WO2023005821A1 - Living body detection method, terminal, and storage medium - Google Patents

Abstract

A living body detection method, a terminal (4), and a computer readable storage medium. The method is applied to a millimeter-wave radar, and comprises: obtaining distance dimension data of the millimeter-wave radar (S101); intercepting the distance dimension data according to a preset effective distance range, and obtaining a target index corresponding to the maximum peak value in the intercepted data (S102); obtaining, according to the target index, n adjacent distance measurement units centered on a distance measurement unit corresponding to the target index, and sequentially obtaining an amplitude difference between the distance dimension data of each distance measurement unit at a kth time point and a (k-1)th time point in N consecutive time points (S103); performing windowing and fast Fourier transform for the amplitude difference corresponding to any distance measurement unit to obtain a spectrum corresponding to the distance measurement unit (S104); and determining, according to the spectrums corresponding to the n distance measurement units, whether a target exists at an Nth time point (S105). The living body detection method can improve the living body detection precision.

Description

Life body detection method, terminal and storage medium

This patent application claims the priority of Chinese Patent Application No.CN202110846368.X filed on July 26, 2021. The disclosure of the prior application is incorporated by reference in its entirety into this application.

technical field

The present application belongs to the technical field of life detection, and in particular relates to a life detection method, a terminal and a computer-readable storage medium.

Background technique

In an outdoor environment with direct sunlight and closed doors and windows, the internal temperature of the car can reach the critical value of heat stroke within 15 minutes. If there is a living body in the car at this time, such as an infant, its life safety will be seriously threatened.

Currently, life detection sensors mainly include infrared detectors, ultrasonic radars, and cameras. Active infrared detectors are easily interfered by various heat sources and sunlight sources. For passive infrared detectors, the infrared radiation of the human body is easily blocked and not easily received by the alarm; especially when the ambient temperature is close to the temperature of the human body, the detection ability and sensitivity of the passive infrared detector will drop significantly, and in severe cases, short-term Failure situation. The resolution of ultrasonic radar is poor, and the detection effect is poor in complex environments; especially at high temperatures, the sensitivity of ultrasonic radar will drop sharply. Cameras have extremely high requirements on light, are easily affected by dust, are expensive, and are not conducive to protecting the privacy of passengers.

How to improve the accuracy of life body detection is an urgent problem to be solved in the prior art.

technical problem

In view of this, the present application provides a living body detection method, a terminal and a storage medium, which can improve the precision of living body detection.

technical solution

The first aspect of the present application provides a living body detection method, the method is applied to millimeter wave radar and includes:

Obtain the distance dimension data of millimeter wave radar;

Intercepting the distance dimension data according to the preset effective distance range, and obtaining the target index corresponding to the maximum peak value in the intercepted data;

According to the target index, obtain n adjacent distance detection units centered on the distance detection unit corresponding to the target index, and for any distance detection unit, according to the distance dimension data corresponding to the distance detection unit, sequentially Acquiring the amplitude difference between the distance dimension data at the kth moment and the k-1th moment of the distance detection unit at consecutive N moments, where N is a positive integer greater than or equal to 3, 1<k<=N;

For the amplitude difference corresponding to any distance detection unit, perform windowing and fast Fourier transform to obtain the frequency spectrum corresponding to the distance detection unit;

According to the frequency spectrum corresponding to the n distance detection units, it is judged whether there is a target at the Nth moment.

In a possible implementation, the acquiring the distance dimension data of the millimeter-wave radar includes:

For the echo signal received by any one of the plurality of receiving antennas of the millimeter-wave radar, the intermediate frequency signal corresponding to the echo signal is obtained through signal processing, and the intermediate frequency signal is fast Fourier transformed to obtain the corresponding signal of the receiving antenna. The distance dimension data; wherein, one transmitting antenna of the millimeter-wave radar transmits a group of chirp signals, and the echo signals received by multiple receiving antennas of the millimeter-wave radar are echoes corresponding to the chirp signals wave signal;

The distance dimension data of the millimeter-wave radar is acquired through the distance dimension data corresponding to at least one receiving antenna.

In a possible implementation manner, the acquiring the distance dimension data of the millimeter wave radar includes:

The distance dimension data corresponding to at least two receiving antennas are coherently accumulated to obtain the distance dimension data of the millimeter wave radar.

In a possible implementation, after obtaining the magnitude difference between the distance dimension data at the kth moment and the k-1th moment, the method further includes:

According to a preset frequency range, the amplitude difference of the distance dimension data at the kth time and the k-1th time is filtered.

In a possible implementation manner, the determining whether there is a target at the Nth moment according to the frequency spectrum corresponding to the n distance detection units includes:

According to the frequency spectrum corresponding to any distance detection unit, search for a maximum value within the preset frequency range, and obtain an index number corresponding to the maximum value;

According to the index number corresponding to the maximum value, obtain an index range of a preset size centered on the index number of the maximum value;

According to the frequency domain values corresponding to all index numbers in the index range and the frequency domain values corresponding to all index numbers in the preset frequency range, obtain the characteristic value corresponding to the distance detection unit;

According to the eigenvalues corresponding to the n distance detection units, it is judged whether there is a target at the Nth moment.

In a possible implementation manner, according to the frequency domain values corresponding to all index numbers in the index range and the frequency domain values corresponding to all index numbers in the preset frequency range, the distance detection unit corresponds to The eigenvalues for include:

summing the frequency domain values corresponding to all index numbers within the index range to obtain the first value sumPeak;

summing frequency domain values corresponding to all index numbers within the preset frequency range to obtain a second value sumSignal;

According to the first value and the second value, the characteristic value conf corresponding to the distance detection unit is obtained through a preset formula, and the preset formula is:

conf=sumPeak / (sumSignal - sumPeak).

In a possible implementation manner, the judging whether there is a target at the Nth moment according to the eigenvalues corresponding to the n distance detection units includes:

If at the Nth moment, the feature values corresponding to the n distance detection units are all greater than or equal to a preset threshold, it is determined that there is a target at the Nth moment.

In a possible implementation, the method further includes:

Judging whether there is a target at each moment in consecutive M moments after the Nth moment including the Nth moment;

If the target exists at R time points among the M time points, it is determined that the target exists, and R is greater than or equal to a preset value.

In a second aspect, the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and operable on the processor. When the processor executes the computer program, the above-mentioned Steps in the method described in one aspect or any possible implementation manner of the first aspect.

In a third aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any one of the above first aspect or the first aspect is implemented. Possible implementations of the steps of the method.

Beneficial effect

This application provides a living body detection method, terminal and storage medium, which can detect the target position through millimeter-wave radar, and calculate the range-dimension signal amplitude of the target position at a certain moment; after continuously accumulating amplitude changes at multiple moments, the data Carry out frequency domain analysis; then use the frequency domain features to judge whether there is a frequency that matches the micro-movement characteristics of the living body, and finally identify whether there is a target living body in the detected space. The living body detection method provided in the present application can accurately identify the micro-movement characteristics of the living body, and improves the detection accuracy of the living body.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description are only for the present application For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without paying creative efforts.

Fig. 1 is the implementation flowchart of the living body detection method provided by one embodiment of the present application;

FIG. 2 is a flow chart of the implementation of a living body detection method provided by another embodiment of the present application;

Fig. 3 is a schematic structural diagram of a living body detection device provided by an embodiment of the present application;

FIG. 4 is a schematic diagram of a terminal provided by an embodiment of the present application.

Detailed ways

In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

In order to make the purpose, technical solution and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

Referring to FIG. 1 , it shows a flow chart of an implementation of a living body detection method provided by an embodiment of the present application. In this embodiment, the living body detection method includes step S101 to step S105. Each step is described in detail below.

S101. Acquire distance dimension data of the millimeter wave radar.

The living body detection method provided in this application can be applied to millimeter wave radar. Millimeter wave radar uses a large bandwidth signal, which has extremely high detection accuracy and is not affected by light, temperature, dust, etc.

Optionally, the millimeter wave radar can have multiple receiving antennas. For the echo signal received by any receiving antenna, the intermediate frequency signal corresponding to the echo signal can be obtained through signal processing; and then the intermediate frequency signal is subjected to fast Fourier transform to obtain the corresponding distance dimension data. Wherein, one transmitting antenna of the millimeter-wave radar can transmit a group of chirp signals; the echo signals received by multiple receiving antennas of the millimeter-wave radar are echo signals corresponding to the chirp signals. The distance dimension data of the millimeter wave radar described in step S101 includes distance dimension data corresponding to at least one receiving antenna.

According to the actual situation of the detected space and the deployment situation of the radar, the distance dimension data corresponding to one or more receiving antennas may be selected as the distance dimension data of the millimeter wave radar.

In this application, the millimeter-wave radar can adopt the method of single transmission and multiple reception, that is, one transmitting antenna can be selected to transmit a group of chirp signals (ie, chirp signals), and correspondingly multiple receiving antennas can receive them. After these receiving antennas receive the echo signals, they convert the echo signals into intermediate frequency signals through signal processing technology; then perform FFT (fast Fourier transform) on the intermediate frequency signals to obtain distance dimension data.

Due to the different layout positions of the radar in the detected space, there may be a situation where the detection ability of a certain direction is weak; in this case, the distance dimension data corresponding to at least two receiving antennas can be coherently accumulated to obtain the millimeter wave radar The distance dimension data of .

That is, according to the actual application situation, the distance dimension data corresponding to two or more receiving antennas are coherently accumulated to improve the signal-to-noise ratio of echo data in a certain direction, thereby improving the detection capability in this direction. Such coherent accumulation is a well known technique in the art.

It should be noted that the space to be detected may be the interior space of a car, or other spaces where life detection is required, which is not limited in this application.

S102. Intercept the distance-dimension data according to the preset effective distance range, and obtain a target index corresponding to the largest peak in the intercepted data.

The preset effective distance range can be expressed as [RangeStart, RangeEnd], which can be reasonably set according to the actual situation of the detected space, the range to be detected, and the layout position of the millimeter-wave radar. In step S102, the distance dimension data obtained in step S101 is intercepted according to the effective distance range. The obtained intercepted data includes a plurality of distance detection units, and each distance detection unit corresponds to a target index targetIndex.

By detecting the largest peak (that is, the strongest echo) and determining the target index of the distance detection unit where the largest peak is located, the approximate distance of the micro-moving target can be determined.

S103, according to the target index, obtain n adjacent distance detection units centered on the distance detection unit corresponding to the target index, for any distance detection unit, according to the distance dimension data corresponding to the distance detection unit, sequentially obtain the distance detection unit In consecutive N time instants, the magnitude difference between the kth instant and the k-1th instant is the distance dimension data.

Wherein, N is a positive integer greater than or equal to 3, and 1<k<=N.

After obtaining the target index, step S103 determines the neighborhood range of the distance detection unit corresponding to the target index, that is, [targetIndex - binIndex, targetIndex + binIndex]; where the size of binIndex can be set in advance. According to the above neighborhood range, namely [targetIndex - binIndex, targetIndex + binIndex], n adjacent distance detection units can be obtained.

In the n distance detection units, the magnitude difference between the distance dimension data at the kth moment and the k-1th moment of each distance detection unit can be obtained sequentially through the first formula; the first formula is:

diffA _m,k = A _m,k - A _m,k-1

Among them, diffA _m,k represents the amplitude difference of the m-th distance detection unit at time k, A _m,k represents the range dimension data amplitude of the m-th distance detection unit at time k, A _m,k-1 represents the time k-1 The magnitude of the distance dimension data of the mth distance detection unit.

In one embodiment, in order to filter out clutter and further improve the accuracy of life detection, any distance detection unit can be processed as follows:

According to a preset frequency range, the amplitude difference of the distance dimension data at the kth time and the k-1th time is filtered.

Wherein, the preset frequency may be the movement frequency of the chest and abdominal cavity when the human body breathes in a state of deep sleep.

In this embodiment, frequency filtering may be performed on the amplitude difference diffA _m,k of the m-th distance detection unit obtained at time k. Considering that a living body is usually a human target, the preset frequency range can be set to 0.1-0.6 Hz. When a person is in a static or deep sleep state, the main micro-movement feature comes from the change of the chest and abdominal cavity during breathing, and the frequency of this change is generally 0.1-0.6Hz. In this embodiment, a Butterworth IIR Bandpass filter can be used to implement filtering. The cutoff frequency of the filter is set to a preset frequency range, such as 0.1-0.6Hz. In this way, the interference caused by out-of-band clutter can be filtered out to a certain extent. The filtered data at time k can be put into the data buffer buffer corresponding to the distance detection unit. Multi-time data accumulation can be carried out in the data buffer buffer.

S104. Perform windowing and fast Fourier transform on the amplitude difference corresponding to any distance detection unit to obtain a frequency spectrum corresponding to the distance detection unit.

At the Nth moment, multiple frames of IIR-filtered data have been accumulated in the data buffer buffer corresponding to the n distance detection units within the target neighborhood. In order to better analyze data characteristics, step S104 performs windowing and FFT processing on the time-domain data in the data buffer buffer, so as to obtain the frequency spectrum corresponding to the n distance detection units within the target neighborhood. It should be noted that the windowing and Fourier transform are well-known technologies in the art.

S105, according to the frequency spectrum corresponding to the n distance detection units, judge whether there is a target at the Nth time.

In step S105, according to the frequency spectrums corresponding to the n distance detection units, it can be judged whether there is a frequency conforming to the breathing characteristics of the human body, so as to judge whether there is a human body in the detected space.

In summary, the life detection method shown in Figure 1 can detect the target position through the millimeter-wave radar, and calculate the range dimension signal amplitude of the target position at a certain moment; after continuously accumulating amplitude changes at multiple moments, the data Carry out frequency domain analysis; then use the frequency domain features to judge whether there is a frequency that matches the micro-movement characteristics of the living body, and finally identify whether there is a target living body in the detected space. The above detection method can accurately identify the micro-movement characteristics of the living body, and improves the detection accuracy of the living body.

Fig. 2 shows a flow chart of implementing a method for detecting a living body provided by another embodiment of the present application. In this embodiment, the living body detection method includes step S201 to step S204. Each step is described in detail below.

S201. According to the frequency spectrum corresponding to any distance detection unit, search for a maximum value within a preset frequency range, and obtain an index number corresponding to the maximum value.

The distance detection unit can be obtained by intercepting the distance dimension data of the millimeter wave radar.

The frequency spectrum of the distance detection unit can be obtained by performing fast Fourier transform on the relevant data corresponding to the distance detection unit.

Optionally, in step S201, after obtaining the frequency spectrum corresponding to n distance detection units at the Nth time, analyze the frequency spectrum characteristics of each distance detection unit at this time. When analyzing the spectrum characteristics, you can first search for the maximum peak within the preset frequency range of 0.1-0.6Hz; if there is a maximum peak, the maximum value in this step refers to the maximum peak value; if there is no maximum peak value, the maximum value in this step refers to is the maximum value in the spectrum.

After searching for the maximum value, record the index number maxIndex corresponding to the maximum value. Each distance detection unit corresponds to an index number.

S202. According to the index number corresponding to the maximum value, acquire an index range of a preset size centered on the index number of the maximum value.

Determine its neighborhood [maxIndex-aroundIndex, maxIndex + aroundIndex] through the index number maxIndex corresponding to the maximum value; the size of aroundIndex is preset. [maxIndex-aroundIndex, maxIndex + aroundIndex] is the index range of the preset size described in this step.

S203. According to the frequency domain values corresponding to all the index numbers in the index range and the frequency domain values corresponding to all the index numbers in the preset frequency range, obtain the characteristic value corresponding to the distance detection unit.

Optionally, this step obtains the eigenvalues in the following way:

summing the frequency domain values corresponding to all index numbers within the index range to obtain the first value sumPeak;

summing frequency domain values corresponding to all index numbers within the preset frequency range to obtain a second value sumSignal;

According to the first value and the second value, the characteristic value conf corresponding to the distance detection unit is obtained through a preset formula; the preset formula is:

conf=sumPeak / (sumSignal - sumPeak).

For n distance detection units in the target neighborhood, it is necessary to calculate the frequency domain feature value conf _m corresponding to the mth distance detection unit at the Nth moment.

S204. According to the characteristic values corresponding to the n distance detection units, it is judged whether there is a target at the Nth moment.

In step S204, count the eigenvalues in the frequency domain corresponding to the n distance detection units in the target neighborhood at the Nth moment; if the eigenvalues in the frequency domain corresponding to the n distance detection units in the target neighborhood all meet the preset The decision-making condition can judge that there is a target at the Nth moment, otherwise there is no target.

Optionally, the aforementioned preset decision-making condition is: at the Nth moment, the feature values corresponding to the n distance detection units are all greater than or equal to a preset threshold Threshold.

Optionally, the detection method provided by this embodiment can also use the following decision-making method: judge whether there is a target at each time in the M consecutive moments after the Nth moment including the Nth moment; , if there is a target at R times, it is judged that there is a target; R is greater than or equal to the preset value.

In summary, the life detection method shown in Figure 2 can obtain the characteristic value of each distance detection unit at the Nth moment through the frequency spectrum corresponding to each distance detection unit, and judge whether there is a target based on this; the detection method can Effective detection of living organisms in the detected space, such as the remaining members of the car; especially for infants and children in a deep sleep state, even if they do not have large-scale movements of their limbs, they can also be detected due to the micro-movement characteristics of the thorax and abdomen. , are accurately identified by millimeter-wave radar.

It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.

The following are device embodiments of the present application, and for details that are not exhaustively described therein, reference may be made to the above-mentioned corresponding method embodiments.

FIG. 3 is a schematic structural diagram of a living body detection device provided by the present application. For ease of description, FIG. 3 only shows the parts related to the present application. The living body detection device provided by the present application will be described in detail below.

As shown in FIG. 3 , the living body detection device 3 includes a distance dimension data acquisition unit 31 , a target index acquisition unit 32 , an amplitude difference acquisition unit 33 , a frequency spectrum acquisition unit 34 and a discrimination unit 35 .

The distance-dimension data acquiring unit 31 is configured to execute the aforementioned step S101, that is, to acquire the distance-dimension data of the millimeter-wave radar.

The target index acquisition unit 32 is configured to perform the aforementioned step S102, that is, intercept the distance dimension data of the millimeter-wave radar according to the preset effective distance range, and acquire the target index corresponding to the maximum peak value in the intercepted data.

The amplitude difference acquisition unit 33 is used to perform the aforementioned step S103, that is, acquire n adjacent distance detection units centered on the distance detection unit corresponding to the target index, and for any distance detection unit, according to the distance detection unit For the corresponding distance dimension data, sequentially acquire the amplitude difference between the distance dimension data of the distance detection unit at the kth moment and the k-1th moment in consecutive N moments, where N is a positive integer greater than or equal to 3, and 1< k<=N.

The spectrum acquisition unit 34 is used to execute the aforementioned step S104, that is, perform windowing and fast Fourier transform on the amplitude difference corresponding to any distance detection unit to obtain the spectrum corresponding to the distance detection unit.

The judging unit 35 is configured to execute the aforementioned step S105, that is, judging whether there is a target at the Nth moment according to the frequency spectrum corresponding to the n distance detection units.

Optionally, the distance-dimension data acquisition unit 31 may also be used to: perform signal processing on the echo signal received by any one of the plurality of receiving antennas of the millimeter-wave radar, to obtain the echo signal corresponding to the echo signal intermediate frequency signal; and perform fast Fourier transform on the intermediate frequency signal to obtain corresponding distance dimension data; wherein, one transmitting antenna of the millimeter wave radar transmits a group of chirp signals, and a plurality of receiving antennas of the millimeter wave radar receive The received echo signal is an echo signal corresponding to the chirp signal.

The distance dimension data of the millimeter wave radar includes distance dimension data received by at least one receiving antenna.

Optionally, the distance dimension data acquiring unit 31 may also be configured to: perform coherent accumulation of distance dimension data corresponding to at least two receiving antennas to obtain the distance dimension data of the millimeter wave radar.

Optionally, the life detection device 3 further includes: a filtering unit 36 . The filtering unit 36 is configured to filter the amplitude difference between the distance dimension data at the kth moment and the k -1th moment according to a preset frequency range.

Optionally, the judging unit 35 can also be used to perform the aforementioned steps S201 to S204, namely:

According to the frequency spectrum corresponding to any distance detection unit, search for a maximum value within the preset frequency range, and obtain an index number corresponding to the maximum value;

According to the index number corresponding to the maximum value, obtain an index range of a preset size centered on the index number of the maximum value;

According to the frequency domain values corresponding to all index numbers in the index range and the frequency domain values corresponding to all index numbers in the preset frequency range, obtain the characteristic value corresponding to the distance detection unit;

According to the eigenvalues corresponding to the n distance detection units, it is judged whether there is a target at the Nth moment.

Optionally, the discrimination unit 35 can also be used for:

summing the frequency domain values corresponding to all index numbers within the index range to obtain the first value sumPeak;

summing the frequency domain values corresponding to all index numbers within the preset frequency range to obtain a second value sumSignal;

According to the first value and the second value, the characteristic value conf corresponding to the distance detection unit is obtained through a preset formula; the preset formula is:

conf=sumPeak / (sumSignal - sumPeak).

Optionally, the judging unit 35 can also be used to: judge at the Nth moment, whether the feature values corresponding to the n distance detection units are all greater than or equal to a preset threshold; if so, judge that there is a target at the Nth moment .

Optionally, the judging unit 35 may also be used for: judging whether there is a target at each moment in consecutive M moments after the Nth moment including the Nth moment;

If the target exists at R time points among the M time points, it is determined that the target exists, and R is greater than or equal to a preset value.

In summary, the life detection device provided by this application can use the detection method provided by this application to detect whether there is life in a certain space, that is, the life detection device provided by this application can detect the target position through millimeter wave radar, and calculate the target position. The distance-dimension signal amplitude at a certain moment in the position; the amplitude changes at multiple moments are continuously accumulated, and the frequency domain analysis is performed on the data at multiple continuous moments; Find out whether there is a target life in the detected space.

Fig. 4 is a schematic diagram of a terminal provided by the present application. As shown in FIG. 4 , the terminal 4 includes a processor 40 , a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40 . When the processor 40 executes the computer program 42, the steps of the living body detection method provided by the above method embodiments are implemented, for example, steps S101 to S105 shown in FIG. 1 . Alternatively, when the processor 40 executes the computer program 42, it realizes the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the units 31 to 36 shown in FIG. 3 .

Exemplarily, the computer program 42 can be divided into one or more modules/units. These modules/units are stored in the memory 41 and executed by the processor 40 to realize the inventive concept of the present application. These modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and these instruction segments are used to describe the execution process of the computer program 42 in the terminal 4 . For example, computer program 42 may be divided into units 31 to 36 shown in FIG. 3 .

The terminal 4 may be a computing device such as a desktop computer, a notebook, a palmtop computer, or a cloud server. The terminal 4 may include, but not limited to, a processor 40 and a memory 41 . Those skilled in the art can understand that FIG. 4 is only an example of the terminal 4 and does not constitute a limitation on the terminal 4 . Terminal 4 may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal 4 may also include an input and output device, a network access device, a bus, and the like.

The processor 40 can be a central processing unit (Central Processing Unit, CPU), and can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor or the like.

The storage 41 may be an internal storage unit of the terminal 4, such as a hard disk or a memory of the terminal 4. The memory 41 can also be an external storage device of the terminal 4, such as a plug-in hard disk equipped on the terminal 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card (Flash Card) wait. Further, the memory 41 may also include both an internal storage unit of the terminal 4 and an external storage device. The memory 41 is used to store the computer program 42 and other programs and data required by the terminal 4 . The memory 41 can also be used to temporarily store data that has been output or will be output.

The present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by the processor, the steps shown in FIG. 1 and/or FIG. 2 can be implemented to complete the detection of living bodies existing in the space.

Those skilled in the art can clearly understand that, for convenience and brevity of description, the present application only uses the division of the above functional units and modules for illustration. In practical applications, the above function allocation can be completed by different functional units or modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the above system, reference may be made to the corresponding process in the foregoing method embodiments, and details will not be repeated here.

In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

In the embodiments provided in this application, it should be understood that the disclosed device/terminal and method may be implemented in other ways, and the above-described device/terminal embodiments are only illustrative. For example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored, or not. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separate. Components shown as units may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the corresponding embodiment.

If the integrated module/unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the implementation of all or part of the processes in the methods of the above-mentioned embodiments in the present application may also be completed by instructing related hardware through computer programs. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of the above-mentioned embodiments of the living body detection method can be realized. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The computer readable medium may be: any entity or device capable of carrying the computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (Read-Only Memory, ROM) , random access memory (Random Access Memory, RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable Excluding electrical carrier signals and telecommunication signals.

The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still implement the foregoing embodiments Modifications to the technical solutions described in the examples, or equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the application, and should be included in the Within the protection scope of this application.

Claims (10)

1. A living body detection method, characterized in that the method is applied to millimeter wave radar and includes:

   Obtaining the distance dimension data of the millimeter wave radar;

   Intercepting the distance dimension data according to the preset effective distance range, and obtaining the target index corresponding to the maximum peak value in the intercepted data;

   According to the target index, obtain n adjacent distance detection units centered on the distance detection unit corresponding to the target index, and for any distance detection unit, according to the distance dimension data corresponding to the distance detection unit, sequentially Acquiring the amplitude difference between the distance dimension data at the kth moment and the k-1th moment of the distance detection unit at consecutive N moments, where N is a positive integer greater than or equal to 3, 1<k<=N;

   For the amplitude difference corresponding to any distance detection unit, perform windowing and fast Fourier transform to obtain the frequency spectrum corresponding to the distance detection unit;

   According to the frequency spectrum corresponding to the n distance detection units, it is judged whether there is a target at the Nth moment.
2. The method according to claim 1, wherein the acquiring the distance dimension data of the millimeter-wave radar comprises:

   For the echo signal received by any one of the plurality of receiving antennas of the millimeter-wave radar, the intermediate frequency signal corresponding to the echo signal is obtained through signal processing, and the intermediate frequency signal is fast Fourier transformed to obtain the corresponding signal of the receiving antenna. The distance dimension data; wherein, one transmitting antenna of the millimeter-wave radar transmits a group of chirp signals, and the echo signals received by multiple receiving antennas of the millimeter-wave radar are echoes corresponding to the chirp signals wave signal;

   The distance dimension data of the millimeter-wave radar is acquired through the distance dimension data corresponding to at least one receiving antenna.
3. The method according to claim 2, wherein the acquiring the distance dimension data of the millimeter-wave radar comprises:

   The distance dimension data corresponding to at least two receiving antennas are coherently accumulated to obtain the distance dimension data of the millimeter wave radar.
4. The method according to claim 1, characterized in that, after obtaining the magnitude difference between the distance dimension data at the kth moment and the k-1th moment, the method further comprises:

   According to a preset frequency range, the amplitude difference of the distance dimension data at the kth time and the k-1th time is filtered.
5. The method according to claim 4, wherein, according to the frequency spectrum corresponding to the n distance detection units, judging whether there is a target at the Nth moment comprises:

   According to the frequency spectrum corresponding to any distance detection unit, search for a maximum value within the preset frequency range, and obtain an index number corresponding to the maximum value;

   According to the index number corresponding to the maximum value, obtain an index range of a preset size centered on the index number of the maximum value;

   According to the frequency domain values corresponding to all index numbers in the index range and the frequency domain values corresponding to all index numbers in the preset frequency range, obtain the characteristic value corresponding to the distance detection unit;

   According to the eigenvalues corresponding to the n distance detection units, it is judged whether there is a target at the Nth moment.
6. The method according to claim 5, wherein the frequency domain values corresponding to all index numbers in the index range and the frequency domain values corresponding to all index numbers in the preset frequency range are used to obtain the The eigenvalues corresponding to the distance detection unit include:

   summing the frequency domain values corresponding to all index numbers within the index range to obtain the first value sumPeak;

   summing frequency domain values corresponding to all index numbers within the preset frequency range to obtain a second value sumSignal;

   According to the first value and the second value, the characteristic value conf corresponding to the distance detection unit is obtained through a preset formula, and the preset formula is:

   conf=sumPeak / (sumSignal - sumPeak).
7. The method according to claim 6, wherein, according to the characteristic values corresponding to the n distance detection units, judging whether there is a target at the Nth moment comprises:

   If at the Nth moment, the feature values corresponding to the n distance detection units are all greater than or equal to a preset threshold, it is determined that there is a target at the Nth moment.
8. The method according to claim 5, characterized in that the method further comprises:

   Judging whether there is a target at each moment in consecutive M moments after the Nth moment including the Nth moment;

   If the target exists at R time points among the M time points, it is determined that the target exists, and R is greater than or equal to a preset value.
9. A terminal, comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, characterized in that, when the processor executes the computer program, the above claims 1 to 1 are implemented. 8. The steps of any one of the methods.
10. A computer-readable storage medium, the computer-readable storage medium stores a computer program, characterized in that, when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 8 above are realized .