Non-contact vital sign data monitoring method based on millimeter-wave radar

Abstract

A non-contact vital sign data monitoring method and system based on a millimeter-wave radar, and a computer-readable storage medium. The method comprises: receiving echo signals in a radar detection space, and on the basis of the echo signals, selecting static detected persons from the radar detection space to serve as target objects (S10, S110); on the basis of echo signals of the target objects, calculating spatial position information of the target objects in the radar detection space (S20, S120); on the basis of the spatial position information, determining the number of target objects equidistant from the radar (S30, S130); if there are at least two target objects equidistant from the radar in the radar detection space, calculating phase information on the basis of azimuth angle information and elevation angle information of the target objects (S40, S150); and calculating vital sign data of the target objects on the basis of the phase information (S50, S160).

Description

Non-contact vital sign data monitoring method based on millimeter wave radar Priority Declaration

[0001] This application claims priority to the prior application document filed with the State Intellectual Property Office of China, with an application date of September 12, 2023, application number 202311176456.9, and application subject matter being a non-contact vital sign data monitoring method based on millimeter wave radar. Technical Field

[0002] The present application relates to the technical field of non-contact vital sign data monitoring, and in particular to a non-contact vital sign data monitoring method, system and computer-readable storage medium based on millimeter wave radar. Background Art

[0003] With the continuous advancement of technology, the demand for contactless vital sign monitoring technology is increasing. This technology can be used in a variety of fields, including medical care, health monitoring, emergency rescue, and security applications. Exemplary vital sign monitoring methods often require contact with the patient or target object, which can be inconvenient and uncomfortable, especially in special environments.

[0004] Contactless vital sign monitoring methods based on radar technology have attracted widespread attention. Radar systems transmit and receive radio wave signals to detect vital signs such as movement and breathing, without requiring direct contact with the subject. This approach reduces invasiveness and improves convenience and comfort.

[0005] However, although radar technology has made significant progress in the field of contactless vital signs monitoring, in some cases, such as when there are multiple people in the radar detection space, there will be multiple situations at the same distance, causing the respiratory signals and heart rate signals to overlap and be unable to be accurately distinguished. Therefore, radar has poor reliability in detecting the respiratory and heart rate of multiple people. Summary of the Invention

[0006] The embodiments of the present application aim to improve the reliability of vital sign monitoring of multiple target objects by providing a non-contact vital sign data monitoring method based on millimeter wave radar.

[0007] To achieve the above objectives, the present invention provides a non-contact vital sign data monitoring method based on millimeter wave radar, comprising:

[0008] Receiving an echo signal in a radar detection space, and screening a static person being measured from the radar detection space as a target object according to the echo signal;

[0009] Calculating spatial position information of the target object in the radar detection space according to the echo signal of the target object, the spatial position information including distance information, azimuth information, and elevation angle information of the target unit compared to the radar;

[0010] Determine the number of target objects equidistant from the radar based on the spatial position information;

[0011] If there are at least two target objects equidistant from the radar in the radar detection space, calculating phase information based on azimuth information and elevation angle information of the target objects;

[0012] Vital sign data of the target object is calculated according to the phase information, where the vital sign data includes at least one of a respiratory rate and a heart rate.

[0013] In one embodiment, determining the number of target objects equidistant from the radar according to the spatial position information includes:

[0014] If there are at least two target objects that do not overlap in azimuth and elevation in the same range unit, it is determined that the at least two target objects are equidistant from the radar, and the number of the at least two target objects is calculated as the number of target objects equidistant from the radar.

[0015] In one embodiment, the millimeter wave radar has multiple virtual antennas;

[0016] Screening out a static person under test as a target object from the radar detection space according to the echo signal includes:

[0017] Performing a range-dimensional fast Fourier transform and a Doppler-dimensional fast Fourier transform on the echo signal to obtain range-Doppler data of each object in the radar detection space;

[0018] performing non-coherent accumulation on the range-Doppler data of all virtual antennas of the radar to obtain accumulated range-Doppler data;

[0019] Performing a two-dimensional constant false alarm detection on the accumulated range-Doppler data, configured to extract objects with a Doppler of 0 from the radar detection space as candidate objects;

[0020] Selecting data with Doppler of 0 for any virtual antenna to perform multi-frame data accumulation processing to obtain range-Doppler data in the slow Doppler dimension; and

[0021] Static filtering is performed on the range-Doppler data in the slow Doppler dimension, configured to remove completely stationary background objects from the candidate objects, and use the remaining candidate objects as the target objects.

[0022] In one embodiment, calculating the spatial position information of the target object in the radar detection space according to the echo signal of the target object includes:

[0023] performing range-dimensional constant false alarm detection on the range-Doppler data of the target object in the slow Doppler dimension, and calculating the range unit of the target object relative to the radar;

[0024] Extracting all antenna data of the target object from the range-Doppler data in the fast Doppler dimension; and

[0025] The azimuth and elevation angles of the target object are calculated based on all antenna data of the target object using an azimuth-elevation combined angle measurement algorithm.

[0026] In one embodiment, the azimuth information includes azimuth frequency domain data, and the elevation information includes elevation frequency domain data;

[0027] Extracting phase information according to the azimuth information and elevation information of the target object includes:

[0028] Phase information is extracted at peaks of the azimuth frequency domain data and the elevation frequency domain data respectively.

[0029] In one embodiment, before calculating the vital sign data of the target object based on the phase information, if there is only one target object in the radar detection space, or there are multiple target objects at unequal distances from the radar, the method further includes:

[0030] Phase information is calculated according to the distance information of the target object.

[0031] In one embodiment, the range information includes Doppler-range data;

[0032] Calculating phase information according to the distance information of the target object includes:

[0033] Phase information is calculated based on the range unit data corresponding to Doppler 0 in the Doppler-range data.

[0034] In one embodiment, calculating phase information based on range cell data corresponding to a Doppler of 0 in the Doppler-range data includes:

[0035] The phase information is obtained by analyzing the phase component of the Doppler-range data.

[0036] In one embodiment, calculating the respiratory rate of the target subject based on the phase information includes:

[0037] Accumulating multiple frames of data on the phase information to obtain phase change data of the target object;

[0038] Using a 6th-order Butterworth filter designed according to the frequency band of the respiratory frequency to filter the phase change data of the target object to extract respiratory waveform data; and

[0039] Performing a fast Fourier transform on the respiratory waveform data to extract the respiratory cycle and calculate the respiratory frequency of the target object.

[0040] In one embodiment, accumulating multiple frames of data on the phase information includes:

[0041] The phase information is accumulated for a period of 10 seconds.

[0042] In one embodiment, performing a fast Fourier transform on the respiratory waveform data to extract the respiratory period and calculate the respiratory frequency of the target subject includes:

[0043] The respiratory frequency of the target object is calculated based on the periodic changes in the frequency components of the respiratory cycle.

[0044] In one embodiment, calculating the heart rate of the target subject based on the phase information includes:

[0045] Accumulating multiple frames of data on the phase information to obtain phase change data of the target object;

[0046] Using a 6th-order Butterworth filter designed according to the frequency band of the heart rate to filter the phase change data of the target object and extract the heart rate waveform data;

[0047] The heart rate waveform data is processed by wavelet transform and then fast Fourier transform to extract the heart rate cycle, and the heart rate of the target object is calculated.

[0048] In one embodiment, accumulating multiple frames of data on the phase information includes:

[0049] The phase information is accumulated for a period of 10 seconds.

[0050] In one embodiment, the heart rate waveform data is subjected to wavelet transform and then fast Fourier transform to extract the heart rate period, and the heart rate of the target object is calculated, including:

[0051] The heart rate of the target subject is calculated based on the periodic changes in the frequency components of the heart rate cycle.

[0052] In one embodiment, the method further comprises:

[0053] If the number of target objects in the radar detection space exceeds a preset threshold, the radar monitoring system will not monitor the vital sign data of the target objects.

[0054] In one embodiment, the preset threshold is set to 4.

[0055] In one embodiment, the millimeter wave radar includes three transmitting antennas and four receiving antennas.

[0056] In one embodiment, the millimeter wave radar adopts a time division multiple access multiple input multiple output signal transmission mode.

[0057] To achieve the above-mentioned objectives, an embodiment of the present application also proposes a millimeter-wave radar-based non-contact vital signs data monitoring system, including a memory, a processor, and a millimeter-wave radar-based non-contact vital signs data monitoring program stored in the memory and runnable on the processor. When the processor executes the millimeter-wave radar-based non-contact vital signs data monitoring program, it implements the millimeter-wave radar-based non-contact vital signs data monitoring method as described in any one of the above items.

[0058] To achieve the above-mentioned objectives, an embodiment of the present application also proposes a computer-readable storage medium, on which a non-contact vital signs data monitoring program based on millimeter wave radar is stored. When the non-contact vital signs data monitoring program based on millimeter wave radar is executed by a processor, the non-contact vital signs data monitoring method based on millimeter wave radar as described in any of the above items is implemented.

[0059] The non-contact vital signs data monitoring method based on millimeter-wave radar of the technical solution of the present application, when multiple target objects equidistant from the radar are detected in space, acquires phase information by introducing azimuth information and pitch angle information, and then calculates the respiratory rate and / or heart rate of each target object based on the acquired phase information to achieve non-contact vital signs data monitoring. Thus, compared with the exemplary contact vital signs monitoring method or millimeter-wave radar vital signs monitoring method, the monitoring method of the present application can not only achieve non-contact monitoring, but also effectively distinguish and monitor the respiratory rate and / or heart rate of multiple equidistant target objects, thereby improving the reliability of vital signs data monitoring for multiple target objects. In other words, the monitoring method of the technical solution of the present application has the advantages of non-contact monitoring and strong monitoring reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0061] FIG1 is a module structure diagram of an embodiment of a non-contact vital sign data monitoring system based on millimeter wave radar of the present application;

[0062] FIG2 is a schematic diagram of the antenna array arrangement of the millimeter wave radar in one embodiment of the non-contact vital sign data monitoring system based on millimeter wave radar of the present application;

[0063] FIG3 is a flow chart of an embodiment of a non-contact vital sign data monitoring method based on millimeter wave radar of the present application;

[0064] FIG4 is a schematic diagram of a module for obtaining an RD-map by performing incoherent accumulation using 12 antennas of a millimeter-wave radar in accordance with an embodiment of a non-contact vital sign data monitoring method based on a millimeter-wave radar of the present application;

[0065] FIG5 is a schematic diagram of a module for performing 2D CFAR detection on an RD-map in an embodiment of a non-contact vital sign data monitoring method based on millimeter-wave radar of the present application;

[0066] FIG6 is a schematic diagram of a module for performing multi-frame data accumulation processing on an RD-map to obtain a slow-Doppler RD-map in an embodiment of a non-contact vital sign data monitoring method based on millimeter-wave radar of the present application;

[0067] FIG7 is a schematic diagram of a module for performing azimuth-elevation joint angle measurement calculation on all antenna data of a target object in an embodiment of a non-contact vital sign data monitoring method based on millimeter-wave radar of the present application;

[0068] FIG8 is a flow chart of another embodiment of the non-contact vital sign data monitoring method based on millimeter-wave radar of the present application.

[0069] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0071] To better understand the above technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. Instead, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0072] It should be noted that in the claims, any reference signs placed between brackets should not be construed as limiting the claims. The presence of "comprising" in the text does not exclude the presence of components or steps not listed in the claims. The quantifier "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim that lists several means, several of these means may be embodied by the same hardware item. The use of "first", "second", and "third" etc. does not indicate any order, and these words may be interpreted as names.

[0073] As shown in Figure 1, Figure 1 is a structural diagram of a non-contact vital sign data monitoring system 1 based on millimeter wave radar according to an embodiment of the present application. The monitoring system of the present application is specifically used to detect the vital sign data of a stationary target.

[0074] As shown in FIG1 , the monitoring system 1 includes: a millimeter wave radar 15 , a memory 11 , a processor 12 and a network interface 13 .

[0075] In this embodiment, the millimeter-wave radar 15 uses a 3Tx4Rx radio frequency chip and includes three transmitting antennas and four receiving antennas. This antenna configuration allows the radar system to send and receive multiple signals simultaneously, thereby helping to improve signal quality and reliability.

[0076] Furthermore, the millimeter-wave radar adopts the TD-MIMO signal transmission mode, with three transmitting antennas transmitting signals in a time-division multiplexing manner and four receiving antennas receiving signals simultaneously.

[0077] Time Division Multiple Access Multiple Input Multiple Output (TD-MIMO) refers to the signal transmission mode used by millimeter wave radar. This mode allows three transmitting antennas to send signals in a time division multiplexing manner, which means that they take turns sending signals at different time intervals to avoid signal interference and improve the system's capacity and efficiency.

[0078] The millimeter-wave radar's four receiving antennas receive signals simultaneously, which helps improve the sensitivity and robustness of signal reception. Simultaneous reception also allows the system to better distinguish and track the vital signs of different target objects in a multi-target environment.

[0079] The antenna array layout of the millimeter-wave radar in this application is shown in Figure 2. The millimeter-wave radar's virtual antenna array is a two-dimensional array consisting of 12 antennas. Virtual antenna arrays are a technology that achieves beamforming and beamforming by rationally arranging antenna elements in space, thereby improving target positioning and signal reception efficiency.

[0080] Optionally, the millimeter-wave radar of the present application is installed on the top, usually on the ceiling or the ceiling of the room, and only performs respiratory and heart rate detection on stationary targets. In actual use, the respiratory and heart rate are only detected when the person is lying on the bed. If there is only one bed in the room, the radar is installed on the ceiling directly above the bed as much as possible. If there are multiple beds in the room, the radar is installed on the ceiling of the room in the middle of the beds. After the radar is installed and powered on, set the installation height of the radar, add the area where the bed is located, and the area where the door is located according to the actual situation of the room, and then it can be used normally.

[0081] Specifically, the memory 11 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the server 1, such as the hard disk of the server 1. In other embodiments, the memory 11 may also be an external storage device of the server 1, such as a plug-in hard disk equipped on the server 1, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card, etc.

[0082] Furthermore, the memory 11 may include both an internal storage unit of the server 1 and an external storage device. The memory 11 can be used not only to store application software and various data installed on the server 1, such as the code of the millimeter-wave radar-based non-contact vital sign data monitoring program 10, but also to temporarily store data that has been output or is about to be output.

[0083] In some embodiments, the processor 12 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run the program code stored in the memory 11 or process data, such as executing the non-contact vital sign data monitoring program 10 based on millimeter wave radar.

[0084] The network interface 13 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface), and is generally used to establish a communication connection between the system 1 and other electronic devices.

[0085] The network may be the Internet, a cloud network, a wireless fidelity (Wi-Fi) network, a personal area network (PAN), a local area network (LAN), and / or a metropolitan area network (MAN). Various devices in the network environment may be configured to connect to the communication network according to various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, at least one of the following: Transmission Control Protocol and Internet Protocol (TCP / IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, IEEE 802.11, Light Fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communication, wireless access point (AP), device-to-device communication, cellular communication protocol, and / or Bluetooth communication protocol, or a combination thereof.

[0086] Optionally, the server may further include a user interface, which may include a display and an input unit such as a keyboard. Optional user interfaces may also include standard wired and wireless interfaces. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display, which may also be referred to as a display screen or display unit, is used to display information processed by server 1 and to display a visual user interface.

[0087] Figure 1 only shows a server 1 having components 11-13 and a non-contact vital sign data monitoring program 10 based on millimeter-wave radar. Those skilled in the art will understand that the structure shown in Figure 1 does not constitute a limitation on the server 1, and may include fewer or more components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0088] In this embodiment, the processor 12 may be configured to call the millimeter-wave radar-based non-contact vital sign data monitoring program stored in the memory 11 and perform the following operations:

[0089] Receiving an echo signal in a radar detection space, and screening a static person being measured from the radar detection space as a target object according to the echo signal;

[0090] Calculating spatial position information of the target object in the radar detection space according to the echo signal of the target object, the spatial position information including distance information, azimuth information, and elevation angle information of the target unit compared to the radar;

[0091] determining the number of target objects equidistant from the radar based on the spatial position information;

[0092] If there are at least two target objects equidistant from the radar in the radar detection space, calculating phase information based on azimuth information and elevation angle information of the target objects;

[0093] Vital sign data of the target object is calculated according to the phase information, where the vital sign data includes at least one of a respiratory rate and a heart rate.

[0094] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0095] If there are at least two target objects that do not overlap in azimuth and elevation in the same range unit, it is determined that the at least two target objects are equidistant from the radar, and the number of the at least two target objects is calculated as the number of target objects equidistant from the radar.

[0096] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0097] Performing a range-dimensional fast Fourier transform and a Doppler-dimensional fast Fourier transform on the echo signal to obtain range-Doppler data of each object in the radar detection space;

[0098] performing non-coherent accumulation on the range-Doppler data of all virtual antennas of the radar to obtain accumulated range-Doppler data;

[0099] Performing a two-dimensional constant false alarm detection on the accumulated range-Doppler data, configured to extract objects with a Doppler of 0 from the radar detection space as candidate objects;

[0100] Selecting data with Doppler of 0 for any virtual antenna to perform multi-frame data accumulation processing to obtain range-Doppler data in the slow Doppler dimension; and

[0101] Static filtering is performed on the range-Doppler data in the slow Doppler dimension, configured to remove completely stationary background objects from the candidate objects, and use the remaining candidate objects as the target objects.

[0102] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0103] performing range-dimensional constant false alarm detection on the range-Doppler data of the target object in the slow Doppler dimension, and calculating the range unit of the target object relative to the radar;

[0104] Extracting all antenna data of the target object from the range-Doppler data in the fast Doppler dimension; and

[0105] The azimuth and elevation angles of the target object are calculated based on all antenna data of the target object using an azimuth-elevation combined angle measurement algorithm.

[0106] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0107] Phase information is extracted at peaks of the azimuth frequency domain data and the elevation frequency domain data respectively.

[0108] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0109] Phase information is calculated according to the distance information of the target object.

[0110] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0111] Phase information is calculated based on the range unit data corresponding to Doppler 0 in the Doppler-range data.

[0112] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0113] The phase information is obtained by analyzing the phase component of the Doppler-range data.

[0114] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0115] Accumulating multiple frames of data on the phase information to obtain phase change data of the target object;

[0116] Using a 6th-order Butterworth filter designed according to the frequency band of the respiratory frequency to filter the phase change data of the target object to extract respiratory waveform data; and

[0117] Performing a fast Fourier transform on the respiratory waveform data to extract the respiratory cycle and calculate the respiratory frequency of the target object.

[0118] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0119] The phase information is accumulated for a period of 10 seconds.

[0120] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0121] The respiratory frequency of the target object is calculated based on the periodic changes in the frequency components of the respiratory cycle.

[0122] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0123] Accumulating multiple frames of data on the phase information to obtain phase change data of the target object;

[0124] Using a 6th-order Butterworth filter designed according to the frequency band of the heart rate to filter the phase change data of the target object and extract the heart rate waveform data;

[0125] The heart rate waveform data is processed by wavelet transform and then fast Fourier transform to extract the heart rate cycle, and the heart rate of the target object is calculated.

[0126] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0127] The phase information is accumulated for a period of 10 seconds.

[0128] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0129] The heart rate of the target subject is calculated based on the periodic changes in the frequency components of the heart rate cycle.

[0130] In one embodiment, the processor 12 may be configured to call the millimeter-wave radar-based bed-fall warning program stored in the memory 11 and perform the following operations:

[0131] If the number of target objects in the radar detection space exceeds a preset threshold, the radar monitoring system will not monitor the vital sign data of the target objects.

[0132] Based on the hardware architecture of the millimeter-wave radar-based non-contact vital sign data monitoring system, an embodiment of the millimeter-wave radar-based non-contact vital sign data monitoring method of the present application is proposed. The millimeter-wave radar-based non-contact vital sign data monitoring method of the present application is intended to improve the reliability of vital sign monitoring of multiple target subjects.

[0133] 3 , which illustrates an embodiment of a non-contact vital sign data monitoring method based on millimeter wave radar of the present application, the non-contact vital sign data monitoring method based on millimeter wave radar includes the following steps:

[0134] S10: receiving an echo signal in a radar detection space, and screening out a static person to be measured from the radar detection space as a target object according to the echo signal.

[0135] Specifically, a millimeter-wave radar system first transmits millimeter-wave signals, which interact with objects within the detection space. When these signals are reflected or scattered by the target object, the radar receives an echo signal. By analyzing these echo signals, the system can identify static people within the radar detection space and mark them as targets.

[0136] Here, the radar detection space refers to the three-dimensional spatial area used by the millimeter-wave radar system for monitoring and detection. In the technical solution of this application, the radar detection space refers to the area covered and monitored by the millimeter-wave radar system. Optionally, in different application scenarios, the radar detection space can be a closed room, an outdoor area, a traffic intersection, a hospital ward, and so on. The millimeter-wave radar system can be designed to adapt to detection spaces of different sizes and shapes to achieve various monitoring and detection tasks.

[0137] S20. Calculate spatial position information of the target object in the radar detection space according to the echo signal of the target object, where the spatial position information includes distance information, azimuth information, and elevation information of the target unit relative to the radar.

[0138] Specifically, once the target object is identified, the system will further analyze its echo signal to calculate the spatial position information of the target object in the radar detection space. This includes the distance, azimuth information, and elevation information of the target object relative to the radar. Among them, azimuth information and elevation information are parameters used to describe the position of the target object in the radar detection space. They are angular measurements in the polar coordinate system and are used to determine the direction and elevation of the target object relative to the radar. The azimuth information describes the horizontal direction of the target object relative to the radar position. The elevation information describes the vertical direction of the target object relative to the radar position.

[0139] Furthermore, spatial position information is typically expressed using a Cartesian coordinate system, where X, Y, and Z represent the target's position in the horizontal, vertical, and range directions, respectively. After obtaining the target's range, azimuth, and elevation angle information, triangulation or three-dimensional coordinate transformation can be performed based on the radar system's geometric parameters and the target's measurement data to determine the target's spatial position.

[0140] S30. Determine the number of target objects that are equidistant from the radar according to the spatial position information.

[0141] Herein, determining the number of target objects equidistant from the radar according to the spatial position information includes:

[0142] If there are at least two target objects that do not overlap in azimuth and elevation in the same range unit, it is determined that the at least two target objects are equidistant from the radar, and the number of the at least two target objects is calculated as the number of target objects equidistant from the radar.

[0143] Specifically, the system first distinguishes targets at different distances based on range information. It then further distinguishes these targets based on their azimuth and elevation information. If there are at least two targets in the same range cell and they do not overlap in azimuth or elevation, the system considers these targets to be equidistant from the radar and counts them as the number of targets equidistant from the radar.

[0144] It can be understood that this method can effectively avoid aliasing or overlap between target objects and ensure that each target object can be accurately identified and monitored.

[0145] S40: If there are at least two target objects equidistant from the radar in the radar detection space, calculate phase information according to azimuth information and elevation information of the target objects.

[0146] Specifically, if there are at least two targets within the radar's equidistant range, the system calculates phase information based on the target's azimuth and elevation information. Phase information refers to the relative phase difference between millimeter-wave signals reflected from different targets.

[0147] As can be appreciated, when faced with multiple equally spaced targets, azimuth and elevation information allows the targets to be located in three-dimensional space. This means that even if multiple targets are at the same distance from the radar, their horizontal and vertical positions can be accurately distinguished. This allows the radar system to separate and identify multiple targets even at equal distances from the radar, effectively distinguishing and monitoring the respiratory rate and heart rate of multiple equally spaced targets, improving the reliability of vital sign data monitoring for multiple targets.

[0148] S50. Calculate vital sign data of the target object according to the phase information, where the vital sign data includes at least one of a respiratory rate and a heart rate.

[0149] Specifically, when a person breathes or their heart beats, the movement of the chest and heart causes the phase of the millimeter wave signal to change. This change typically manifests as a phase shift or rate of change in the signal. The phase change caused by breathing typically has different frequencies and characteristics than the phase change caused by a heartbeat. Breathing typically has a lower frequency (usually between 0.1 Hz and 0.8 Hz), while the heartbeat typically has a higher frequency (usually above 0.8 Hz to 2.5 Hz). Based on the above principles, by monitoring the phase changes in the echo signal caused by the person's breathing and heartbeat, the respiratory rate and / or heart rate of each person can be calculated using methods such as fast Fourier transform, thereby achieving non-contact vital sign data monitoring.

[0150] It can be understood that the non-contact vital signs data monitoring method based on millimeter-wave radar of the technical solution of the present application, when detecting the presence of multiple target objects equidistant from the radar in space, introduces azimuth information and pitch angle information to obtain phase information, and then calculates the respiratory rate and / or heart rate of each target object based on the acquired phase information to achieve non-contact vital signs data monitoring. In this way, compared with the exemplary contact vital signs monitoring method or millimeter-wave radar vital signs monitoring method, the monitoring method of the present application can not only achieve non-contact monitoring, but also effectively distinguish and monitor the respiratory rate and / or heart rate of multiple equidistant target objects, thereby improving the reliability of vital signs data monitoring for multiple target objects. In other words, the monitoring method of the technical solution of the present application has the advantages of non-contact monitoring and strong monitoring reliability.

[0151] In addition, the monitoring method based on the technical solution of the present application can simultaneously detect the vital signs data of multiple people in one space using only one device, thereby helping to reduce the monitoring cost of multi-object vital signs data monitoring and improving the cost-effectiveness of monitoring.

[0152] Referring to FIG. 4 to FIG. 6 , in some embodiments, selecting a static person under test as a target object from the radar detection space according to the echo signal includes the following steps:

[0153] S11 . Performing range-dimensional fast Fourier transform (RangeFFT) and Doppler-dimensional fast Fourier transform (DopplerFFT) on the echo signal to obtain range-Doppler data of each object in the thunder detection space.

[0154] Among them, in radar signal processing, the range dimension is used to measure the distance between the target object and the radar, and the Doppler dimension is used to measure the speed of the target object.

[0155] Specifically, in the technical solution of this application, the millimeter-wave radar adopts the TD-MIMO signal transmission mode. Three transmitting antennas transmit signals in a time-division multiplexing manner, and four receiving antennas receive signals simultaneously, forming 12 virtual antennas. Each antenna transmits M chirps, and the number of ADC sampling points for each chirp is N. A chirp is a linear frequency modulation signal whose frequency varies linearly with time. It can improve the radar's resolution and anti-interference capabilities. An ADC is an analog-to-digital converter that converts analog signals into digital signals for subsequent signal processing. The amount of ADC data in the current frame is 12*M*N.

[0156] In step S11, an N-point RangeFFT is performed on the ADC data to obtain the distance information for each object. FFT is a transform that converts a signal into a frequency domain representation, used to extract the signal's period or frequency. RangeFFT is a method that uses the relationship between the frequency variation of a chirp signal and the target distance to measure the target distance.

[0157] Next, an M-point Doppler FFT is performed on the RangeFFT to obtain a range-Doppler dimension map (RD-map, i.e., range-Doppler data). The RD-map contains N*M data. This data can be used to represent the position and velocity of each target within the radar detection volume. The Doppler FFT is a method for measuring target velocity by exploiting the relationship between the frequency variation of the echo signal caused by target motion and the target velocity.

[0158] S12. Performing non-coherent accumulation on the range-Doppler data of all virtual antennas of the radar to obtain accumulated range-Doppler data.

[0159] Specifically, referring to Figure 4 , in the technical solution of this application, after obtaining the RD-map for each antenna, the RD-maps of the 12 virtual antennas can be non-coherently accumulated to obtain an accumulated RD-map. Non-coherent accumulation is a method that adds the RD-maps of different antennas or frames based on amplitude without considering phase differences.

[0160] It can be understood that the signal-to-noise ratio of the target can be improved by incoherent accumulation, making it easier to detect the object when performing constant false alarm detection in subsequent steps.

[0161] S13 , performing a two-dimensional constant false alarm rate (2D CFAR) detection on the accumulated range-Doppler data to extract objects with a Doppler of 0 from the radar detection space as candidate objects.

[0162] Among them, 2D CFAR detection is a method that performs constant false alarm detection in two dimensions: distance and Doppler. It can determine whether a target exists based on the average value of each unit and the surrounding units in the RD-map.

[0163] Specifically, 2D CFAR detection applies a two-dimensional threshold decision to the accumulated range-Doppler data based on a given false alarm rate threshold. Data points exceeding the threshold are considered to be present. Therefore, 2D CFAR detection can filter out objects in the radar detection space.

[0164] Furthermore, since the respiration and heart rate detection is only performed on stationary targets, objects with a Doppler of 0 are selected from the existing objects as candidate objects. Among them, a Doppler of 0 indicates that the target has no speed change, that is, it is stationary.

[0165] Referring to Figure 5, the center cell represents the detected cell, the cell being determined to be a target. The middle cell represents the guard cell, adjacent to the detected cell, used to mitigate the target's edge effect. The outer cells represent the noise cells, adjacent to the guard cell, used to estimate the average noise level. When the detected cell is larger than a times the noise cell, the detection cell is detected, where a is a preset threshold parameter used to control detection sensitivity.

[0166] S14, selecting the data of any virtual antenna with Doppler of 0 to perform multi-frame data accumulation processing to obtain range-Doppler data in the slow Doppler dimension.

[0167] Specifically, referring to Figure 6 , in this embodiment, data with Doppler of antenna 1 at 0 is selected and accumulated over 5 seconds to obtain the range-Doppler data (RD-map) in the slow Doppler dimension. This allows the temporal variation of this data to be determined.

[0168] Among them, slow Doppler refers to the change in echo signal frequency caused by the target's slight movement, which is related to the target's breathing and heart rate.

[0169] It can be understood that by selecting data with a Doppler of 0 and performing multi-frame data accumulation processing on them, and by superimposing the multi-frame data, the observation time of the target object can be increased, thereby improving the reliability of detection.

[0170] S15 . Perform static filtering on the range-Doppler data in the slow Doppler dimension to remove completely stationary background objects from the candidate objects, and use the remaining candidate objects as the target objects.

[0171] Specifically, static filtering of slow Doppler data (subtracting the average of multiple frames from each frame) can exploit the stability of background signals to remove completely static backgrounds, retaining only the changing signals, i.e., the target signals. The objects corresponding to these target signals (i.e., the remaining candidate objects) can then be considered the target objects.

[0172] Through the above steps S11 to S15, static persons under detection can be identified from the radar detection space. By combining distance, Doppler and constant false alarm detection technologies, the accuracy and reliability of static targets can be improved.

[0173] With reference to FIG. 7 , in some embodiments, calculating the spatial position information of the target object in the radar detection space based on the echo signal of the target object includes the following steps:

[0174] S21 . Performing range-dimensional constant false alarm detection on the range-Doppler data of the target object in the slow Doppler dimension, and calculating a range unit of the target object relative to the radar.

[0175] Among them, distance-dimensional constant false alarm detection is a method for constant false alarm detection in the distance dimension. It can judge whether there is a target based on the average value of each unit and the surrounding units in the slow Doppler spectrum.

[0176] Specifically, after performing range-Doppler constant false alarm detection on the range-Doppler data of the target object in the slow Doppler dimension, the range unit of the stationary target with breathing and heart rate (ie, the target object) can be extracted.

[0177] S22. Extract all antenna data of the target object from the range-Doppler data in the fast Doppler dimension.

[0178] The fast-Doppler range-Doppler data is collected by the radar system and contains information about the signals received by each antenna. The signal received by each antenna includes the target's range and Doppler frequency, which are related to the target's characteristics in terms of distance and speed.

[0179] Specifically, after identifying the target object, the echo signals associated with the target object are identified to confirm the data points associated with the target object. Next, the antennas that received this data are determined. Finally, all antenna data related to the target object can be extracted from the range-Doppler data in the Fast Doppler dimension.

[0180] S23. Calculate the azimuth and elevation angles of the target object using an azimuth-elevation combined angle measurement algorithm based on all antenna data of the target object.

[0181] The azimuth-elevation joint angle measurement algorithm is a method for calculating the azimuth and elevation angles of a target object within the radar detection space. The core principle of the azimuth-elevation joint angle measurement algorithm is to use multiple antennas or sensors to measure the target object's signals and combine the phase and amplitude information of these signals to calculate the azimuth and elevation angles.

[0182] Specifically, based on all the antenna data extracted from the target object, an azimuth-elevation joint angle measurement algorithm is used to calculate the target object's azimuth and elevation angles. Once the azimuth and elevation information are calculated, the target object's three-dimensional position within the radar detection space can be obtained.

[0183] In some embodiments, the azimuth information includes azimuth frequency domain data, and the elevation information includes elevation frequency domain data.

[0184] Specifically, by performing an angle-dimensional fast Fourier transform on the target object's echo signal, the azimuth frequency domain data and the elevation frequency domain data of the target object can be obtained. The azimuth frequency domain data and the elevation frequency domain data can display the amplitude and phase information of the frequency components.

[0185] Correspondingly, extracting phase information according to the azimuth information and the elevation information of the target object includes: extracting phase information at peaks of the azimuth frequency domain data and the elevation frequency domain data, respectively.

[0186] In frequency domain data, phase information is usually associated with the peak value of the frequency component. A peak value indicates that the signal intensity reaches a maximum value at a specific frequency.

[0187] Specifically, for azimuth information, phase information can be extracted at the peak value (the frequency component with the largest amplitude) of the azimuth frequency domain data. Similarly, for elevation information, phase information can be extracted at the peak value (the frequency component with the largest amplitude) of the elevation frequency domain data.

[0188] As shown in FIG8 , in some embodiments, before calculating the vital sign data of the target object based on the phase information, if there is only one target object in the radar detection space, or there are multiple target objects at unequal distances from the radar, the method further includes:

[0189] Phase information is calculated according to the distance information of the target object.

[0190] Specifically, when there is only one target object or multiple target objects in the radar detection space, but the distances between the multiple target objects and the radar are not equal, the system will obtain its phase information based on the distance information of the target object.

[0191] It's understandable that when there's only one target or multiple targets at unequal distances within the radar detection space, the radar can accurately identify multiple targets based on distance information. Therefore, obtaining phase information based on distance information can reduce computational complexity, reduce the system's computational load, conserve computing resources, and improve efficiency, all while ensuring reliable vital sign monitoring of the target.

[0192] In some embodiments, the range information comprises Doppler-range data.

[0193] Specifically, by performing a Doppler Fast Fourier Transform on the echo signal of the target object, the Doppler-range data of the target object can be obtained. The Doppler-range data includes the distance information of the target object from the radar and the Doppler frequency information.

[0194] Furthermore, calculating the phase information according to the distance information of the target object includes:

[0195] Phase information is calculated according to range unit data corresponding to a Doppler of 0 in the Doppler-range data.

[0196] Among them, a Doppler of 0 corresponds to a target object having a speed of zero relative to the radar, that is, the target object is stationary or has a very small speed relative to the radar.

[0197] Specifically, by analyzing the Doppler-range data, the range unit data corresponding to Doppler 0 can be determined. The range unit data represents the position of all stationary or nearly stationary target objects relative to the radar.

[0198] Furthermore, after determining the range unit data corresponding to Doppler 0, the phase information can be obtained by analyzing the phase component of the Doppler-range data.

[0199] In some embodiments, calculating the respiratory rate of the target subject based on the phase information includes the following steps:

[0200] S51 , accumulating multiple frames of phase information to obtain phase change data of the target object.

[0201] Specifically, multiple frames of phase information from the target object can be accumulated, for example, for 10 seconds. Through multi-frame accumulation, the phase change waveform / curve (i.e., phase change curve) of each target object over the multiple frames can be extracted.

[0202] Among them, the accumulation of multi-frame data helps to increase the signal-to-noise ratio, thereby improving the accuracy of respiratory rate monitoring.

[0203] S52 , using a 6th-order Butterworth filter designed according to the frequency band of the respiratory frequency to filter the phase change data of the target object and extract respiratory waveform data.

[0204] The Butterworth filter is a digital filter commonly used in signal processing. It enhances signals within a specific frequency range while suppressing unwanted frequency components. A sixth-order Butterworth filter designed for the respiratory frequency range can emphasize respiratory waveform signals while reducing noise.

[0205] Specifically, after filtering the phase change data, the obtained signal can be considered as respiratory waveform data, which reflects the periodic changes in the respiration of the target object.

[0206] S53: Perform fast Fourier transform on the respiratory waveform data to extract the respiratory cycle, so as to calculate the respiratory frequency of the target object.

[0207] Specifically, by performing a fast Fourier transform (FFT) on the extracted respiratory waveform data, the frequency components of the target object's respiratory cycle can be extracted from the waveform. Based on the periodic changes in the frequency components of the respiratory cycle, the target object's respiratory frequency can be calculated.

[0208] In addition, by analyzing the FFT results, the amplitude and phase information corresponding to the respiratory frequency in the spectrum can be extracted. Among them, the amplitude information of the respiratory frequency can be used to indicate the intensity of breathing.

[0209] In some embodiments, calculating the heart rate of the target subject based on the phase information includes the following steps:

[0210] S54: Accumulate multiple frames of phase information to obtain phase change data of the target object.

[0211] Specifically, multiple frames of phase information from the target object can be accumulated, for example, for 10 seconds. Through multi-frame accumulation, the phase change waveform / curve (i.e., phase change curve) of each target object over the multiple frames can be extracted.

[0212] Among them, the accumulation of multi-frame data helps to increase the signal-to-noise ratio, thereby improving the accuracy of respiratory rate monitoring.

[0213] S55, filtering the phase change data of the target object using a 6th-order Butterworth filter designed according to the frequency band of the heart rate to extract the heart rate waveform data;

[0214] The Butterworth filter is a digital filter commonly used in signal processing. It enhances signals within a specific frequency range while suppressing unwanted frequency components. A sixth-order Butterworth filter designed for the heart rate frequency range can emphasize the heartbeat waveform signal and reduce noise.

[0215] Specifically, after filtering the phase change data, the obtained signal can be considered as heart rate waveform data, which reflects the periodic changes in the heart rate of the target object.

[0216] S56 , performing wavelet transform on the heart rate waveform data and then performing fast Fourier transform to extract the heart rate cycle, so as to calculate the heart rate of the target object.

[0217] Among them, wavelet transform is a transform that can decompose the signal into basis functions of different scales and frequencies, which is used to extract the detailed features of the signal or remove noise.

[0218] Specifically, by performing wavelet transform and fast Fourier transform (FFT) on the extracted respiratory waveform data, the frequency components of the target object's heart rate cycle can be extracted from the waveform. Based on the periodic changes in the frequency components of the heart rate cycle, the target object's heart rate can be calculated.

[0219] In addition, by analyzing the FFT results, we can also extract the amplitude and phase information corresponding to the heart rate in the spectrum. Among them, the amplitude information of the heart rate can be used to indicate the intensity of the heartbeat.

[0220] In some embodiments, if the number of target objects in the radar detection space exceeds a preset threshold, the radar monitoring system does not monitor the vital sign data of the target objects.

[0221] Exemplarily, the preset threshold may be set to 4.

[0222] It can be understood that when the number of target objects in a specific space (such as a ward) exceeds the set number of beds (i.e., the set number), it indicates that there are more people than expected in the space. Vital sign monitoring at this time may detect vital sign data of non-recorded personnel (patients), affecting data security and reliability. Controlling the radar system to stop vital sign monitoring at this time not only saves monitoring resources but also ensures the purity of the monitored data.

[0223] In addition, embodiments of the present application further provide a computer-readable storage medium. The computer-readable storage medium may be any one of, or any combination of, a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), or a USB memory device. The computer-readable storage medium includes a millimeter-wave radar-based non-contact vital sign data monitoring program 10. The specific implementation of the computer-readable storage medium of the present application is substantially the same as the specific implementation of the millimeter-wave radar-based non-contact vital sign data monitoring method and server 1 described above, and will not be further described herein.

[0224] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0225] This application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing system to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing system produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more blocks in the block diagram.

[0226] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing system to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0227] These computer program instructions can also be loaded onto a computer or other programmable data processing system so that a series of operating steps are executed on the computer or other programmable system to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable system provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0228] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0229] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A non-contact vital sign data monitoring method based on millimeter wave radar, wherein: include: Receiving echo signals in the radar detection space, and selecting static persons to be detected from the radar detection space as target objects according to the echo signals; Calculating spatial position information of the target object in the radar detection space according to the echo signal of the target object, the spatial position information including distance information, azimuth information and elevation angle information of the target unit compared to the radar; Determine the number of target objects that are equidistant from the radar according to the spatial position information; If there are at least two target objects equidistant from the radar in the radar detection space, calculating phase information according to azimuth information and elevation information of the target objects; as well as The vital sign data of the target object is calculated according to the phase information, where the vital sign data includes at least one of a respiratory rate and a heart rate.

2. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 1, wherein: Determining the number of target objects equidistant from the radar according to the spatial position information includes: If there are at least two target objects that do not overlap in azimuth and elevation in the same distance unit, it is determined that the at least two target objects are equidistant from the radar, and the number of the at least two target objects is calculated as the number of target objects equidistant from the radar.

3. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 1, wherein: The millimeter wave radar has multiple virtual antennas; Screening out a static person to be detected as a target object from the radar detection space according to the echo signal includes: Performing a range-dimensional fast Fourier transform and a Doppler-dimensional fast Fourier transform on the echo signal to obtain range-Doppler data of each object in the radar detection space; Performing incoherent accumulation of the range-Doppler data of all virtual antennas of the radar to obtain accumulated range-Doppler data; Performing a two-dimensional constant false alarm detection on the accumulated range-Doppler data, configured to extract an object with a Doppler of 0 from the radar detection space as a candidate object; Select the data with Doppler of any virtual antenna being 0 to perform multi-frame data accumulation processing to obtain the range-Doppler data of the slow Doppler dimension; and Static filtering is performed on the range-Doppler data in the slow Doppler dimension, configured to remove completely stationary background objects from the candidate objects, and use the remaining candidate objects as the target objects.

4. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 3, wherein: Calculating the spatial position information of the target object in the radar detection space according to the echo signal of the target object includes: Performing a range-dimensional constant false alarm detection on the range-Doppler data of the target object in the slow Doppler dimension, and calculating a distance unit of the target object compared to the radar; Extracting all antenna data of the target object from the range-Doppler data in the fast Doppler dimension; and According to all antenna data of the target object, an azimuth-elevation joint angle measurement algorithm is adopted to calculate the azimuth and elevation angles of the target object.

5. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 3, wherein: The azimuth information includes azimuth frequency domain data, and the elevation information includes elevation frequency domain data; Extracting phase information according to the azimuth information and elevation information of the target object includes: Phase information is extracted at peaks of the azimuth frequency domain data and the elevation frequency domain data respectively.

6. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 3, wherein: Before calculating the vital sign data of the target object according to the phase information, if there is only one target object in the radar detection space, or there are multiple target objects that are not equidistant from the radar, the method further includes: Phase information is calculated according to the distance information of the target object.

7. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 6, wherein: The range information includes Doppler-range data; Calculating phase information according to the distance information of the target object includes: The phase information is calculated according to the range unit data corresponding to the Doppler value of 0 in the Doppler-range data.

8. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 6, wherein: The phase information is calculated based on the distance unit data corresponding to Doppler 0 in the Doppler-range data, including: The phase information is obtained by analyzing the phase component of the Doppler-range data.

9. The non-contact vital sign data monitoring method based on millimeter wave radar according to claim 1 or 6, wherein: Calculating the respiratory frequency of the target object according to the phase information includes: Accumulating multiple frames of data on the phase information to obtain phase change data of the target object; Using a 6th-order Butterworth filter designed according to the frequency band of the respiratory frequency to filter the phase change data of the target object to extract the respiratory waveform data; and Performing a fast Fourier transform on the respiratory waveform data to extract the respiratory cycle, and calculating the respiratory frequency of the target object.

10. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 9, wherein: Accumulating multiple frames of data on the phase information, including: The phase information is accumulated for a period of 10 seconds.

11. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 9, wherein: Performing a fast Fourier transform on the respiratory waveform data to extract the respiratory cycle and calculate the respiratory frequency of the target object includes: The respiratory frequency of the target object is calculated based on the periodic changes in the frequency components of the respiratory cycle.

12. The non-contact vital sign data monitoring method based on millimeter wave radar according to claim 1 or 6, wherein: Calculating the heart rate of the target object according to the phase information includes: Accumulating multiple frames of data on the phase information to obtain phase change data of the target object; Using a 6th-order Butterworth filter designed according to the frequency band of the heart rate to filter the phase change data of the target object, and extract the heart rate waveform data; The heart rate waveform data is processed by wavelet transform and then fast Fourier transform to extract the heart rate cycle, and the heart rate of the target object is calculated.

13. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 12, wherein: Accumulating multiple frames of data on the phase information, including: The phase information is accumulated for a period of 10 seconds.

14. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 12, wherein: The heart rate waveform data is subjected to wavelet transform and then fast Fourier transform to extract the heart rate cycle, and the heart rate of the target object is calculated, including: The heart rate of the target subject is calculated based on the periodic changes in the frequency components of the heart rate cycle.

15. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 1, wherein: The method further comprises: If the number of target objects in the radar detection space exceeds a preset threshold, the radar monitoring system will not monitor the vital sign data of the target objects.

16. The non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 15, wherein: The preset threshold is set to 4.

17. The non-contact vital sign data monitoring method based on millimeter wave radar according to claim 1, wherein: The millimeter wave radar includes three transmitting antennas and four receiving antennas.

18. The non-contact vital sign data monitoring method based on millimeter wave radar according to claim 1, wherein: The millimeter wave radar adopts a time division multiple access multiple input multiple output signal transmission mode.

19. A non-contact vital sign data monitoring system based on millimeter wave radar, wherein: The invention comprises a millimeter wave radar, a memory, a processor and a non-contact vital sign data monitoring program based on the millimeter wave radar which is stored in the memory and can be run on the processor. When the processor executes the millimeter-wave radar-based non-contact vital sign data monitoring program, the millimeter-wave radar-based non-contact vital sign data monitoring method according to claim 1 is implemented.

20. A computer-readable storage medium, wherein: The computer-readable storage medium stores a non-contact vital sign data monitoring program based on millimeter wave radar, and when the non-contact vital sign data monitoring program based on millimeter wave radar is executed by the processor, the non-contact vital sign data monitoring method based on millimeter wave radar as claimed in claim 1 is implemented.

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