Physiological parameter separation monitoring method, device, equipment and medium

Abstract

The invention discloses a physiological parameter separation monitoring method, device and equipment and a medium. In a target monitoring area, an FMCW millimeter wave radar is indicated to transmit an FMCW signal and receive at least one group of target reflection signals; performing position processing on each group of target reflection signals through a wave velocity forming algorithm to obtain each target position, and performing grouping processing through a clustering algorithm to obtain target identity information corresponding to each target position; respectively calculating corresponding target reflection signal frequency domain characteristics according to the target reflection signals respectively matched with the target identity information, and respectively obtaining corresponding physiological parameter separation values; the breathing frequency band signal value and the heart rate frequency band signal value are sent to a target cloud server, and the target cloud server is indicated to conduct real-time monitoring processing operation according to the target identity information. The problems that multi-target monitoring cannot be effectively carried out, and physiological parameter separation cannot be carried out are solved, the monitoring efficiency is improved, and the nursing cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method, device, equipment and medium for separating and monitoring physiological parameters. Background Art

[0002] With the problem of population aging, more medical staff are needed to ensure people's safety, resulting in high labor costs, and timely medical first aid treatment operations are required.

[0003] In the process of implementing the present invention, the inventor found that the existing technology has the following defects: At present, for user contact devices, there are problems of cumbersome wearing and poor comfort, and it is difficult to synchronously monitor multiple people; for existing user non-contact technologies, it is impossible to effectively distinguish individual physiological signals in a multi-person scenario, resulting in data confusion, unable to effectively monitor multiple targets, with low monitoring efficiency and high nursing costs. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for separating and monitoring physiological parameters to improve the monitoring efficiency and reduce the nursing cost.

[0005] According to one aspect of the present invention, there is provided a method for separating and monitoring physiological parameters, including:

[0006] In a target monitoring area, instruct a pre-installed FMCW (Frequency Modulated Continuous Wave) millimeter wave radar to transmit an FMCW signal and receive at least one set of target reflection signals;

[0007] Through a pre-set wave velocity shaping algorithm, perform position processing on each set of target reflection signals to obtain each target position, and perform grouping processing through a pre-set clustering algorithm to obtain target identity information corresponding to each target position;

[0008] Calculate the frequency domain characteristics of the corresponding target reflection signals respectively according to the target reflection signals respectively matched with each target identity information, and obtain the corresponding physiological parameter separation values respectively according to the frequency domain characteristics of each target reflection signal;

[0009] Wherein, the physiological parameter separation values include respiratory frequency band signal values and heart rate frequency band signal values;

[0010] Send the respiratory frequency band signal value and the heart rate frequency band signal value to a target cloud server, and instruct the target cloud server to perform real-time monitoring and processing operations according to each target identity information.

[0011] According to another aspect of the present invention, there is provided a device for separating and monitoring physiological parameters, including:

[0012] A target reflection signal receiving module, configured to indicate a pre-installed FMCW millimeter-wave radar to transmit an FMCW signal in a target monitoring area and receive at least one set of target reflection signals;

[0013] A target identity information determination module, configured to perform position processing on the sets of target reflection signals through a pre-set wave velocity shaping algorithm to obtain the positions of the targets, and perform grouping processing through a pre-set clustering algorithm to obtain the target identity information corresponding to the positions of the targets respectively;

[0014] A physiological parameter separation value determination module, configured to calculate the corresponding frequency domain characteristics of the target reflection signals respectively according to the target reflection signals respectively matched with the target identity information, and obtain the corresponding physiological parameter separation values respectively according to the frequency domain characteristics of the target reflection signals;

[0015] Wherein, the physiological parameter separation values include respiration frequency band signal values and heart rate frequency band signal values;

[0016] A real-time monitoring and processing module, configured to send the respiration frequency band signal values and the heart rate frequency band signal values to a target cloud server, and instruct the target cloud server to perform real-time monitoring and processing operations according to the target identity information.

[0017] According to another aspect of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the physiological parameter separation monitoring method according to any embodiment of the present invention is implemented.

[0018] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the physiological parameter separation monitoring method according to any embodiment of the present invention when executed.

[0019] In the technical solution of the embodiment of the present invention, in a target monitoring area, an FMCW millimeter-wave radar installed in advance is instructed to transmit an FMCW signal, and at least one group of target reflection signals is received; through a preset wave velocity shaping algorithm, position processing is performed on each group of target reflection signals to obtain each target position, and grouping processing is performed through a preset clustering algorithm to obtain target identity information corresponding to each target position; corresponding frequency-domain characteristics of the target reflection signals are respectively calculated according to the target reflection signals respectively matched with each target identity information, and corresponding physiological parameter separation values are respectively obtained according to the frequency-domain characteristics of each target reflection signal; wherein, the physiological parameter separation values include respiration frequency band signal values and heart rate frequency band signal values; the respiration frequency band signal values and the heart rate frequency band signal values are sent to a target cloud server, and the target cloud server is instructed to perform real-time monitoring processing operations according to each target identity information. The problems of ineffective multi-target monitoring and inability to separate physiological parameters are solved, the monitoring efficiency is improved, and the nursing cost is reduced.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0022] Figure 1 is a flowchart of a physiological parameter separation monitoring method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flowchart of another physiological parameter separation monitoring method provided according to Embodiment 2 of the present invention;

[0024] Figure 3 is a schematic structural diagram of a physiological parameter separation monitoring device provided according to Embodiment 3 of the present invention;

[0025] Figure 4 is a schematic structural diagram of an electronic device provided according to Embodiment 4 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solution in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "target", "current", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 It is a flowchart of a physiological parameter separation monitoring method provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of separating and monitoring the physiological parameters of multiple targets. This method can be executed by a physiological parameter separation monitoring device, and the physiological parameter separation monitoring device can be implemented in the form of hardware and / or software.

[0030] Correspondingly, as Figure 1 shown, the method includes:

[0031] S110. In the target monitoring area, instruct the pre-installed FMCW millimeter-wave radar to transmit an FMCW signal and receive at least one set of target reflection signals.

[0032] Among them, the FMCW millimeter-wave radar includes a MIMO array antenna, and the MIMO array antenna is used for spatial filtering and transmission of the FMCW signal; among them, the MIMO array antenna performs spatial filtering processing on the FMCW signal through a pre-set adaptive weight allocation algorithm; among them, the FMCW millimeter-wave radar is connected to the embedded processor through a PCI-E3.0 interface.

[0033] Among them, MIMO (Multiple-Input Multiple-Output) is multiple input and multiple output.

[0034] Specifically, the PCI-E 3.0 interface is the third generation of the PCI Express technical standard organization, PCI-SIG. The embedded processor can be an ARM Cortex A72. It can be understood that the FMCW millimeter-wave radar can be connected to the ARM Cortex A72 through the PCI-E 3.0 interface, thereby realizing high-speed data processing operations.

[0035] In this embodiment, the FMCW millimeter-wave radar emits a frequency-modulated continuous wave signal. Specifically, the frequency-modulated continuous wave signal can be emitted through the frequency band of 24 GHz or 60 GHz. By sending the FMCW signal, when the FMCW signal contacts multiple target users, multiple target reflection signals will be formed and the target reflection signals will be fed back.

[0036] In this embodiment, in the target monitoring area, the FMCW millimeter-wave radar is pre-installed, and the coverage range of the FMCW millimeter-wave radar can be greater than or equal to 15 square meters. For the monitoring of the target monitoring area, a certain time delay can be set, for example, 50 milliseconds, which can meet the requirement of real-time monitoring of users in the target monitoring area.

[0037] S120. Through a pre-set wave velocity shaping algorithm, perform position processing on each group of target reflection signals to obtain each target position, and perform grouping processing through a pre-set clustering algorithm to obtain the target identity information corresponding to each target position.

[0038] Among them, the wave velocity shaping algorithm can be a signal processing technology mainly used for the orientation and enhancement of signal sources in MIMO arrays. In this embodiment, the wave velocity shaping algorithm is used to locate each group of target reflection signals to obtain the target position. Specifically, the accuracy of target position determination can be improved through this wave velocity shaping algorithm, and the position information of different users can be effectively distinguished.

[0039] Among them, the target position can be used to represent the positions of each target user. The clustering algorithm can be an algorithm for clustering different positions, and it can be an algorithm for better determining the identity information of target users. The target identity information can be used to characterize the specific identity of each target user.

[0040] Optionally, perform position processing on the groups of target reflection signals through a pre-set wave velocity shaping algorithm to obtain respective target positions, and perform grouping processing through a pre-set clustering algorithm to obtain target identity information corresponding to each target position, including: performing position processing on the groups of target reflection signals through a pre-set wave velocity shaping algorithm, and calculating the target azimuth and target distance corresponding to each target position; respectively performing grouping processing on each of the target azimuths and each of the target distances through a pre-set clustering algorithm to obtain target identity information corresponding to each target position.

[0041] In this embodiment, the target position may include a target azimuth and a target distance. Specifically, assuming that 5 groups of target reflection signals are obtained, position processing needs to be performed on the 5 groups of target reflection signals respectively, and the target azimuth (which can be represented by θ) and target distance (which can be represented by r) corresponding to the 5 groups of target reflection signals are calculated.

[0042] Furthermore, the target azimuths corresponding to the 5 groups of target reflection signals (θ1, θ2, θ3, θ4, and θ5 respectively) can be subjected to clustering and grouping processing. The specific clustering algorithm can be the DBSCAN algorithm, and an azimuth clustering and grouping result can be obtained. Similarly, r1, r2, r3, r4, and r5 can be subjected to clustering and grouping processing through the DBSCAN algorithm respectively, and a distance clustering and grouping result can be obtained.

[0043] Correspondingly, the target identity information corresponding to each target position can be bound according to the azimuth clustering and grouping result and the distance clustering and grouping result.

[0044] S130. Calculate the frequency domain features of the corresponding target reflection signals respectively according to the target reflection signals respectively matched with each of the target identity information, and obtain the corresponding physiological parameter separation values respectively according to the frequency domain features of each of the target reflection signals.

[0045] Among them, the physiological parameter separation values include respiratory frequency band signal values and heart rate frequency band signal values.

[0046] Among them, the target reflection signal is a time domain signal, and the frequency domain feature of the target reflection signal can be the frequency domain feature of the target reflection signal obtained by converting the time domain signal into the frequency domain.

[0047] Among them, the physiological parameter separation value can be separated from the frequency domain feature of the target reflection signal and is used to describe the physiological parameter value of the target user.

[0048] Optionally, calculating corresponding frequency domain features of the target reflection signals respectively matched according to the respective target identity information, and obtaining corresponding physiological parameter separation values according to the frequency domain features of the respective target reflection signals, includes: performing frequency domain transformation on the target reflection signals respectively matched according to the respective target identity information through fast Fourier transform, and respectively calculating the frequency domain features of the respective target reflection signals; performing denoising processing on the frequency domain features of the respective target reflection signals respectively through a preset wavelet denoising method to obtain corresponding respiration frequency band signals and heart rate frequency band signals; performing value calculation on the respective respiration frequency band signals and the respective heart rate frequency band signals respectively through a preset peak detection algorithm to obtain respiration frequency band signal values and heart rate frequency band signal values.

[0049] Optionally, performing denoising processing on the frequency domain features of the respective target reflection signals respectively through a preset wavelet denoising method to obtain corresponding respiration frequency band signals and heart rate frequency band signals, includes: performing denoising processing on the frequency domain features of the respective target reflection signals respectively through the Daubechies basis function in the wavelet denoising method to obtain corresponding respiration frequency band signals and heart rate frequency band signals respectively.

[0050] In this embodiment, it is necessary to perform frequency domain transformation processing on the target reflection signals through fast Fourier transform to obtain the frequency domain features of the target reflection signals. Furthermore, performing denoising processing on the frequency domain features of the respective target reflection signals through the Daubechies basis function can achieve the functions of eliminating noise and separating overlapping spectra, and can further adaptively adjust the filtering parameters according to the signal strength to improve the monitoring reliability in low signal-to-noise ratio scenarios.

[0051] Specifically, a wavelet denoising method can be applied to remove power frequency interference (specifically, it can be 50Hz or 60Hz) and high-frequency noise, and retain the respiration and heart rate frequency band signals. Correspondingly, the respiration frequency band signals and heart rate frequency band signals can be obtained.

[0052] Furthermore, the value calculation can be performed on the respective respiration frequency band signals and the respective heart rate frequency band signals respectively through a peak detection algorithm to obtain respiration frequency band signal values and heart rate frequency band signal values.

[0053] S140: Sending the respiration frequency band signal values and the heart rate frequency band signal values to a target cloud server, and instructing the target cloud server to perform real-time monitoring processing operations according to the respective target identity information.

[0054] In this embodiment, after determining the respiratory frequency band signal value and the heart rate frequency band signal value, real-time monitoring and processing operations can be performed through the target cloud server, so that when it is found that the user's corresponding respiration or heart rate is abnormal, early warnings and emergency operations can be carried out in a timely manner to ensure the user's life safety and improve the monitoring efficiency.

[0055] The technical solution of the embodiment of the present invention is as follows: in the target monitoring area, an FMCW millimeter-wave radar installed in advance is instructed to transmit an FMCW signal and at least one group of target reflection signals is received; through a preset wave velocity shaping algorithm, position processing is performed on each group of target reflection signals to obtain each target position, and grouping processing is performed through a preset clustering algorithm to obtain target identity information corresponding to each target position; according to the target reflection signals respectively matched with each target identity information, the frequency domain characteristics of the corresponding target reflection signals are calculated respectively, and corresponding physiological parameter separation values are obtained respectively according to the frequency domain characteristics of each target reflection signal; wherein, the physiological parameter separation values include a respiratory frequency band signal value and a heart rate frequency band signal value; the respiratory frequency band signal value and the heart rate frequency band signal value are sent to the target cloud server, and the target cloud server is instructed to perform real-time monitoring and processing operations according to each target identity information. This solves the problems of ineffective multi-target monitoring and inability to separate physiological parameters, improves the monitoring efficiency, and reduces the nursing cost.

[0056] Embodiment 2

[0057] Figure 2 FIG. 10 is a flowchart of another physiological parameter separation monitoring method provided by the second embodiment of the present invention. This embodiment is refined based on the above embodiments. In this embodiment, further refinement operations are performed on the operation of sending the respiratory frequency band signal value and the heart rate frequency band signal value to the target cloud server and instructing the target cloud server to perform real-time monitoring and processing operations according to each target identity information.

[0058] Correspondingly, as Figure 2 shown, the method includes:

[0059] S210. In the target monitoring area, an FMCW millimeter-wave radar installed in advance is instructed to transmit an FMCW signal and at least one group of target reflection signals is received.

[0060] S220. Through a preset wave velocity shaping algorithm, position processing is performed on each group of target reflection signals to obtain each target position, and grouping processing is performed through a preset clustering algorithm to obtain target identity information corresponding to each target position.

[0061] S230. Calculate the corresponding frequency domain characteristics of the target reflection signals respectively matched according to each of the target identity information, and obtain the corresponding physiological parameter separation values according to the frequency domain characteristics of each of the target reflection signals.

[0062] Among them, the physiological parameter separation values include the respiratory frequency band signal value and the heart rate frequency band signal value.

[0063] S240. Instruct the target cloud server to perform real-time monitoring on the received respiratory frequency band signal value and heart rate frequency band signal value according to a preset threshold alarm rule to obtain each real-time monitoring result.

[0064] Among them, the threshold alarm rule includes a respiratory frequency band signal value threshold and a heart rate frequency band signal value threshold; the real-time monitoring result is an abnormal real-time monitoring result or a normal real-time monitoring result;

[0065] S250. If there is at least one of the real-time monitoring results that is an abnormal real-time monitoring result, obtain the current abnormal target identity information that matches the abnormal real-time monitoring result.

[0066] S260. According to the current abnormal target identity information, instruct the humanoid health care robot to perform an emergency handling operation.

[0067] Among them, the threshold alarm rule can be the preset threshold sizes of the respiratory frequency band signal value and the heart rate frequency band signal value.

[0068] Exemplarily, assume that 3 target users are monitored, and the corresponding respiratory frequency band signal values and heart rate frequency band signal values are respectively (the respiratory frequency band signal value of target user 1 is a1 and the heart rate frequency band signal value is b1; the respiratory frequency band signal value of target user 2 is a2 and the heart rate frequency band signal value is b2; the respiratory frequency band signal value of target user 3 is a3 and the heart rate frequency band signal value is b3). Assume that the respiratory frequency band signal value threshold is a th and the heart rate frequency band signal value threshold is b th .

[0069] Assume a1≥a th and b1≥b th , then both the respiratory frequency band signal value and the heart rate frequency band signal value exceed the threshold, and the real-time monitoring result is an abnormal real-time monitoring result.

[0070] Assume a2≥a th and b2<b th , then the respiratory frequency band signal value exceeds the threshold, and the real-time monitoring result is an abnormal real-time monitoring result. Or assume a2<a th and b2≥b th , then the heart rate frequency band signal value exceeds the threshold, and the real-time monitoring result is an abnormal real-time monitoring result.

[0071] Assume a3 < a th and b3 < b th , then both the respiratory frequency band signal value and the heart rate frequency band signal value are less than the threshold, and the real-time monitoring result is a normal real-time monitoring result.

[0072] Furthermore, assume there is an abnormal real-time monitoring result, and obtain the current abnormal target identity information that matches the abnormal real-time monitoring result, which can be target user 1 and target user 2 respectively. Further, according to the current abnormal target identity information corresponding to target user 1 and target user 2 respectively, the humanoid health care robot can be instructed to perform emergency handling operations.

[0073] The technical solution of the embodiment of the present invention is as follows: in the target monitoring area, instruct the pre-installed FMCW millimeter wave radar to emit FMCW signals and receive at least one group of target reflection signals; through the pre-set wave velocity shaping algorithm, perform position processing on the groups of target reflection signals to obtain the positions of each target, and perform grouping processing through the pre-set clustering algorithm to obtain the target identity information corresponding to each target position; calculate the corresponding target reflection signal frequency domain characteristics according to the target reflection signals respectively matched with each target identity information, and obtain the corresponding physiological parameter separation values according to the frequency domain characteristics of each target reflection signal; wherein, the physiological parameter separation values include the respiratory frequency band signal value and the heart rate frequency band signal value; send the respiratory frequency band signal value and the heart rate frequency band signal value to the target cloud server, and instruct the target cloud server to perform real-time monitoring processing operations according to each target identity information. This solves the problems of being unable to effectively monitor multiple targets and being unable to separate physiological parameters, improves the monitoring efficiency, and reduces the nursing cost. By setting a threshold, it is possible to monitor and give early warnings for abnormal real-time monitoring results, and by instructing the humanoid health care robot to perform emergency handling operations, the safety of users is guaranteed and the user experience is improved.

[0074] Embodiment III

[0075] Figure 3 It is a schematic structural diagram of a physiological parameter separation monitoring device provided in Embodiment III of the present invention. The physiological parameter separation monitoring device provided in this embodiment can be implemented through software and / or hardware, and can be configured in a terminal device or a server to implement a physiological parameter separation monitoring method in the embodiment of the present invention. As Figure 3 shown, the device includes: a target reflection signal receiving module 310, a target identity information determining module 320, a physiological parameter separation value determining module 330, and a real-time monitoring processing module 340.

[0076] Among them, the target reflected signal receiving module 310 is used to indicate a pre-installed FMCW millimeter-wave radar in a target monitoring area to transmit an FMCW signal and receive at least one set of target reflected signals;

[0077] The target identity information determining module 320 is used to perform position processing on the groups of target reflected signals through a pre-set wave velocity shaping algorithm to obtain the positions of each target, and perform grouping processing through a pre-set clustering algorithm to obtain the target identity information corresponding to each target position;

[0078] The physiological parameter separation value determining module 330 is used to calculate the corresponding frequency domain characteristics of the target reflected signals respectively according to the target reflected signals respectively matched with the target identity information, and obtain the corresponding physiological parameter separation values respectively according to the frequency domain characteristics of the target reflected signals;

[0079] Among them, the physiological parameter separation value includes a respiration frequency band signal value and a heart rate frequency band signal value;

[0080] The real-time monitoring and processing module 340 is used to send the respiration frequency band signal value and the heart rate frequency band signal value to a target cloud server, and instruct the target cloud server to perform real-time monitoring and processing operations according to the target identity information.

[0081] The technical solution of the embodiment of the present invention is as follows: In a target monitoring area, it indicates a pre-installed FMCW millimeter-wave radar to transmit an FMCW signal and receive at least one set of target reflected signals; through a pre-set wave velocity shaping algorithm, it performs position processing on the groups of target reflected signals to obtain the positions of each target, and performs grouping processing through a pre-set clustering algorithm to obtain the target identity information corresponding to each target position; it calculates the corresponding frequency domain characteristics of the target reflected signals respectively according to the target reflected signals respectively matched with the target identity information, and obtains the corresponding physiological parameter separation values respectively according to the frequency domain characteristics of the target reflected signals; among them, the physiological parameter separation value includes a respiration frequency band signal value and a heart rate frequency band signal value; it sends the respiration frequency band signal value and the heart rate frequency band signal value to a target cloud server, and instructs the target cloud server to perform real-time monitoring and processing operations according to the target identity information. It solves the problems of ineffective multi-target monitoring and inability to separate physiological parameters, improves the monitoring efficiency, and reduces the nursing cost.

[0082] Based on the above embodiments, in an FMCW millimeter-wave radar, a multiple-input multiple-output (MIMO) array antenna is included, and spatial filtering and transmission of FMCW signals are performed through the MIMO array antenna. Among them, the MIMO array antenna performs spatial filtering processing on the FMCW signals through a preset adaptive weight allocation algorithm. Among them, the FMCW millimeter-wave radar is connected to an embedded processor through a PCI-E 3.0 interface.

[0083] Based on the above embodiments, the target identity information determination module 320 may specifically be configured to: perform position processing on each group of target reflection signals through a preset wave velocity shaping algorithm, and calculate the target azimuth and target distance corresponding to each target position; through a preset clustering algorithm, perform grouping processing on each of the target azimuths and each of the target distances to obtain the target identity information corresponding to each target position.

[0084] Based on the above embodiments, the physiological parameter separation value determination module 330 may specifically include: a target reflection signal frequency-domain feature calculation unit, configured to perform frequency-domain transformation on the target reflection signals respectively matched with each piece of target identity information through fast Fourier transform, and calculate the frequency-domain features of each target reflection signal respectively; a respiration frequency band signal and heart rate frequency band signal determination unit, configured to perform denoising processing on the frequency-domain features of each target reflection signal respectively through a preset wavelet denoising method to obtain the corresponding respiration frequency band signal and heart rate frequency band signal; a respiration frequency band signal value and heart rate frequency band signal value determination unit, configured to perform value calculation on each of the respiration frequency band signals and each of the heart rate frequency band signals respectively through a preset peak detection algorithm to obtain the respiration frequency band signal value and the heart rate frequency band signal value.

[0085] Based on the above embodiments, the respiration frequency band signal and heart rate frequency band signal determination unit may specifically be configured to: perform denoising processing on the frequency-domain features of each target reflection signal respectively through the Daubechies basis function in the wavelet denoising method to obtain the corresponding respiration frequency band signal and heart rate frequency band signal respectively.

[0086] Based on the above embodiments, the real-time monitoring processing module 340 includes: a real-time monitoring result obtaining unit, configured to instruct the target cloud server to perform real-time monitoring on the received respiration frequency band signal value and heart rate frequency band signal value according to a preset threshold alarm rule to obtain each real-time monitoring result; among them, the threshold alarm rule includes a respiration frequency band signal value threshold and a heart rate frequency band signal value threshold; an emergency processing unit, configured to perform emergency processing operations according to each real-time monitoring result and the target identity information corresponding to each real-time monitoring result respectively.

[0087] Based on the above embodiments, wherein the real-time monitoring result is an abnormal real-time monitoring result or a normal real-time monitoring result; the emergency processing unit can be specifically configured to: if there is at least one abnormal real-time monitoring result among the real-time monitoring results, obtain the current abnormal target identity information matching the abnormal real-time monitoring result; and according to the current abnormal target identity information, instruct the humanoid health care robot to perform an emergency processing operation.

[0088] The physiological parameter separation monitoring device provided by the embodiments of the present invention can execute the physiological parameter separation monitoring method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0089] Embodiment 4

[0090] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0091] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0092] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0093] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the physiological parameter separation monitoring method.

[0094] In some embodiments, the physiological parameter separation monitoring method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the physiological parameter separation monitoring method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the physiological parameter separation monitoring method in any other suitable manner (e.g., by means of firmware).

[0095] The method includes: in a target monitoring area, instructing a pre-installed frequency-modulated continuous-wave (FMCW) millimeter-wave radar to transmit an FMCW signal and receiving at least one set of target reflected signals; performing position processing on the groups of target reflected signals through a pre-set wave velocity shaping algorithm to obtain each target position, and performing grouping processing through a pre-set clustering algorithm to obtain target identity information corresponding to each target position; calculating frequency-domain characteristics of the corresponding target reflected signals respectively according to the target reflected signals respectively matched with the target identity information, and obtaining corresponding physiological parameter separation values respectively according to the frequency-domain characteristics of the target reflected signals; wherein the physiological parameter separation values include respiratory frequency band signal values and heart rate frequency band signal values; sending the respiratory frequency band signal values and the heart rate frequency band signal values to a target cloud server, and instructing the target cloud server to perform real-time monitoring processing operations according to each target identity information.

[0096] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0097] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0098] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0100] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0101] A computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0102] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0103] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0104] Embodiment 5

[0105] Embodiment 5 of the present invention further provides a computer-readable storage medium, and the computer-readable instructions are used to execute a physiological parameter separation monitoring method when executed by a computer processor. The method includes: in a target monitoring area, instructing a pre-installed frequency-modulated continuous wave (FMCW) millimeter-wave radar to transmit FMCW signals and receiving at least one set of target reflection signals; through a pre-set wave velocity shaping algorithm, performing position processing on the groups of target reflection signals to obtain each target position, and performing grouping processing through a pre-set clustering algorithm to obtain target identity information corresponding to each target position; respectively calculating frequency-domain characteristics of the corresponding target reflection signals according to the target reflection signals respectively matched with the target identity information, and respectively obtaining corresponding physiological parameter separation values according to the frequency-domain characteristics of the target reflection signals; where the physiological parameter separation values include respiration frequency band signal values and heart rate frequency band signal values; sending the respiration frequency band signal values and the heart rate frequency band signal values to a target cloud server, and instructing the target cloud server to perform real-time monitoring processing operations according to the target identity information.

[0106] Certainly, the computer-executable instructions of a computer-readable storage medium provided by the embodiments of the present invention are not limited to the method operations described above, and can also execute related operations in the physiological parameter separation monitoring methods provided by any embodiments of the present invention.

[0107] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments of the present invention.

[0108] It should be noted that in the embodiments of the above physiological parameter separation monitoring device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for easy distinction from each other and do not limit the protection scope of the present invention.

[0109] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for separately monitoring physiological parameters, characterized in that Comprising: In a target monitoring area, instruct a pre-installed Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar to transmit an FMCW signal and receive at least one set of target reflected signals; Through a pre-set wave velocity shaping algorithm, perform position processing on each set of target reflected signals to obtain the positions of each target, and perform grouping processing through a pre-set clustering algorithm to obtain the target identity information corresponding to each target position; Calculate the corresponding frequency domain characteristics of the target reflected signals respectively according to the target reflected signals respectively matched with each of the target identity information, and obtain the corresponding physiological parameter separation values respectively according to the frequency domain characteristics of each of the target reflected signals; Wherein, the physiological parameter separation values include respiration frequency band signal values and heart rate frequency band signal values; Send the respiration frequency band signal values and the heart rate frequency band signal values to a target cloud server, and instruct the target cloud server to perform real-time monitoring processing operations according to each target identity information.

2. The method according to claim 1, wherein Comprising: Wherein, the FMCW millimeter-wave radar includes a Multiple-Input Multiple-Output (MIMO) array antenna, and the MIMO array antenna is used for spatial filtering and transmission of the FMCW signal; Wherein, the MIMO array antenna performs spatial filtering processing on the FMCW signal through a pre-set adaptive weight allocation algorithm; Wherein, the FMCW millimeter-wave radar is connected to an embedded processor through a PCI-E 3.0 interface.

3. The method according to claim 2, wherein The step of performing position processing on each set of target reflected signals through a pre-set wave velocity shaping algorithm to obtain the positions of each target, and performing grouping processing through a pre-set clustering algorithm to obtain the target identity information corresponding to each target position includes: Perform position processing on each set of target reflected signals through a pre-set wave velocity shaping algorithm, and calculate the target azimuth and target distance corresponding to each target position; Through a pre-set clustering algorithm, perform grouping processing on each of the target azimuths and each of the target distances respectively to obtain the target identity information corresponding to each target position.

4. The method according to claim 3, characterized in that, The step of calculating the corresponding frequency domain characteristics of the target reflected signals respectively according to the target reflected signals respectively matched with each of the target identity information, and obtaining the corresponding physiological parameter separation values respectively according to the frequency domain characteristics of each of the target reflected signals includes: Through Fast Fourier Transform (FFT), perform frequency domain transformation on the target reflected signals respectively matched with each of the target identity information, and calculate the frequency domain characteristics of each of the target reflected signals respectively; Through a pre-set wavelet denoising method, perform denoising processing on the frequency domain characteristics of each of the target reflected signals respectively to obtain the corresponding respiration frequency band signal and heart rate frequency band signal; Through a pre-set peak detection algorithm, perform value calculation on each of the respiration frequency band signals and each of the heart rate frequency band signals respectively to obtain the respiration frequency band signal values and the heart rate frequency band signal values.

5. The method according to claim 4, wherein The step of performing denoising processing on the frequency domain characteristics of each of the target reflected signals respectively through a pre-set wavelet denoising method to obtain the corresponding respiration frequency band signal and heart rate frequency band signal includes: The frequency domain features of each target reflection signal are denoised respectively by the Daubechies basis function in the wavelet denoising method, so as to obtain the corresponding respiration frequency band signal and heart rate frequency band signal respectively.

6. The method according to claim 5, wherein Sending the respiration frequency band signal value and the heart rate frequency band signal value to a target cloud server, and instructing the target cloud server to perform real-time monitoring processing operations according to each target identity information, including: Instructing the target cloud server to perform real-time monitoring on the received respiration frequency band signal value and heart rate frequency band signal value according to a preset threshold alarm rule, so as to obtain each real-time monitoring result; Wherein, the threshold alarm rule includes a respiration frequency band signal value threshold and a heart rate frequency band signal value threshold; Performing emergency processing operations according to each real-time monitoring result and the target identity information corresponding to each real-time monitoring result respectively.

7. The method according to claim 6, characterized in that, The real-time monitoring result is an abnormal real-time monitoring result or a normal real-time monitoring result; The performing emergency processing operations according to the real-time monitoring result and the target identity information corresponding to the real-time monitoring result includes: If there is at least one real-time monitoring result that is an abnormal real-time monitoring result, obtaining the current abnormal target identity information matching the abnormal real-time monitoring result; According to the current abnormal target identity information, instructing a humanoid health care robot to perform emergency processing operations.

8. A physiological parameter separation monitoring device, characterized in that, Including: A target reflection signal receiving module, configured to instruct a pre-installed frequency modulated continuous wave (FMCW) millimeter wave radar in a target monitoring area to emit an FMCW signal and receive at least one group of target reflection signals; A target identity information determining module, configured to perform position processing on each group of target reflection signals through a preset wave velocity shaping algorithm to obtain each target position, and perform grouping processing through a preset clustering algorithm to obtain the target identity information corresponding to each target position respectively; A physiological parameter separation value determining module, configured to calculate the corresponding frequency domain features of the target reflection signals respectively according to the target reflection signals respectively matched with each target identity information, and obtain the corresponding physiological parameter separation values respectively according to the frequency domain features of each target reflection signal; Wherein, the physiological parameter separation value includes a respiration frequency band signal value and a heart rate frequency band signal value; A real-time monitoring processing module, configured to send the respiration frequency band signal value and the heart rate frequency band signal value to a target cloud server, and instruct the target cloud server to perform real-time monitoring processing operations according to each target identity information.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that When the processor executes the computer program, it implements the physiological parameter separation monitoring method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the processor to implement the physiological parameter separation monitoring method according to any one of claims 1-7 when executed.

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