Composite detection device for smart home, indoor human body detection method and medium

Abstract

The invention relates to the technical field of smart home, and particularly discloses a composite detection device for smart home, an indoor human body detection method and a medium, and the detection method comprises the steps: transmitting a millimeter wave frequency band radar signal, collecting an echo signal, and obtaining a first judgment result based on the echo signal; infrared signals are collected, and a second judgment result is obtained based on the infrared signals; performing data fusion on the first judgment result and the second judgment result by adopting a D-S evidence theory to obtain a detection result; the step of obtaining the first judgment result based on the echo signal comprises the steps of judging whether a moving target exists in the detection space or not based on the echo signal, and if the moving target exists, executing the step of judging whether the moving target is a dynamic human body or not; judging whether a static human body exists in the detection space based on the echo signal; the step of judging whether the moving target is a dynamic human body comprises the steps of detecting whether the mechanical motion of the moving target has strict periodicity, and if so, judging that the moving target is an interference source; and performing adaptive-MSSA filtering processing on the echo signal to obtain signal characteristics, and judging whether the moving target is a dynamic human body according to the signal characteristics, so that the accuracy and reliability of indoor human body detection can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and in particular, to a composite detection device for smart home, an indoor human body detection method, and a medium. Background Art

[0002] Detecting whether there is a human body in a room is a key link in smart home control. For example, according to the detection result of whether there is a human body in the detection space, intelligent devices such as lamps, air conditioners, doors, and windows are controlled to perform corresponding opening and closing.

[0003] Millimeter Wave Radar is a radar that operates in the millimeter wave band, that is, the operating frequency range is 30 GHz to 300 GHz. In radar detection, due to the diffraction effect of electromagnetic waves, the resolution of electromagnetic waves for detecting objects is proportional to the wavelength of electromagnetic waves. The shorter the wavelength of electromagnetic waves, the higher the resolution. In addition, the millimeter wave band belongs to the atmospheric window, is less affected by natural light and thermal radiation sources, and has a small propagation attenuation. Therefore, millimeter wave radar has the advantages of high resolution, strong anti-interference ability, and all-weather and all-time operation, which makes millimeter wave radar show strong capabilities in the field of indoor personnel detection.

[0004] The pyroelectric infrared sensing unit for human body can detect the temperature change in the environment and the movement of objects by using the pyroelectric effect. When the energy in the detection range changes, the sensor will correspondingly generate a current change and give a trigger signal. This sensor is very sensitive to the infrared radiation released by the human body and is very suitable for human presence detection.

[0005] In the traditional method of using millimeter wave radar for indoor human body detection, many interferences such as furniture, green plants, curtains, and fans existing in the indoor scene are often ignored. The clutter brought by these interferences will cause false alarm problems and seriously affect the indoor human body detection performance. The infrared sensing unit has poor penetrability and is easily affected by the environment. Especially when the human body temperature is equal to the environment temperature, the detection effect will be seriously reduced.

[0006] Although there are currently some human presence detection methods that have a scheme of installing both millimeter wave radar and infrared sensing unit at the same time, there is still a lack of means to deal with interference factors, resulting in poor anti-interference ability and affecting the detection accuracy. Summary of the Invention

[0007] The purpose of the present invention is to provide a composite detection device for smart home, an indoor human body detection method, and a medium in view of the existing technical status.

[0008] The composite detection device and detection method of the present invention fuse the judgment results of millimeter-wave band radar detection and infrared detection, integrate the information represented by independent data from different information sources, eliminate the one-sidedness of information, obtain a more reliable and effective judgment result, and in the process of millimeter-wave band radar detection, make full use of the difference characteristics between interference signals and human signals to perform targeted processing on the echo signal, thereby effectively improving the accuracy and reliability of indoor human detection as a whole. When applied to smart home control, it can effectively improve the precise control of smart home control. At the same time, it can ensure the confidentiality of detection and has higher privacy protection in indoor monitoring.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] First, the present invention provides an indoor human detection method, including:

[0011] Transmit millimeter-wave band radar signals and collect echo signals, and obtain a first judgment result based on the echo signals;

[0012] Collect infrared signals and obtain a second judgment result based on the infrared signals;

[0013] Use the D-S evidence theory to perform data fusion on the first judgment result and the second judgment result to obtain a detection result;

[0014] The step of obtaining the first judgment result based on the echo signals includes:

[0015] Judge whether there is a moving target in the detection space based on the echo signals. If there is a moving target, execute the step of judging whether the moving target is a dynamic human body;

[0016] Judge whether there is a static human body in the detection space based on the echo signals;

[0017] The step of judging whether the moving target is a dynamic human body includes:

[0018] Detect whether the mechanical movement of the moving target has strict periodicity. If there is strict periodicity, it is determined that the moving target is an interference source;

[0019] Perform adaptive-MSSA filtering processing on the echo signals to obtain signal features, and judge whether the moving target is a dynamic human body according to the signal features.

[0020] In some embodiments, the step of judging whether there is a moving target in the detection space based on the echo signals includes:

[0021] Process the echo signal to obtain range-slow time dimension data. After static clutter filtering, perform Fourier transform on the slow time dimension to construct range-Doppler dimension data;

[0022] Based on the range-Doppler dimension data, determine whether there are moving targets in the space.

[0023] In some embodiments, the step of detecting whether the mechanical motion of the moving target has strict periodicity includes:

[0024] Calculate the autocorrelation function for each range unit of the range-slow time dimension data, calculate the autocorrelation coefficients at different time delays, and form an autocorrelation coefficient function curve;

[0025] Confirm whether there are periodic peaks greater than a preset peak threshold in the autocorrelation coefficient function curve. If so, determine that the mechanical motion of the moving target has strict periodicity; otherwise, determine that the mechanical motion of the moving target does not have strict periodicity.

[0026] In some embodiments, the step of determining whether there is a static human body in the detection space based on the echo signal includes:

[0027] Obtain the phase information of the echo signal based on the echo signal;

[0028] Sparsely reconstruct the phase information of the echo signal to obtain a reconstructed signal;

[0029] Calculate the error value between the reconstructed signal and the ideal sine signal. If the error value is less than a preset threshold, determine that there is a static human body in the detection space; otherwise, determine that there is no static human body in the detection space.

[0030] In some embodiments, the step of collecting infrared signals and obtaining a second judgment result based on the infrared signals includes:

[0031] Judge whether there are human targets in the room through multiple joint statistics.

[0032] Second, the present invention provides a composite detection device for smart home, including:

[0033] A millimeter-wave radar sensing unit for transmitting radar signals in the millimeter-wave band and collecting echo signals;

[0034] An infrared sensing unit for collecting infrared signals;

[0035] A control unit, respectively communicatively connected to the millimeter-wave radar sensing unit and the infrared sensing unit. The control unit includes a first judgment module, a second judgment module, and a fusion analysis module;

[0036] The first judgment module can obtain a first judgment result based on the echo signal;

[0037] The second judgment module can obtain a second judgment result based on the infrared signal,

[0038] The fusion analysis module can perform data fusion on the first judgment result and the second judgment result by using the D-S evidence theory to obtain a detection result;

[0039] The first judgment module includes a dynamic human body judgment module and a static human body judgment module,

[0040] The dynamic human body judgment module can judge whether there is a moving target in the detection space based on the echo signal. If there is a moving target, execute the step of judging whether the moving target is a dynamic human body;

[0041] The static human body judgment module can judge whether there is a static human body in the detection space based on the echo signal,

[0042] The step of judging whether the moving target is a dynamic human body includes:

[0043] Detect whether the mechanical movement of the moving target has strict periodicity. If there is strict periodicity, determine that the moving target is an interference source;

[0044] Perform adaptive-MSSA filtering processing on the echo signal, obtain signal features, and judge whether the moving target is a dynamic human body according to the signal features.

[0045] In some embodiments, the infrared sensing unit is a pyroelectric infrared sensing unit for the human body, the detection wavelength is 5 μm to 14 μm, and the operating frequencies of the millimeter-wave radar sensing unit include 24 GHz and 60 GHz.

[0046] In some embodiments, the step of judging whether there is a moving target in the detection space based on the echo signal includes:

[0047] Process the echo signal, obtain range-slow time dimension data, perform static clutter filtering, and then perform Fourier transform on the slow time dimension to construct range-Doppler dimension data;

[0048] Judge whether there is a moving target in the space based on the range-Doppler dimension data;

[0049] The step of detecting whether the mechanical movement of the moving target has strict periodicity includes:

[0050] Calculate the autocorrelation function for each range unit of the range-slow time dimension data, calculate the autocorrelation coefficients at different time delays, and form an autocorrelation coefficient function curve;

[0051] Confirm whether there are periodic peaks greater than a preset peak threshold in the autocorrelation coefficient function curve. If so, determine that the mechanical motion of the moving target has strict periodicity; otherwise, determine that the mechanical motion of the moving target does not have strict periodicity.

[0052] In some embodiments, the step of determining whether there is a static human body in the detection space based on the echo signal includes:

[0053] Obtain the phase information of the echo signal based on the echo signal;

[0054] Sparsely reconstruct the phase information of the echo signal to obtain a reconstructed signal;

[0055] Calculate the error value between the reconstructed signal and the ideal sine signal. If the error value is less than a preset threshold, determine that there is a static human body in the detection space; otherwise, determine that there is no static human body in the detection space.

[0056] Third, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0057] The beneficial effects of the present invention are as follows:

[0058] In the present invention, after detecting by millimeter-wave band radar and infrared detection, the D-S evidence theory is used to fuse the judgment results of the two, so as to comprehensively judge the presence state of the indoor human body. The D-S evidence theory constructs a general framework of an uncertain reasoning model by associating the belief function of evidence with the maximum and minimum values of the probability space, and fuses the information represented by independent data from different information sources with the evidence combination rule, eliminating the one-sidedness of information and obtaining a more reliable and effective judgment result, thereby improving the accuracy of indoor human body detection. On the other hand, during the detection process by millimeter-wave band radar, whether there are dynamic human bodies and static human bodies are detected respectively, so as to perform corresponding data processing according to different state characteristics and improve the detection accuracy. At the same time, during the dynamic human body detection process, in view of the interference of the oscillating fan, the mechanical movement of the oscillating fan has a more regular and strict periodicity than human movement. In view of this characteristic, by detecting whether the mechanical movement of the moving target has a strict periodicity, the detection interference caused by the oscillating fan is excluded, and the echo signal is subjected to adaptive-MSSA filtering processing, and the echo signal is reconstructed through multi-channel singular spectrum analysis (MSSA). During the reconstruction process, the signal subspace is adaptively identified by using the periodicity and range correlation characteristics of the response, effectively suppressing interference signals such as green plants and curtains, highlighting the human movement signal, and further identifying and distinguishing dynamic human bodies by analyzing the signal characteristics of the filtered signal, thereby effectively improving the accuracy of human body recognition.

[0059] The present invention fuses the judgment results of millimeter-wave band radar detection and infrared detection, fuses the information represented by independent data from different information sources, eliminates the one-sidedness of information, obtains a more reliable and effective judgment result, and during the millimeter-wave band radar detection process, makes full use of the difference characteristics between interference signals and human signals to perform targeted processing on the echo signal, thereby effectively improving the accuracy and reliability of indoor human body detection as a whole, being able to quickly and accurately judge whether there are people in the room, and when applied to smart home control, being able to effectively improve the precise control of smart home control. At the same time, it can ensure the confidentiality of detection and has higher privacy protection in indoor monitoring. Description of the Drawings

[0060] Figure 1 It is a flowchart of an indoor human body detection method according to an embodiment of the present invention.

[0061] Figure 2 It is a flowchart of the step of obtaining a first judgment result based on the echo signal according to an embodiment of the present invention.

[0062] Figure 3 It is a flowchart of the step of judging whether the moving target is a dynamic human body according to an embodiment of the present invention.

[0063] Figure 4 Flow chart of the step of judging whether there is a static human body in the detection space based on the echo signal according to an embodiment of the present invention.

[0064] Figure 5 Schematic diagram of the principle of applying a composite detection device for a smart home to home intelligent control according to an embodiment of the present invention. Specific embodiments

[0065] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and cannot be construed as a limitation of the present invention. In addition, it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0066] In the description of the present invention, terms such as "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meanings of "a plurality" and "several" are two or more, unless otherwise specifically defined.

[0067] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0068] First, as shown in Figures 1 to 3 The present invention provides an indoor human body detection method, including:

[0069] S100. Transmit a radar signal in the millimeter wave band and collect the echo signal, and obtain a first judgment result based on the echo signal. Specifically, the millimeter wave radar sensing unit can be used to transmit a radar signal in the millimeter wave band and collect the echo signal;

[0070] S200. Collect the infrared signal, and obtain a second judgment result based on the infrared signal. Specifically, the infrared sensing unit can be used to collect the infrared signal;

[0071] S300. Use the D-S evidence theory to perform data fusion on the first judgment result and the second judgment result to obtain a detection result;

[0072] Among them, in step S100, the step of obtaining the first judgment result based on the echo signal includes:

[0073] S110. Determine whether there is a moving target in the detection space based on the echo signal. If there is a moving target, perform the step of determining whether the moving target is a dynamic human. Otherwise, do not perform the step of determining whether the moving target is a dynamic human;

[0074] S120. Determine whether there is a static human in the detection space based on the echo signal;

[0075] In step S110, the step of determining whether the moving target is a dynamic human (step S112) includes:

[0076] S1121. Detect whether the mechanical movement of the moving target has strict periodicity. If there is strict periodicity, determine that the moving target is an interference source;

[0077] S1122. Perform adaptive-MSSA (multichannel singular spectrum analysis) filtering on the echo signal to obtain signal features, and determine whether the moving target is a dynamic human according to the signal features.

[0078] In the present invention, after detecting by millimeter-wave band radar and infrared detection, the D-S evidence theory is used to fuse the judgment results of the two, so as to comprehensively judge the presence state of humans indoors. The D-S evidence theory constructs a general framework of an uncertain inference model by associating the belief function of evidence with the maximum and minimum values of the probability space, and fuses the information represented by independent data from different information sources with the evidence combination rule, eliminating the one-sidedness of information and obtaining a more reliable and effective judgment result, thereby improving the accuracy of indoor human detection. On the other hand, during the detection process by millimeter-wave band radar, it is respectively detected whether there are dynamic humans and static humans, so as to perform corresponding data processing according to different state characteristics, improving the detection accuracy. At the same time, during the dynamic human detection process, aiming at the interference of the oscillating fan, the mechanical movement of the oscillating fan has a more regular and strict periodicity than human movement. For this characteristic, by detecting whether the mechanical movement of the moving target has strict periodicity, the detection interference caused by the oscillating fan is excluded, and the echo signal is subjected to adaptive-MSSA filtering processing, and the echo signal is reconstructed through multichannel singular spectrum analysis (MSSA). During the reconstruction process, the response periodicity and range correlation characteristics are used to adaptively identify the signal subspace, effectively suppressing interference signals such as green plants and curtains, highlighting the human movement signal, and further identifying and distinguishing dynamic humans by analyzing the signal features after filtering, thereby effectively improving the accuracy of human recognition.

[0079] The present invention combines the judgment results of millimeter-wave band radar detection and infrared detection, integrates the information represented by independent data from different information sources, eliminates the one-sidedness of the information, and obtains a more reliable and effective judgment result. During the process of millimeter-wave band radar detection, the difference characteristics between the interference signal and the human body signal are fully utilized to perform targeted processing on the echo signal. As a result, the accuracy and reliability of indoor human detection are effectively improved as a whole, and it can quickly and accurately judge whether there are people in the room. When applied to smart home control, it can effectively improve the precise control of smart home control. At the same time, it can ensure the confidentiality of detection and has higher privacy protection in indoor monitoring.

[0080] In some embodiments, in step S110, the step of determining whether there is a moving target in the detection space based on the echo signal includes:

[0081] S1111. Process the echo signal to obtain range-slow time dimension data. After static clutter filtering, perform Fourier transform on the slow time dimension to construct range-Doppler dimension data. Specifically:

[0082] Perform one-dimensional Fourier transform on the echo signal of each range cell, convert the echo signal from the time domain to the frequency domain, and then organize it according to the range and slow time dimensions to form a range-slow time dimension data matrix;

[0083] Subsequently, perform static clutter filtering to remove the interference generated by stationary targets (static clutter) in the echo signal, then perform Fourier transform on the slow time dimension data after filtering the static clutter to obtain the Doppler frequency shift, and then organize it according to the range and Doppler frequency shift dimensions to construct a range-Doppler dimension data matrix.

[0084] S1112. Determine whether there is a moving target in the space based on the range-Doppler dimension data.

[0085] The range-slow time dimension data is used to capture the range change of the target at different time points. Through static clutter filtering, the interference of stationary targets can be removed, and the signal of moving targets can be extracted. The range-Doppler dimension data is a data form that organizes the echo signal according to the range and Doppler frequency shift. Among them, the range dimension reflects the range information of the target, and the Doppler dimension reflects the speed information of the target. Thus, the range and speed information of the target can be obtained simultaneously, and the moving target in the detection space can be identified more accurately, improving the accuracy and reliability of human detection.

[0086] In some embodiments, in step S1121, the step of detecting whether the mechanical movement of the moving target has strict periodicity includes:

[0087] 1) calculating an autocorrelation function for each distance unit of the distance-slow time dimension data, calculating autocorrelation coefficients under different time delays, and forming an autocorrelation coefficient function curve;

[0088] 2) confirm whether the autocorrelation coefficient function curve has a periodic peak value greater than a preset peak threshold value. If so, it is determined that the mechanical movement of the moving target has strict periodicity. Otherwise, it is determined that the mechanical movement of the moving target does not have strict periodicity.

[0089] Specifically, it is confirmed whether the autocorrelation coefficient function curve has a periodic peak value greater than a preset peak threshold value, that is, the autocorrelation coefficient function curve needs to simultaneously satisfy:

[0090] Condition 1: There is a periodic peak to indicate its periodicity;

[0091] Condition 2: The periodic peak is greater than the preset peak threshold, that is, the periodic peak needs to be highly significant to indicate that its periodicity is highly regular, which is strictly periodic rather than non-strictly periodic. If the autocorrelation coefficient is lower than the preset peak threshold or the peak is not obvious, then it can be considered that the signal is more likely to be a human signal, because the periodic movements of the human body (such as breathing and heartbeat) are usually not as regular as mechanical interference.

[0092] Regarding the interference of an oscillating fan, the mechanical movement of an oscillating fan has a more regular and strict periodicity than human movement. The autocorrelation coefficient function curve can be used to more accurately determine whether the signal has strict periodicity, thereby distinguishing the interference source from the dynamic human body, accurately identifying the interference source, and further improving the accuracy of human body detection.

[0093] In some embodiments, see Figure 4 As shown, in step S120, the step of judging whether there is a static human body in the detection space based on the echo signal includes:

[0094] S121. Acquire phase information of the echo signal based on the echo signal;

[0095] S122. Sparsely reconstruct the phase information of the echo signal to obtain a reconstructed signal;

[0096] S123. Calculate the error value between the reconstructed signal and the ideal sinusoidal signal. If the error value is less than a preset threshold, determine that there is a static human body in the detection space; otherwise, determine that there is no static human body in the detection space.

[0097] When the human body is stationary, breathing causes periodic fluctuations in the chest cavity, which are manifested as a low-frequency sine signal in the phase information of the radar echo signal. In the present invention, through the sparse reconstruction technology, a reconstructed signal is obtained, and then by calculating the error value between the reconstructed signal and the ideal sine signal, the reconstructed signal is matched with the ideal sine signal to confirm whether the reconstructed signal is close to the ideal sine signal, thereby distinguishing whether the static target is a static human body.

[0098] In some embodiments, in step S200, the step of collecting the infrared signal and obtaining the second judgment result based on the infrared signal includes:

[0099] Judging whether there is a human target in the room through multiple joint statistics.

[0100] Among them, multiple joint statistics can be multiple detections. By performing joint statistical analysis on the data of multiple detections, a more reliable and accurate judgment result can be obtained. Or, it can be through multiple infrared sensing units arranged at different positions in the detection space for detection, obtaining multiple detection data at the same time, and performing joint statistical analysis on the multiple detection data at the same time to obtain a more reliable and accurate judgment result. Through multiple joint statistics, the accuracy and reliability of infrared detection are improved.

[0101] Second, refer to Figures 1 to 3 and Figure 5 As shown, the present invention provides a composite detection device for a smart home, including:

[0102] A millimeter-wave radar sensing unit for transmitting radar signals in the millimeter-wave band and collecting echo signals;

[0103] An infrared sensing unit for collecting infrared signals;

[0104] A control unit is respectively communicatively connected to the millimeter-wave radar sensing unit and the infrared sensing unit. The control unit includes a first judgment module, a second judgment module and a fusion analysis module;

[0105] The first judgment module can obtain a first judgment result based on the echo signal;

[0106] The second judgment module can obtain a second judgment result based on the infrared signal,

[0107] The fusion analysis module can perform data fusion on the first judgment result and the second judgment result by using the D-S evidence theory to obtain a detection result;

[0108] The first judgment module includes a dynamic human body judgment module and a static human body judgment module,

[0109] The dynamic human body judgment module can judge whether there is a moving target in the detection space based on the echo signal. If there is a moving target, it executes the step of judging whether the moving target is a dynamic human body;

[0110] The static human body judgment module can judge whether there is a static human body in the detection space based on the echo signal,

[0111] The step of judging whether the moving target is a dynamic human body includes:

[0112] Detect whether the mechanical movement of the moving target has strict periodicity. If there is strict periodicity, it is determined that the moving target is an interference source;

[0113] Perform adaptive-MSSA filtering processing on the echo signal to obtain signal features, and judge whether the moving target is a dynamic human body according to the signal features.

[0114] The composite detection device of the present invention fuses the judgment results of millimeter-wave band radar detection and infrared detection, combines the information represented by independent data from different information sources, eliminates the one-sidedness of information, and obtains a more reliable and effective judgment result. During the millimeter-wave band radar detection process, it makes full use of the difference characteristics between interference signals and human body signals to perform targeted processing on the echo signal, thereby effectively improving the accuracy and reliability of indoor human body detection as a whole, and can quickly and accurately judge whether there are people in the room. When applied to smart home control, it can effectively improve the precise control of smart home control.

[0115] See Figure 5 As shown, in some embodiments, the processing unit can be arranged on the main control MCU chip.

[0116] See Figure 5 As shown, in some embodiments, the millimeter-wave radar sensing unit can be arranged on a radar chip connected with an antenna, which can not only transmit millimeter-wave band radar signals and collect echo signals, but also perform preprocessing such as amplification, filtering, and mixing on the echo signals.

[0117] Exemplarily, see Figure 5 As shown, taking the detection device of the present invention applied to home intelligent lighting as an example:

[0118] The millimeter-wave radar sensing unit transmits millimeter-wave band radar signals and collects echo signals, the infrared sensing unit collects infrared signals, the control unit processes the echo signals and infrared signals, outputs the detection result, sends the judgment result to the Bluetooth module, and the Bluetooth module then sends the judgment result to the smart home control platform. When the detection result is that there is someone, it reads the value of the photosensitive sensor and judges whether it is less than the preset light threshold. If it is less than the preset light threshold, it outputs an enhanced lighting instruction.

[0119] In some embodiments, the infrared sensing unit is a pyroelectric infrared sensing unit for the human body, with a detection wavelength of 5 μm to 14 μm, and the operating frequencies of the millimeter-wave radar sensing unit include 24 GHz and 60 GHz.

[0120] The pyroelectric infrared sensing unit for the human body has a detection wavelength of 5 μm to 14 μm and is sensitive to the infrared radiation (wavelength about 9 μm to 10 μm) emitted by the human body, and is suitable for detecting the presence of a human body.

[0121] The technology of the 24 GHz millimeter-wave radar is mature, and it can achieve accurate detection of the presence of a stationary human body and trajectory tracking, with controllable costs and higher cost performance.

[0122] The 60 GHz millimeter-wave radar has a shorter wavelength, a wider bandwidth, and higher resolution. This enables it to detect even weaker object displacement changes, such as the micro-motions of human breathing and heartbeat.

[0123] In some embodiments, the step of determining whether there is a moving target in the detection space based on the echo signal includes:

[0124] Processing the echo signal to obtain range-slow time dimension data, performing static clutter filtering, and then performing Fourier transform on the slow time dimension to construct range-Doppler dimension data;

[0125] Determining whether there is a moving target in the space based on the range-Doppler dimension data;

[0126] The step of detecting whether the mechanical motion of the moving target has strict periodicity includes:

[0127] Calculating the autocorrelation function for each range unit of the range-slow time dimension data, calculating the autocorrelation coefficients at different time delays, and forming an autocorrelation coefficient function curve;

[0128] Confirming whether there are periodic peaks greater than a preset peak threshold in the autocorrelation coefficient function curve. If so, it is determined that the mechanical motion of the moving target has strict periodicity; otherwise, it is determined that the mechanical motion of the moving target does not have strict periodicity.

[0129] In some embodiments, the step of determining whether there is a static human body in the detection space based on the echo signal includes:

[0130] Obtaining the phase information of the echo signal based on the echo signal;

[0131] Sparsely reconstructing the phase information of the echo signal to obtain a reconstructed signal;

[0132] Calculate the error value between the reconstructed signal and the ideal sine signal. If the error value is less than a preset threshold, it is determined that there is a static human body in the detection space; otherwise, it is determined that there is no static human body in the detection space.

[0133] Thirdly, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0134] In the description of this specification, the description with reference to terms such as "some embodiments", "exemplary", "example", or "for example" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0135] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications equivalent to the equivalent embodiments by using the technical content prompted above within the scope of the technical solution of the present invention. However, as long as the content does not depart from the technical solution of the present invention, any simple modification, equivalent change, and modification made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An indoor human body detection method, characterized in that, Including: Transmitting millimeter-wave band radar signals and collecting echo signals, and obtaining a first judgment result based on the echo signals; Collecting infrared signals and obtaining a second judgment result based on the infrared signals; Using the D-S evidence theory to perform data fusion on the first judgment result and the second judgment result to obtain a detection result; The step of obtaining the first judgment result based on the echo signals includes: Judging whether there is a moving target in the detection space based on the echo signals. If there is a moving target, execute the step of judging whether the moving target is a dynamic human body; Judging whether there is a static human body in the detection space based on the echo signals; The step of judging whether the moving target is a dynamic human body includes: Detecting whether the mechanical motion of the moving target has strict periodicity. If there is strict periodicity, it is determined that the moving target is an interference source; Performing adaptive-MSSA filtering processing on the echo signals to obtain signal features, and judging whether the moving target is a dynamic human body according to the signal features.

2. The indoor human body detection method according to claim 1, characterized in that, The step of judging whether there is a moving target in the detection space based on the echo signals includes: Processing the echo signals to obtain range-slow time dimension data, performing static clutter filtering, and then performing Fourier transform on the slow time dimension to construct range-Doppler dimension data; Judging whether there is a moving target in the space based on the range-Doppler dimension data.

3. The indoor human body detection method according to claim 2, characterized in that, The step of detecting whether the mechanical motion of the moving target has strict periodicity includes: Calculating the autocorrelation function for each range unit of the range-slow time dimension data, calculating the autocorrelation coefficients at different time delays, and forming an autocorrelation coefficient function curve; Confirming whether there is a periodic peak greater than a preset peak threshold in the autocorrelation coefficient function curve. If so, it is determined that the mechanical motion of the moving target has strict periodicity; otherwise, it is determined that the mechanical motion of the moving target does not have strict periodicity.

4. The indoor human body detection method according to claim 1, characterized in that, The step of judging whether there is a static human body in the detection space based on the echo signals includes: Obtaining the phase information of the echo signals based on the echo signals; Sparsely reconstructing the phase information of the echo signals to obtain a reconstructed signal; Calculating the error value between the reconstructed signal and an ideal sine signal. If the error value is less than a preset threshold, it is determined that there is a static human body in the detection space; otherwise, it is determined that there is no static human body in the detection space.

5. A method for indoor human body detection according to claim 1, characterized in that, The step of collecting infrared signals and obtaining a second judgment result based on the infrared signals includes: Judging whether there is a human target in the room through multiple joint statistics.

6. A composite detection device for smart home, characterized in that, Including: A millimeter-wave radar sensing unit for transmitting millimeter-wave band radar signals and collecting echo signals; An infrared sensing unit for collecting infrared signals; A control unit, which is communicatively connected to the millimeter-wave radar sensing unit and the infrared sensing unit respectively. The control unit includes a first judgment module, a second judgment module, and a fusion analysis module; The first judgment module can obtain a first judgment result based on the echo signals; The second judgment module can obtain a second judgment result based on the infrared signals, The fusion analysis module can perform data fusion on the first judgment result and the second judgment result by using the D-S evidence theory to obtain a detection result; The first judgment module includes a dynamic human body judgment module and a static human body judgment module. The dynamic human body judgment module can judge whether there is a moving target in the detection space based on the echo signal. If there is a moving target, it executes the step of judging whether the moving target is a dynamic human body. The static human body judgment module can judge whether there is a static human body in the detection space based on the echo signal. The step of judging whether the moving target is a dynamic human body includes: Detect whether the mechanical movement of the moving target has strict periodicity. If there is strict periodicity, it is determined that the moving target is an interference source; Perform adaptive-MSSA filtering processing on the echo signal to obtain signal characteristics, and judge whether the moving target is a dynamic human body according to the signal characteristics.

7. The composite detection device for smart home according to claim 6, wherein, The infrared sensing unit is a pyroelectric infrared sensing unit for the human body, and the detection wavelength is 5μm to 14μm. The operating frequencies of the millimeter-wave radar sensing unit include 24GHz and 60GHz.

8. The composite detection device for smart home according to claim 1, characterized in that, The step of judging whether there is a moving target in the detection space based on the echo signal includes: Process the echo signal to obtain range-slow time dimension data. After static clutter filtering, perform Fourier transform on the slow time dimension to construct range-Doppler dimension data; Judge whether there is a moving target in the space based on the range-Doppler dimension data; The step of detecting whether the mechanical movement of the moving target has strict periodicity includes: Calculate the autocorrelation function for each range unit of the range-slow time dimension data, calculate the autocorrelation coefficients at different time delays, and form an autocorrelation coefficient function curve; Confirm whether there is a periodic peak greater than the preset peak threshold in the autocorrelation coefficient function curve. If so, it is determined that the mechanical movement of the moving target has strict periodicity; otherwise, it is determined that the mechanical movement of the moving target does not have strict periodicity.

9. The composite detection device for smart home according to claim 1, characterized in that The step of judging whether there is a static human body in the detection space based on the echo signal includes: Obtain the phase information of the echo signal based on the echo signal; Sparsely reconstruct the phase information of the echo signal to obtain a reconstructed signal; Calculate the error value between the reconstructed signal and the ideal sine signal. If the error value is less than the preset threshold, it is determined that there is a static human body in the detection space; otherwise, it is determined that there is no static human body in the detection space.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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