An object state sensing and detection method and system

Abstract

The present invention relates to the field of inductive recognition technology, and particularly relates to a method and system for detecting the state of an object. The method includes: obtaining intermediate frequency data, and obtaining the velocity component of the current intermediate frequency data through fast Fourier transform; analyzing and judging the obtained velocity component, and judging the target object corresponding to the velocity component by clustering the velocity component and analyzing the value-taking position; detecting the maximum energy value of each velocity component of the target object, and judging the distance and action amplitude of the target object corresponding to the maximum energy value by comparing the maximum energy value with a preset energy value range; and outputting an inductive detection result according to the distance and action amplitude of the target object. The present invention can judge the category of the target object and judge the action amplitude of the target object by calculating the maximum energy value of the velocity component, and can detect whether the target object is in a large movement, a micro movement or a stationary state.

Description

Technical Field

[0001] The present invention relates to the technical field of inductive identification, and particularly relates to an object state induction detection method and system for object detection. Background Art

[0002] Currently, infrared sensors are mainly used in the market to detect corresponding application scenarios to determine whether there is a person. However, when using an infrared sensor for inductive detection, it is often affected by strong light and high-temperature objects, resulting in the inability to effectively detect the human body and accurately detect the target. Moreover, when the infrared sensor is detecting, it can only detect a single point or a certain angular range, and the detection range is small, which cannot meet the inductive detection requirements in various application scenarios.

[0003] A microwave sensor is a device that uses microwave characteristics to detect some physical quantities. Currently, there are two modulation methods for microwave sensors in the industry: FMCW and CW. Among them, CW modulation is also called continuous wave (CW) modulation, which is a continuous wave signal. In fact, it is a sine wave as the carrier wave. CW can include linear modulation (AM, DSB, SSB, VSB, etc.), non-linear modulation (FM, PM), digital modulation (ASK, FSK, etc.). Since CW modulation can only detect dynamic objects and cannot detect the micro-motion and stationary states. Therefore, there is a need to provide an object state induction detection method and system. Summary of the Invention

[0004] In order to solve the problem that the existing modulation can only detect dynamic objects and cannot detect micro-motion and stationary states, the present invention constructs an object state induction detection method and system. By taking advantage of the microwave sensor, it can detect large movements, micro-motions, and stationary state objects, and has a large detection range.

[0005] The present invention is implemented by the following technical solutions:

[0006] An object state induction detection method includes the following steps:

[0007] Obtain intermediate frequency data, and obtain the velocity component of the current intermediate frequency data through fast Fourier transform;

[0008] Analyze and judge the obtained velocity component. By clustering the velocity component and analyzing the value position, judge the target object corresponding to the velocity component;

[0009] Detect the maximum energy value of each velocity component of the target object. By comparing the maximum energy value with a preset energy value interval, judge the distance and action amplitude of the target object corresponding to the maximum energy value;

[0010] Output an inductive detection result according to the distance and action amplitude of the target object.

[0011] As a further solution of the present invention, the method for obtaining the velocity component of the current intermediate frequency data includes:

[0012] Grab the intermediate frequency data;

[0013] Remove the direct current from the grabbed intermediate frequency data and apply a Hanning window;

[0014] Obtain the velocity component of the current intermediate frequency data through fast Fourier transform.

[0015] Furthermore, the intermediate frequency data is a signal that is emitted by a sensor as an electromagnetic wave with a fixed frequency, reflected by an object, received by the sensor, and obtained after mixing. The mixing formula of the intermediate frequency data is:

[0016] If 混 =sin[(ω1 - ω2)]

[0017] where ω1 is the transmission frequency and ω2 is the reception frequency.

[0018] Furthermore, the intermediate frequency data reflects the velocity components of all velocities, and the sampling frequency for grabbing the intermediate frequency data is 315 Hz.

[0019] Furthermore, the velocity components of all velocities reflected by the intermediate frequency signal of the intermediate frequency data are the velocities reflected by the moving object. The velocity reflected by the moving object is calculated according to the Doppler effect. The Doppler effect formula is:

[0020]

[0021] where f D : Doppler frequency or difference frequency;

[0022] f0: Transmission frequency of the radar;

[0023] v: Velocity range of the moving object;

[0024] c0: Speed of light;

[0025] α: Angle between the actual direction of movement and the line connecting the sensor to the target object.

[0026] Furthermore, when removing the direct current from the intermediate frequency data, since the direct current signal is a fixed value, all the collected intermediate frequency data is added and divided by the average value, and then subtracted. The calculation formula for the value y k1 after the intermediate frequency signal is processed is:

[0027]

[0028] where x kis the intermediate frequency data collected, and N is the number of intermediate frequency data collected this time.

[0029] Further, after the intermediate frequency data is DC-removed, the method of adding a Hanning window is to multiply the collected data by a function, that is, to add a window; the value y of the intermediate frequency signal after DC-removal and then windowing k The calculation formula is:

[0030]

[0031] where: x k is the intermediate frequency data collected, and N is the number of intermediate frequency data collected this time.

[0032] Further, the FFT function formula f of the velocity component of the current intermediate frequency data is obtained by fast Fourier transform (FFT) as:

[0033] f = (a0, a1, …, a n-1 )

[0034] Separate the odd and even terms and construct coefficients of the following length , where a is the intermediate frequency signal after windowing and DC-removal;

[0035] f0 = (a0, a2, …, a n-2 )

[0036] f1 = (a1, a3, …, a n-1 )

[0037] There is f(x) = f0(x 2 ) + xf1(x 2 );

[0038] For There is

[0039]

[0040]

[0041] Let There is

[0042]

[0043]

[0044] The above series of operations is called the butterfly operation, is called the rotation factor;

[0045] Recursively obtain y [0] , y [1] , and the scales are all Combine to obtain y;

[0046]

[0047] After this calculation, the frequency information of the current waveform is obtained. In the FFT, the interval df between adjacent spectral lines is:

[0048] where fs is the sampling frequency, N is the number of intermediate-frequency data collected this time, Ts is the sampling time interval, and the spectral line interval determines the frequency resolution of the FFT. When the spectral line interval is large, useful information will be lost due to the fence effect.

[0049] As a further solution of the present invention, the value positions after clustering of the velocity components corresponding to different target objects are different. For example, when judging whether the target object is a person or a fan, since the velocity components of the fan have values at only a few fixed positions, while the human body will have a variety of positions and continuous velocity components. Therefore, after clustering the velocity components, judge which of the above logics the velocity components conform to, and obtain whether the target object is a person or a fan.

[0050] Furthermore, the method for clustering the velocity components is as follows:

[0051] Set the frequency threshold for the clustering operation;

[0052] Find the frequency points greater than the threshold. After finding, mark this frequency point, and call the value of this frequency point the head, and then continue this round of loop;

[0053] Find the frequency points less than the threshold. After finding, mark this frequency point, and call the value of this frequency point the tail, and end the loop.

[0054] Furthermore, the method for judging the target object corresponding to the velocity component is as follows:

[0055] Subtract the value corresponding to the head from the value corresponding to the tail to obtain a difference value;

[0056] Judge the category of the target object according to the magnitude of the difference value.

[0057] The present invention also includes an object state induction detection system. The object state induction detection system uses the foregoing induction detection method to judge the category and action amplitude of the target object, and detects the large movement, micro movement and static state of the target object; the object state induction detection system includes a velocity component calculation module, a target object judgment module and a state analysis module.

[0058] The velocity component calculation module is used to obtain the velocity components of the current intermediate-frequency data by performing a fast Fourier transform on the acquired intermediate-frequency data;

[0059] The target object judgment module is used to analyze and judge the obtained velocity components. By clustering the velocity components and analyzing the value-taking positions, it judges and obtains the target object corresponding to the velocity components;

[0060] The state analysis module is used to detect the maximum energy value of each velocity component of the target object. By comparing the maximum energy value with a preset energy value range, it judges the distance and action amplitude of the target object corresponding to the maximum energy value. According to the distance and action amplitude of the target object, it analyzes and outputs the induction detection result.

[0061] The present invention also includes a computer device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the object state induction detection method described above.

[0062] The present invention also includes a computer-readable storage medium storing computer instructions for causing the computer to execute the object state induction detection method.

[0063] The technical solution provided by the present invention has the following beneficial effects:

[0064] The present invention detects the intermediate frequency data of the electromagnetic wave emitted and reflected by the sensor device, analyzes and judges the velocity components of the intermediate frequency data, and uses the different position values of the velocity components of different types of target objects to judge the type of the target object and judges the action amplitude of the target object by calculating the maximum energy value of the velocity components. It can detect whether the target object is in a large movement, a micro movement or a static state, and can obtain the approximate distance of the target object. According to the output result, it judges whether there is a person, and is applicable to the induction detection of target objects in application scenarios such as offices, meeting rooms or hospital beds.

[0065] These aspects or other aspects of the present invention will be more clearly understood in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following will briefly introduce the drawings required for the description of the exemplary embodiments or related technologies. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0067] Figure 1 Flow chart of an object state sensing and detection method according to the present invention.

[0068] Figure 2 Flow chart of obtaining the velocity component of intermediate frequency data in the object state sensing and detection method in an embodiment of the present invention.

[0069] Figure 3 Schematic structural diagram of the α angle in the object state sensing and detection method in an embodiment of the present invention.

[0070] Figure 4 Schematic diagram of the intermediate frequency signal collected in the object state sensing and detection method in an embodiment of the present invention.

[0071] Figure 5 Schematic diagram of the value after fast Fourier transform in the object state sensing and detection method in an embodiment of the present invention.

[0072] Figure 6 System block diagram of the object state sensing and detection system in an embodiment of the present invention. Detailed implementation manners

[0073] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. 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.

[0074] In some processes described in the specification, claims and above-mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The operation numbers such as 101, 102, etc. are only used to distinguish different operations, and the numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent a sequence, and do not limit that "first" and "second" are of different types.

[0075] The technical solutions in the exemplary embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the exemplary embodiments of the present invention. Obviously, the described exemplary embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0076] Refer to Figure 1 as shownFigure 1 Flow chart of an object state sensing and detecting method provided by the present invention. An object state sensing and detecting method provided by the present invention, the method comprising the following steps:

[0077] S1. Obtain intermediate frequency data, and obtain the velocity component of the current intermediate frequency data through fast Fourier transform.

[0078] It should be particularly noted that the method for obtaining the velocity component of the current intermediate frequency data includes:

[0079] S101. Grab the intermediate frequency data;

[0080] S102. Remove the direct current from the grabbed intermediate frequency data and add a Hanning window;

[0081] S103. Obtain the velocity component of the current intermediate frequency data through fast Fourier transform.

[0082] In this embodiment, the intermediate frequency data reflects the velocity components of all velocities. The intermediate frequency (IF) data is the data that is emitted as an electromagnetic wave, reflected back, and this data reflects the velocity components of all velocities. In this embodiment, the sampling frequency for grabbing the intermediate frequency data is 315 Hz, and the data collected at other frequencies is incomplete.

[0083] Among them, the intermediate frequency data is a signal that is emitted by a sensor as an electromagnetic wave with a fixed frequency, reflected by an object, received by the sensor, and then obtained through mixing. The mixing formula for the intermediate frequency data is:

[0084] If 混 =sin[(ω1 - ω2)]

[0085] Among them, ω1 is the transmission frequency and ω2 is the reception frequency.

[0086] The velocity components of all velocities reflected by the intermediate frequency signal of the intermediate frequency data are the velocities reflected by the moving object. The velocity reflected by the moving object is calculated according to the Doppler effect. The Doppler effect formula is:

[0087]

[0088] Among them, f D : Doppler frequency or difference frequency;

[0089] f0: Transmission frequency of the radar;

[0090] v: Velocity range of the moving object;

[0091] c0: Speed of light;

[0092] α: The angle between the actual direction of movement and the line connecting the sensor to the target object. A schematic diagram of α is shown as Figure 3 shown below.

[0093] In this embodiment, when removing the DC component from the intermediate frequency data, since the DC signal is a fixed value, all the collected intermediate frequency data are added and divided by the average value, and then subtracted. The value y of the intermediate frequency signal after processing is k1 calculated by the formula:

[0094]

[0095] where x k is the collected intermediate frequency data, and N is the number of the collected intermediate frequency data this time.

[0096] After removing the DC component from the intermediate frequency data, the method of adding a Hanning window is to multiply the collected data by a function, that is, adding a window. The value y of the intermediate frequency signal after removing the DC component and then adding a window is k calculated by the formula:

[0097]

[0098] where: x k is the collected intermediate frequency data, and N is the number of the collected intermediate frequency data this time.

[0099] It should be particularly noted that if the FFT is directly performed on the data without adding a window, there will be a problem of spectral leakage, while adding a window can reduce the spectral leakage after FFT.

[0100] In this embodiment, the FFT function formula f for obtaining the velocity component of the current intermediate frequency data through the fast Fourier transform (FFT) is:

[0101] f = (a0, a1, …, a n-1 )

[0102] Separate the odd terms and even terms and construct the following length of coefficient representation. In the formula, a is the intermediate frequency signal after adding a window and removing the DC component;

[0103] f0 = (a0, a2, …, a n-2 )

[0104] f1 = (a1, a3, …, a n-1 )

[0105] There is f(x) = f0(x 2 ) + xf1(x 2 );

[0106] For there is

[0107]

[0108]

[0109] Let There is

[0110]

[0111]

[0112] The above series of operations is called the butterfly operation, is called the rotation factor;

[0113] Recursively obtain y [0] , y [1] , and the scale is Merge to obtain y

[0114]

[0115] What is obtained after this calculation is the frequency information of the current waveform. Since in FFT, the interval df between adjacent spectral lines is:

[0116] where fs is the sampling frequency, N is the number of intermediate-frequency data collected this time, Ts is the sampling time interval, and the spectral line interval determines the frequency resolution of FFT. When the spectral line interval is large, useful information will be lost due to the fence effect.

[0117] In this embodiment, taking the sampling rate of fs = 315 Hz as an example and the number of sampling points being 128, the frequency resolution is: Calculate the speed of the radar according to the radar formula as 1 m / s: When calculating the Doppler speed of 1 m / s for the sine wave with an intermediate-frequency signal output of 38.6 Hz, substituting into the frequency resolution gives: That is, the resolution of one velocity component is: 0.06375 m / s. Since this sensor does not support angle measurement, there is no direction judgment.

[0118] For example, it is illustrated as follows:

[0119] Figure 4 and Figure 5 is the waveform of the signal received by a radar, where Figure 4 is the collected intermediate-frequency signal, which has undergone windowing and DC removal, Figure 5 is the value after FFT (Fast Fourier Transform). View the picture shown in Figure 5 where the abscissa is the frequency resolution of FFT and the ordinate is the energy value.

[0120] S2. Analyze and judge the obtained velocity components. By clustering the velocity components and analyzing the value positions, judge the target object corresponding to the velocity components.

[0121] In this embodiment, the value positions after clustering of the velocity components corresponding to different target objects are different. For example, when judging whether the target object is a person or a fan, since the velocity components of the fan have values at only a few fixed positions, while the human body will have velocity components at multiple positions and continuously, therefore, after clustering the velocity components, judge which logic the velocity components conform to above, and obtain whether the target object is a person or a fan.

[0122] Taking the judgment of whether it is a person or a fan as an example, by clustering the velocity components, since the velocity components of the fan have values at only a few fixed positions, while the human body will have velocity components at multiple positions and continuously, judge which logic the velocity components conform to above, and obtain whether the target object is a person or a fan. It should be particularly noted that the sampling frequency will not be zero point several Hertz.

[0123] In this embodiment, the method for clustering the velocity components is as follows:

[0124] Set the frequency point threshold for the clustering operation;

[0125] Find the frequency points greater than the threshold. After finding, mark this frequency point, and call the value of this frequency point the head, then continue this round of loop;

[0126] Find the frequency points less than the threshold. After finding, mark this frequency point, and call the value of this frequency point the tail, and end the loop.

[0127] Specifically, taking the frequency point threshold as 200 as an example. 1. Find the frequency points greater than the threshold of 200. After finding, mark this point, and call this value the head, then continue this round of loop. 2. Find the frequency points less than the threshold of 200. After finding, mark this point, and call this value the tail, and end the loop. The purpose of this embodiment is to find continuous frequency points greater than 200. Since the human body is a non-rigid body movement, the speeds of different parts are different when a person moves, but there must be many continuous speeds.

[0128] In this embodiment, the method for judging the target object corresponding to the velocity components is as follows:

[0129] Subtract the value corresponding to the head from the value corresponding to the tail to obtain a difference value;

[0130] Judge the category of the target object according to the magnitude of the difference value.

[0131] For example, since different movement amplitudes of a person can be used to judge the state of the person at this time, the following defines the frequency point situation of a person at 1 m:

[0132] (1) Breathing: Defined as a person sitting still with their limbs not moving. At this time, the frequency points show that only 1 - 3 frequency points are greater than the threshold value of 200.

[0133] (2) Slight movement: Defined as a person's slight swaying, with their limbs only making small movements such as typing on a keyboard. At this time, 3 - 10 frequency points are greater than the threshold value of 200.

[0134] (3) Large movement: Defined as a person making large movements such as walking. At this time, more than 10 frequency points are greater than 200.

[0135] S3. Detect the maximum energy value of each velocity component of the target object, and compare the maximum energy value with a preset energy value range to determine the distance and movement amplitude of the target object corresponding to the maximum energy value.

[0136] In this embodiment, detect the energy value of each velocity component of the target object, compare the energy value with a preset energy value range, determine which object's energy value range the above energy value belongs to, and obtain the approximate distance and movement amplitude (large movement, slight movement, or stationary) of the target object.

[0137] When making a judgment, first determine whether the target object is a person or a fan, and at the same time obtain the maximum energy value of the velocity component. Determine the movement amplitude of the target object through the maximum energy value, and determine whether it belongs to a large movement, a slight movement, or a stationary state.

[0138] In this embodiment, due to the attenuation of electromagnetic waves, as the distance changes, the detected energy value attenuates more as the distance gets farther. Therefore, it is necessary to judge the current person's distance through the maximum energy value, and apply different frequency point situations according to the distance between the person and the radar.

[0139] Since the fan belongs to rigid body motion and only has a few frequency points, but the speed is too fast, that position can be identified as the fan.

[0140] S4. Output the induction detection result according to the distance and movement amplitude of the target object.

[0141] In this embodiment, judge whether there is a person according to the output induction detection result. The present invention does not need to distinguish between single and multiple persons during induction detection.

[0142] The present invention detects intermediate frequency data emitted and reflected by an electromagnetic wave through a sensor device, analyzes and judges the velocity component of the intermediate frequency data, and utilizes the fact that the position values of the velocity components of different types of target objects are different to judge the type of the target object and judge the action amplitude of the target object by calculating the maximum energy value of the velocity component. It can detect whether the target object is in a large movement, a micro movement or a stationary state, and can obtain the approximate distance of the target object, and judge whether there is a person according to the output result, and is applicable to the induction detection of target objects in application scenarios such as offices, meeting rooms or hospital beds.

[0143] It should be understood that although the above is described in a certain order, these steps are not necessarily executed in the above order successively. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, a part of the steps of this embodiment may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0144] In one embodiment, as Figure 6 shown, an object state induction detection system is provided, including a velocity component calculation module 100, a target object judgment module 200, and a state analysis module 300. Among them:

[0145] The velocity component calculation module 100 is used to obtain the velocity component of the current intermediate frequency data by performing a fast Fourier transform on the acquired intermediate frequency data.

[0146] The target object judgment module 200 is used to analyze and judge the obtained velocity component, and judge the target object corresponding to the velocity component by clustering the velocity component and analyzing the value position.

[0147] The state analysis module 300 is used to detect the maximum energy value of each velocity component of the target object, compare the maximum energy value with a preset energy value interval, judge the distance and action amplitude of the target object corresponding to the maximum energy value, and analyze and output an induction detection result according to the distance and action amplitude of the target object.

[0148] In this embodiment, the induction detection system adopts the steps of an object state induction detection method as described above during execution. Therefore, the operation process of the induction detection system in this embodiment will not be introduced in detail.

[0149] In one embodiment, an embodiment of the present invention further provides a computer device, including at least one processor, and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor is caused to execute the object state sensing and detection method, and when the processor executes the instructions, the steps in the above method embodiments are implemented:

[0150] Obtain intermediate frequency data, and obtain the velocity component of the current intermediate frequency data through fast Fourier transform;

[0151] Analyze and judge the obtained velocity component. By clustering the velocity component and analyzing the value position, judge and obtain the target object corresponding to the velocity component;

[0152] Detect the maximum energy value of each velocity component of the target object. By comparing the maximum energy value with a preset energy value range, judge the distance and action amplitude of the target object corresponding to the maximum energy value;

[0153] Output the sensing and detection result according to the distance and action amplitude of the target object.

[0154] In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions for causing the computer to execute the object state sensing and detection method:

[0155] Obtain intermediate frequency data, and obtain the velocity component of the current intermediate frequency data through fast Fourier transform;

[0156] Analyze and judge the obtained velocity component. By clustering the velocity component and analyzing the value position, judge and obtain the target object corresponding to the velocity component;

[0157] Detect the maximum energy value of each velocity component of the target object. By comparing the maximum energy value with a preset energy value range, judge the distance and action amplitude of the target object corresponding to the maximum energy value;

[0158] Output the sensing and detection result according to the distance and action amplitude of the target object.

[0159] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program characterized by computer instructions. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above various methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories.

[0160] Non-volatile memory may include read-only memory, magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory may include random access memory or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory or dynamic random access memory, etc.

[0161] In summary, the present invention detects the intermediate frequency data emitted and reflected by the electromagnetic wave through the sensor device, analyzes and judges the velocity component of the intermediate frequency data. By using the fact that the position values of the velocity components of different types of target objects are different, it judges the type of the target object and judges the action amplitude of the target object by calculating the maximum energy value of the velocity component. It can detect whether the target object is in a large movement, a micro movement, or a stationary state, and can obtain the approximate distance of the target object. According to the output result, it judges whether there is a person, and is applicable to the inductive detection of target objects in application scenarios such as offices, meeting rooms, or hospital beds.

[0162] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting the state of an object, characterized in that, Including: Obtain intermediate frequency data, and obtain the velocity component of the current intermediate frequency data through fast Fourier transform; Analyze and judge the obtained velocity component. By clustering the velocity component and analyzing the value position, judge and obtain the target object corresponding to the velocity component; Detect the maximum energy value of each velocity component of the target object. By comparing the maximum energy value with a preset energy value range, judge the distance and action amplitude of the target object corresponding to the maximum energy value; Output an induction detection result according to the distance and action amplitude of the target object; The method for clustering the velocity component is: set the frequency point threshold for the clustering operation; Find the frequency points greater than the threshold. After finding, mark this frequency point, and call the value of this frequency point the head, and then continue this round of loop; Find the frequency points less than the threshold. After finding, mark this frequency point, and call the value of this frequency point the tail, and end the loop.

2. The method for detecting the state of an object according to claim 1, characterized in that: The method for obtaining the velocity component of the current intermediate frequency data includes: Grab intermediate frequency data; Remove the direct current from the grabbed intermediate frequency data and add a Hanning window; Obtain the velocity component of the current intermediate frequency data through fast Fourier transform.

3. The method for detecting the state of an object according to claim 1 or 2, characterized in that: The intermediate frequency data is a signal emitted by a sensor with a fixed frequency of electromagnetic wave, reflected by an object and received by the sensor, and the waveform obtained after mixing. The mixing formula of the intermediate frequency data is: If 混 = sin[(ω1 - ω2)] Where, ω1 is the transmission frequency and ω2 is the reception frequency.

4. The method for detecting the state of an object according to claim 3, characterized in that: All velocity components reflected by the intermediate frequency signal of the intermediate frequency data are the velocities reflected by the moving object. The velocity reflected by the moving object is calculated according to the Doppler effect. The Doppler effect formula is: where f D : Doppler frequency or difference frequency; f0: Transmission frequency of the radar; v: Velocity range of the moving object; c0: Speed of light; α: Angle between the actual direction of movement and the line connecting the sensor to the target object.

5. The method for detecting the state of an object according to claim 2, characterized in that: When removing the DC component from the intermediate frequency data, since the DC signal is a fixed value, all the collected intermediate frequency data can be added up and divided by the average value, and then subtracted. The value y of the processed intermediate frequency signal k1 is calculated by the formula: Among them, x k is the intermediate frequency data collected, and N is the number of intermediate frequency data collected this time.

6. The method for detecting the state of an object according to claim 5, characterized in that: After removing the direct current from the intermediate frequency data, the method of adding a Hanning window is to multiply the collected data by a function, that is, add a window; The value y of the intermediate-frequency signal after DC removal and windowing again k is calculated by the formula: Where: x k is the intermediate frequency data collected, and N is the number of intermediate frequency data collected this time.

7. The method for detecting the state of an object according to claim 6, characterized in that: The FFT function formula f for obtaining the velocity component of the current intermediate frequency data through fast Fourier transform is: f = (a0, a1, …, a n-1 ) Separate the odd and even terms and construct the coefficient representation of the following length where a is the intermediate frequency signal after windowing and removing DC. In the fast Fourier transform, the interval df between adjacent spectral lines is: Where, fs is the sampling frequency, N is the number of intermediate frequency data collected this time, Ts is the sampling time interval, and the spectral line interval determines the frequency resolution of the FFT. When the spectral line interval is large, useful information will be lost due to the fence effect.

8. The object state sensing and detection method according to claim 1, characterized in that: The method for judging and obtaining the target object corresponding to the velocity component is: Subtract the value corresponding to the tail from the value corresponding to the head to obtain a difference value; Judge the category of the target object according to the magnitude of the difference value.

9. An object state sensing and detection system, characterized in that: The object state induction detection system uses the object state induction detection method described in any one of claims 1-8 to judge the category and action amplitude of the target object, and detect the large movement, micro movement and static state of the target object; The object state induction detection system includes: A velocity component calculation module, configured to obtain the velocity component of the current intermediate frequency data through fast Fourier transform for the obtained intermediate frequency data; A target object judgment module, configured to analyze and judge the obtained velocity component. By clustering the velocity component and analyzing the value position, judge and obtain the target object corresponding to the velocity component; and A state analysis module, which is used to detect the maximum energy value of each velocity component of the target object, compare the maximum energy value with a preset energy value range, judge the distance and action amplitude of the target object corresponding to the maximum energy value, and analyze and output an induction detection result according to the distance and action amplitude of the target object; The method for clustering the velocity components is as follows: set the frequency point threshold for the clustering operation; find the frequency points greater than the threshold, mark this frequency point after finding it, call the value of this frequency point the head, and then continue this round of loop; find the frequency points less than the threshold, mark this frequency point after finding it, call the value of this frequency point the tail, and end the loop.

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