A Temperature-Robust Liquid Identification Method Based on FMCW Technology

Abstract

This invention discloses a temperature-robust liquid identification method based on FMCW technology, belonging to the field of liquid identification technology. The method includes the following steps: placing the liquid to be tested in front of a transceiver; transmitting a frequency-modulated continuous wave (FMCH) signal using a millimeter-wave radar; receiving the signal reflected back from the liquid; eliminating interference from the DC component of the hardware itself; extracting liquid information; extracting temperature-robust liquid features; and achieving liquid identification. This invention proposes a temperature-robust liquid identification method based on FMCW technology, which continuously transmits chirp signals using an FMCW millimeter-wave radar, receives the signal reflected back from the liquid to be tested, filters the signal to remove the DC component, extracts liquid information, obtains the millimeter-wave spectrum of the liquid to be tested, simplifies and quantizes the millimeter-wave spectrum, thereby obtaining liquid features robust to temperature fluctuations, and thus achieving liquid identification.

Description

Technical Field

[0001] This invention relates to the field of liquid identification technology, and in particular to a temperature-robust liquid identification method based on FMCW technology. Background Technology

[0002] Material sensing of liquids plays a vital role in fields such as security inspection, smart logistics, and industrial production. Traditional material sensing methods include hyperspectral cameras, mass spectrometers, and high-frequency X-rays, but these devices are typically expensive and complex to operate. Furthermore, these methods are invasive and can lead to liquid contamination.

[0003] In recent years, researchers have utilized wireless technologies such as RFID, WiFi, UWB, and FMCW to propose many innovative liquid sensing methods. These systems rely on the fact that the dielectric constant is a unique characteristic of liquids, affecting signals that reflect or penetrate the liquid (e.g., amplitude and phase). By analyzing changes in the amplitude or phase of the received signal, non-contact and non-invasive liquid identification can be achieved. Although some current non-contact identification methods perform well at room temperature, they rarely consider the impact of temperature fluctuations. In fact, temperature fluctuations directly affect certain properties of liquids, such as dielectric constant and viscosity, which are commonly used in most wireless-based liquid identification methods. Ignoring the effects of temperature fluctuations can lead to liquid identification errors, potentially causing serious consequences. For example, in industrial production, misclassifying sodium hydroxide solution as hydrochloric acid could lead to production accidents, while misclassifying flammable and explosive products such as alcohol and gasoline as pure water could pose significant safety risks.

[0004] FMCW (Frequency Modulated Continuous Wave) refers to a technology where the received echo frequency follows the same triangular wave pattern as the transmitted frequency, but with a time difference. This small time difference can be used to calculate the target distance. FMCW technology and pulse radar technology are two techniques used in high-precision radar ranging. This invention aims to achieve non-contact identification of liquids using FMCW technology. Summary of the Invention

[0005] The purpose of this invention is to provide a temperature-robust liquid identification method based on FMCW technology. This method involves continuously transmitting chirp signals using an FMCW millimeter-wave radar, receiving signals reflected from the liquid being tested, filtering the signals to remove DC components, extracting liquid information, obtaining the millimeter-wave spectrum of the liquid, and simplifying and quantizing the millimeter-wave spectrum to obtain liquid characteristics robust to temperature fluctuations. This enables liquid identification and addresses the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a temperature-robust liquid identification method based on FMCW technology, comprising the following steps:

[0007] S1: Place the liquid to be tested in front of the transceiver;

[0008] S2: Millimeter-wave radar transmits frequency-modulated continuous wave signals;

[0009] S3: Receive the signal reflected back by the liquid to be tested;

[0010] S4: Eliminate interference from the DC component of the hardware itself;

[0011] S5: Extract liquid information;

[0012] S6: Extract temperature-robust liquid features;

[0013] S7: Enables liquid recognition.

[0014] Preferably, step S2 specifically includes the following:

[0015] With the millimeter-wave radar transceiver antenna pointed towards the liquid being tested, the FMCW millimeter-wave radar continuously transmits chirp signals. The frequency of these chirp signals changes linearly with time, ranging from 77 GHz to 81 GHz.

[0016] Preferably, step S4 specifically includes the following:

[0017] In the frequency domain of the original signal, there is a significant peak between 0cm and 10cm, which is an inherent characteristic of FMCW. This peak is considered as a DC component. Different devices or different parameters of the same device will produce different DC components. A high-pass filter is used to filter the DC component and remove it, so the signal after 10cm is taken.

[0018] Preferably, the liquid information extracted in step S5 includes the following:

[0019] An empirical threshold is set. The first peak value that exceeds the empirical threshold is the reflection peak of the liquid surface. The second maximum point that exceeds the empirical threshold after the first peak value is the reflection peak of the liquid back side. A bandpass filter is used to extract the frequency band information between the two peaks. This frequency band information is the liquid information.

[0020] Preferably, the magnitude of the empirical threshold is the magnitude of the DC component.

[0021] Preferably, the specific content of step S6 is as follows:

[0022] The frequency response curve is obtained from the extracted liquid information. For different liquids, no matter how the temperature fluctuates, although the amplitude of the received signal changes, the frequency response curve will only shift up and down in the whole spectrum. This stable frequency response curve is called millimeter wave spectrum. Simplifying the millimeter wave spectrum and quantizing and amplifying the corresponding trend features are the liquid features.

[0023] Preferably, step S7 specifically includes the following:

[0024] By combining classic machine learning methods, a liquid feature database is constructed, which stores the liquid features corresponding to different liquids, and the liquid category corresponding to the acquired liquid features is identified.

[0025] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a temperature-robust liquid identification method based on FMCW technology. It continuously transmits chirp signals using an FMCW millimeter-wave radar, receives signals reflected from the liquid under test, filters the signals to remove DC components, extracts liquid information, and obtains the millimeter-wave spectrum of the liquid under test. The millimeter-wave spectrum is then simplified and quantized to obtain liquid characteristics robust to temperature fluctuations, thereby enabling liquid identification. In this invention, by removing DC components, the location of liquid information is determined, further locking the corresponding information and removing interference from multipath and DC components, thus improving identification accuracy. Furthermore, this invention obtains a frequency response curve from the extracted liquid information, and experiments confirm that although the amplitude of the received signal changes, the frequency response curve only shifts up and down across the entire spectrum, providing a stable feature for identifying certain liquids. This feature is robust to temperature fluctuations, overcoming the technical defect in existing technologies where temperature fluctuations can lead to incorrect liquid identification. Attached Figure Description

[0026] Figure 1 This is a flowchart of the liquid identification method of the present invention;

[0027] Figure 2 This is a schematic diagram illustrating the change in dielectric constant of pure water caused by a temperature change of 10°C according to the present invention.

[0028] Figure 3 This is a schematic diagram showing the peak amplitude changes of whole milk and vinegar with concentrations of 24% and 26% respectively under temperature fluctuations, according to the present invention.

[0029] Figure 4 This is a schematic diagram illustrating the effect of temperature on liquid recognition according to the present invention;

[0030] Figure 5 This is the time-frequency domain diagram of the original signal of this invention;

[0031] Figure 6This is a schematic diagram of the liquid information extraction components of the present invention;

[0032] Figure 7 The images show millimeter-wave spectra extracted from 10% saline solution at different temperatures according to the present invention.

[0033] Figure 8 This is a schematic diagram illustrating the liquid characteristics of a 10% saline solution according to the present invention.

[0034] Figure 9 The images show millimeter-wave spectra of the 10% sugar solution of this invention at different temperatures.

[0035] Figure 10 This is a schematic diagram illustrating the liquid characteristics of a 10% sugar solution according to the present invention;

[0036] Figure 11 The images show millimeter-wave spectra of iced black tea extracted at different temperatures according to the present invention.

[0037] Figure 12 This is a schematic diagram illustrating the liquid characteristics of the iced black tea of ​​the present invention;

[0038] Figure 13 The images show millimeter-wave spectra of the lemon tea from this invention at different temperatures.

[0039] Figure 14 This is a schematic diagram illustrating the liquid characteristics of the lemon tea of ​​the present invention;

[0040] Figure 15 The images show millimeter-wave spectra of green plum tea extracted at different temperatures according to the present invention.

[0041] Figure 16 This is a schematic diagram illustrating the liquid characteristics of the green plum tea of ​​the present invention;

[0042] Figure 17 The images show millimeter-wave spectra of 68% ethanol at different temperatures.

[0043] Figure 18 This is a schematic diagram illustrating the liquid characteristics of 68% alcohol according to the present invention;

[0044] Figure 19 The images show millimeter-wave spectra of 69% ethanol at different temperatures.

[0045] Figure 20 This is a schematic diagram illustrating the liquid characteristics of 69% alcohol according to the present invention;

[0046] Figure 21 The images show millimeter-wave spectra of 72% ethanol at different temperatures.

[0047] Figure 22 This is a schematic diagram illustrating the liquid characteristics of 72% alcohol according to the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see Figure 1 A temperature-robust liquid identification method based on FMCW technology includes the following steps:

[0050] Step 1: Place the liquid to be tested in front of the transceiver;

[0051] Step 2: The millimeter-wave radar transmits a frequency-modulated continuous wave signal. The transceiver antenna of the millimeter-wave radar is pointed towards the liquid to be tested. The FMCW millimeter-wave radar continuously transmits chirp signals. The frequency of the chirp signal changes linearly with time, and the frequency range is 77GHz-81GHz.

[0052] Step 3: Receive the signal reflected back from the liquid being tested;

[0053] When a signal encounters a liquid, it is reflected back and received by the radar receiving antenna, which can be expressed by the formula:

[0054]

[0055] Where S input G represents the intensity of the signal entering the liquid. t and G r R represents the gain of the transmitting and receiving antennas, d represents the distance from the radar, λ is the wavelength of the millimeter wave, and R liquid denoted as the reflectance coefficient of the liquid.

[0056] In other words, when identifying liquids at a specific distance, the reflected signal of the liquid is determined by its reflection coefficient. The reflection coefficient of a liquid can be expressed as:

[0057]

[0058] Where n liquid n air Let represent the refractive indices of the two media, one before the signal enters the liquid and the other before. The refractive indices are represented by the dielectric constant ∈ = ∈′―i∈″.

[0059]

[0060] The above indicates that the received intensity of the reflected signal reflects the unique properties of the liquid and can be used to distinguish different liquids.

[0061] However, the dielectric constant of a liquid is closely related to temperature, and the dielectric constant can be expressed as:

[0062] ∈(f,T)=∈′(f,T)―i∈″(f,T)

[0063] Where f represents frequency, T represents temperature, ∈′ represents the energy storage capacity of an object when its material is exposed to an electromagnetic field, and ∈″ represents the loss factor, which affects energy absorption and attenuation. For example, as Figure 2 As shown, for pure water, a temperature change of 10°C will result in a corresponding change of 5 in the dielectric constant.

[0064] Three liquids were identified in a laboratory setting: whole milk, vinegar with concentrations of 24% and 26%, respectively. A constant-temperature heating plate was used to simulate liquid temperature fluctuations, and data were collected in 5-degree Celsius increments from 25°C to 95°C. Results are as follows: Figure 3 As shown, the peak amplitudes of different liquids overlap and intersect as the temperature changes.

[0065] For example, vinegar with a concentration of 24% at 35°C and whole milk with a concentration of 24% at 55°C have very similar amplitudes. Using the received signal amplitude as a feature parameter to input machine learning for liquid identification may lead to incorrect liquid identification.

[0066] like Figure 4 As shown, when the temperature fluctuation range is within 10 degrees Celsius, the recognition accuracy is about 90%. When the temperature fluctuation range is above 30 degrees Celsius, the liquid recognition performance drops sharply. In the prior art, ignoring the influence of temperature fluctuation may lead to liquid recognition errors, which may cause serious consequences. Therefore, in this invention, steps four to six are performed to overcome the influence of temperature fluctuation on recognition accuracy.

[0067] Step 4: Eliminate interference from the hardware's own DC component.

[0068] like Figure 5 As shown, the raw data can be divided into three frequency components:

[0069] First, there is a distinct peak between 0cm and 10cm, which is an inherent characteristic of FMCW and can be considered as a DC component.

[0070] Second, in addition to the DC component, the original signal also contains a large number of reflections from the background environment, such as tables, walls, and books, which are multipath components.

[0071] Third, there is a distinct peak between the DC component and the multipath component, representing the reflection of the liquid. Due to the interference of the multipath component and the DC component, the liquid information in the original signal may be masked.

[0072] In the frequency domain of the original signal, different devices or different parameters of the same device will produce different DC components. A high-pass filter is used to filter the DC components and remove them. The signal is taken from 10cm away to eliminate the interference of the DC components of the hardware itself.

[0073] Step 5:

[0074] like Figure 6 As shown, at the location of the liquid being tested, there is a maximum peak, which is a reflection from the front of the liquid container. After the maximum peak, there is a tiny peak. The distance between these two peaks is approximately equal to the width of the container. This tiny peak may be a reflection generated from the back of the container. To explain this experimental phenomenon, the propagation of the FMCW signal in the liquid was modeled.

[0075] Signals are reflected when passing through media with different impedances. Specifically, the reflected signal mainly consists of the following parts: First, when the incident signal reaches the surface of the container, a reflected signal containing liquid information is generated, and another part of the incident signal penetrates and enters the liquid; Second, after the incident signal enters the liquid, the signal will pass through the liquid and reach the rear of the container, still generating a reflected signal containing information that has passed through the liquid; Third, a small amount of signal will still penetrate out of the liquid container and be reflected when it encounters walls and other obstacles.

[0076] Liquid information is extracted, and an empirical threshold is set. The first peak value greater than the empirical threshold is the reflection peak about the liquid surface, and the second maximum point after the first peak value that is greater than the empirical threshold value is the reflection peak about the back of the liquid. Because it has been observed through experiments that the maximum peak value is always greater than the DC component regardless of temperature fluctuations, the empirical threshold value is set to the DC component value. A bandpass filter is used to extract the frequency band information between the two peaks. This frequency band information is the liquid information, and other reflection signals are removed.

[0077] Step Six: Extract temperature-robust liquid characteristics. Obtain the frequency response curve from the extracted liquid information. Experiments show that for different liquids, regardless of temperature fluctuations, although the amplitude of the received signal changes, the frequency response curve will only shift up and down in the entire spectrum. This stable frequency response curve is called the millimeter wave spectrum. Simplify the millimeter wave spectrum and quantize and amplify the corresponding trend characteristics to obtain the liquid characteristics.

[0078] During the experiment, the dielectric constant was shown to be a function not only of temperature but also of frequency. The frequency response of pure water was collected in a laboratory environment at different temperatures between 25℃ and 95℃ (temperature steps of 5℃). Liquid characteristic extraction was performed on 10% saline solution and 10% sugar solution. Figures 7-10 As shown, regardless of temperature fluctuations, although the amplitude of the received signal varies, the frequency response curve remains stable. This means that from a frequency domain perspective, a stable characteristic can be obtained to identify certain liquids, and this characteristic is robust to temperature fluctuations.

[0079] In a laboratory setting, liquid characterization was performed on commercially purchased iced black tea, lemon tea, and plum green tea. The temperature range was 25℃-95℃, with a temperature step of 10℃. The results are as follows: Figures 11-16 As shown, different beverages have significantly different liquid characteristics;

[0080] Simultaneously, liquid characteristic extraction was performed on ethanol at concentrations of 68%, 69%, and 72%, with a temperature range of 25℃-45℃ and a temperature step size of 10℃. The results are as follows. Figures 17-22 As shown in the figure, the liquid feature extraction results of alcohol with concentrations of 68%, 69%, and 72% can be seen that the liquid features of alcohol with only minor differences and changes in concentration can be distinguished, indicating that the recognition accuracy of the present invention is high.

[0081] The experimental data are not limited to this; only a portion is listed in this invention. Based on the above data, it can be concluded that the liquid characteristics are robust to temperature fluctuations.

[0082] Step 7: Achieve liquid recognition. Combining classic machine learning methods, construct a liquid feature database. The liquid feature database stores the liquid features corresponding to different liquids, and identifies the liquid category corresponding to the acquired liquid features.

[0083] When building a liquid feature database, the liquid features corresponding to liquids of different concentrations are stored. When identifying a liquid, the corresponding liquid category is found from the acquired liquid features.

[0084] In summary, this invention proposes a temperature-robust liquid identification method based on FMCW technology. This method continuously transmits chirp signals using an FMCW millimeter-wave radar, receives signals reflected from the liquid being tested, filters the signals to remove DC components, extracts liquid information, and obtains the millimeter-wave spectrum of the liquid. The millimeter-wave spectrum is then simplified and quantized to obtain liquid characteristics robust to temperature fluctuations, thereby enabling liquid identification. By removing DC components, the location of liquid information is determined, further locking onto the corresponding information and eliminating interference from multipath and DC components, thus improving identification accuracy. Furthermore, this invention obtains a frequency response curve from the extracted liquid information. Experiments demonstrate that although the amplitude of the received signal varies, the frequency response curve only shifts vertically across the entire spectrum, providing a stable feature for identifying certain liquids. This feature is robust to temperature fluctuations, overcoming the technical shortcomings of existing technologies where temperature fluctuations can lead to incorrect liquid identification.

[0085] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A temperature-robust liquid identification method based on FMCW technology, characterized in that, Includes the following steps: S1: Place the liquid to be tested in front of the transceiver; S2: Millimeter-wave radar transmits frequency-modulated continuous wave signals; S3: Receive the signal reflected back by the liquid to be tested; S4: Eliminate interference from the DC component of the hardware itself; S5: Extract liquid information; S6: Extract temperature-robust liquid features and obtain the frequency response curve from the extracted liquid information. For different liquids, no matter how the temperature fluctuates, although the amplitude of the received signal changes, the frequency response curve will only shift up and down in the whole spectrum. This stable frequency response curve is called millimeter wave spectrum. Simplify the millimeter wave spectrum and quantize and amplify the corresponding trend features, which are the liquid features. S7: Enables liquid recognition.

2. The temperature-robust liquid identification method based on FMCW technology as described in claim 1, characterized in that: Step S2 specifically includes the following: With the millimeter-wave radar transceiver antenna pointed towards the liquid being tested, the FMCW millimeter-wave radar continuously transmits chirp signals. The frequency of these chirp signals changes linearly with time, ranging from 77 GHz to 81 GHz.

3. The temperature-robust liquid identification method based on FMCW technology as described in claim 1, characterized in that: Step S4 specifically includes the following: In the frequency domain of the original signal, there is a significant peak between 0cm and 10cm, which is an inherent characteristic of FMCW. This peak is considered as a DC component. Different devices or different parameters of the same device will produce different DC components. A high-pass filter is used to filter the DC component and remove it, so the signal after 10cm is taken.

4. The temperature-robust liquid identification method based on FMCW technology as described in claim 1, characterized in that: The liquid information extracted in step S5 includes the following: An empirical threshold is set. The first peak value that exceeds the empirical threshold is the reflection peak of the liquid surface. The second maximum point that exceeds the empirical threshold after the first peak value is the reflection peak of the liquid back side. A bandpass filter is used to extract the frequency band information between the two peaks. This frequency band information is the liquid information.

5. The temperature-robust liquid identification method based on FMCW technology as described in claim 4, characterized in that: The magnitude of the empirical threshold is the magnitude of the DC component.

6. The temperature-robust liquid identification method based on FMCW technology as described in claim 1, characterized in that: Step S7 specifically includes the following: By combining classic machine learning methods, a liquid feature database is constructed, which stores the liquid features corresponding to different liquids, and the liquid category corresponding to the acquired liquid features is identified.

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