Metal detection identification method and system based on metal-based phase spectrum

Abstract

The application provides a metal detection and identification method and system based on metal phase spectrum, and relates to the technical field of metal detection and identification.The method comprises the following steps: acquiring real-time induction signal data through a receiving coil; acquiring a human body pure signal; acquiring processed real-time induction signal data and processed human body pure signal according to the real-time induction signal data and the human body pure signal; determining a phase feature angle; determining a mixed identification result, a main detection material, a detection result confidence and a shielding effect identification result according to the phase feature angle; determining a dynamic alarm threshold according to the processed human body pure signal; and determining a detection and identification result according to the mixed identification result, the main detection material, the detection result confidence and the dynamic alarm threshold.The metal detection and identification system comprises a security gate body, a display unit, a metal detection unit, a long-distance non-contact card reading unit and a processor.The application can improve the accuracy of metal detection and identification.

Description

Technical Field

[0001] This invention relates to the field of metal detection and identification technology, and in particular to a metal detection and identification method and system based on the phase spectrum of metals. Background Technology

[0002] In related technologies, alarms are mainly triggered by detecting changes in the electromagnetic field amplitude caused by metal objects. However, these technologies have difficulty distinguishing the material of the metal (e.g., conductive aluminum, magnetic steel, and non-magnetic stainless steel). They are prone to false alarms or missed alarms due to small metal objects on the wearer's person (e.g., belt buckles or buttons) or environmental interference. In other words, these technologies have difficulty distinguishing the material of the metal and cannot guarantee the accuracy of the detection results.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a metal detection and identification method and system based on the phase spectrum of metals, which can solve the technical problems that related technologies have difficulty in distinguishing the material of metals and in ensuring the accuracy of detection results.

[0005] According to a first aspect of the present invention, a metal detection and identification method based on the phase spectrum of metals is provided, comprising:

[0006] At multiple points during the detection cycle, real-time sensing signal data is acquired through the receiving coil;

[0007] Acquire pure signals from the human body;

[0008] Based on the real-time sensing signal data and the human body pure signal, obtain the processed real-time sensing signal data and the processed human body pure signal;

[0009] The phase characteristic angle is determined based on the processed real-time sensing signal data;

[0010] Based on the phase characteristic angle, determine the hybrid recognition result, the main detection material, the confidence level of the detection result, and the shielding effect recognition result;

[0011] Based on the processed human body clean signal, determine the dynamic alarm threshold;

[0012] The detection and identification results are determined based on the hybrid identification results, the main detection material, the confidence level of the detection results, and the dynamic alarm threshold.

[0013] According to the present invention, obtaining processed real-time sensing signal data and processed human body pure signal based on the real-time sensing signal data and the human body pure signal includes:

[0014] Based on the real-time sensing signal data and the human body pure signal, determine the first original signal amplitude and the second original signal amplitude;

[0015] Obtain real-time drift and noise basis estimates;

[0016] Obtain the real-time reference signal amplitude and the ideal reference signal amplitude;

[0017] Based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude, the processed real-time sensing signal data and the processed pure human body signal are determined.

[0018] According to the present invention, determining processed real-time sensing signal data and processed human body pure signal based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude includes: according to the formula:

[0019]

[0020] Confirm that the processed real-time sensing signal data and processed pure human signals ,in, The amplitude of the first original signal. The amplitude of the second original signal. This represents the real-time drift and noise basis estimate at time i of the detection period. For the ideal reference signal amplitude, The real-time reference signal amplitude is the value at the i-th moment of the detection period.

[0021] According to the present invention, determining the phase characteristic angle based on the processed real-time sensing signal data includes:

[0022] Based on the processed real-time sensing signal data, determine the first orthogonal component and the second orthogonal component;

[0023] The phase characteristic angle is determined based on the first orthogonal component and the second orthogonal component.

[0024] According to the present invention, determining the phase characteristic angle based on the first orthogonal component and the second orthogonal component includes: according to the formula:

[0025]

[0026] Determine the phase characteristic angle ,in, The first orthogonal component, It is the second orthogonal component.

[0027] According to the present invention, based on the phase characteristic angle, determining the hybrid identification result, the main detection material, the detection result confidence level, and the shielding effect identification result includes:

[0028] Based on the phase characteristic angle, determine the phase characteristic angle time series;

[0029] Determine the standard deviation of the phase characteristic angle based on the phase characteristic angle time series;

[0030] The hybrid recognition result is determined based on the standard deviation of the phase feature angle and the preset judgment threshold;

[0031] The average value of the phase characteristic angle is determined based on the phase characteristic angle time series;

[0032] The main material to be detected is determined based on the average value of the phase characteristic angles.

[0033] The confidence level of the detection result is determined based on the phase characteristic angle and the standard deviation of the phase characteristic angle.

[0034] The shielding effect identification result is determined based on the phase characteristic angle time series, the target signal amplitude vector, and the confidence level of the detection result.

[0035] According to the present invention, determining the confidence level of the detection result based on the phase characteristic angle and the standard deviation of the phase characteristic angle includes:

[0036] Determine the phase matching coefficient, phase stability coefficient, and trend tolerance coefficient;

[0037] Obtain the target signal amplitude vector and the standard signal amplitude vector;

[0038] Determine the rate of change of the phase characteristic angle based on the phase characteristic angle;

[0039] Obtain the standard phase characteristic angle change rate of the standard material and the standard phase characteristic angle of the target being detected;

[0040] The confidence level of the detection result is determined based on the standard phase characteristic angle, the phase characteristic angle, the standard deviation of the phase characteristic angle, the phase matching coefficient, the phase stability coefficient, the trend tolerance coefficient, the target signal amplitude vector, the standard signal amplitude vector, the rate of change of the phase characteristic angle, and the rate of change of the standard phase characteristic angle.

[0041] According to the present invention, the confidence level of the detection result is determined based on the standard phase characteristic angle, the phase characteristic angle, the standard deviation of the phase characteristic angle, the phase matching coefficient, the phase stability coefficient, the trend tolerance coefficient, the target signal amplitude vector, the standard signal amplitude vector, the rate of change of the phase characteristic angle, and the rate of change of the standard phase characteristic angle, including: according to the formula:

[0042]

[0043] Determine the confidence level of the detection result at time i in the detection period. , , and The preset weights are used, and max is the function to find the maximum value. To detect the phase characteristic angle at the i-th time step of the period, The standard phase characteristic angle at the i-th moment of the detection period. This is the phase tolerance coefficient. This is the phase stability coefficient. Let be the standard deviation of the phase characteristic angle at the i-th time point of the detection period. Let be the target signal amplitude vector at the i-th time point of the detection period. Let be the standard signal amplitude vector at the i-th time point of the detection period. The Pearson correlation coefficient is the amplitude vector of the target signal and the amplitude vector of the standard signal. This is the trend tolerance coefficient. To detect the rate of change of the phase characteristic angle at the i-th moment of the period, The standard phase characteristic angle change rate is the rate of change of the i-th time step in the detection period.

[0044] According to the present invention, determining a dynamic alarm threshold based on the human body purity signal includes:

[0045] Based on the processed pure human body signal, determine the dynamic human body background phase angle;

[0046] The optimal detection phase is determined based on the dynamic human body background phase angle;

[0047] Determine the typical average amplitude of the pure human signal at the optimal detection phase;

[0048] Based on the amplitude of the pure human body signal, determine the standard deviation of the human body background signal amplitude;

[0049] Determine the configurable sensitivity coefficient;

[0050] The dynamic alarm threshold is determined based on the typical amplitude average value, the standard deviation of the human body background signal amplitude, and the configurable sensitivity coefficient.

[0051] According to a second aspect of the present invention, a metal detection and identification system based on the phase spectrum of metals is provided, comprising:

[0052] The security gate body, display unit, metal detection unit, long-range contactless card reading unit, and processor, wherein the processor is used to execute a metal detection and identification method based on the phase spectrum of metals.

[0053] Technical Effects: According to the present invention, the collected real-time sensing signal data and human body clean signal can be compensated and processed. Based on the compensated real-time sensing signal data, the phase characteristic angle is determined. Based on the compensated human body clean signal, the dynamic alarm threshold is determined. Furthermore, based on the phase characteristic angle, the accuracy of the detection result is analyzed to determine whether the detection target is mainly composed of mixed materials, the main material of the detection target, and the accuracy of the detection result. The mixed identification result, the main detection material, the confidence level of the detection result, and the shielding effect identification result are determined. Combined with the dynamic alarm threshold, the detection identification result is determined, thus improving the accuracy of metal detection and identification. When determining the processed real-time sensing signal data and the processed human body clean signal, the processed real-time sensing signal data and the processed human body clean signal can be determined based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude. During the calculation process, the influence of the real-time drift and noise floor estimate and environmental drift on signal detection can be fully analyzed. Based on the above influences, the processed real-time sensing signal data and the processed human body clean signal are determined, thus improving the accuracy of the processed real-time sensing signal data and the processed human body clean signal. When determining the phase characteristic angle, it can be determined based on the first and second orthogonal components. During the calculation process, the phase characteristic angle in radians can be calculated using the first and second orthogonal components, and then converted to a preset unit, improving the accuracy of the phase characteristic angle. When determining the confidence level of the detection result, it can be determined based on the standard phase characteristic angle, the phase characteristic angle itself, the standard deviation of the phase characteristic angle, the phase matching coefficient, the phase stability coefficient, the trend tolerance coefficient, the target signal amplitude vector, the standard signal amplitude vector, the rate of change of the phase characteristic angle, and the rate of change of the standard phase characteristic angle. During the calculation process, the confidence level of the detection result can be determined from three aspects: phase consistency, overall contour matching, and dynamic behavior consistency, improving the comprehensiveness and accuracy of the confidence level.

[0054] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0056] Figure 1 A schematic flowchart of a metal detection and identification method based on phase spectrum of metals according to an embodiment of the present invention is shown as an example.

[0057] Figure 2 An exemplary schematic diagram illustrates the acquisition of processed real-time sensor signal data and processed pure human body signals according to an embodiment of the present invention;

[0058] Figure 3 A schematic diagram illustrating the determination of the phase characteristic angle according to an embodiment of the present invention is shown exemplarily;

[0059] Figure 4 An exemplary schematic diagram illustrating the determination of hybrid identification results, main detection materials, detection result confidence levels, and shielding effect identification results according to embodiments of the present invention is shown.

[0060] Figure 5 An exemplary schematic diagram illustrating the determination of dynamic alarm thresholds according to an embodiment of the present invention is shown;

[0061] Figure 6 A block diagram of a metal detection and identification system based on the phase spectrum of metals according to an embodiment of the present invention is shown as an example;

[0062] Figure 7 A phase diagram according to an embodiment of the present invention is shown as an example;

[0063] Figure 8 An exemplary schematic diagram of polarity change according to an embodiment of the present invention is shown;

[0064] Figure 9 An exemplary phase spectrum curve of a single metal according to an embodiment of the present invention is shown;

[0065] Figure 10 An exemplary table of phase characteristic angle data for a fixed-track experiment according to an embodiment of the present invention is shown;

[0066] Figure 11 Phase spectrum curves of various metal materials according to embodiments of the present invention are shown exemplarily;

[0067] Figure 12 A phase spectrum diagram of a metal exhibiting a shielding effect according to an embodiment of the present invention is shown as an example.

[0068] Figure 13 An exemplary graph of the fixed-frequency detection results according to an embodiment of the present invention is shown.

[0069] Figure 14 A phase spectrum curve of the shielding effect of the graphite layer according to an embodiment of the present invention is shown as an example;

[0070] Figure 15 A schematic diagram of a security gate according to an embodiment of the present invention is shown as an example;

[0071] Figure 16 A schematic diagram illustrating the content displayed by the display unit of a security gate according to an embodiment of the present invention is shown. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0073] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0074] Figure 1 An exemplary flowchart of a metal detection and identification method based on the phase spectrum of metals according to an embodiment of the present invention is shown, the method comprising:

[0075] Step S1: At multiple moments during the detection cycle, real-time sensing signal data is acquired through the receiving coil;

[0076] Step S2: Obtain pure signals from the human body;

[0077] Step S3: Based on the real-time sensing signal data and the human body pure signal, obtain the processed real-time sensing signal data and the processed human body pure signal;

[0078] Step S4: Determine the phase characteristic angle based on the processed real-time sensing signal data;

[0079] Step S5: Based on the phase characteristic angle, determine the hybrid recognition result, the main detection material, the confidence level of the detection result, and the shielding effect recognition result;

[0080] Step S6: Determine the dynamic alarm threshold based on the processed human body purity signal;

[0081] Step S7: Determine the detection and identification result based on the hybrid identification result, the main detection material, the confidence level of the detection result, and the dynamic alarm threshold.

[0082] According to the metal detection and identification method based on phase spectrum of metals according to embodiments of the present invention, the real-time sensing signal data and the pure human body signal are compensated and processed. Based on the real-time sensing signal data after compensation and processing, the phase characteristic angle is determined. Based on the pure human body signal after compensation and processing, the dynamic alarm threshold is determined. Furthermore, based on the phase characteristic angle, the method analyzes whether the detection target is mainly a mixed material, the main material of the detection target, and the accuracy of the detection result, and determines the mixed identification result, the main detection material, the confidence level of the detection result, and the shielding effect identification result. Combined with the dynamic alarm threshold, the detection and identification result is determined, thereby improving the accuracy of metal detection and identification.

[0083] According to one embodiment of the present invention, in step S1, real-time sensing signal data is acquired by receiving coil at multiple moments during the detection cycle.

[0084] For example, when a metal target passes through an electromagnetic field, it disturbs the magnetic field and generates an induced signal in the receiving coil. The original induced signal, i.e., the real-time induced signal data, is obtained through the receiving coil in the security gate. At this time, the real-time induced signal may include the target metal signal, the human body background signal, hardware DC offset, environmental electromagnetic noise, and device thermal noise.

[0085] According to one embodiment of the present invention, in step S2, a human body purity signal is acquired.

[0086] For example, during the self-test phase after the equipment starts up, and during the long idle period between two alarms, one or more known "clean" people (i.e., "naked people") are allowed to pass through the security gate normally. The detection system collects the signals of these clean passers-by, that is, the human body clean signal. At this time, the human body clean signal may include the human body background signal, hardware DC offset, environmental electromagnetic noise and device thermal noise.

[0087] According to an embodiment of the present invention, in step S3, processed real-time sensing signal data and processed human body pure signal are obtained based on the real-time sensing signal data and the human body pure signal.

[0088] Figure 2 An exemplary schematic diagram illustrates the acquisition of processed real-time sensor signal data and processed pure human body signals according to an embodiment of the present invention.

[0089] According to an embodiment of the present invention, step S3 includes:

[0090] Step S31: Determine the first original signal amplitude and the second original signal amplitude based on the real-time sensing signal data and the human body pure signal;

[0091] Step S32: Obtain real-time drift and noise basis estimates;

[0092] Step S33: Obtain the real-time reference signal amplitude and the ideal reference signal amplitude;

[0093] Step S34: Based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude, determine the processed real-time sensing signal data and the processed pure human body signal.

[0094] For example, acquiring the signal amplitude of real-time sensing signal data, i.e., the first raw signal amplitude; acquiring the signal amplitude of pure human body signal, i.e., the second raw signal amplitude; acquiring the average level of inherent DC offset and low-frequency noise of the sensing device when no target passes by, i.e., real-time drift and noise floor estimates. Real-time drift and noise floor estimates mainly include: hardware DC offset (e.g., the inherent zero-point error of amplifiers, ADCs, etc., which changes slowly with temperature) and environmental constants / low-frequency interference (e.g., the constant response generated by stable background electromagnetic fields in the environment (e.g., the geomagnetic field, distant radio stations) in the system). The specific determination of real-time drift and noise floor estimates... The method involves continuously collecting a series of real-time inductive signal amplitude values ​​within a short time window (typically a few seconds, where the system definitively determines that no personnel or large metal targets will pass through; this can be determined through beam sensors or activity detection of the signal itself). The median of this series of values ​​is then used as the estimate of the real-time drift and noise floor. This method of using the median as the estimate of the real-time drift and noise floor robustly estimates the true "center position" of the signal, thus ensuring the stability of the compensation. (The average value is highly sensitive to extreme values ​​(e.g., a sudden, brief strong electromagnetic pulse interference), which can lead to real-time drift and noise floor.) The acoustic floor estimate is incorrect, while the median is insensitive to extreme values. Furthermore, the real-time drift and noise floor estimates are updated in real-time. The system continuously and continuously updates this value in the background; for example, it calculates the median every second using the most recent 5 seconds of targetless data as the latest real-time drift and noise floor estimate. This allows for real-time tracking of slow baseline drift caused by temperature and power fluctuations. Inside the detection device (usually in a concealed location), there is a standard reference metal object of known material, shape, size, and location (e.g., a small aluminum sheet or steel ball fixed in a corner). When the detection device is idle (e.g., after startup in the early morning, or when no one has passed through for a long time), the system automatically... The control transmission circuit measures the internal reference object, and the signal amplitude obtained at this time is the real-time reference signal amplitude. The real-time reference signal amplitude reflects the system's response strength to a "known standard metal" under the current ambient temperature and hardware conditions. The ideal reference signal amplitude is the standard value measured and recorded on the same internal reference object under standard temperature and humidity laboratory conditions, representing the response strength of the detection system under ideal conditions. Based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude, the processed real-time sensing signal data and the processed pure human body signal are determined.

[0095] According to an embodiment of the present invention, step S34 includes: determining the processed real-time sensing signal data according to formula (1). and processed pure human signals ,

[0096] (1)

[0097] in, The amplitude of the first original signal. The amplitude of the second original signal. This represents the real-time drift and noise basis estimate at time i of the detection period. For the ideal reference signal amplitude, The real-time reference signal amplitude is the value at the i-th moment of the detection period.

[0098] According to one embodiment of the present invention, Let be the real-time drift and noise floor estimate at the i-th moment of the detection period, representing the zero-point drift value or additive interference, those interference components that do not change with the target signal but will raise or lower the overall amplitude baseline of the first original signal. This means that by subtracting zero-point background interference from the total contaminated signal, the variations caused by the target metal and the human body background are highlighted. Equivalent to , This is the ratio of the real-time reference signal amplitude to the ideal reference signal amplitude. If the detection system is perfectly ideal and drift-free, then... equal , If the temperature rises and causes a decrease in coil efficiency, the amplitude of the real-time reference signal will be less than the amplitude of the ideal reference signal. Greater than 1, As a denominator, it will be magnified. The value of this, thus causing a decrease in sensitivity due to increased temperature, This represents the compensated real-time sensing signal data, i.e., the processed real-time sensing signal data. Similarly, This means that by subtracting zero-bit background interference from the total polluted signal, the changes caused by the human body's own background are highlighted. This indicates the compensated pure human signal, that is, the processed pure human signal.

[0099] In this way, the processed real-time sensing signal data and the processed pure human body signal can be determined based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude. During the calculation process, the influence of the real-time drift and noise floor estimate and environmental drift on signal detection can be fully analyzed, and the processed real-time sensing signal data and the processed pure human body signal can be determined based on the above influences, thereby improving the accuracy of the processed real-time sensing signal data and the processed pure human body signal.

[0100] According to one embodiment of the present invention, in step S4, the phase characteristic angle is determined based on the processed real-time sensing signal data.

[0101] Figure 3 A schematic diagram illustrating the determination of the phase characteristic angle according to an embodiment of the present invention is shown.

[0102] According to an embodiment of the present invention, step S4 includes:

[0103] Step S41: Determine the first orthogonal component and the second orthogonal component based on the processed real-time sensing signal data;

[0104] Step S42: Determine the phase characteristic angle based on the first orthogonal component and the second orthogonal component.

[0105] For example, the receiver of a detection system (such as a security gate) adopts an orthogonal dual-channel (or multi-channel) design, which can simultaneously or time-divisionally demodulate two induced signal components with a phase difference of 90 degrees (i.e., 50 units phase difference). The detection system uses two orthogonal reference signals (such as sin(ωt) and cos(ωt), where t is a time variable) with the same frequency as the transmitted signal to demodulate, filter, and extract the amplitude of the processed real-time induced signal data. After demodulation and filtering, two DC or low-frequency amplitude values ​​are obtained, which represent the projections of the metal target signal onto two orthogonal phase axes, namely, the first orthogonal component and the second orthogonal component. The phase characteristic angle is calculated based on the first and second orthogonal components. Different individual metals, due to their unique conductivity and magnetic permeability, produce different phase shifts. When the phase of the relevant detection sampling signal changes, the detected amplitude is different, with maximum values ​​and zero points. If polar coordinates are used to represent the different sampling signal phases and detected amplitudes, it will be a zero-crossing circle diagram, such as... Figure 7 The phase diagram shows the trajectory indicated by the dashed line. At a specific phase angle (i.e., the phase characteristic angle), there is a maximum detection amplitude. At a phase characteristic angle ±90°, there is a detection zero point or minimum amplitude. At a phase characteristic angle +180°, the detection amplitude has a reverse maximum value. At the same audio frequency, different metals have different phase characteristic angles. This is related to the material, shape, size, and spatial trajectory, but primarily depends on whether the material is conductive or magnetic. Therefore, different materials have different phase spectra, and measuring the phase characteristic angle of its phase spectrum can largely identify the material.

[0106] According to an embodiment of the present invention, step S42 includes: determining the phase characteristic angle according to formula (2). ,

[0107] (2)

[0108] Among them, is the first orthogonal component, is the second orthogonal component.

[0109] According to an embodiment of the present invention, is a two-parameter arctangent function, indicating the calculation of the angle between the ray pointing to the point and the positive x-axis corresponding to the first orthogonal component, is the unit conversion coefficient. Since the function outputs a radian value, and through the unit conversion coefficient the radian value is converted into 0 to 200 units (corresponding to 0 to 360 degrees). For the convenience of calculation, here the 360° angular coordinate of the phase plane is represented by 200, that is, 0° = 0, 90° = 50, 180° = 100, 360° = 200, and so on. The angle zero means that the relevant detection square wave is in phase with the spatial magnetic field. The amplitude absolute value M is represented in logarithmic coordinates, and the maximum value is 100. Its conversion relationship with the absolute value of the AD sampling value A is: when M = 0, |A| = 0; when 0 < M < 100: |A| = 32 * (1.073 ^ (M - 1)). Or, 0.3 * M is the decibel number of the phase difference. The polarity of M is defined by the polarity change of the detection signal. If the detection signal is first positive and then negative, it is defined as positive; if it is first negative and then positive, it is defined as negative, as shown in the schematic diagram of the polarity change of Figure 8 shown.

[0110] In this way, the phase characteristic angle can be determined according to the first orthogonal component and the second orthogonal component. During the calculation process, the phase characteristic angle of the radian value can be calculated through the first orthogonal component and the second orthogonal component, and the phase characteristic angle of the radian value is converted into a preset unit, which improves the accuracy of the phase characteristic angle. [[ID=,28]]

[0111] According to an embodiment of the present invention, in step S5, according to the phase characteristic angle, determine the hybrid recognition result, the main detected material, the detection result confidence level, and the shielding effect recognition result.

[0112] Figure 4 Exemplarily shows a schematic diagram of determining the hybrid recognition result, the main detected material, the detection result confidence level, and the shielding effect recognition result according to an embodiment of the present invention.

[0113] According to an embodiment of the present invention, step S5 includes:

[0114] Step S51, according to the phase characteristic angle, determine the phase characteristic angle time series;

[0115] Step S52, according to the phase characteristic angle time series, determine the phase characteristic angle standard deviation;​

[0116] Step S53: Determine the hybrid recognition result based on the standard deviation of the phase feature angle and the preset judgment threshold;

[0117] Step S54: Determine the average value of the phase characteristic angle based on the phase characteristic angle time series;

[0118] Step S55: Determine the main detection material based on the average value of the phase characteristic angle;

[0119] Step S56: Determine the confidence level of the detection result based on the phase characteristic angle and the standard deviation of the phase characteristic angle;

[0120] Step S57: Determine the shielding effect identification result based on the phase characteristic angle time series, the target signal amplitude vector, and the detection result confidence level.

[0121] For example, a phase feature angle time series is constructed based on the phase feature angles of the same target at multiple instants during its passage through the target detection area. The standard deviation of the multiple phase feature angles in the time series is calculated; this standard deviation reflects the dispersion and volatility of the phase feature angle time series. Based on the standard deviation and a preset judgment threshold, the mixed recognition result is determined. For instance, if the standard deviation is greater than the preset judgment threshold, it is judged as multi-material mixing, and the mixed recognition result is 1. If there are two or more metal materials, the situation is more complex. Figure 11 The phase spectrum curves of various metal materials are shown in the test results of T42 and T62, and T61 and T62 passing through a security gate simultaneously. It can be seen that the phase characteristic angle of the composite result shifts to a value between the phase characteristic angles of the two metals. T42+T62 only deviates slightly to the right from the T62 curve, while T61+T62 deviates slightly to the left from the T61 curve. If the standard deviation of the phase characteristic angle is less than or equal to the preset judgment threshold, it is judged as a single material, and the mixed recognition result is 0. Regarding the measured phase spectrum curves of a single metal... Figure 9 The measured phase spectrum curves of a single metal are the phase-amplitude detection curves of three different national standard samples moving on a fixed track at a frequency of 6.62 kHz. Figure 9 , Figures 11-14The horizontal axis is in degrees, and the vertical axis is in dB. T42 is non-magnetic stainless steel, T61 is ordinary magnetic stainless steel, and T62 is conductive aluminum. The amplitude M is the result of detection using correlation signals of different phases. From the measured results, the phase characteristic angles of the phase spectra of the three types of materials—magnetic, conductive, and non-magnetic alloy stainless steel—differ significantly, which can be used to identify the materials. However, the phase characteristic angles exhibit some dispersion due to differences in size, shape, and spatial position of the trajectory. Therefore, it is difficult to distinguish between copper and aluminum, and between 45# steel and ordinary stainless steel, even when both are conductive. Figure 10The phase characteristic angle data table for the fixed-track experiment shows the phase characteristic angle data obtained by converting the detection results of two orthogonal correlation detection signals with arbitrary phases in the aforementioned fixed-track experiment. With two orthogonal correlation detection results, accurate phase characteristic angle results can be calculated for a specific single-material metal object, with very small dispersion. The phase characteristic angles below are calculated from two detection results separated by 50° (corresponding to 90°) in the aforementioned phase spectrum, and show high consistency with the curve formed by the global results.The preset judgment threshold can be obtained through experimental calibration. For example, before the detection system is put into use, a large number of known standard samples of single materials with different shapes (such as aluminum blocks and steel sheets) are repeatedly passed through the detection area. The standard deviation of their respective phase characteristic angles is calculated and statistically analyzed. The upper limit of their statistical distribution (such as the mean plus three times the standard deviation) is taken as the reference threshold for that material. Different thresholds are set for several major metal materials (such as copper, aluminum, and steel), and a conservative maximum allowable value that covers all cases is taken as the unified preset judgment threshold. Furthermore, the detection system can record the standard deviation of the phase characteristic angle when the sample passes safely (such as without alarm) and dynamically fine-tune it. This threshold is set to adapt to specific usage environments. Based on the phase characteristic angle time series, the average phase characteristic angle of multiple phase characteristic angles in the sequence is determined. Based on the average phase characteristic angle, the main detection material is determined. For example, when it is determined to be a "multi-material mixture," the main detection material may include multiple materials. The estimated dominant material type of the dominant material in the mixture is approximately equal to the average phase characteristic angle (even if materials are mixed, their combined electromagnetic effect will show a centroid or average tendency in the phase characteristic angle). The average phase characteristic angle can be compared with a standard material library (e.g., the phase characteristic angle of aluminum is approximately 20°, and the phase characteristic angle of steel is approximately 140°) to infer which material is in the mixture. The contribution of quality is greater. For example, if the average phase characteristic angle is around 60°, it may mean that the mixture is mainly composed of non-ferrous metals with good conductivity. In practical applications of detection systems, transient electromagnetic interference (e.g., an external electric spark) can also cause drastic jumps in the phase characteristic angle time series, resulting in extremely high standard deviations of the phase characteristic angle. However, the phase characteristic angle time series of transient electromagnetic interference consists of irregular, discontinuous peaks, while the sequence of real objects is a continuous, smooth curve. The average value of the phase characteristic angle time series of transient electromagnetic interference is a random number without physical meaning, while the average value of the sequence of real mixtures has a clear material orientation. Therefore, the detection system can combine the phase characteristic angle standard deviation with the phase characteristic angle time series. The system comprehensively distinguishes between multi-material mixed targets and transient electromagnetic interference by considering factors such as whether the standard deviation is too high, whether the phase characteristic angle time series is smooth, and whether the mean is meaningful, and filters out the latter. The confidence level of the detection result is determined based on the phase characteristic angle and its standard deviation. If the target is identified as the target metal at time i, the higher the confidence level, the greater the likelihood that the target belongs to that type of material, and the greater the accuracy of the detection result. The shielding effect identification result is determined based on the phase characteristic angle time series, the target signal amplitude vector, and the confidence level of the detection result. For example, graphite, as a conductive non-metal, typically has a phase characteristic angle within a specific range (e.g., significantly different from common metals).If the average value of the phase characteristic angle falls within this range, and the shape of the target signal amplitude vector conforms to the typical response of graphite, then it is preliminarily determined that the outer material may be graphite. Based on the preliminary determination that there may be a graphite shielding layer, if one or more of the following conditions are met at the same time, it is inferred that there is a shielding effect (the shielding effect identification result is 1): 1. Confidence contradiction: Although the system determines that the main detection material is graphite based on the average value of the phase characteristic angle, the confidence of the calculated detection result for the graphite material is always lower than a low threshold (e.g., lower than 0.3). This may mean that the signal is mixed with interference from the shielded object; 2. Abnormal signal amplitude: The overall energy or specific frequency response of the target signal amplitude vector has a non-negligible difference from the standard signal amplitude vector of the pure graphite sample. This difference pattern is consistent with the theoretical model of internal metal objects; 3. The phase characteristic angle time series shows atypical fluctuations that cannot be explained by a single material during the target's passage. This fluctuation may be related to the local field disturbance of the internal metal object under the shielding layer. If the shielding effect identification result is 1, it indicates a high probability of the existence of a shielding effect. If the shielding effect identification result is 0, it indicates no obvious shielding effect. If one material completely wraps another material, only one material can be detected due to the shielding effect. For example, if there is a time difference between the entry and exit of the two materials into the detection channel, the calculation of the phase characteristic angle as shown in the table above will change with the phase of the relevant detection signal and will no longer be a stable value as shown in the table. This is because the proportion of the two materials obtained when detecting different phase signals is different. Figure 12 The phase spectrum curve of the shielding effect of the metal is shown in the test results of a short knife with a brass sheath along a fixed track. Due to the high symmetry of the test results within a 360° range, to save testing time, the test was only performed within the range of -90° to 90° (coordinates -50 to 50). Figure 12 It can be seen that when a steel knife is sheathed in a brass sheath, it will be identified as a copper object due to the shielding effect. During the detection of the brass sheath, the calculated phase characteristic is -8. When the knife is sheathed, the phase characteristic is -5. However, when a copper-handled knife enters the detection channel alone, if the steel knife is in front and the copper handle is behind, the proportions of the two metals intercepted under different detection phases are different, causing the calculated phase characteristic angle to be unstable, ranging from 36 to 63. Therefore, if two orthogonal phase signals are used for detection, it has a high ability to distinguish whether a single-material metal is a conductor, a magnet, or a stainless steel alloy. However, difficulties arise when two or more materials are involved. Figure 14The phase spectrum curves of the graphite shielding effect are shown as a comparison of the detection results of a naked person by the left coil (without graphite shielding) and the right coil (with shielding). A small piece of cardboard coated with graphite shielding material was also placed in the curves. The cardboard was placed close to the left coil during testing; the absolute magnitude of the amplitude is meaningless, only reflecting the position of the phase characteristic angle. It can be seen that because the graphite shielding layer has the same phase characteristics (phase characteristic angle) as the naked person, it can significantly reduce the naked person signal. From the maximum value of the curve, it can reduce the signal by more than 0.3 * 20 = 6 (dB) and shift the phase characteristic angle of the naked person's phase spectrum curve by approximately 90°. The effect is best at the phase characteristic angle (peak position) of the unshielded naked person curve, reducing it by nearly 0.3 * 60 = 18 (dB), becoming zero. At a 90° difference, near the zero point of the unshielded curve, the two curves intersect, and the graphite layer has no effect on the naked person signal.

[0122] According to an embodiment of the present invention, step S56 includes:

[0123] Step S561: Determine the phase matching coefficient, phase stability coefficient, and trend tolerance coefficient;

[0124] Step S562: Obtain the target signal amplitude vector and the standard signal amplitude vector;

[0125] Step S563: Determine the rate of change of the phase characteristic angle based on the phase characteristic angle;

[0126] Step S564: Obtain the standard phase characteristic angle change rate of the standard material and the standard phase characteristic angle of the target being detected;

[0127] Step S565: Determine the confidence level of the detection result based on the standard phase characteristic angle, the phase characteristic angle, the standard deviation of the phase characteristic angle, the phase matching coefficient, the phase stability coefficient, the trend tolerance coefficient, the target signal amplitude vector, the standard signal amplitude vector, the rate of change of the phase characteristic angle, and the rate of change of the standard phase characteristic angle.

[0128] For example, determining the phase matching coefficient, phase stability coefficient, and trend tolerance coefficient: The phase matching coefficient represents the tolerance window for matching the phase characteristic angle with the standard phase characteristic angle of the detection target. A larger phase matching coefficient indicates a higher tolerance for deviations and a smoother decrease in the confidence level of the detection result; a smaller phase matching coefficient indicates a lower tolerance for deviations and a more drastic decrease in the confidence level of the detection result. Specifically, it is determined by testing a large number of pure samples of known materials, calculating the standard deviation of their phase characteristic angles, and setting the phase matching coefficient to twice the standard deviation. The phase stability coefficient represents the penalty for phase jitter on the phase consistency component. A larger phase stability coefficient means that the same amount of phase jitter will lead to a greater decrease in the confidence level of the detection result. Specifically, it is determined by testing a large number of pure samples with regular shapes and single materials, and mixed samples with multiple material combinations or highly irregular shapes, determining the average phase characteristic angle of the mixed samples and the average phase characteristic angle of the pure samples, and then taking... The trend tolerance coefficient is determined by taking the intermediate value of multiple phase temperature coefficients within the inequality range; the trend tolerance coefficient represents the tolerance for the standard phase characteristic angle change rate and the matching of the phase characteristic angle change rate. The larger the trend tolerance coefficient, the more severe the penalty for the deviation between the standard phase characteristic angle change rate and the phase characteristic angle change rate, and the more drastic the decrease in the confidence of the test results. The trend tolerance coefficient is determined by analyzing the variance of the phase characteristic angle change rate of the standard material under the standard passage mode (a series of well-defined, controllable, and representative target passage scenarios used to establish performance benchmarks and set parameters during the development, calibration, and optimization phases of the test system). The trend tolerance coefficient can take values ​​of [value missing]. The process involves: acquiring the target signal amplitude vector and the standard signal amplitude vector. For example, the target signal amplitude vector is composed of the signal amplitudes of the target measured at different detection phases within a short historical window prior to the current moment. The standard signal amplitude vector is composed of the expected standard signal amplitudes of the standard material (e.g., if the detection system considers the current target to be metal A, then the standard material is metal A) at the same set of detection phases, obtained from a known experimental database. This vector can be obtained through extensive testing and learning of standard material samples. The rate of change of the phase characteristic angle is determined based on the slopes of multiple phase characteristic angles. The standard phase change rate of the standard material under a typical passage trajectory is obtained, i.e., the standard phase characteristic angle change rate. The standard phase characteristic angle of the main detection material (i.e., the target material) at the current detection location is read from the standard material library, i.e., the standard phase characteristic angle of the target. The accuracy of the detection results is evaluated and the confidence level of the detection results is determined based on the standard phase characteristic angle, phase characteristic angle, standard deviation of phase characteristic angle, phase matching coefficient, phase stability coefficient, trend tolerance coefficient, target signal amplitude vector, standard signal amplitude vector, rate of change of phase characteristic angle, and standard phase characteristic angle change rate.

[0129] According to an embodiment of the present invention, step S565 includes: determining the confidence level of the detection result at the i-th moment of the detection period according to formula (3). ,

[0130]

[0131] in, , and The preset weights are used, and max is the function to find the maximum value. To detect the phase characteristic angle at the i-th time step of the period, The standard phase characteristic angle at the i-th moment of the detection period. This is the phase tolerance coefficient. This is the phase stability coefficient. Let be the standard deviation of the phase characteristic angle at the i-th time point of the detection period. Let be the target signal amplitude vector at the i-th time point of the detection period. Let be the standard signal amplitude vector at the i-th time point of the detection period. The Pearson correlation coefficient is the amplitude vector of the target signal and the amplitude vector of the standard signal. This is the trend tolerance coefficient. To detect the rate of change of the phase characteristic angle at the i-th moment of the period, The standard phase characteristic angle change rate is the rate of change of the i-th time step in the detection period.

[0132] According to one embodiment of the present invention, The difference between the phase characteristic angle at the i-th moment of the detection period and the standard phase characteristic angle is the smaller the difference. The smaller the difference, the more similar the electromagnetic characteristics of the current target are to the standard material (determined based on the main detection material at that time). The phase tolerance coefficient represents the range of natural deviations during operation. Let be the ratio of the difference between the phase characteristic angle and the standard phase characteristic angle at the i-th moment of the detection period to the phase tolerance coefficient, representing the normalization and dimensionless elimination of the difference between the phase characteristic angle and the standard phase characteristic angle. This represents the instantaneous degree of matching between the phase characteristic angle and the standard phase characteristic angle. The larger the value, the higher the instantaneous matching degree. The smaller the value, and, in When the value is small, the instantaneous matching degree It is extremely sensitive to errors; even a small error can affect the instantaneous matching accuracy. A significant decrease was observed, ensuring high-precision matching. Let be the standard deviation of the phase characteristic angle at the i-th moment of the detection period, representing the stability of the battery fingerprint of the current detection target during the detection process. For an ideal, singular, and pure metal sphere passing through at a constant speed, Approaching zero, for targets composed of multiple materials, due to different parts entering the detection area at different times and targets subject to random electromagnetic interference, It will be relatively large. This is the phase stability coefficient. This indicates the penalty caused by phase jitter. Indicates the phase stability decay status. Indicates taking 0 and The maximum value, as described above, indicates that when the phase angle is extremely unstable during the detection process, Set to 0, regardless of instantaneous matching degree. How big? The values ​​are all 0. This indicates the phase consistency status.

[0133] According to one embodiment of the present invention, Let be the target signal amplitude vector at the i-th moment of the detection period, representing the complete shape of the electromagnetic response of the detected target as the system detects the phase change. Let be the standard signal amplitude vector at the i-th moment of the detection period, representing the most fundamental and stable electromagnetic response profile of the material of the target being detected. The Pearson correlation coefficient is calculated between the target signal amplitude vector and the standard signal amplitude vector. Calculating the Pearson correlation coefficient between the target signal amplitude vector and the standard signal amplitude vector eliminates the influence of the overall DC offset (background intensity) of the signal, focusing only on changes in the contour shape. Furthermore, it makes the comparison unaffected by the absolute magnitude of the two signals, focusing solely on the similarity of the shapes. The value of is in [-1, 1], when When the value equals 1, it indicates that the two signal profiles are perfectly positively correlated. When the value is -1, it indicates that the two signal profiles are completely negatively correlated. A value of 0 indicates no linear correlation. It can be used for overall pattern recognition of "electromagnetic fingerprints" on metals, and is extremely sensitive to material purity and shape regularity.

[0134] According to the first embodiment of the present invention, The phase characteristic angle change rate at the i-th moment of the detection period directly reflects the relative motion state (approaching, moving away, sweeping) of the target within the detection area and the rotational changes of its own attitude. Let be the standard phase characteristic angle change rate at the i-th moment of the detection cycle, representing the typical change rate of the material corresponding to the detection target during normal and standard passage. This is the difference between the rate of change of the phase characteristic angle and the standard rate of change of the phase characteristic angle. The larger the difference, the more serious the deviation of the current target's movement trend from the normal pattern. For example, if a coin in a pocket that should be moving slowly is detected to have a rapidly fluctuating phase characteristic angle, it may indicate that the item is being moved or shaken quickly, and the behavior is more likely to be abnormal. The product of the difference between the rate of change of the phase characteristic angle and the standard rate of change of the phase characteristic angle and the trend tolerance coefficient represents the conversion of a deviation with physical units into a dimensionless trend deviation penalty coefficient. Indicates consistency in dynamic behavior. Indicates taking 0 and The maximum value mentioned above, which takes the maximum value, can be used to set the lower limit of the value for consistent dynamic behavior to 0.

[0135] According to one embodiment of the present invention, This indicates that the confidence level of the detection results is determined by considering phase consistency, overall contour matching, and dynamic behavior consistency. , and The preset weights can be set to 0.6, 0.25, and 0.15 respectively.

[0136] In this way, the confidence level of the detection result can be determined based on the standard phase characteristic angle, phase characteristic angle, standard deviation of phase characteristic angle, phase matching coefficient, phase stability coefficient, trend tolerance coefficient, target signal amplitude vector, standard signal amplitude vector, rate of change of phase characteristic angle, and rate of change of standard phase characteristic angle. During the calculation process, the confidence level of the detection result can be determined from three aspects: phase consistency, overall contour matching, and dynamic behavior consistency, thereby improving the comprehensiveness and accuracy of the confidence level of the detection result.

[0137] According to one embodiment of the present invention, in step S6, a dynamic alarm threshold is determined based on the processed human body purity signal.

[0138] Figure 5 An exemplary schematic diagram illustrating the determination of dynamic alarm thresholds according to an embodiment of the present invention is shown.

[0139] According to an embodiment of the present invention, step S6 includes:

[0140] Step S61: Determine the dynamic human background phase angle based on the processed human pure signal;

[0141] Step S62: Determine the optimal detection phase based on the dynamic human body background phase angle;

[0142] Step S63: Determine the typical amplitude average of the pure human signal on the optimal detection phase;

[0143] Step S64: Determine the standard deviation of the human background signal amplitude based on the amplitude of the pure human signal.

[0144] Step S65: Determine the configurable sensitivity coefficient;

[0145] Step S66: Determine the dynamic alarm threshold based on the typical amplitude average value, the standard deviation of the human body background signal amplitude, and the configurable sensitivity coefficient.

[0146] For example, based on the processed pure human signal, the phase characteristic angle of the pure human body on the phase spectrum, i.e., the dynamic human body background phase angle, is determined under the current environment and the current carrier state. Its value cycles between 0 and 200. According to the principle of electromagnetic induction, the receiving point least sensitive to a specific signal is often the point orthogonal (perpendicular) to the phase of that signal. Therefore, the optimal detection phase is determined by adding 50 units (50 units represent 90 degrees of orthogonality) to the dynamic human body background phase angle. Figure 13 The graph showing the results at a fixed frequency is for a frequency of 6.62 kHz. The measured phase characteristic angle will differ at different frequencies. A phase characteristic angle also exists for the naked person carrying the target. The following are test curves for naked individuals with a graphite shielding layer outside the security gate coil (the function of which will be analyzed later), and curves for T42, T61, and T62 are also included for comparison. The typical average amplitude of the pure human signal at the optimal detection phase is determined. Based on the amplitude of the pure human signal, the standard deviation of the human background signal amplitude is determined. A configurable sensitivity coefficient is determined; this coefficient is a system-preset constant, usually an integer (e.g., 3, 4, and 5). A larger configurable sensitivity coefficient results in a less sensitive alarm system, only alarming for large signals much stronger than the background noise, with a low false alarm rate, but potentially missing small metal objects. A smaller configurable sensitivity coefficient results in a more sensitive alarm system, triggering an alarm even with weak signals, with a low false alarm rate, but potentially causing false alarms due to normal background fluctuations. The dynamic alarm threshold is determined by adding the product of the standard deviation of the human background signal amplitude and the configurable sensitivity coefficient to the typical average amplitude. Any signal amplitude measured at the detection phase exceeding the dynamic alarm threshold triggers an alarm.

[0147] According to an embodiment of the present invention, in step S7, the detection and identification result is determined based on the hybrid identification result, the main detection material, the confidence level of the detection result, and the dynamic alarm threshold.

[0148] For example, if the signal amplitude measured on the detection phase exceeds the dynamic alarm threshold, an alarm is triggered. When the mixed identification result is 0, it indicates that the detection target is a single material. Based on the fact that the main detection material contains only one type, the category of the detection target is determined to be a prohibited material (e.g., T61-steel). The alarm level is then determined based on the confidence level of the detection result (e.g., the highest level is triggered when the confidence level of the detection result is greater than 0.85, the lowest level is triggered when it is less than 0.4, and a warning alarm is issued in other cases). If the mixed identification result is 1, it indicates that the detection target is a mixed material. The main detection material includes multiple possibilities. The confidence level of the detection result for the detection target and multiple possible materials is determined separately. An early warning is issued based on the type of material with the highest confidence level of the detection result. At the same time, the presence and priority of each component in the mixed material can be determined based on the confidence levels of multiple detection results.

[0149] According to one embodiment of the present invention, the security gate in this embodiment is a multifunctional security gate integrating metal detection and long-distance identity recognition. It includes a gate body and a display unit, a metal detection unit and a long-distance contactless card reader, both electrically connected to the display unit. A metal detection antenna electrically connected to the metal detection unit is installed on the inner side of the gate body, and a card reading antenna connected to the long-distance contactless card reader is also installed on the inner side of the gate body. A band-stop filter unit is connected between the metal detection antenna and the metal detection unit, and a band-pass filter unit is connected between the card reading antenna and the long-distance contactless card reader. The long-distance contactless card reader operates at a frequency of 13.56MHz and has an operating distance greater than 30cm.

[0150] In this embodiment, the width ratio of the metal detection antenna to the card reading antenna is 50:34; the height ratio of the metal detection antenna to the card reading antenna is 1:2. This embodiment also includes a switch for electrical connection to the monitoring unit; the switch is electrically connected to both the metal detection unit and the long-range contactless card reading unit. This embodiment integrates the metal detection unit and the long-range contactless card reading unit into one unit through the design of the switch electrically connecting them separately, providing a simple way to connect them to the monitoring unit, which can be a computer or a management control backend.

[0151] The switch in this embodiment is a serial port switch or a network port switch. The type of switch can be selected according to the interfaces of the metal detection unit and the long-range contactless card reading unit. This embodiment also includes a face recognition unit with temperature detection function, an audible and visual alarm unit, a barcode scanning unit, and a display unit electrically connected to the switch. This embodiment, through the design including a face recognition unit with temperature detection function, an audible and visual alarm unit, a barcode scanning unit, and a display unit electrically connected to the switch, allows the face recognition unit with temperature detection function to capture static images, record dynamic videos, and detect human body temperature. The audible and visual alarm unit uses light and sound signals to provide prompts and alarms. The barcode scanning unit can be used to read one-dimensional or two-dimensional codes for ticket and other identification purposes. Integrating the face recognition unit with temperature detection function, the audible and visual alarm unit, the barcode scanning unit, and the display unit into one unit gives the security gate multiple functions, reduces the use of other equipment, and greatly reduces the occupation of site and human resources. It ensures the safety of people entering and exiting while allowing security personnel to pass through quickly.

[0152] The security gate body of this embodiment includes at least two spaced-apart side panels, with a housing between the two side panels. The housing and the two side panels enclose an access opening. Each side panel is detachably connected to the housing. Each side panel has a metal detection antenna and a card reading antenna on its inner side. The metal detection unit and the long-range contactless card reading unit are installed inside the housing, and the display unit is installed on the housing.

[0153] This embodiment features a security gate body comprising at least two spaced-apart side panels, with a housing between the two side panels. The housing and the two side panels enclose a passageway. Each side panel is detachably connected to the housing. Each side panel has a metal detection antenna and a card reading antenna on its inner side. The metal detection unit and the long-range contactless card reading unit are installed inside the housing, and the display unit is installed on the housing. This design makes the security gate simpler, easier to install and disassemble, and improves detection efficiency and the passage experience for security personnel.

[0154] In this embodiment, the chassis is also equipped with an audible and visual alarm unit and a facial recognition unit; a barcode scanning unit is installed on the inner side of the side door panel. This design, with the audible and visual alarm unit and facial recognition unit installed on the chassis, and the barcode scanning unit installed on the inner side of the side door panel, makes the security gate simpler, easier to install and remove, and improves the passage experience for security personnel. In this embodiment, the audible and visual alarm unit is installed on the side of the chassis where personnel enter, the facial recognition unit is installed at the bottom of the chassis, and the barcode scanning unit is installed on the inner side of the side door panel, improving detection efficiency and the passage experience for security personnel. The barcode scanning unit in this embodiment can be a one-dimensional barcode scanner or a two-dimensional barcode scanner, installed according to requirements.

[0155] This embodiment also provides a security gate system, including a monitoring unit and a multi-functional security gate integrating metal detection and long-range identity recognition, as described above, electrically connected to the monitoring unit. The metal detection unit and the long-range contactless card reading unit are also electrically connected to the monitoring unit. Monitoring by the monitoring unit ensures the safe entry of personnel. A switch is electrically connected to the monitoring unit; this connection simplifies the wiring and facilitates installation and disassembly. The type of switch is selected based on the type of monitoring unit. The switch and monitoring unit can be connected via a serial port or a network port. Functions such as face recognition, barcode scanning, temperature detection, and display can be integrated into the switch. Connecting the switch to the monitoring unit facilitates installation, information collection, backend comparison, intelligent monitoring, and ensures rapid personnel passage. The structure of the security gate is as follows... Figure 15 The diagram shows a security gate. Figure 15 The rectangle at the top of the security gate is the display unit. Figure 16 This is a schematic diagram showing the content displayed by the display unit of the security gate. Figure 16 The dotted box in the left half of the screen indicates the location of the metal object, the upper right half displays the real-time monitoring video, and the lower right half displays the photo of the person being identified and the text information of their ID card.

[0156] According to one embodiment of the present invention, the security gate integrates long-range ID card reading technology and metal detection function, enabling real-time, non-intrusive collection of ID card and facial information of passersby. It performs full-body metal detection from head to toe, and quickly verifies the consistency between the person and the ID card using facial information and ID card photos. Persons do not need to stay inside the gate during the security check. The collected facial and identity information, along with the results of the identity verification and metal detection, are sent to a platform for statistical analysis. The platform also analyzes the data against blacklists and whitelists, allowing for real-time checks to determine if passersby are considered high-risk individuals, and guiding on-site police deployment and response based on the results. The security gate features long-range ID card reading technology, breaking the limitations of near-field communication (compliant with ISO / IEC 14443 protocol). It extends the ID card reading distance from 0-3 cm as specified in GA450-2013 to over 50 cm, enabling seamless ID card information collection in areas ranging from 30 cm to 170 cm above the ground within the channel. It also allows for long-distance reading of ID cards in various postures and movements. With 18-zone metal detection, it can accurately detect metal objects carried by individuals from head to toe. Dual-channel high-definition cameras enable rapid facial capture, and the captured face can be accurately verified against the ID card photo. The system allows for data collection, detection, and verification without requiring the user to stop or present their ID card. The throughput can exceed 60 people per minute. A local ID card blacklist database can be deployed based on the level of importance, enabling precise verification and real-time control. Detection results can be reported to the backend for comprehensive analysis and rapid warning of individuals using both blacklists and whitelists.

[0157] The metal detection and identification method based on phase spectrum of metals according to embodiments of the present invention can compensate for the acquired real-time sensing signal data and human body clean signal, and determine the phase characteristic angle based on the compensated real-time sensing signal data, and determine the dynamic alarm threshold based on the compensated human body clean signal. Furthermore, based on the phase characteristic angle, it analyzes whether the detection target is mainly composed of mixed materials, the main material of the detection target, and the accuracy of the detection result, and determines the mixed identification result, the main detection material, the confidence level of the detection result, and the shielding effect identification result. Combined with the dynamic alarm threshold, the detection identification result is determined, thus improving the accuracy of metal detection and identification. When determining the processed real-time sensing signal data and the processed human body clean signal, the processed real-time sensing signal data and the processed human body clean signal can be determined based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude. During the calculation process, the influence of the real-time drift and noise floor estimate and environmental drift on signal detection can be fully analyzed, and the processed real-time sensing signal data and the processed human body clean signal can be determined based on the above influences, thus improving the accuracy of the processed real-time sensing signal data and the processed human body clean signal. When determining the phase characteristic angle, it can be determined based on the first and second orthogonal components. During the calculation process, the phase characteristic angle in radians can be calculated using the first and second orthogonal components, and then converted to a preset unit, improving the accuracy of the phase characteristic angle. When determining the confidence level of the detection result, it can be determined based on the standard phase characteristic angle, the phase characteristic angle itself, the standard deviation of the phase characteristic angle, the phase matching coefficient, the phase stability coefficient, the trend tolerance coefficient, the target signal amplitude vector, the standard signal amplitude vector, the rate of change of the phase characteristic angle, and the rate of change of the standard phase characteristic angle. During the calculation process, the confidence level of the detection result can be determined from three aspects: phase consistency, overall contour matching, and dynamic behavior consistency, improving the comprehensiveness and accuracy of the confidence level.

[0158] Figure 6 An exemplary block diagram of a metal detection and identification system based on the phase spectrum of metals according to an embodiment of the present invention is shown, the system comprising:

[0159] The security gate body, display unit, metal detection unit, long-range contactless card reading unit, and processor, wherein the processor is used to execute a metal detection and identification method based on the phase spectrum of metals.

[0160] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0161] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A metal detection and identification method based on the phase spectrum of metals, characterized in that, include: At multiple points during the detection cycle, real-time sensing signal data is acquired through the receiving coil; Acquire pure signals from the human body; Based on the real-time sensing signal data and the human body pure signal, obtain the processed real-time sensing signal data and the processed human body pure signal; The phase characteristic angle is determined based on the processed real-time sensing signal data; Based on the phase characteristic angle, determine the hybrid recognition result, the main detection material, the confidence level of the detection result, and the shielding effect recognition result; Based on the processed human body clean signal, determine the dynamic alarm threshold; The detection and identification result is determined based on the hybrid identification result, the main detection material, the confidence level of the detection result, and the dynamic alarm threshold; Based on the processed real-time sensing signal data, the phase characteristic angle is determined, including: Based on the processed real-time sensing signal data, determine the first orthogonal component and the second orthogonal component; The phase characteristic angle is determined based on the first orthogonal component and the second orthogonal component; Determining the phase characteristic angle based on the first orthogonal component and the second orthogonal component includes: according to the formula: Determine the phase characteristic angle ,in, The first orthogonal component, It is the second orthogonal component; Based on the phase characteristic angle, the hybrid recognition result, the main detection material, the confidence level of the detection result, and the shielding effect recognition result are determined, including: Based on the phase characteristic angle, determine the phase characteristic angle time series; Determine the standard deviation of the phase characteristic angle based on the phase characteristic angle time series; The hybrid recognition result is determined based on the standard deviation of the phase feature angle and the preset judgment threshold; The average value of the phase characteristic angle is determined based on the phase characteristic angle time series; The main material to be detected is determined based on the average value of the phase characteristic angles. The confidence level of the detection result is determined based on the phase characteristic angle and the standard deviation of the phase characteristic angle. The shielding effect identification result is determined based on the phase characteristic angle time series, the target signal amplitude vector, and the confidence level of the detection result.

2. The metal detection and identification method based on the phase spectrum of metals according to claim 1, characterized in that, Based on the real-time sensing signal data and the human body pure signal, the processed real-time sensing signal data and the processed human body pure signal are obtained, including: Based on the real-time sensing signal data and the human body pure signal, determine the first original signal amplitude and the second original signal amplitude; Obtain real-time drift and noise basis estimates; Obtain the real-time reference signal amplitude and the ideal reference signal amplitude; Based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude, the processed real-time sensing signal data and the processed pure human body signal are determined.

3. The metal detection and identification method based on the phase spectrum of metals according to claim 2, characterized in that, Based on the first original signal amplitude, the second original signal amplitude, the real-time drift and noise floor estimate, the real-time reference signal amplitude, and the ideal reference signal amplitude, the processed real-time sensing signal data and the processed pure human body signal are determined, including: according to the formula: Confirm that the processed real-time sensing signal data and processed pure human signals ,in, The amplitude of the first original signal. The amplitude of the second original signal. This represents the real-time drift and noise basis estimate at time i of the detection period. For the ideal reference signal amplitude, The real-time reference signal amplitude is the value at the i-th moment of the detection period.

4. The metal detection and identification method based on the phase spectrum of metals according to claim 1, characterized in that, The confidence level of the detection result is determined based on the phase characteristic angle and the standard deviation of the phase characteristic angle, including: Determine the phase matching coefficient, phase stability coefficient, and trend tolerance coefficient; Obtain the target signal amplitude vector and the standard signal amplitude vector; Determine the rate of change of the phase characteristic angle based on the phase characteristic angle; Obtain the standard phase characteristic angle change rate of the standard material and the standard phase characteristic angle of the target being detected; The confidence level of the detection result is determined based on the standard phase characteristic angle, the phase characteristic angle, the standard deviation of the phase characteristic angle, the phase matching coefficient, the phase stability coefficient, the trend tolerance coefficient, the target signal amplitude vector, the standard signal amplitude vector, the rate of change of the phase characteristic angle, and the rate of change of the standard phase characteristic angle.

5. The metal detection and identification method based on the phase spectrum of metals according to claim 4, characterized in that, The confidence level of the detection result is determined based on the standard phase characteristic angle, the phase characteristic angle, the standard deviation of the phase characteristic angle, the phase matching coefficient, the phase stability coefficient, the trend tolerance coefficient, the target signal amplitude vector, the standard signal amplitude vector, the rate of change of the phase characteristic angle, and the rate of change of the standard phase characteristic angle, including: according to the formula: Determine the confidence level of the detection result at time i in the detection period. , , and The preset weights are used, and max is the function to find the maximum value. To detect the phase characteristic angle at the i-th time step of the period, The standard phase characteristic angle at the i-th moment of the detection period. This is the phase tolerance coefficient. This is the phase stability coefficient. Let be the standard deviation of the phase characteristic angle at the i-th moment of the detection period. Let be the target signal amplitude vector at the i-th time point of the detection period. Let be the standard signal amplitude vector at the i-th time point of the detection period. The Pearson correlation coefficient is the amplitude vector of the target signal and the amplitude vector of the standard signal. This is the trend tolerance coefficient. To detect the rate of change of the phase characteristic angle at the i-th moment of the period, The standard phase characteristic angle change rate is the rate of change of the i-th time step in the detection period.

6. The metal detection and identification method based on the phase spectrum of metals according to claim 1, characterized in that, Based on the aforementioned human body purity signal, a dynamic alarm threshold is determined, including: Based on the processed pure human body signal, determine the dynamic human body background phase angle; The optimal detection phase is determined based on the dynamic human body background phase angle; Determine the typical average amplitude of the pure human signal at the optimal detection phase; Based on the amplitude of the pure human body signal, determine the standard deviation of the human body background signal amplitude; Determine the configurable sensitivity coefficient; The dynamic alarm threshold is determined based on the typical amplitude average value, the standard deviation of the human body background signal amplitude, and the configurable sensitivity coefficient.

7. A metal detection and identification system based on the phase spectrum of metals, characterized in that, include: The security gate body, display unit, metal detection unit, long-range contactless card reading unit, and processor, wherein the processor is used to execute the method described in any one of claims 1-6.

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