Method and device for detecting handheld electronic product based on semiconductor medium analysis

Abstract

The invention discloses a handheld electronic product detection method and device based on semiconductor medium analysis, and the method comprises the steps: transmitting a multi-band electromagnetic excitation signal; receiving a response signal of the target detection object to the multi-band electromagnetic excitation signal; processing the response signal to extract response features; based on the response features, feature matching is carried out in a semiconductor component feature database, a feature matching result is obtained, and whether the target detection object contains electronic equipment or not is judged; and when it is judged that the target detection object contains the electronic equipment, warning is performed through the acousto-optic unit. The technical problem that the electronic equipment in various states cannot be effectively detected in the prior art is solved, and the technical effects of accurately detecting the electronic equipment in various states and timely warning are achieved through semiconductor medium feature analysis.

Description

Technical Field

[0001] The present invention relates to the field of electronic equipment detection, and in particular to a handheld electronic product detection method and device based on semiconductor medium analysis. Background Art

[0002] With the rapid development of electronic technology, the penetration rate of various portable electronic devices has continued to increase. While these devices bring convenience to people's lives and work, they also pose challenges to the safety management of certain special places.

[0003] At present, the common electronic device detection devices on the market are mainly based on metal detection technology. Although such devices can detect electronic products containing metal components, they face many technical limitations. First, metal detection cannot effectively distinguish between ordinary metal objects and electronic devices; second, today's electronic devices are becoming more and more miniaturized and lightweight, and the metal content is reduced, making it difficult for the sensitivity of traditional metal detectors to meet detection needs; more importantly, the existing technology is difficult to cope with the detection needs of electronic devices in various situations such as shutting down, removing the battery, and pulling out the SIM card. For example, the detection ability of small electronic products such as micro electronic devices (such as MiLi headphones) is extremely limited. Therefore, the existing technology cannot effectively detect electronic devices in various states. Summary of the invention

[0004] The present application aims to solve the technical problem that the prior art cannot effectively detect electronic devices in various states by providing a handheld electronic product detection method and device based on semiconductor medium analysis.

[0005] The first aspect disclosed in the present application provides a handheld electronic product detection method based on semiconductor medium analysis, the method comprising: transmitting a multi-band electromagnetic excitation signal, the multi-band electromagnetic excitation signal comprising a low-band electromagnetic excitation signal, a mid-band electromagnetic excitation signal and a high-band electromagnetic excitation signal; receiving a response signal of a target detection object to the multi-band electromagnetic excitation signal; processing the response signal to extract response characteristics, the response characteristics comprising spectrum characteristics, harmonic characteristics, impedance characteristics and phase characteristics; based on the response characteristics, performing feature matching in a semiconductor component feature database to obtain feature matching results; judging whether the target detection object contains an electronic device according to the feature matching results; and giving an alarm through an acousto-optic unit when it is judged that the target detection object contains an electronic device.

[0006] Optionally, the response signal is processed to extract response features, including: preprocessing the response signal to obtain a preprocessed response signal; performing time-domain analysis and frequency-domain analysis on the preprocessed response signal to obtain time-domain analysis results and frequency-domain analysis results; extracting the spectral features and harmonic features from the frequency-domain analysis results; and extracting the impedance features and phase features based on the time-domain analysis results and frequency-domain analysis results.

[0007] Optionally, before obtaining the feature matching result by performing feature matching in the semiconductor component feature database based on the response features, the method includes: obtaining a plurality of electronic device samples, where the plurality of electronic device samples include electronic devices of different types, different brands, and different models; setting a variety of device operating states, where the variety of device operating states include the power-on state, the power-off state, the battery-removed state, and the SIM-card-removed state; and constructing a conductor component feature database according to the multi-band electromagnetic excitation signal, the plurality of electronic device samples, and the variety of device operating states in a normal electromagnetic environment.

[0008] Optionally, constructing a conductor component feature database according to the multi-band electromagnetic excitation signal, the plurality of electronic device samples, and the variety of device operating states includes: traversing the plurality of electronic device samples to obtain a first electronic device sample; obtaining a first sample response feature of the first electronic device sample to the multi-band electromagnetic excitation signal in the variety of device operating states according to the multi-band electromagnetic excitation signal; and associating the first electronic device sample and the first sample response feature and adding them to the conductor component feature database.

[0009] Optionally, obtaining a first sample response feature of the first electronic device sample to the multi-band electromagnetic excitation signal in the variety of device operating states according to the multi-band electromagnetic excitation signal includes: traversing the variety of device operating states to obtain a first device operating state; transmitting a multi-band electromagnetic excitation signal to the first electronic device sample in the first device operating state and receiving a first state response feature of the first electronic device sample to the multi-band electromagnetic excitation signal; and associating the first device operating state and the first state response feature and adding them to the first sample response feature of the first electronic device sample.

[0010] Optionally, obtaining the feature matching result by performing feature matching in the semiconductor component feature database based on the response features includes: extracting a plurality of sample response features from the semiconductor component feature database according to the maximum number of synchronous processes; synchronously calculating the matching degrees of the response feature and the plurality of sample response features based on a feature matching calculation formula to obtain a plurality of response matching degrees; and using the plurality of response matching degrees as the feature matching result.

[0011] Optionally, the feature matching calculation formula is as follows: where is the matching degree between the response feature and the sample response feature ; is the spectral feature distance between the response feature and the sample response feature ; is the harmonic feature distance between the response feature and the sample response feature ; is the impedance feature distance between the response feature and the sample response feature ; is the phase feature distance between the response feature and the sample response feature ; , , respectively represent the weight coefficients of the spectral feature, harmonic feature, impedance feature, and phase feature, and satisfy .

[0012] Another aspect disclosed in the present application provides a handheld electronic product detection device based on semiconductor medium analysis. The device includes: an electromagnetic signal transmitting unit for transmitting multi-band electromagnetic excitation signals, where the multi-band electromagnetic excitation signals include low-band electromagnetic excitation signals, mid-band electromagnetic excitation signals, and high-band electromagnetic excitation signals; a signal receiving unit for receiving response signals of a target detection object to the multi-band electromagnetic excitation signals; a feature extraction unit for processing the response signals to extract response features, where the response features include spectral features, harmonic features, impedance features, and phase features; a feature matching unit for performing feature matching in a semiconductor component feature database based on the response features to obtain a feature matching result; a judgment and analysis unit for judging whether the target detection object contains an electronic device according to the feature matching result; and an acoustic-optic warning unit for giving an alarm through an acoustic-optic unit when it is judged that the target detection object contains an electronic device.

[0013] The handheld electronic product detection method and device based on semiconductor medium analysis provided by this application first emit multi-band electromagnetic excitation signals, where the multi-band electromagnetic excitation signals include low-band electromagnetic excitation signals, medium-band electromagnetic excitation signals, and high-band electromagnetic excitation signals. This multi-band excitation method can comprehensively stimulate the electromagnetic response characteristics of different types of semiconductor components, improving the comprehensiveness and adaptability of detection compared to single-frequency detection. Then, the response signals of the target detection object to the multi-band electromagnetic excitation signals are received to capture the electromagnetic responses generated by the target object under electromagnetic excitation in different bands, providing raw data for subsequent analysis. Next, the received response signals are processed to extract response characteristics, where the response characteristics include spectral characteristics, harmonic characteristics, impedance characteristics, and phase characteristics. By extracting multi-dimensional characteristics, the electromagnetic characteristics of semiconductor components can be comprehensively reflected, providing a basis for accurate identification. Subsequently, based on the extracted response characteristics, feature matching is performed in the semiconductor component feature database to obtain the feature matching result, comparing the detected features with the pre-established database, and identifying the electronic device through pattern recognition. Subsequently, it is determined whether the target detection object contains an electronic device based on the feature matching result, thereby identifying the electronic device. When it is determined that the target detection object contains an electronic device, an alarm is given through the sound and light unit, providing a warning message to the detection personnel in a timely manner, and effectively detecting prohibited electronic devices.

[0014] Through the combination of multi-band excitation and multi-dimensional feature analysis, the above technical solution can effectively identify electronic devices in various states, including communication products in states such as powered on, powered off, battery removed, or SIM card removed, solving the technical problem in the prior art that microelectronic devices cannot be effectively detected. Description of the Drawings

[0015] Figure 1 It is a schematic flowchart of the handheld electronic product detection method based on semiconductor medium analysis provided by an embodiment of this application; Figure 2 It is a schematic structural diagram of the handheld electronic product detection device based on semiconductor medium analysis provided by an embodiment of this application.

[0016] Description of the Reference Numerals: 11. Electromagnetic signal transmitting unit; 12. Signal receiving unit; 13. Feature extraction unit; 14. Feature matching unit; 15. Judgment and analysis unit; 16. Sound and light warning unit. Detailed Embodiments

[0017] The general idea of the technical solution provided by this application is as follows: The embodiments of the present application provide a method and device for detecting handheld electronic products based on semiconductor medium analysis. By utilizing the specific response characteristics exhibited by semiconductor components under the action of an electromagnetic field, electronic devices are identified, thereby solving the technical problem in the prior art of being unable to effectively detect electronic devices in various states. This method does not rely on the working state of the electronic device, but is based on the essential characteristics of the semiconductor medium material contained in all electronic devices, achieving efficient and accurate detection of electronic products.

[0018] First, a multi-band electromagnetic excitation signal including a low-frequency band, a middle-frequency band, and a high-frequency band is emitted to form a detection electromagnetic field with full-band coverage, so as to maximally stimulate the electromagnetic responses of different types of semiconductor components. Subsequently, a high-sensitivity receiving module captures the response signals generated by the target detection object and digitally processes these signals to extract multi-dimensional response characteristics including spectral characteristics, harmonic characteristics, impedance characteristics, and phase characteristics. These characteristics constitute the electromagnetic fingerprint of the semiconductor component, reflecting the unique attributes of the electronic device that distinguish it from ordinary objects. Then, the extracted characteristics are intelligently matched with a pre-established semiconductor component characteristic database to analyze and determine whether the target detection object contains an electronic device. When an electronic device is detected, an alarm is immediately issued through the sound and light unit to remind the inspector to take further action. The entire detection process is fast, accurate, and not affected by the state of the detection object, and can detect electronic devices in any state, such as powered on, powered off, battery removed, or SIM card removed.

[0019] Through an innovative approach combining multi-band excitation and multi-dimensional feature analysis, the limitations of traditional metal detection technology are broken through, achieving comprehensive and accurate identification of electronic devices in various states, especially efficient detection of micro communication devices, greatly improving the effectiveness and reliability of security inspections.

[0020] After introducing the basic principle of the present application, the various non-limiting embodiments of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification.

[0021] Embodiment 1, as Figure 1 shown, Embodiment 1 of the present application provides a method for detecting handheld electronic products based on semiconductor medium analysis, and this method includes: S100: Transmit a multi-band electromagnetic excitation signal, and the multi-band electromagnetic excitation signal includes a low-frequency band electromagnetic excitation signal, a middle-frequency band electromagnetic excitation signal, and a high-frequency band electromagnetic excitation signal.

[0022] Specifically, first, a multi-band electromagnetic excitation signal is transmitted to the target detection area. Among them, the multi-band electromagnetic excitation signal refers to electromagnetic waves in different frequency bands, specifically including low-frequency electromagnetic excitation signals, medium-frequency electromagnetic excitation signals, and high-frequency electromagnetic excitation signals. The low-frequency electromagnetic excitation signal can be an electromagnetic wave with a frequency range between 30 Hz and 300 kHz. The electromagnetic wave in this frequency band has strong penetration ability and can effectively excite the semiconductor components inside the object to be detected. The medium-frequency electromagnetic excitation signal can be an electromagnetic wave with a frequency range between 300 kHz and 30 MHz. The electromagnetic wave in this frequency band has a certain penetration ability and can produce obvious resonance responses with a variety of electronic components. The high-frequency electromagnetic excitation signal can be an electromagnetic wave with a frequency range between 30 MHz and 3 GHz. The electromagnetic wave in this frequency band has high recognition sensitivity for radio frequency circuits, communication modules, etc. in electronic devices.

[0023] By transmitting multi-band electromagnetic excitation signals simultaneously or according to a preset sequence, various semiconductor devices that may exist in the target detection object can be comprehensively excited, improving the accuracy and reliability of detection. Compared with the single-band detection method, the multi-band detection method can effectively reduce environmental interference and false alarm rate, and at the same time improve the recognition ability for different types of electronic devices.

[0024] S200: Receive the response signal of the target detection object to the multi-band electromagnetic excitation signal.

[0025] Specifically, receive the response signal generated by the target detection object to the multi-band electromagnetic excitation signal. When the transmitted multi-band electromagnetic excitation signal irradiates the target detection object, if there are electronic devices inside the target detection object, its semiconductor components will interact with the electromagnetic wave and generate specific electromagnetic responses. The response signal of the target detection object to the multi-band electromagnetic excitation signal has various forms, including but not limited to reflection signals, harmonic signals, re-radiation signals, and electromagnetic interference signals. Among them, the reflection signal is mainly determined by the physical structure characteristics of the target detection object; the harmonic signal mainly originates from the non-linear characteristics of semiconductor components; the re-radiation signal is the secondary radiation generated by semiconductor components under the action of the electromagnetic field; the electromagnetic interference signal mainly comes from the abnormal working state of the internal circuit of the electronic device under external excitation.

[0026] By capturing various response signals generated by the target detection object under low-frequency, medium-frequency, and high-frequency electromagnetic excitations, it lays a foundation for subsequent feature extraction and analysis.

[0027] S300: Process the response signal to extract response features, and the response features include spectral features, harmonic features, impedance features, and phase features.

[0028] Specifically, after obtaining the response signal, the received response signal is systematically processed to extract multi-dimensional response features that can effectively characterize the electromagnetic characteristics of semiconductor components. The response features specifically include spectral features, harmonic features, impedance features, and phase features, and these features together constitute the electromagnetic fingerprint of the electronic device. For example, first, digital signal processing techniques and intelligent feature extraction algorithms are used to perform preprocessing operations such as denoising, filtering, and normalization on the response signal to eliminate the influence of environmental interference and random noise, and improve the signal-to-noise ratio and analyzability of the signal; then, the preprocessed response signal is deeply analyzed in the time domain and frequency domain respectively to extract various response features, and spectral features, harmonic features, impedance features, and phase features are obtained.

[0029] Among them, the spectral feature refers to the energy distribution characteristics of the response signal in the frequency domain, which is obtained through time-frequency analysis methods such as fast Fourier transform. The spectral feature can reflect the working frequencies and energy characteristics of different functional modules in the electronic device and is a key index for identifying the device type. The harmonic feature refers to the various harmonic components contained in the response signal and their amplitude ratio relationships. The semiconductor component will generate rich harmonic components under electromagnetic excitation, and the harmonic feature can effectively reflect the nonlinear characteristics and working state of the semiconductor component and has a high recognition value. The impedance feature refers to the impedance response characteristics of the target detection object to the electromagnetic field, including parameters such as resistance, reactance, and conductance. Different types of electronic devices and their internal components have unique impedance features. By analyzing the law of the impedance of the target detection object changing with frequency, the material and structure information of the semiconductor component can be obtained. The phase feature refers to the phase difference between the response signal and the excitation signal and its law of changing with frequency. The phase feature is closely related to the circuit topology and component parameters inside the electronic device and can provide supplementary information for identifying the electronic device.

[0030] By extracting the above multi-dimensional response features, a feature vector that comprehensively reflects the electromagnetic characteristics of the electronic device is constructed, providing a reliable basis for subsequent feature matching and device identification.

[0031] S400: Based on the response features, perform feature matching in the semiconductor component feature database to obtain the feature matching result.

[0032] Specifically, after obtaining the response features, perform a matching operation on the response features in the pre-constructed semiconductor component feature database to obtain a feature matching result, thereby comparing the features of the unknown electronic device with those of the known devices to achieve the identification and classification of electronic devices. Among them, the semiconductor component feature database is a knowledge base of the electromagnetic characteristics of electronic devices established in advance through a large number of experiments and samplings. This database contains the electromagnetic response features of various types, different brands, and various models of electronic devices under various working conditions, specifically including the standard values and their variation ranges of spectral features, harmonic features, impedance features, and phase features. The feature matching process uses a similarity calculation method for multi-dimensional feature vectors, and evaluates the similarity between the target response features and the sample response features stored in the database through a specific matching algorithm to obtain the feature matching result.

[0033] S500: Determine whether the target detection object contains an electronic device according to the feature matching result.

[0034] Specifically, based on the obtained feature matching result, determine whether the target detection object contains an electronic device. The specific determination method can be achieved by comparing the feature matching degree with a preset threshold. When it is found from the feature matching result that there is a situation where the matching degree exceeds the preset threshold, it is determined that the target detection object contains an electronic device; otherwise, if all matching degrees are lower than the preset threshold, it is determined that the target detection object does not contain an electronic device. Among them, the preset threshold is determined through statistical analysis of a large amount of experimental data, which can effectively balance the sensitivity and specificity of detection.

[0035] S600: When it is determined that the target detection object contains an electronic device, give an alarm through the acoustic-optic unit.

[0036] Specifically, when it is determined according to the feature matching result that the target detection object contains an electronic device, send an alarm signal to the operator through the acoustic-optic unit to prompt the detection of the presence of an electronic device. Among them, the acoustic-optic unit refers to a dedicated hardware unit for emitting sound and light warning signals, mainly including a sound warning sub-unit and an optical warning sub-unit. The sound warning sub-unit can emit warning sounds with different frequencies, rhythms, and volumes to provide auditory feedback; the optical warning sub-unit provides intuitive visual feedback through LED indicators or displays, etc.

[0037] Further, process the response signal to extract response features, including: S310: Preprocess the response signal to obtain the preprocessed response signal; S320: Perform time-domain analysis and frequency-domain analysis on the preprocessed response signal to obtain time-domain analysis results and frequency-domain analysis results; S330: Extract spectral features and harmonic features from the frequency-domain analysis results; S340: Extract impedance features and phase features based on the time-domain analysis results and frequency-domain analysis results.

[0038] Specifically, when processing the response signal to extract response features, first, preprocess the received response signal to improve the accuracy and reliability of subsequent analysis. Among them, the preprocessing includes operations such as denoising, filtering, baseline correction, and normalization. The denoising process uses wavelet transform or adaptive filtering technology to effectively remove random noise and pulse interference; the filtering process removes out-of-band interference through a band-pass filter bank; the baseline correction is used to eliminate the DC bias and slow-varying drift of the signal; the normalization process normalizes the signal amplitude to facilitate signal comparison under different detection conditions. Through these preprocessing operations, a response signal with a higher signal-to-noise ratio and a more stable baseline is obtained, laying a good foundation for subsequent feature extraction. Then, perform time-domain analysis and frequency-domain analysis on the preprocessed response signal simultaneously. The time-domain analysis focuses on the characteristics of the signal changing with time, including the calculation of parameters such as amplitude statistics, peak detection, zero-crossing rate, and autocorrelation; the frequency-domain analysis obtains the frequency components and their energy distribution of the signal through methods such as fast Fourier transform, short-time Fourier transform, or wavelet transform. Through the dual analysis of the time domain and the frequency domain, various characteristic information in the response signal can be comprehensively captured, providing rich data support for subsequent feature extraction.

[0039] Next, extract spectral features and harmonic features from the frequency-domain analysis results. The spectral features mainly include parameters such as power spectral density, frequency band energy distribution, main frequency component and its amplitude; the harmonic features include parameters such as the amplitude, phase, and harmonic distortion rate of each harmonic. These features can effectively reflect the nonlinear characteristics and working states of semiconductor components and are important bases for identifying electronic devices. Subsequently, impedance features and phase features are comprehensively extracted based on the time-domain analysis results and frequency-domain analysis results. The impedance feature is derived by calculating the amplitude ratio relationship between the response signal and the excitation signal and combining electromagnetic field theory; the phase feature is obtained by analyzing the phase difference between the response signal and the excitation signal. These two types of features are closely related to the circuit topology and component parameters inside the electronic device and provide supplementary information for identifying the electronic device.

[0040] By extracting comprehensive and accurate various response features from the original response signal, these features together constitute a feature vector that can uniquely identify the electronic device, providing a reliable basis for subsequent feature matching and device identification.

[0041] Further, before performing feature matching in the semiconductor component feature database based on the response features and obtaining the feature matching result, the method includes: S710: Obtain multiple electronic device samples, where the multiple electronic device samples include electronic devices of different types, different brands, and different models; S720: The working states of multiple devices, where the working states of multiple devices include the power-on state, the power-off state, the battery-removed state, and the SIM-card-removed state; S730: In a normal electromagnetic environment, construct a characteristic database of conductor components based on multi-band electromagnetic excitation signals, multiple electronic device samples, and multiple device working states.

[0042] Specifically, before performing feature matching, first establish a complete characteristic database of semiconductor components. First, obtain multiple representative electronic device samples to ensure the comprehensiveness and coverage of the database. Among them, the multiple electronic device samples include electronic devices of different types, different brands, and different models. Specifically, different types refer to various common portable electronic devices such as mobile phones, tablet computers, laptop computers, smart watches, miniature cameras, and voice recorders; different models refer to product models of different series and different configurations under the same brand. By collecting diverse electronic device samples, a characteristic database with wide applicability can be established to adapt to various electronic devices that may be encountered in actual detection.

[0043] Then, set multiple device working states for the obtained electronic device samples to simulate various situations that may occur in the actual detection environment. Among them, the multiple device working states include the power-on state, the power-off state, the battery-removed state, and the SIM-card-removed state. By setting different working states, the electromagnetic response characteristics of electronic devices under various conditions can be comprehensively captured, improving the adaptability and accuracy of detection. Next, in a normal electromagnetic environment, construct a characteristic database of semiconductor components based on multi-band electromagnetic excitation signals, multiple electronic device samples, and multiple device working states. A normal electromagnetic environment refers to a test environment with a standardized background electromagnetic noise level to ensure the consistency and comparability of the characteristic data in the database. For each electronic device sample, in each of its working states, use multi-band electromagnetic excitation signals for testing, obtain its response characteristics, and associate and store these characteristics with the device information in the database. This database adopts a structured design, supporting efficient data storage, retrieval, and update operations, providing data support for subsequent feature matching.

[0044] By establishing a comprehensive and accurate characteristic database of semiconductor components, containing the electromagnetic response characteristics of a large number of electronic devices in various working states, it provides a reliable reference basis for feature matching in actual detection.

[0045] Furthermore, construct a characteristic database of conductor components based on multi-band electromagnetic excitation signals, multiple electronic device samples, and multiple device working states, including: S731: Traverse multiple electronic device samples to obtain a first electronic device sample; S732: Obtain the first sample response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal under various device operating states according to the multi-band electromagnetic excitation signal; S733: Associate the first electronic device sample with the first sample response characteristics and add them to the conductor component feature database.

[0046] Specifically, the traversal method is used to process multiple electronic device samples one by one to ensure that all samples are included in the conductor component feature database. Here, traversal means selecting each sample from multiple electronic device samples in a preset order for processing until all samples are processed. During each traversal, a specific electronic device sample is obtained, called the first electronic device sample. This sample can be an electronic device of any type, brand, or model, and its detailed information will be recorded, including parameters such as device type, brand, model, and hardware configuration. These information will be stored in the database as the index basis for feature data. Then, according to the multi-band electromagnetic excitation signal, obtain the first sample response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal under various device operating states. Specifically, first place the first electronic device sample in a preset test environment, and then, according to a predetermined protocol, sequentially or simultaneously transmit low-band, mid-band, and high-band electromagnetic excitation signals, and record the response of this sample to the multi-band electromagnetic excitation signal in each operating state (including the power-on state, power-off state, battery-removed state, and SIM-card-removed state). Process these response signals to extract spectral characteristics, harmonic characteristics, impedance characteristics, and phase characteristics, forming a feature set that comprehensively describes the electromagnetic response characteristics of this sample, that is, the first sample response characteristics. Then, associate the first electronic device sample with the first sample response characteristics and add this associated information to the semiconductor component feature database. The association process is achieved by establishing a mapping relationship between device information and response characteristics, using a unique identifier (such as a device ID) to link the device information with its corresponding response characteristics to form a complete data entry. These data entries are stored in the feature database according to a specific structure and indexing method to support subsequent efficient retrieval and matching operations.

[0047] By processing each electronic device sample, a semiconductor component feature database containing a large number of electromagnetic response characteristics of electronic devices is finally constructed. This database has comprehensive sample coverage and rich feature information, providing a reliable reference basis for electronic device detection. With the emergence of new electronic devices and the development of technology, this database can also be continuously updated and expanded by the same method to maintain its effectiveness and applicability.

[0048] Further, according to the multi-band electromagnetic excitation signal, obtaining the first sample response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal under various device operating states, including: S7321: Traverse various device operating states to obtain the first device operating state; S7322: Under the first device operating state, transmit a multi-band electromagnetic excitation signal to the first electronic device sample and receive the first state response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal; S7323: Correlate the first device operating state and the first state response characteristics and add them to the first sample response characteristics of the first electronic device sample.

[0049] Specifically, when obtaining the response characteristics of the first electronic device sample under various device operating states, a traversal method is used to process each of the various device operating states one by one to ensure that all possible operating states are covered. For the first electronic device sample, each state is sequentially selected from the set of device operating states (including the power-on state, power-off state, battery-removed state, SIM-card-removed state, etc.) for processing until all states are processed. During each traversal, a specific device operating state is obtained, called the first device operating state. This operating state corresponds to a specific state that the electronic device may exhibit during actual use or storage.

[0050] Under the first device operating state, transmit a multi-band electromagnetic excitation signal to the first electronic device sample and receive the first state response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal. First, adjust the first electronic device sample to the first device operating state (such as powering on the device, powering off the device, removing the battery, or removing the SIM card, etc.), and then transmit a multi-band electromagnetic excitation signal to the sample, including low-frequency band, medium-frequency band, and high-frequency band electromagnetic waves, which can comprehensively excite various semiconductor components inside the device. At the same time, capture the device's response to these excitation signals, process the response signals, and extract the spectral characteristics, harmonic characteristics, impedance characteristics, and phase characteristics under this specific operating state to form the first state response characteristics. Then, correlate the first device operating state and the first state response characteristics and add this associated information to the first sample response characteristics of the first electronic device sample.

[0051] By processing each device operating state, the complete response characteristic data of the first electronic device sample under all operating states is obtained, ensuring the comprehensiveness and accuracy of the characteristic database, and enabling effective identification of electronic devices in various states during actual detection.

[0052] Further, based on the response characteristics, perform feature matching in the semiconductor component feature database to obtain the feature matching result, including: S410: Extract multiple sample response characteristics from the semiconductor component feature database according to the maximum number of synchronous processes; S420: Based on the feature matching calculation formula, synchronously calculate the matching degrees between the response characteristic and multiple sample response characteristics to obtain multiple response matching degrees; S430: Take the multiple response matching degrees as the feature matching result.

[0053] Specifically, when performing feature matching in the semiconductor component feature database based on the response characteristics, first, according to the maximum number of synchronous processes of the processor, extract multiple sample response characteristics from the semiconductor component feature database. The maximum number of synchronous processes refers to the maximum amount of data that the hardware platform can process in parallel at the same time, and this value depends on factors such as the number of cores of the processor, memory capacity, and computing power. Determine the data batch size for each matching calculation based on this value to achieve the optimal utilization of computing resources. The extraction process uses a pre-constructed index structure to quickly obtain sample response characteristic data that meets specific conditions. These sample response characteristics represent the electromagnetic response characteristics of various electronic devices stored in the database under different working states and will be used as a reference basis for the matching calculation. Then, based on the feature matching calculation formula, synchronously calculate the matching degrees between the target response characteristic and multiple sample response characteristics to obtain multiple response matching degrees. Among them, synchronous calculation means using parallel computing technology to calculate the matching degrees of multiple sample response characteristics simultaneously, greatly improving the calculation efficiency. The feature matching calculation formula is a multi-dimensional similarity calculation method that comprehensively considers spectral characteristics, harmonic characteristics, impedance characteristics, and phase characteristics, and calculates the comprehensive matching degree by weighted summation. The calculation result is a series of response matching degree values, and each value corresponds to the similarity degree between a sample response characteristic and the target response characteristic. After that, take the multiple response matching degrees as the feature matching result for output. These matching degree values are arranged in descending order to form an ordered matching result set. The higher the matching degree value, the more similar the corresponding sample response characteristic is to the target response characteristic, which also means that the target detection object is more likely to contain the electronic device corresponding to this sample. These feature matching results will be used as the input for subsequent judgment steps to determine whether the target detection object contains an electronic device.

[0054] Further, the feature matching calculation formula is: Wherein, is the response characteristic and the sample response characteristic between the matching degrees, is the response characteristic and the sample response characteristic The spectral feature distance is the response feature and the sample response feature of the harmonic feature distance is the response feature and the sample response feature of the impedance feature distance is the response feature and the sample response feature of the phase feature distance , , respectively represent the weight coefficients of the spectral feature, harmonic feature, impedance feature, and phase feature, and satisfy .

[0055] Specifically, the following feature matching calculation formula is adopted: where represents the matching degree between the response feature and the sample response feature , and the value range of this value is from 0 to 1. The closer the value is to 1, the higher the matching degree; represents the spectral feature distance between the response feature and the sample response feature , which is used to quantify the difference degree of the two features in the frequency energy distribution; represents the harmonic feature distance between the response feature and the sample response feature , which is used to quantify the difference degree of the two features in the harmonic components; represents the impedance feature distance between the response feature and the sample response feature , which is used to quantify the difference degree of the two features in the electromagnetic impedance characteristics; represents the phase feature distance between the response feature and the sample response feature , which is used to quantify the difference degree of the two features in the phase response. In the formula, , , respectively represent the weight coefficients of the spectral feature, harmonic feature, impedance feature, and phase feature. These coefficients determine the importance of various features in the overall matching degree calculation. To ensure the normalization of the calculation results, these weight coefficients satisfy constraint conditions.

[0056] The calculation methods for each feature distance can adopt methods such as standardized Euclidean distance, Manhattan distance, or Mahalanobis distance. The specific selection depends on the distribution characteristics of the feature data and the system's requirements for calculation efficiency. The specific values of each weight coefficient are determined through statistical analysis of a large amount of experimental data and can be adjusted and optimized by an expert group according to different application scenarios and detection requirements.

[0057] Through the above feature matching calculation formula, multiple dimensions of the electronic device response characteristics can be comprehensively considered, providing accurate and reliable matching results and providing a scientific basis for the judgment of the electronic device.

[0058] Embodiment 2, based on the same inventive concept as the handheld electronic product detection method based on semiconductor medium analysis in the foregoing embodiment, as Figure 2 shown, Embodiment 2 of the present application provides a handheld electronic product detection device based on semiconductor medium analysis. The device includes: An electromagnetic signal transmitting unit 11 for transmitting multi-band electromagnetic excitation signals, where the multi-band electromagnetic excitation signals include low-band electromagnetic excitation signals, mid-band electromagnetic excitation signals, and high-band electromagnetic excitation signals; A signal receiving unit 12 for receiving the response signals of the target detection object to the multi-band electromagnetic excitation signals; A feature extraction unit 13 for processing the response signals to extract response features, where the response features include spectral features, harmonic features, impedance features, and phase features; A feature matching unit 14 for performing feature matching in the semiconductor component feature database based on the response features to obtain a feature matching result; A judgment and analysis unit 15 for judging whether the target detection object contains an electronic device according to the feature matching result; An acoustic and optical warning unit 16 for giving an alarm through the acoustic and optical unit when it is judged that the target detection object contains an electronic device.

[0059] Further, the execution steps of the feature extraction unit 13 include: Preprocessing the response signals to obtain preprocessed response signals; Performing time-domain analysis and frequency-domain analysis on the preprocessed response signals to obtain time-domain analysis results and frequency-domain analysis results; Extracting spectral features and harmonic features from the frequency-domain analysis results; Extracting impedance features and phase features based on the time-domain analysis results and the frequency-domain analysis results.

[0060] Further, the present application embodiment also includes a feature database construction unit. The execution steps of this unit include: Obtaining multiple electronic device samples, where the multiple electronic device samples include electronic devices of different types, different brands, and different models; The device has multiple device working states, where the multiple device working states include the power-on state, the power-off state, the battery-removed state, and the SIM-card-removed state; Under normal electromagnetic environment, a conductor component feature database is constructed according to multi-band electromagnetic excitation signals, multiple electronic device samples, and multiple device working states.

[0061] Furthermore, the execution steps of the feature database construction unit further include: Traverse multiple electronic device samples to obtain the first electronic device sample; According to the multi-band electromagnetic excitation signal, obtain the first sample response feature of the first electronic device sample to the multi-band electromagnetic excitation signal under multiple device working states; Associate the first electronic device sample and the first sample response feature, and add them to the conductor component feature database.

[0062] Furthermore, the execution steps of the feature database construction unit further include: Traverse multiple device working states to obtain the first device working state; Under the first device working state, transmit a multi-band electromagnetic excitation signal to the first electronic device sample, and receive the first state response feature of the first electronic device sample to the multi-band electromagnetic excitation signal; Associate the first device working state and the first state response feature, and add them to the first sample response feature of the first electronic device sample.

[0063] Furthermore, the execution steps of the feature matching unit 14 further include: Extract multiple sample response features from the semiconductor component feature database according to the maximum number of synchronous processes; Based on the feature matching calculation formula, synchronously calculate the matching degrees between the response feature and the multiple sample response features to obtain multiple response matching degrees; Take the multiple response matching degrees as the feature matching results.

[0064] Furthermore, the feature matching calculation formula is: Among them, is the response feature and the sample response feature the matching degree between them, is the response feature and the sample response feature the spectral feature distance of, is the response feature and the sample response feature the harmonic feature distance of, is the response feature and the impedance feature distance of the sample response feature The impedance feature distance is the response feature and the phase feature distance of the sample response feature The phase feature distance , , respectively represent the weight coefficients of the spectral feature, harmonic feature, impedance feature and phase feature, and satisfy .

[0065] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A handheld electronic product detection method based on semiconductor medium analysis, characterized in that: include: Transmitting a multi-band electromagnetic excitation signal, wherein the multi-band electromagnetic excitation signal includes a low-band electromagnetic excitation signal, a mid-band electromagnetic excitation signal, and a high-band electromagnetic excitation signal; Receiving a response signal of a target detection object to the multi-band electromagnetic excitation signal; Processing the response signal to extract response features, wherein the response features include spectrum features, harmonic features, impedance features, and phase features; Based on the response characteristics, feature matching is performed in a semiconductor component feature database to obtain feature matching results; Determining whether the target detection object includes an electronic device according to the feature matching result; When it is determined that the target detection object contains an electronic device, an alarm is given through the sound and light unit.

2. The handheld electronic product detection method based on semiconductor medium analysis according to claim 1 is characterized in that: Processing the response signal to extract response features includes: Preprocessing the response signal to obtain a preprocessed response signal; Performing time domain analysis and frequency domain analysis on the preprocessed response signal to obtain time domain analysis results and frequency domain analysis results; Extracting the spectrum characteristics and harmonic wave characteristics from the frequency domain analysis result; The impedance feature and the phase feature are extracted based on the time domain analysis result and the frequency domain analysis result.

3. The handheld electronic product detection method based on semiconductor medium analysis according to claim 1 is characterized in that: Before performing feature matching in a semiconductor component feature database based on the response feature and obtaining a feature matching result, the following steps are included: Acquire multiple electronic device samples, wherein the multiple electronic device samples include electronic devices of different types, brands, and models; The device has multiple device working states, where the multiple device working states include power-on state, power-off state, battery removal state, and SIM card removal state; In a normal electromagnetic environment, a conductor component feature database is constructed based on multi-band electromagnetic excitation signals, multiple electronic equipment samples and various equipment working conditions.

4. The handheld electronic product detection method based on semiconductor medium analysis according to claim 3 is characterized in that: The method of constructing a conductor component feature database according to multi-band electromagnetic excitation signals, multiple electronic equipment samples and multiple equipment working states includes: Traversing multiple electronic device samples to obtain a first electronic device sample; According to the multi-band electromagnetic excitation signal, obtaining first sample response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal under multiple device working states; The first electronic device sample and the first sample response feature are associated and added to the conductor component feature database.

5. The handheld electronic product detection method based on semiconductor medium analysis according to claim 4 is characterized in that: The step of obtaining, according to the multi-band electromagnetic excitation signal, first sample response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal under multiple device working states includes: Traverse the working states of multiple devices and obtain the working state of the first device; In the working state of the first device, transmitting a multi-band electromagnetic excitation signal to the first electronic device sample, and receiving a first state response characteristic of the first electronic device sample to the multi-band electromagnetic excitation signal; The first device working state and the first state response feature are associated and added to the first sample response feature of the first electronic device sample.

6. The handheld electronic product detection method based on semiconductor medium analysis according to claim 1 is characterized in that: The performing feature matching in a semiconductor component feature database based on the response feature to obtain a feature matching result includes: Extracting a plurality of sample response features from the semiconductor component feature database according to the maximum number of simultaneous processing; Based on the feature matching calculation formula, synchronously calculating the matching degree of the response feature and multiple sample response features to obtain multiple response matching degrees; The multiple response matching degrees are used as feature matching results.

7. The handheld electronic product detection method based on semiconductor medium analysis according to claim 6 is characterized in that: The feature matching calculation formula is: in, Response characteristics and sample response characteristics The matching degree between Response characteristics and sample response characteristics The spectral feature distance, For the response characteristics and sample response characteristics The harmonic characteristic distance of For the response characteristics and sample response characteristics The impedance characteristic distance, Response characteristics and sample response characteristics The phase characteristic distance, , , Respectively represent the weight coefficients of spectrum characteristics, harmonic characteristics, impedance characteristics and phase characteristics, and satisfy .

8. A handheld electronic product detection device based on semiconductor medium analysis, characterized in that: A handheld electronic product detection method based on semiconductor medium analysis according to any one of claims 1 to 7 comprises: An electromagnetic signal transmitting unit, used for transmitting a multi-band electromagnetic excitation signal, wherein the multi-band electromagnetic excitation signal includes a low-band electromagnetic excitation signal, a mid-band electromagnetic excitation signal and a high-band electromagnetic excitation signal; A signal receiving unit, used to receive a response signal of a target detection object to the multi-band electromagnetic excitation signal; A feature extraction unit, used for processing the response signal to extract response features, wherein the response features include spectrum features, harmonic features, impedance features and phase features; A feature matching unit, configured to perform feature matching in a semiconductor component feature database based on the response feature and obtain a feature matching result; A judgment and analysis unit, used for judging whether the target detection object contains an electronic device according to the feature matching result; The sound and light warning unit is used to give an alarm through the sound and light unit when it is determined that the target detection object contains an electronic device.

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