Handheld Electronic Product Detection Method and Device Based on Semiconductor Medium Analysis

Abstract

The present invention discloses a method and device for detecting handheld electronic products based on semiconductor medium analysis. The method includes: transmitting multi-band electromagnetic excitation signals; receiving response signals of a target detection object to the multi-band electromagnetic excitation signals; processing the response signals to extract response features; based on the response features, performing feature matching in a semiconductor component feature database to obtain a feature matching result, and determining whether the target detection object contains an electronic device; when it is determined that the target detection object contains an electronic device, giving an alarm through an acoustic-optic unit. This application solves the technical problem in the prior art that electronic devices in various states cannot be effectively detected, and achieves the technical effect of accurately detecting and timely warning electronic devices in various states through semiconductor medium feature analysis.

Description

Technical Field

[0001] The present invention relates to the field of electronic device detection, and particularly to a detection method and device for handheld electronic products based on semiconductor medium analysis. Background Art

[0002] With the rapid development of electronic technology, the popularity of various portable electronic devices has been continuously increasing. While these devices bring convenience to people's lives and work, they also pose challenges to the security management of certain special places.

[0003] Currently, 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 ordinary metal items from electronic devices; second, today's electronic devices are becoming more and more miniaturized and lightweight, with a reduced metal content, making it difficult for traditional metal detectors to meet the detection requirements; more importantly, the existing technology is difficult to cope with the detection requirements of electronic devices in various situations such as the shutdown state, removal of the battery, and removal of the SIM card. For example, the detection ability for small electronic products such as micro electronic devices (such as rice grain earphones) is extremely limited. Therefore, the existing technology cannot effectively detect electronic devices in various states. Summary of the Invention

[0004] The present application provides a detection method and device for handheld electronic products based on semiconductor medium analysis, aiming to solve the technical problem that electronic devices in various states cannot be effectively detected in the existing technology.

[0005] In the first aspect disclosed by the present application, a detection method for handheld electronic products based on semiconductor medium analysis is provided. The method includes: transmitting 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; receiving response signals of a target detection object to the multi-band electromagnetic excitation signals; processing the response signals to extract response features, where the response features include spectral features, harmonic features, impedance features, and phase features; based on the response features, performing feature matching in a semiconductor component feature database to obtain a feature matching result; judging whether the target detection object contains an electronic device according to the feature matching result; and when it is judged that the target detection object contains an electronic device, giving an alarm through an acoustic-optic unit.

[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, according to the multi-band electromagnetic excitation signal, 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; and associating the first electronic device sample with the first sample response feature and adding them to the conductor component feature database.

[0009] Optionally, obtaining, according to the multi-band electromagnetic excitation signal, 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 includes: traversing the variety of device operating states to obtain a first device operating state; transmitting the 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 with 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 between 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:

[0012]

[0013] Wherein, is the response feature and the sample response feature is the matching degree between them, is the response feature and the sample response feature is the spectral feature distance, is the response feature and the sample response feature is the harmonic feature distance, is the response feature and the sample response feature is the impedance feature distance, is the response feature and the sample response feature is the phase feature distance, , , respectively represent the weight coefficients of the spectral feature, harmonic feature, impedance feature and phase feature, and satisfy .

[0014] Another aspect disclosed in this 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, medium-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 device 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; an acoustic and optical warning unit for giving an alarm through an acoustic and optical unit when it is judged that the target detection object contains an electronic device.

[0015] 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, capturing 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 features, where the response features include spectral features, harmonic features, impedance features, and phase features. By extracting multi-dimensional features, the electromagnetic characteristics of semiconductor components can be comprehensively reflected, providing a basis for accurate identification. Subsequently, based on the extracted response features, 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 discovering prohibited electronic devices.

[0016] 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

[0017] 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;

[0018] 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.

[0019] Description of the Reference Numerals:

[0020] 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

[0021] The general idea of the technical solution provided by this application is as follows:

[0022] The embodiments of the present application provide a method and device for detecting handheld electronic products based on semiconductor medium analysis, which uses the specific response characteristics of semiconductor components under the action of an electromagnetic field to identify electronic devices, thereby solving the technical problem in the prior art that electronic devices in various states cannot be effectively detected. This method does not depend 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 to achieve efficient and accurate detection of electronic products.

[0023] 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 covering the entire frequency band 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 different from ordinary items. Then, the extracted characteristics are intelligently matched with the 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, a warning is immediately issued through the sound and light unit to remind the inspector to perform further processing. 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.

[0024] Through an innovative method combining multi-band excitation and multi-dimensional feature analysis, the limitations of traditional metal detection technology are broken through, and comprehensive and accurate identification of electronic devices in various states is achieved, especially the efficient detection of micro communication devices, greatly improving the effectiveness and reliability of security inspections.

[0025] 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.

[0026] 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 the method includes:

[0027] 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.

[0028] 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 within different frequency bands, specifically including a low-frequency band electromagnetic excitation signal, a medium-frequency band electromagnetic excitation signal, and a high-frequency band electromagnetic excitation signal. The low-frequency band 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 semiconductor components inside the object to be detected. The medium-frequency band 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 also generate obvious resonance responses with a variety of electronic components. The high-frequency band 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.

[0029] By transmitting the multi-band electromagnetic excitation signal 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.

[0030] S200: Receive the response signal of the target detection object to the multi-band electromagnetic excitation signal.

[0031] 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; and the electromagnetic interference signal mainly comes from the abnormal working state of the internal circuit of the electronic device under external excitation.

[0032] By capturing various response signals generated by the target detection object under low-frequency band, medium-frequency band, and high-frequency band electromagnetic excitations, it lays a foundation for subsequent feature extraction and analysis.

[0033] S300: Process the response signal to extract response features, and the response features include spectral features, harmonic features, impedance features, and phase features.

[0034] 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.

[0035] 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 components of each harmonic and their amplitude ratio relationships contained in the response signal. The semiconductor component will generate rich harmonic components under electromagnetic excitation, and the harmonic feature can effectively reflect the non-linear 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 object to be detected 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 object to be detected 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 change 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.

[0036] 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.

[0037] S400: Based on the response features, perform feature matching in the semiconductor component feature database to obtain the feature matching result.

[0038] Specifically, after obtaining the response features, perform a matching operation on the response features in the pre-constructed semiconductor component feature database to obtain the feature matching result, so as to compare the features of the unknown electronic device with those of the known devices and achieve the identification and classification of the electronic devices. Among them, the semiconductor component feature database is an electromagnetic characteristic knowledge base of electronic devices established in advance through a large number of experiments and samplings. The 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.

[0039] S500: Determine whether the target detection object contains an electronic device according to the feature matching result.

[0040] 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; on the contrary, if all the 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 and can effectively balance the sensitivity and specificity of the detection.

[0041] S600: When it is determined that the target detection object contains an electronic device, give an alarm through the sound and light unit.

[0042] 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 sound and light unit to prompt the presence of the detected electronic device. Among them, the sound and light 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.

[0043] Furthermore, process the response signal to extract the response features, including:

[0044] S310: Preprocess the response signal to obtain the preprocessed response signal;

[0045] S320: Perform time-domain analysis and frequency-domain analysis on the preprocessed response signal to obtain the time-domain analysis result and the frequency-domain analysis result;

[0046] S330: Extract spectral features and harmonic features from the frequency-domain analysis results;

[0047] S340: Extract impedance features and phase features based on the time-domain analysis results and the frequency-domain analysis results.

[0048] 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 bandpass 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 for 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.

[0049] 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 components and their amplitudes; the harmonic features include parameters such as the amplitudes, phases, and harmonic distortion rates 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 the frequency-domain analysis results. The impedance features are derived by calculating the amplitude ratio relationship between the response signal and the excitation signal and combining electromagnetic field theory; the phase features are 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.

[0050] 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.

[0051] 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:

[0052] S710: Obtain multiple electronic device samples, where the multiple electronic device samples include electronic devices of different types, different brands, and different models;

[0053] S720: Set 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;

[0054] S730: In a normal electromagnetic environment, construct a conductor component feature database based on multi-band electromagnetic excitation signals, multiple electronic device samples, and multiple device working states.

[0055] Specifically, before performing feature matching, first establish a complete semiconductor component feature database. 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, micro 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 feature database with wide applicability can be established to adapt to various electronic devices that may be encountered in actual detection.

[0056] 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 semiconductor component feature database 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 feature 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. The database adopts a structured design to support efficient data storage, retrieval, and update operations, providing data support for subsequent feature matching.

[0057] By establishing a comprehensive and accurate semiconductor component feature database, which contains the electromagnetic response characteristics of a large number of electronic devices in various working states, a reliable reference basis is provided for feature matching in actual detection.

[0058] Further, a characteristic database of conductor components is constructed according to the multi-band electromagnetic excitation signal, multiple electronic device samples, and multiple device operating states, including:

[0059] S731: Traverse multiple electronic device samples to obtain a first electronic device sample;

[0060] S732: 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 multiple device operating states;

[0061] S733: Associate the first electronic device sample with the first sample response characteristics and add them to the characteristic database of conductor components.

[0062] Specifically, a traversal method is used to process multiple electronic device samples one by one to ensure that all samples are included in the characteristic database of conductor components. 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 any type, brand, or model of electronic device, 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 multiple 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 emit low-band, mid-band, and high-band electromagnetic excitation signals, and record the response of the sample to the multi-band electromagnetic excitation signal in each operating state (including 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 set of characteristics that comprehensively describe the electromagnetic response characteristics of the 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 characteristic database. The association process is achieved by establishing a mapping relationship between device information and response characteristics, using a unique identifier (such as device ID) to link the device information with its corresponding response characteristics to form complete data entries. These data entries are stored in the characteristic database according to a specific structure and indexing method to support subsequent efficient retrieval and matching operations.

[0063] By processing each electronic device sample, a semiconductor component feature database containing electromagnetic response features of a large number of electronic devices is ultimately 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 through the same method to maintain its effectiveness and applicability.

[0064] Furthermore, according to the multi-band electromagnetic excitation signal, the first sample response features of the first electronic device sample to the multi-band electromagnetic excitation signal under multiple device operating states are obtained, including:

[0065] S7321: Traverse multiple device operating states to obtain the first device operating state;

[0066] S7322: Under the first device operating state, emit a multi-band electromagnetic excitation signal to the first electronic device sample and receive the first state response features of the first electronic device sample to the multi-band electromagnetic excitation signal;

[0067] S7323: Correlate the first device operating state and the first state response features and add them to the first sample response features of the first electronic device sample.

[0068] Specifically, when obtaining the response features of the first electronic device sample under multiple device operating states, a traversal method is used to process each of the multiple 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 power-on state, power-off state, battery-removed state, SIM-card-removed state, etc.) for processing until all states are processed. In each traversal process, a specific device operating state is obtained, which is called the first device operating state. This operating state corresponds to a specific state that the electronic device may appear in during actual use or storage.

[0069] In the first device working state, a multi-band electromagnetic excitation signal is transmitted to the first electronic device sample, and the first state response characteristics of the first electronic device sample to the multi-band electromagnetic excitation signal are received. First, the first electronic device sample is adjusted to the first device working state (such as turning on the device, turning off the device, removing the battery or removing the SIM card, etc.), and then a multi-band electromagnetic excitation signal is transmitted to the sample, including low-band, mid-band, and high-band electromagnetic waves, which can comprehensively excite various semiconductor components inside the device. At the same time, the response of the device to these excitation signals is captured, and the response signal is processed to extract the spectral characteristics, harmonic characteristics, impedance characteristics, and phase characteristics in this specific working state, forming the first state response characteristics. Then, the first device working state and the first state response characteristics are correlated, and this correlation information is added to the first sample response characteristics of the first electronic device sample.

[0070] By processing each device working state, the complete response characteristic data of the first electronic device sample in all working 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.

[0071] Furthermore, based on the response characteristics, feature matching is performed in the semiconductor component characteristic database to obtain the feature matching results, including:

[0072] S410: Extract multiple sample response characteristics from the semiconductor component characteristic database according to the maximum number of synchronous processes;

[0073] S420: Based on the feature matching calculation formula, synchronously calculate the matching degrees of the response characteristics and multiple sample response characteristics to obtain multiple response matching degrees;

[0074] S430: Use the multiple response matching degrees as the feature matching results.

[0075] Specifically, when performing feature matching in the semiconductor component feature database based on response features, first, according to the maximum number of synchronous processes of the processor, multiple sample response features are extracted 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 simultaneously, and this value depends on factors such as the number of processor cores, memory capacity, and computing power. Based on this value, the data batch for each matching calculation is determined to achieve the optimal utilization of computing resources. The extraction process uses a pre-constructed index structure to quickly obtain sample response feature data that meet specific conditions. These sample response features represent the electromagnetic response features of various electronic devices stored in the database under different working states and will be used as a reference for the matching calculation. Then, based on the feature matching calculation formula, the matching degrees between the target response feature and multiple sample response features are calculated synchronously, obtaining multiple response matching degrees. Among them, synchronous calculation means using parallel computing technology to calculate the matching degrees of multiple sample response features simultaneously, greatly improving the calculation efficiency. The feature matching calculation formula is a multi-dimensional similarity calculation method that comprehensively considers spectral features, harmonic features, impedance features, and phase features, 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 feature and the target response feature. After that, the multiple response matching degrees are output as the feature matching results. These matching degree values are arranged in descending order to form an ordered set of matching results. The higher the matching degree value, the more similar the corresponding sample response feature is to the target response feature, 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.

[0076] Further, the feature matching calculation formula is:

[0077]

[0078] Wherein, is the response feature and the sample response feature between the matching degrees, is the response feature and the sample response feature of 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 The phase feature distance , , respectively represent the weight coefficients of the spectral feature, harmonic feature, impedance feature and phase feature, and satisfy .

[0079] Specifically, the following feature matching calculation formula is adopted:

[0080]

[0081] where represents the matching degree between the response feature and the sample response feature . The value range of this value is from 0 to 1, and 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.

[0082] The calculation methods of each feature distance can adopt methods such as standardized Euclidean distance, Manhattan distance or Mahalanobis distance, etc. The specific selection depends on the distribution characteristics of the feature data and the requirements of the system 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 the expert group according to different application scenarios and detection requirements.

[0083] Through the above feature matching calculation formula, multiple dimensions of the response feature of the electronic device can be comprehensively considered, providing accurate and reliable matching results and providing a scientific basis for the judgment of the electronic device.

[0084] 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:

[0085] An electromagnetic signal transmitting unit 11, configured to transmit 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;

[0086] A signal receiving unit 12, configured to receive response signals of a target detection object to the multi-band electromagnetic excitation signals;

[0087] A feature extraction unit 13, configured to process the response signals to extract response features, where the response features include spectral features, harmonic features, impedance features, and phase features;

[0088] A feature matching unit 14, configured to perform feature matching in a semiconductor component feature database based on the response features to obtain a feature matching result;

[0089] A judgment and analysis unit 15, configured to judge whether the target detection object contains an electronic device according to the feature matching result;

[0090] An acoustic and optical warning unit 16, configured to give a warning through an acoustic and optical unit when it is judged that the target detection object contains an electronic device.

[0091] Further, the execution steps of the feature extraction unit 13 include:

[0092] Preprocess the response signals to obtain preprocessed response signals;

[0093] Perform time-domain analysis and frequency-domain analysis on the preprocessed response signals to obtain time-domain analysis results and frequency-domain analysis results;

[0094] Extract spectral features and harmonic features from the frequency-domain analysis results;

[0095] Extract impedance features and phase features based on the time-domain analysis results and the frequency-domain analysis results.

[0096] Further, the embodiment of the present application further includes a feature database construction unit. The execution steps of this unit include:

[0097] Obtain a plurality of electronic device samples, where the plurality of electronic device samples include electronic devices of different types, different brands, and different models;

[0098] The device has multiple device operating states, where the multiple device operating states include the power-on state, the power-off state, the battery-removed state, and the SIM-card-removed state;

[0099] Under normal electromagnetic environment, according to the multi-band electromagnetic excitation signal, multiple electronic device samples, and multiple device operating states, construct a conductor component feature database.

[0100] Furthermore, the execution steps of the feature database construction unit further include:

[0101] Traverse multiple electronic device samples to obtain the first electronic device sample;

[0102] 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 operating states;

[0103] Associate the first electronic device sample and the first sample response feature, and add them to the conductor component feature database.

[0104] Furthermore, the execution steps of the feature database construction unit further include:

[0105] Traverse multiple device operating states to obtain the first device operating state;

[0106] Under the first device operating state, emit 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;

[0107] Associate the first device operating state and the first state response feature, and add them to the first sample response feature of the first electronic device sample.

[0108] Furthermore, the execution steps of the feature matching unit 14 further include:

[0109] According to the maximum number of synchronous processes, extract multiple sample response features from the semiconductor component feature database;

[0110] Based on the feature matching calculation formula, synchronously calculate the matching degrees between the response feature and multiple sample response features to obtain multiple response matching degrees;

[0111] Take the multiple response matching degrees as the feature matching result.

[0112] Furthermore, the feature matching calculation formula is:

[0113]

[0114] Wherein, is the response feature and the sample response feature The matching degree between 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 .

[0115] 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 included in the patent protection scope of the present application by the same token.

Claims

1. A handheld electronic product detection method based on semiconductor medium analysis, characterized in that, Including: 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; Receiving response signals of a target detection object to the multi-band electromagnetic excitation signals; Processing the response signals to extract response features, where the response features include spectral features, harmonic features, impedance features, and phase features; Based on the response features, performing feature matching in a semiconductor component feature database to obtain a feature matching result; Judging whether the target detection object contains an electronic device according to the feature matching result; When it is judged that the target detection object contains an electronic device, giving an alarm through an acousto-optic unit; Processing the response signals to extract response features includes: 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 the spectral features and harmonic features from the frequency-domain analysis results; Extracting the impedance features and phase features based on the time-domain analysis results and frequency-domain analysis results; Before performing feature matching in the semiconductor component feature database based on the response features to obtain a feature matching result, the following steps are included: Obtaining multiple electronic device samples, where the multiple electronic device samples include electronic devices of different types, different brands, and different models; Setting multiple device working states, where the multiple device working states include the powered-on state, the powered-off state, the battery-removed state, and the SIM-card-removed state; In a normal electromagnetic environment, constructing a semiconductor component feature database according to the multi-band electromagnetic excitation signals, the multiple electronic device samples, and the multiple device working states.

2. The handheld electronic product detection method based on semiconductor medium analysis according to claim 1, characterized in that, The constructing a semiconductor component feature database according to the multi-band electromagnetic excitation signals, the multiple electronic device samples, and the multiple device working states includes: Traversing the multiple electronic device samples to obtain a first electronic device sample; According to the multi-band electromagnetic excitation signals, obtaining first sample response features of the first electronic device sample to the multi-band electromagnetic excitation signals in multiple device working states; Associating the first electronic device sample and the first sample response features and adding them to the semiconductor component feature database.

3. The handheld electronic product detection method based on semiconductor medium analysis according to claim 2, wherein The obtaining first sample response features of the first electronic device sample to the multi-band electromagnetic excitation signals in multiple device working states according to the multi-band electromagnetic excitation signals includes: Traversing the multiple device working states to obtain a first device working state; In the first device working state, transmitting multi-band electromagnetic excitation signals to the first electronic device sample and receiving first state response features of the first electronic device sample to the multi-band electromagnetic excitation signals; Associating the first device working state and the first state response features and adding them to the first sample response features of the first electronic device sample.

4. The handheld electronic product detection method based on semiconductor medium analysis according to claim 1, wherein, Performing feature matching in the semiconductor component feature database based on the response features to obtain a feature matching result, including: extracting a plurality of 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 calculating the matching degrees between the response feature and the plurality of sample response features to obtain a plurality of response matching degrees; Taking the plurality of response matching degrees as the feature matching result.

5. The method for detecting a handheld electronic product based on semiconductor medium analysis according to claim 4, wherein The feature matching calculation formula is: Among them, S(V1, V2) is the matching degree between the response feature V1 and the sample response feature V2, D spec is the spectral feature distance between the response feature V1 and the sample response feature V2, D harm is the harmonic feature distance between the response feature V1 and the sample response feature V2, D imp is the impedance feature distance between the response feature V1 and the sample response feature V2, D phasc is the phase feature distance between the response feature V1 and the sample response feature V2. α, β, γ, and δ respectively represent the weight coefficients of the spectral feature, harmonic feature, impedance feature, and phase feature, and satisfy α + β + γ + δ = 1.

6. A handheld electronic product detection device based on semiconductor medium analysis, characterized in that, Using the handheld electronic product detection method based on semiconductor medium analysis according to any one of claims 1-5, including: An electromagnetic signal transmitting unit for transmitting a multi-band electromagnetic excitation signal, where the multi-band electromagnetic excitation signal includes a low-band electromagnetic excitation signal, a middle-band electromagnetic excitation signal, and a high-band electromagnetic excitation signal; A signal receiving unit for receiving a response signal of a target detection object to the multi-band electromagnetic excitation signal; A feature extraction unit for processing the response signal 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 the 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; An acoustic-optic warning unit for giving an alarm through the acoustic-optic unit when it is judged that the target detection object contains an electronic device.

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