Multi-mode peep-proof detection method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a multi-mode peep-proof detection method and device, electronic equipment and a storage medium, and relates to the technical field of information security and anti-theft, and the method comprises the steps: obtaining multi-mode environment data in an omnibearing manner through radio frequency signal scanning, magnetic induction, active infrared detection and sound wave detection; and through a multi-source data fusion algorithm and decision tree model analysis, a sleep device activation mechanism is combined, and a detection result is accurately obtained. On the basis, an AR visual guiding and self-adaptive shielding technology is used for generating a peep-proof scheme, and positioning guiding and interference elimination of dangerous equipment are achieved. If the threat is not eliminated, continuously monitoring the environment, and dynamically adjusting the strategy; and if the threat is eliminated, optimizing the decision tree model and the wake-up signal parameter according to the detection data. The problem of missing detection caused by diversification and miniaturization of hidden equipment in the prior art and the problem that detection is difficult in the quiet period of dormant equipment are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of information security and anti-espionage technology, and in particular to a multimodal anti-spy detection method, device, electronic device and storage medium. Background Technology

[0002] With the rapid development of information technology and micro-manufacturing processes, the misuse of covert devices such as voyeurism, eavesdropping, and GPS tracking has become increasingly prominent, posing a major threat to personal privacy and information security.

[0003] These devices are not only characterized by their diversity, miniaturization, and camouflage, making them easy to integrate into everyday items such as chargers, smoke detectors, and car accessories, but they are also extremely concealed, greatly increasing the difficulty of detection.

[0004] Traditional single detection technologies, such as those relying solely on infrared scanning or signal detection, are no longer sufficient to address the complex challenges posed by modern covert devices, easily leading to missed detections or false alarms. This is especially true when facing GPS trackers or eavesdropping devices operating in sleep mode, where conventional radio frequency detection equipment is virtually undetectable during their silent period.

[0005] Meanwhile, interference from numerous legitimate wireless signals in complex environments such as hotels, vehicles, and offices further exacerbates the false alarm rate of traditional detectors, making it difficult to accurately locate the source of the threat. Furthermore, most anti-spy camera devices require users to possess a high level of professional knowledge and patience, resulting in a high barrier to entry for ordinary users and hindering their effective utilization.

[0006] Therefore, there is an urgent need for a multimodal anti-spying detection method that integrates anti-spying, eavesdropping, and GPS tracking, which is efficient, accurate, easy to use, and adaptable to complex environments. Summary of the Invention

[0007] The embodiments of this invention provide a multimodal anti-spy detection method to address the problems of existing technologies, such as easy missed detection, difficulty in detecting dormant devices during their silent period, high false alarm rate due to interference in complex environments, and high user skill requirements and steep learning curve. The technical solution is as follows: According to one aspect of the present invention, a multimodal anti-spy detection method is provided, the method comprising: acquiring multimodal environmental data through radio frequency signal scanning, magnetic field sensing by a magnetic induction array, active infrared detection, and acoustic wave detection; the multimodal environmental data including wireless signal characteristics, magnetic field data, infrared detection data, and acoustic wave detection data; performing fusion analysis and wake-up processing on the multimodal environmental data through a multi-source data fusion algorithm and a decision tree model, combined with a dormant device activation mechanism, to obtain detection results; the dormant device activation mechanism is used to wake up dormant dangerous devices; generating an anti-spy scheme through AR visualization guidance and adaptive shielding based on the detection results, and providing location guidance and interference elimination for dangerous devices based on the anti-spy scheme; the AR visualization is used to mark the location of the dangerous device in real time and provide elimination guidance; the adaptive shielding is used to interfere with the tracker; if the threat of the dangerous device is not completely eliminated, the current environment is continuously monitored; if the threat of the dangerous device has been completely eliminated, the detection and analysis strategy is adjusted based on the current detection data; the adjustment of the detection and analysis strategy includes optimizing the decision tree model and adjusting the wake-up signal parameters.

[0008] In one embodiment, acquiring multimodal environmental data through radio frequency signal scanning, magnetic field sensing by a magnetic induction array, active infrared detection, and acoustic wave detection is achieved through the following steps: continuously monitoring and recording all wireless signal characteristics through radio frequency signal scanning; identifying suspicious signals by comparing the wireless signal characteristics with a preset signal fingerprint database; the wireless signal characteristics include signal strength, frequency, and transmission mode; obtaining magnetic field data by sensing the magnetic field of the surrounding environment through a high-sensitivity Hall sensor; identifying abnormal magnetic sources by analyzing the magnetic field data through a gradient measurement algorithm; obtaining infrared detection data by emitting infrared light of a set wavelength to reflect light off suspicious lenses and using filters to suppress ambient light interference; obtaining echo signals by emitting ultrasonic waves of a set frequency to generate echoes to eavesdropping devices; and obtaining acoustic wave detection data by performing feature analysis on the echo signals; the set wavelength includes 940nm.

[0009] In one embodiment, the detection results are obtained by performing fusion analysis and wake-up processing on the multimodal environmental data through a multi-source data fusion algorithm and a decision tree model, combined with a dormant device activation mechanism. This is achieved through the following steps: the multimodal environmental data is weighted and fused according to the reliability and importance of the data source to obtain a dataset; the dataset is analyzed using a decision tree model according to preset rules and conditions to obtain detection results; for dangerous devices in dormant mode, the dormant device activation mechanism is activated to send a wake-up signal to the surrounding environment, so that the dormant dangerous device generates a danger signal after receiving the wake-up signal, and the detection results are updated according to the danger signal.

[0010] In one embodiment, an anti-spy scheme is generated based on the detection results using AR visualization guidance and adaptive shielding. The location guidance and interference elimination of dangerous devices based on the anti-spy scheme are achieved through the following steps: when the detection results indicate the presence of dangerous devices, an AR interface matching virtual information and the real scene is generated based on real-time collected environmental information and detection data to generate location guidance for the dangerous devices; the location guidance uses markers and text descriptions, and elimination guidance is provided based on the type and characteristics of the dangerous devices according to real-time collected environmental information and detection data; the elimination guidance includes operating methods.

[0011] In one embodiment, an anti-spying scheme is generated based on the detection results through AR visualization guidance and adaptive shielding. The location guidance and interference elimination of dangerous devices based on the anti-spying scheme are achieved through the following steps: analyzing the feature information in the multimodal environmental data based on the detection results, and identifying the type of dangerous device in combination with a preset device feature library; if the dangerous device is a wireless eavesdropping device, then transmitting electromagnetic noise of a set frequency and intensity to interfere with the communication of the eavesdropping device; if the dangerous device is a GPS tracker, then sending fake base station signals to interfere with the positioning function of the GPS tracker.

[0012] In one embodiment, if the threat of a hazardous device is not completely eliminated, continuous monitoring of the current environment is achieved through the following steps: when it is determined that the threat of a hazardous device is not completely eliminated, monitoring parameters are set according to the type, characteristics, and detection data of the hazardous device; the monitoring parameters include the monitoring time interval and frequency; current environmental data is collected in real time according to the monitoring parameters, and the current environmental data is compared and analyzed in real time with the detection data to determine the operating status of the hazardous device; if the operating status reaches the set conditions, an alarm is issued; if the hazardous device is found to be running again during the monitoring process, an alarm is issued, and the monitoring strategy is adjusted according to the anti-spy scheme and the current situation, and interference elimination measures are initiated.

[0013] In one embodiment, if the threat of the dangerous device has been completely eliminated, the detection and analysis strategy is adjusted based on the detection data through the following steps: After the threat of the dangerous device has been completely eliminated, all data from the detection process is comprehensively collected, classified, and stored to establish a detailed data archive; the decision tree model is optimized based on the data archive; the optimization includes adjusting rules and conditions, adding judgment nodes, modifying judgment nodes, adjusting weights, and optimizing the decision path; the wake-up signal parameters in the dormant device activation mechanism are adjusted based on the data archive; the parameter adjustment includes adjusting the frequency range, intensity, and transmission time.

[0014] According to one aspect of the present invention, a multimodal anti-spy detection device is provided, the device comprising: a multimodal data acquisition module, used to acquire multimodal environmental data through radio frequency signal scanning, magnetic field sensing by a magnetic induction array, active infrared detection, and acoustic wave detection; the multimodal environmental data including wireless signal characteristics, magnetic field data, infrared detection data, and acoustic wave detection data; and a data fusion and device wake-up module, used to perform fusion analysis and wake-up processing on the multimodal environmental data through a multi-source data fusion algorithm and a decision tree model, combined with a dormant device activation mechanism, to obtain a detection result; the dormant device activation mechanism is used to wake up dormant dangerous devices. The intelligent guidance and shielding module is used to generate an anti-spying scheme based on the detection results through AR visualization guidance and adaptive shielding, and to locate and eliminate interference with dangerous devices according to the anti-spying scheme; the AR visualization is used to mark the location of the dangerous device in real time and provide elimination guidance; the adaptive shielding is used to interfere with the tracker; the dynamic monitoring and optimization module is used to continuously monitor the current environment if the threat of the dangerous device is not completely eliminated; if the threat of the dangerous device has been completely eliminated, the detection and analysis strategy is adjusted according to the detection data; the adjustment of the detection and analysis strategy includes optimizing the decision tree model and adjusting the wake-up signal parameters.

[0015] According to one aspect of the present invention, an electronic device includes at least one processor and at least one memory, wherein computer-readable instructions are stored on the memory; the computer-readable instructions are executed by one or more of the processors to cause the electronic device to implement the multimodal anti-spy detection method as described above.

[0016] According to one aspect of the present invention, a storage medium has computer-readable instructions stored thereon, which are executed by one or more processors to implement the multimodal anti-spy detection method as described above.

[0017] The beneficial effects of the technical solution provided by this invention are: In the aforementioned technical solution, this invention acquires multimodal environmental data comprehensively through radio frequency signal scanning, magnetic induction, active infrared detection, and acoustic wave detection. The data is then analyzed using a multi-source data fusion algorithm and decision tree model, combined with a dormant device activation mechanism, to accurately obtain detection results. Based on this, an anti-spying scheme is generated using AR visualization guidance and adaptive shielding technology, enabling the location guidance and interference elimination of dangerous devices. If the threat persists, the environment is continuously monitored, and the strategy is dynamically adjusted; if the threat has been eliminated, the decision tree model and wake-up signal parameters are optimized based on the detection data. This solution overcomes the limitations of traditional single detection technologies, effectively solving the problem of missed detection caused by the diversification and miniaturization of concealed devices, as well as the difficulty in detecting dormant devices during their silent period. Simultaneously, it reduces the false alarm rate in complex environments and improves the ease of user operation. Through an intelligent guidance and elimination closed-loop system, the entire process from detection to intervention is automated, significantly enhancing the protection of personal privacy and information security. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a multimodal anti-spy detection method according to an exemplary embodiment; Figure 2 This is a block diagram illustrating a multimodal anti-spy detection device according to an exemplary embodiment; Figure 3 This is a hardware structure diagram of an electronic device according to an exemplary embodiment; Figure 4 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0021] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this disclosure means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0022] This invention provides a multimodal anti-spy detection method. Through multimodal collaborative detection and intelligent analysis, it achieves efficient and accurate detection and elimination of spying, eavesdropping, and GPS tracking devices. It solves the problems of high false alarm rates, high missed detection rates, difficulty in handling dormant devices, and high user operation barriers associated with traditional technologies. This multimodal anti-spy detection method is applicable to multimodal anti-spy detection devices, which can be electronic devices. The multimodal anti-spy detection method in this invention can be applied to various scenarios, such as multimodal anti-spy detection.

[0023] Please see Figure 1 This invention provides a multimodal anti-spy detection method applicable to electronic devices.

[0024] In the following method embodiments, for ease of description, the execution subject of each step of the method is an electronic device, but this does not constitute a specific limitation.

[0025] like Figure 1 As shown, the method may include the following steps: Step 110: Obtain multimodal environmental data through radio frequency signal scanning, magnetic field sensing by magnetic induction array, active infrared detection, and acoustic wave detection.

[0026] In one possible implementation, all wireless signal characteristics are continuously monitored and recorded through radio frequency signal scanning. Suspicious signals are identified by comparing the wireless signal characteristics with a preset signal fingerprint database. Magnetic field data is obtained by sensing the magnetic field of the surrounding environment through a high-sensitivity Hall sensor. Abnormal magnetic sources are identified by analyzing the magnetic field data through a gradient measurement algorithm. Infrared detection data is obtained by emitting infrared light of a set wavelength to reflect light off suspicious lenses and using filters to suppress ambient light interference. Echoes are emitted at a set frequency to generate echoes to the eavesdropping device, and acoustic wave detection data is obtained by performing feature analysis on the echoes.

[0027] The multimodal environmental data includes wireless signal characteristics, magnetic field data, infrared detection data, acoustic wave detection data, etc.; wireless signal characteristics include signal strength, frequency, transmission mode, etc.; the set wavelength includes 940nm, etc., which can be set according to the specific application scenario, and are not limited here.

[0028] Specifically, a full-band (20MHz-6GHz) radio frequency signal scanning module continuously monitors and records wireless signal characteristics (such as signal strength, frequency, and transmission mode), and compares them with a preset signal fingerprint database to identify suspicious transmissions (such as intermittent burst signals). A high-sensitivity Hall sensor is used to detect the ambient magnetic field, and a gradient measurement algorithm is used to distinguish abnormal magnetic sources (such as the strong magnetic attraction of vehicle-mounted GPS trackers). 940nm wavelength infrared light is emitted, and hidden cameras are identified through lens reflection, while filters suppress ambient light interference. 20kHz-50kHz ultrasonic waves are emitted, and echo signal characteristics are analyzed to detect the diaphragm structure of eavesdropping devices.

[0029] The system incorporates several key technologies: radio frequency scanning covers the entire frequency band, ensuring no 4G / 5G / Wi-Fi / Bluetooth signals are missed; the signal fingerprint database contains transmission characteristics of known hidden cameras, improving the identification rate of suspicious signals; the magnetic array uses a gradient algorithm to eliminate environmental interference (such as metal furniture), marking only abnormal magnetic sources and reducing false alarms; infrared detection employs a narrow-band filter to suppress ambient light such as sunlight and LEDs, improving the accuracy of hidden camera detection; and acoustic detection analyzes the resonant frequency of the diaphragm to distinguish eavesdropping devices from ordinary objects (such as clocks).

[0030] In the above process, the embodiments of the present invention overcome the limitations of a single sensor through multimodal collaborative acquisition, covering radio frequency, magnetic field, optical and acoustic dimensions, which greatly improves the detection rate of concealed devices, provides full-scene perception capabilities, and realizes accurate identification of hidden cameras disguised as everyday objects.

[0031] Step 120: Multi-source data fusion algorithm and decision tree model, combined with dormant device activation mechanism, are used to perform fusion analysis and wake-up processing on multi-modal environmental data to obtain detection results.

[0032] In one possible implementation, multimodal environmental data is weighted and fused according to the reliability and importance of the data source to obtain a dataset. A decision tree model is used to analyze the dataset according to preset rules and conditions to obtain detection results. For dangerous devices in dormant mode, a dormant device activation mechanism is activated to send a wake-up signal to the surrounding environment. When the dormant dangerous device receives the wake-up signal, it generates a danger signal, and the detection results are updated according to the danger signal.

[0033] The detection results are judgments generated by the system after comprehensive analysis of multimodal environmental data, including device type, threat level, and status information. For example, when the radio frequency signal scanning module detects an intermittent burst signal at 2.4 GHz, and the infrared detection module finds a lens reflection at the corresponding location, the decision tree model will output a detection result of "active wireless eavesdropping device present." If only an abnormal magnetic source is detected but no radio frequency activity is detected, a preliminary result of "dormant GPS tracker present" may be generated. After the dormant device activation mechanism induces it to connect to the network, it will be further updated to "activated GPS tracker (IMEI code: XXXX)," providing accurate guidance for subsequent interference elimination. The dormant device activation mechanism is used to wake up dormant dangerous devices.

[0034] Specifically, weights are assigned based on the reliability of the data source (e.g., the directness of radio frequency signals > the indirectness of magnetic fields), and multimodal data is fused through a decision tree model to reduce false alarms (e.g., no alarm is triggered if only Wi-Fi signals are detected, but an alarm is triggered if Wi-Fi + magnetic anomalies are detected). A wake-up signal simulating a base station command is transmitted to the environment to induce a dormant GPS device or eavesdropping device to briefly connect to the network, thereby being captured by the radio frequency signal scanning module.

[0035] The decision tree model has pre-defined rules: for example, if both a 2.4GHz Wi-Fi signal and a magnetic anomaly are detected simultaneously, it is considered a high-risk threat; if only a Bluetooth signal is detected, it is considered a low-risk threat. The wake-up signal simulates a base station paging protocol (such as GSM's PAGING REQUEST), ensuring compatibility with the communication protocols of mainstream GPS trackers and guaranteeing response from dormant devices. Upon activation, the RF signal scanning module immediately captures the device ID or location signal and updates the detection results.

[0036] In the above process, the embodiments of the present invention solve the problem that traditional technologies cannot detect the "quiet period" of dormant devices through data fusion and biomimetic activation, which greatly improves the detection rate of dormant GPS / eavesdropping devices, provides dynamic threat perception capabilities, and realizes the leap from passive alarm to active trapping.

[0037] Step 130: Based on the detection results, generate an anti-spy scheme through AR visualization guidance and adaptive shielding, and locate and eliminate interference with dangerous equipment according to the anti-spy scheme.

[0038] In one possible implementation, when the detection result indicates the presence of hazardous equipment, an AR interface matching virtual information and real scene is generated based on real-time collected environmental information and detection data to generate location guidance for the hazardous equipment; the location guidance uses markers and text descriptions, and provides elimination guidance based on the type and characteristics of the hazardous equipment according to the real-time collected environmental information and detection data.

[0039] AR visualization is used to mark the location of hazardous equipment in real time and provide removal guidance; adaptive shielding is used to interfere with the tracker. The removal guidance includes operating procedures, etc., which are not specified here.

[0040] In one possible implementation, the characteristic information in the multimodal environmental data is analyzed based on the detection results, and the type of dangerous equipment is identified by combining it with a preset equipment feature library. If the dangerous equipment is a wireless eavesdropping device, electromagnetic noise of a set frequency and intensity is emitted to interfere with the communication of the eavesdropping device. If the dangerous equipment is a GPS tracker, a fake base station signal is sent to interfere with the positioning function of the GPS tracker.

[0041] The electromagnetic noise setting frequency can be in the range of 1.8GHz-2.4GHz, and the intensity can be -70dBm, etc. It can be set according to the actual application scenario, and there is no limitation here.

[0042] Furthermore, the parameters of the shielding signal are dynamically adjusted based on environmental factors and equipment characteristics. The shielding effect is monitored in real time during the transmission of the shielding signal. The effectiveness of the shielding is determined by detecting changes in the data of hazardous equipment. If the shielding is ineffective, the parameters of the shielding signal are adjusted.

[0043] Specifically, by overlaying an AR interface onto a mobile app or device screen, the location of the threat can be marked in real time (e.g., "The suspicious device is located 5cm above the left of the socket"), and physical removal instructions can be provided (e.g., "Shield the area with a metal sheet").

[0044] The system employs adaptive shielding: against wireless eavesdropping devices, it emits electromagnetic noise to interfere with their communication; against GPS trackers, it sends fake base station signals to interfere with positioning. Shielding parameters (such as frequency and intensity) are dynamically adjusted, and the shielding effect is monitored in real time. The AR interface uses SLAM (Simultaneous Localization and Mapping) technology to align virtual markers with the real scene in real time, with an error of <2cm. The electromagnetic noise is customized for the communication frequency band of the eavesdropping device (e.g., 1.8GHz-2.4GHz) to avoid interfering with legitimate devices. The fake base station signal simulates the C / A code of GPS satellites, forcing the tracker to receive incorrect positioning data.

[0045] In the above process, the embodiments of the present invention reduce the user's operating threshold (no professional training required) through AR guidance and intelligent shielding, shorten the threat elimination time from 10 minutes to 1 minute, provide WYSIWYG intervention capability, and realize full automation of the process from detection to elimination.

[0046] Step 140: If the threat of the dangerous equipment has not been completely eliminated, continue to monitor the current environment; if the threat of the dangerous equipment has been completely eliminated, adjust the detection and analysis strategy based on the detection data.

[0047] In one possible implementation, when it is determined that the threat of hazardous equipment has not been completely eliminated, monitoring parameters are set according to the type, characteristics and detection data of the hazardous equipment; current environmental data is collected in real time according to the monitoring parameters, and the current environmental data is compared and analyzed with the detection data in real time to determine the operating status of the hazardous equipment. If the operating status meets the set conditions, an alarm is issued; if the hazardous equipment is found to be running again during the monitoring process, an alarm is issued, and the monitoring strategy is adjusted according to the anti-spy scheme and the current situation, and interference elimination measures are initiated.

[0048] In one possible implementation, once the threat of the dangerous device has been completely eliminated, all data from the detection process is comprehensively collected, categorized, and stored to create a detailed data archive. The decision tree model is then optimized based on the data archive, and the wake-up signal parameters in the dormant device activation mechanism are adjusted accordingly.

[0049] Among them, monitoring parameters include setting the monitoring time interval and frequency, adjusting detection and analysis strategies include optimizing the decision tree model, adjusting wake-up signal parameters, etc. Optimization includes adjusting rules and conditions, adding judgment nodes, modifying judgment nodes, adjusting weights, optimizing decision paths, etc. Parameter adjustment includes adjusting frequency range, intensity, transmission time, etc., none of which are limited here.

[0050] Specifically, if the threat is not eliminated, monitoring parameters are set according to the device type (e.g., a GPS tracker scans the signal every 5 seconds), and environmental data and detection records are compared in real time to trigger an alarm.

[0051] Optimize the decision tree model: adjust rule nodes (e.g., add "5GHz Wi-Fi + magnetic anomaly" as a high-risk condition); adjust wake-up signal parameters: modify frequency range or transmission time to improve activation rate.

[0052] Among these features, monitoring parameters are dynamically adjusted: for example, in vehicle scenarios, the chassis GPS may move, increasing the monitoring frequency to once every 2 seconds. Decision tree optimization is based on historical data: if a GPS tracker frequently disguises itself as a charging head, a new criterion of "charging head shape + magnetic anomaly" is added. Wake-up signal parameter optimization: the optimal transmission strength (e.g., -70dBm) is determined through A / B testing.

[0053] In the above process, the embodiments of the present invention solve the rigidity problem of "one detection, fixed strategy" in traditional technology through dynamic feedback and adaptive optimization, which greatly improves the system's adaptability, provides a continuously evolving defense system, and realizes the upgrade from static response to dynamic evolution.

[0054] Through the above process, this invention achieves full-scenario privacy protection through four core steps: multimodal data acquisition integrates radio frequency, magnetic field, infrared, and acoustic wave detection, breaking through the limitations of single technologies and improving the detection rate of concealed devices; intelligent analysis and activation employ data fusion algorithms and biomimetic wake-up signals to solve the problem of missed detection during the dormant period of dormant devices, thus improving the detection rate; AR guidance and adaptive shielding shorten threat elimination time and lower the user's operational threshold through a visual interface and dynamic interference technology; dynamic monitoring and strategy optimization continuously track threats and adaptively adjust the decision model to achieve continuous evolution of the defense system. This solution covers complex scenarios such as hotels and vehicles, and combines high precision, ease of use, and adaptability, effectively solving the pain points of traditional technologies such as high missed detection rate, high false alarm rate, and difficulty in detecting dormant devices, and has significant industrial application value.

[0055] In one application scenario, the multimodal anti-spy detection method of the present invention is used to set up a multimodal anti-spy detection system for hotel room privacy and security detection. The system includes an active infrared detection module and a radio frequency signal scanning module.

[0056] In this application, the radio frequency signal scanning module refers to a signal scanning unit covering the entire frequency band from 20MHz to 6GHz (including 4G / 5G / Wi-Fi / Bluetooth, etc.). This module continuously monitors the characteristics of wireless signals in the environment (such as signal strength, frequency, and transmission mode) and compares them with a preset signal fingerprint database to identify suspicious transmissions (such as intermittent burst signals), thereby marking potentially dangerous devices.

[0057] The active infrared detection module is the optical detection unit in the multimodal perception layer. It identifies hidden cameras by emitting infrared light of a specific wavelength (e.g., 940nm) and utilizing the lens reflection principle. This module is equipped with filters to suppress ambient light interference, ensuring accurate detection of reflective points even under complex lighting conditions. Its output data (such as reflectivity and location coordinates) is fused and analyzed with radio frequency and magnetic sensing data to form the basis for threat assessment.

[0058] The radio frequency signal scanning module continuously monitors wireless signals in the environment and marks potential wireless eavesdropping or tracking devices; the active infrared detection module uses optical means to locate hidden cameras and supplement the blind spots of radio frequency detection; the data from all modules are weighted and analyzed by a multi-source fusion algorithm, and the final detection results are output by a decision tree model to guide subsequent intervention measures.

[0059] Using this system to conduct privacy and security checks in hotel rooms can specifically include the following steps: Step S1: Environmental pre-scanning and multimodal data acquisition.

[0060] Specifically, the user places the portable detection device on the hotel table. After activation, the radio frequency signal scanning module covers the 20MHz-6GHz frequency band and detects an abnormal 2.4GHz Wi-Fi signal and intermittent burst transmission characteristics in the socket area. The active infrared detection module emits 940nm wavelength infrared light and identifies a reflective point 5cm above the left of the socket through lens reflection recognition. After the filter suppresses ambient light, it is confirmed that the reflection intensity exceeds the threshold.

[0061] In the above process, the embodiments of the present invention use radio frequency-infrared timing correlation analysis to locate the hidden camera inside the socket, breaking through the limitations of the accuracy of traditional single-mode detection.

[0062] Step S2: Activate and re-verify the hibernation device.

[0063] Specifically, the intelligent analysis center determines that the socket device may be in a dormant state, activates the bionic base station wake-up mechanism, transmits a 4G paging signal to the environment, and the dormant GPS tracker briefly connects to the network after responding to the signal. The radio frequency signal scanning module captures its IMEI code and compares it with the signal fingerprint database to confirm the device type and threat level.

[0064] In the above process, the embodiments of the present invention solve the problem of missed detection of devices during the silent period by activating the simulated communication protocol, thereby improving the detection success rate.

[0065] Step S3: AR-guided intervention and physical exclusion.

[0066] Specifically, the guidance and intervention layer generates an AR overlay interface through a mobile app, marking the threat location in real time as "a suspicious camera exists 5cm above the left of the socket," and provides step-by-step operation instructions. Users can use metal tape to cover the lens according to the instructions, and the adaptive shielding technology simultaneously emits 2.4GHz electromagnetic noise to block the camera's wireless transmission.

[0067] In the above process, the embodiments of the present invention shorten the threat handling time through a "detection-location-elimination" closed-loop system, and require no professional operational knowledge.

[0068] This embodiment uses a hotel scenario as an example to verify the effectiveness of the integrated detection system based on multimodal perception.

[0069] Through the above process, this invention quickly locates the position of a disguised camera inside a socket using radio frequency scanning and infrared reflection analysis, overcoming the bottleneck of traditional equipment in detecting miniaturized concealed devices. Subsequently, it utilizes biomimetic base station wake-up technology to activate a dormant GPS tracker, solving the problem of missed detection during the silent period. Finally, it guides the user through an AR interface to complete physical shielding, combined with adaptive electromagnetic interference technology to block wireless transmission. The entire process achieves full automation of "collection-analysis-intervention," and the device, measuring only 9cm × 5cm and supporting USB-C power supply, perfectly adapts to mobile scenarios. Compared to traditional single-mode detectors, this solution significantly reduces the user's operational threshold through multi-mode collaboration and intelligent closed-loop design, while simultaneously addressing core pain points such as interference in complex environments and the concealment of dormant devices, demonstrating high practicality and industrial promotion value.

[0070] The following are embodiments of the device of the present invention, which can be used to execute the multimodal anti-spy detection method involved in the present invention. For details not disclosed in the embodiments of the device of the present invention, please refer to the method embodiments of the multimodal anti-spy detection method involved in the present invention.

[0071] Please see Figure 2 This invention provides a multimodal anti-spy detection device 800.

[0072] The multimodal anti-spy detection device 800 includes, but is not limited to: a multimodal data acquisition module 810, a data fusion and device wake-up module 830, an intelligent guidance and shielding module 850, and a dynamic monitoring and optimization module 870.

[0073] The multimodal data acquisition module 810 is used to acquire multimodal environmental data through radio frequency signal scanning, magnetic field sensing by magnetic induction array, active infrared detection and acoustic wave detection; the multimodal environmental data includes wireless signal characteristics, magnetic field data, infrared detection data and acoustic wave detection data.

[0074] The data fusion and device wake-up module 830 is used to perform fusion analysis and wake-up processing on multimodal environmental data through multi-source data fusion algorithms and decision tree models, combined with a dormant device activation mechanism, to obtain detection results; the dormant device activation mechanism is used to wake up dormant dangerous devices.

[0075] The intelligent guidance and shielding module 850 is used to generate an anti-spy scheme based on the detection results through AR visualization guidance and adaptive shielding. The anti-spy scheme is used to locate and eliminate interference with dangerous equipment. AR visualization is used to mark the location of dangerous equipment in real time and provide elimination guidance. Adaptive shielding is used to interfere with the tracker.

[0076] The dynamic monitoring and optimization module 870 is used to continuously monitor the current environment if the threat of dangerous equipment has not been completely eliminated; if the threat of dangerous equipment has been completely eliminated, the detection and analysis strategy is adjusted based on the detection data. The adjustment of the detection and analysis strategy includes optimizing the decision tree model and adjusting the wake-up signal parameters.

[0077] It should be noted that the multimodal anti-spy detection provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed. That is, the internal structure of the multimodal anti-spy detection device will be divided into different functional modules to complete all or part of the functions described above.

[0078] Furthermore, the multimodal anti-spy detection device and the multimodal anti-spy detection method provided in the above embodiments belong to the same concept. The specific way in which each module performs its operation has been described in detail in the method embodiments, and will not be repeated here.

[0079] Figure 3 A schematic diagram of the structure of an electronic device according to an exemplary embodiment is shown.

[0080] It should be noted that this electronic device is merely an example adapted to the present invention and should not be construed as providing any limitation on the scope of use of the present invention. Furthermore, this electronic device should not be interpreted as requiring or depending on having... Figure 3 One or more components of the exemplary electronic device 2000 shown.

[0081] The hardware structure of electronic devices 2000 can vary significantly due to differences in configuration or performance, such as... Figure 3 As shown, the electronic device 2000 includes: a power supply 210, an interface 230, at least one memory 250, and at least one central processing unit (CPU) 270.

[0082] Specifically, power supply 210 is used to provide operating voltage for various hardware devices on electronic device 2000.

[0083] Interface 230 includes at least one wired or wireless network interface 231 for interacting with external devices. Of course, in other examples adapted to this invention, interface 230 may further include at least one serial-to-parallel conversion interface 233, at least one input / output interface 235, and at least one USB interface 237, etc. Figure 3 As shown, this does not constitute a specific limitation.

[0084] The memory 250 serves as a carrier for resource storage and can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored on it include the operating system 251, application programs 253, and data 255, etc., and the storage method can be temporary storage or permanent storage.

[0085] The operating system 251 is used to manage and control the various hardware devices and application programs 253 on the electronic device 2000, so as to enable the central processing unit 270 to perform calculations and processing on the massive data 255 in the memory 250. It can be Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0086] Application 253 is a computer-readable instruction that performs at least one specific task on top of operating system 251, and each module may contain computer-readable instructions for electronic device 2000. For example, a multimodal privacy detection device can be considered as application 253 deployed on electronic device 2000.

[0087] Data 255 may be signal information, etc., and is stored in memory 250.

[0088] The central processing unit 270 may include one or more processors and is configured to communicate with the memory 250 via at least one communication bus to read computer-readable instructions stored in the memory 250, thereby performing operations and processing on massive amounts of data 255 stored in the memory 250. For example, a multimodal anti-spy detection method may be implemented by the central processing unit 270 reading a series of computer-readable instructions stored in the memory 250.

[0089] Furthermore, the present invention can also be implemented through hardware circuits or a combination of hardware circuits and software. Therefore, the implementation of the present invention is not limited to any specific hardware circuit, software, or combination thereof.

[0090] Please see Figure 4 This invention provides an electronic device 4000, which may include: a desktop computer, a laptop computer, a server, etc., with sensor recognition capabilities.

[0091] exist Figure 4 In this context, the electronic device 4000 includes at least one processor 4001 and at least one memory 4003.

[0092] The data interaction between the processor 4001 and the memory 4003 can be achieved through at least one communication bus 4002. This communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0093] Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0094] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0095] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program instructions or code in the form of instructions or data structures and accessible by the electronic device 4000, but not limited thereto.

[0096] The memory 4003 stores computer-readable instructions, and the processor 4001 can read the computer-readable instructions stored in the memory 4003 through the communication bus 4002.

[0097] The computer-readable instructions are executed by one or more processors 4001 to implement the multimodal anti-spy detection methods in the above embodiments.

[0098] Furthermore, this embodiment of the invention provides a storage medium storing computer-readable instructions, which are executed by one or more processors to implement the multimodal anti-spy detection method described above.

[0099] This invention provides a computer program product, which includes computer-readable instructions stored in a storage medium. One or more processors of an electronic device read the computer-readable instructions from the storage medium, load and execute the computer-readable instructions, thereby enabling the electronic device to implement the multimodal anti-spy detection method as described above.

[0100] Compared with related technologies, the beneficial effects of the present invention are: 1. This invention can significantly improve the detection accuracy of concealed devices; through a multimodal collaborative detection mechanism, it integrates the time-series correlation analysis of radio frequency, magnetic induction, infrared and sound waves, which greatly improves the detection rate of traditional single-modal devices, especially achieving efficient identification of miniature cameras disguised as charging heads and smoke alarms and dormant GPS trackers.

[0101] 2. This invention has the ability to actively activate dormant devices; by using a biomimetic communication protocol (such as simulating a base station to send a paging signal), it induces GPS or eavesdropping devices to briefly connect to the network during the silent period, solving the problem that traditional radio frequency detectors cannot detect devices during "non-working hours", and greatly improving the success rate of dormant device detection.

[0102] 3. This invention can lower the user's operating threshold; through AR visualization guidance technology, threat location markings (such as "suspicious device is located 5cm above the left of the socket") and physical elimination instructions (such as "shield the area with a metal sheet") are superimposed on the mobile APP or device screen, transforming professional operations into a simple interactive process, avoiding the problem of traditional devices "only alarming but not solving".

[0103] 4. This invention has strong environmental adaptability; through multi-source data fusion algorithm and decision tree model, it performs weighted analysis of radio frequency, magnetic induction and infrared data, effectively distinguishes legitimate wireless signals (such as Wi-Fi and Bluetooth) from threat signals, and reduces the false alarm rate in complex scenarios.

[0104] 5. This invention enables lightweight embedded deployment; through chip-level signal processing algorithms (such as compressed sensing technology), multimodal sensors are integrated into portable devices with a size of <10cm×6cm, supporting USB-C power supply, and meeting the real-time detection needs of mobile scenarios such as vehicles and hotels.

[0105] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0106] The above description is only a partial embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multimodal anti-spy detection method, characterized in that, The method includes: Multimodal environmental data is acquired through radio frequency signal scanning, magnetic field sensing using a magnetic induction array, active infrared detection, and acoustic wave detection; the multimodal environmental data includes wireless signal characteristics, magnetic field data, infrared detection data, and acoustic wave detection data. The multi-source data fusion algorithm and decision tree model, combined with the dormant device activation mechanism, are used to perform fusion analysis and wake-up processing on the multimodal environmental data to obtain detection results; the dormant device activation mechanism is used to wake up dormant dangerous equipment. Based on the detection results, an anti-spyware scheme is generated using AR visualization guidance and adaptive shielding. The anti-spyware scheme is used to locate and eliminate interference with dangerous devices. The AR visualization is used to mark the location of the dangerous devices in real time and provide elimination guidance. The adaptive shielding is used to interfere with the tracker. If the threat of dangerous equipment is not completely eliminated, the current environment will be continuously monitored; if the threat of dangerous equipment has been completely eliminated, the detection and analysis strategy will be adjusted based on the detection data. The adjustment of the detection and analysis strategy includes optimizing the decision tree model and adjusting the wake-up signal parameters.

2. The multimodal anti-spy detection method as described in claim 1, characterized in that, The acquisition of multimodal environmental data through radio frequency signal scanning, magnetic field sensing by a magnetic induction array, active infrared detection, and acoustic wave detection includes: All wireless signal characteristics are continuously monitored and recorded by scanning radio frequency signals. Suspicious signals are identified by comparing the wireless signal characteristics with a preset signal fingerprint database. The wireless signal characteristics include signal strength, frequency, and transmission mode. The magnetic field data of the surrounding environment is obtained by sensing the magnetic field through a high-sensitivity Hall sensor, and the abnormal magnetic source is obtained by analyzing the magnetic field data through a gradient measurement algorithm. Infrared detection data is obtained by emitting infrared light of a set wavelength to reflect light off a suspicious lens and using a filter to suppress ambient light interference. Ultrasonic waves of a set frequency are emitted to generate an echo signal to the eavesdropping device. The echo signal is then analyzed to obtain acoustic detection data. The set wavelength includes 940nm.

3. The multimodal anti-spy detection method as described in claim 1, characterized in that, The detection results are obtained by fusing and analyzing the multimodal environmental data through a multi-source data fusion algorithm and decision tree model, combined with a dormant device activation mechanism, to achieve the following: The multimodal environmental data is weighted and fused according to the reliability and importance of the data source to obtain a dataset. The dataset is then analyzed using a decision tree model according to preset rules and conditions to obtain detection results. For dangerous devices in hibernation mode, the hibernation device activation mechanism is activated to send a wake-up signal to the surrounding environment. Upon receiving the wake-up signal, the hibernating dangerous device generates a danger signal, and the detection result is updated based on the danger signal.

4. The multimodal anti-spy detection method as described in claim 1, characterized in that, The process of generating an anti-spying scheme based on the detection results using AR visualization guidance and adaptive shielding, and locating and eliminating interference with dangerous devices based on the anti-spying scheme, includes: When the detection result indicates the presence of dangerous equipment, an AR interface matching virtual information and real scene is generated based on real-time collected environmental information and detection data to generate location guidance for the dangerous equipment; The location guidance uses markers and text descriptions, and provides elimination guidance based on the type and characteristics of hazardous equipment according to real-time collected environmental information and detection data; the elimination guidance includes operating methods.

5. The multimodal anti-spy detection method as described in claim 1, characterized in that, The process of generating an anti-spying scheme based on the detection results using AR visualization guidance and adaptive shielding, and locating and eliminating interference with dangerous devices based on the anti-spying scheme, includes: Based on the detection results, the feature information in the multimodal environmental data is analyzed, and the type of hazardous equipment is identified by combining it with a preset equipment feature library; If the dangerous device is a wireless eavesdropping device, then electromagnetic noise of a set frequency and intensity is emitted to interfere with the communication of the eavesdropping device; if the dangerous device is a GPS tracker, then fake base station signals are sent to interfere with the positioning function of the GPS tracker.

6. The multimodal anti-spy detection method as described in claim 1, characterized in that, If the threat from hazardous equipment is not completely eliminated, the current environment will be continuously monitored, including: When it is determined that the threat from hazardous equipment has not been completely eliminated, monitoring parameters are set according to the type, characteristics, and detection data of the hazardous equipment; the monitoring parameters include the monitoring time interval and frequency; The current environmental data is collected in real time according to the monitoring parameters. The current environmental data is compared and analyzed in real time with the detection data to determine the operating status of the dangerous equipment. If the operating status reaches the set conditions, an alarm is issued. If dangerous equipment is detected to be running again during monitoring, an alarm will be issued, and the monitoring strategy will be adjusted according to the anti-spy scheme and the current situation, and interference elimination measures will be initiated.

7. The multimodal anti-spy detection method as described in claim 1, characterized in that, If the threat from the dangerous equipment has been completely eliminated, the detection and analysis strategy will be adjusted based on the detection data, including: Once the threat of dangerous equipment has been completely eliminated, all data from the detection process will be collected comprehensively, categorized and stored, and a detailed data archive will be established. The decision tree model is optimized based on the data archive; the optimization includes adjusting rules and conditions, adding judgment nodes, modifying judgment nodes, adjusting weights, and optimizing the decision path; The wake-up signal parameters in the hibernation device activation mechanism are adjusted according to the data file; the parameter adjustment includes adjusting the frequency range, intensity, and transmission time.

8. A multimodal anti-spy detection device, characterized in that, The device includes: The multimodal data acquisition module is used to acquire multimodal environmental data through radio frequency signal scanning, magnetic field sensing by a magnetic induction array, active infrared detection, and acoustic wave detection; the multimodal environmental data includes wireless signal characteristics, magnetic field data, infrared detection data, and acoustic wave detection data; The data fusion and device wake-up module is used to perform fusion analysis and wake-up processing on the multimodal environmental data through multi-source data fusion algorithms and decision tree models, combined with a dormant device activation mechanism, to obtain detection results; the dormant device activation mechanism is used to wake up dormant dangerous devices. The intelligent guidance and shielding module is used to generate an anti-spy scheme based on the detection results through AR visualization guidance and adaptive shielding, and to locate and eliminate interference with dangerous devices according to the anti-spy scheme; the AR visualization is used to mark the location of the dangerous devices in real time and provide elimination guidance; the adaptive shielding is used to interfere with the tracker. The dynamic monitoring and optimization module is used to continuously monitor the current environment if the threat of dangerous equipment has not been completely eliminated; if the threat of dangerous equipment has been completely eliminated, the detection and analysis strategy is adjusted based on the detection data. The adjustment of the detection and analysis strategy includes optimizing the decision tree model and adjusting the wake-up signal parameters.

9. An electronic device, characterized in that, include: At least one processor and at least one memory, wherein, The memory stores computer-readable instructions; The computer-readable instructions are executed by one or more of the processors, causing the electronic device to implement the multimodal anti-spy detection method as described in any one of claims 1 to 7.

10. A storage medium having computer-readable instructions stored thereon, characterized in that, The computer-readable instructions are executed by one or more processors to implement the multimodal anti-spy detection method as described in any one of claims 1 to 7.

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