Method, device and equipment for detecting peripheral cameras by smart television and storage medium

Abstract

The invention discloses a peripheral camera detection method, device and equipment for a smart television and a storage medium, and relates to the technical field of intelligent detection, and the method comprises the steps: receiving a detection instruction, and calling a signal scanning assembly in the smart television according to the detection instruction; performing signal data acquisition on the surrounding environment of the smart television through a signal scanning assembly to obtain to-be-detected data of the surrounding environment; and analyzing and evaluating the to-be-detected data, and determining a camera detection result of the surrounding environment. According to the invention, the signal scanning assembly is integrated in the smart television, so that data acquisition, analysis and evaluation are carried out on the surrounding environment where the smart television is located, the camera detection result of the surrounding environment is obtained, and the effect of accurately detecting the hidden camera through the smart television configured in a room is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent detection, and particularly to a method, device, equipment and storage medium for detecting surrounding cameras of an intelligent TV. Background Art

[0002] Miniature cameras, as the crystallization of modern technology, are famous for their small size and powerful functions. Such cameras are designed to be concealed and are widely used in many fields such as aviation, commerce, media, enterprises and households. However, the popularization of this technology has also brought some negative impacts, such as the risk of leakage of corporate secrets and personal privacy. And the pinhole camera, as a special type of miniature camera, gets its name because it uses a fish-eye lens, a planar lens or a conical lens, and it has stronger concealment and is more likely to be used for illegal monitoring. Some pinhole cameras can be concealedly installed in daily items such as routers, electronic clocks, and smoke alarms. Lawbreakers modify these devices to cleverly hide the cameras, making it difficult for non-professionals to detect them. Hotels and guesthouses have become the hardest hit areas for such concealed cameras, which not only makes the guests extremely uneasy but also seriously threatens the security of personal privacy.

[0003] Currently, most of the detection methods for concealed cameras are to scan the room with a handheld mobile device, and the accuracy of the detection results is directly related to the professionalism of the mobile device. Users often do not purchase and carry high-cost professional detection equipment due to the need to detect the room, resulting in difficulty in simultaneously meeting the user's privacy protection needs and cost requirements.

[0004] Therefore, how to accurately detect concealed cameras at low cost is an urgent problem to be solved currently. Summary of the Invention

[0005] The main purpose of the present application is to provide a method, device, equipment and storage medium for detecting surrounding cameras of an intelligent TV, aiming to solve the technical problem of how to accurately detect concealed cameras at low cost.

[0006] To achieve the above object, the present application proposes a method for detecting surrounding cameras of an intelligent TV, and the method includes:

[0007] Receiving a detection instruction and invoking a signal scanning component in the intelligent TV according to the detection instruction;

[0008] Collecting signal data of the surrounding environment of the intelligent TV through the signal scanning component to obtain the data to be detected of the surrounding environment;

[0009] Analyzing and evaluating the data to be detected to determine the camera detection result of the surrounding environment.

[0010] In addition, to achieve the above object, the present application further provides an intelligent TV peripheral camera detection device, and the intelligent TV peripheral camera detection device includes:

[0011] A start module, configured to receive a detection instruction and call a signal scanning component in the intelligent TV according to the detection instruction;

[0012] An acquisition module, configured to collect signal data of the peripheral environment of the intelligent TV through the signal scanning component to obtain data to be detected of the peripheral environment;

[0013] A detection module, configured to analyze and evaluate the data to be detected to determine a camera detection result of the peripheral environment.

[0014] In addition, to achieve the above object, the present application further provides an electronic device, and the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the intelligent TV peripheral camera detection method as described above.

[0015] In addition, to achieve the above object, the present application further provides a storage medium, and the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the intelligent TV peripheral camera detection method as described above are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the intelligent TV peripheral camera detection method of the present application;

[0019] Figure 2 It is a schematic flowchart provided for Embodiment 2 of the intelligent TV peripheral camera detection method of the present application;

[0020] Figure 3 It is a schematic flowchart provided for Embodiment 3 of the intelligent TV peripheral camera detection method of the present application;

[0021] Figure 4Schematic flowchart of the method for detecting peripheral cameras of the smart TV provided in Embodiment 3 of this application;

[0022] Figure 5 Schematic diagram of the module structure of the device for detecting peripheral cameras of the smart TV in the embodiment of this application;

[0023] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the method for detecting peripheral cameras of the smart TV in the embodiment of this application.

[0024] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0025] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0026] In order to better understand the technical solutions of this application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0027] The main solution of the embodiment of this application is: receive a detection instruction, and call the signal scanning component in the smart TV according to the detection instruction; collect signal data of the surrounding environment of the smart TV through the signal scanning component to obtain the data to be detected of the surrounding environment; analyze and evaluate the data to be detected to determine the camera detection result of the surrounding environment.

[0028] Since most of the current detection methods for hidden cameras are to scan and detect the room through a handheld mobile device, and the accuracy of the detection result is directly related to the professionalism of the mobile device. Users often do not purchase and carry high-cost professional detection equipment due to the detection needs of the room, resulting in it being difficult to meet both the user's privacy protection needs and the cost requirements at the same time. Therefore, how to accurately detect hidden cameras at low cost is an urgent problem to be solved currently.

[0029] This application provides a solution. By integrating a signal scanning component in the smart TV, data collection and analysis and evaluation of the surrounding environment where the smart TV is located are carried out to obtain the camera detection result of the surrounding environment, achieving the effect of accurately detecting hidden cameras through the smart TV already configured in the room.

[0030] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a smart TV, etc. that can implement the above functions. The following takes the smart TV as an example to illustrate this embodiment and the following embodiments.

[0031] Based on this, an embodiment of the present application provides a method for an intelligent TV to detect surrounding cameras. Referring to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the method for an intelligent TV to detect surrounding cameras in the present application.

[0032] In this embodiment, the method for an intelligent TV to detect surrounding cameras includes steps S10 to S30:

[0033] Step S10: Receive a detection instruction and call a signal scanning component in the intelligent TV according to the detection instruction;

[0034] It should be noted that the detection instruction is a command to start the camera detection triggered by a user or a system, such as an instruction sent through the remote control, voice assistant, or mobile application of the intelligent TV; the signal scanning component can be one or more of signal scanning components such as an infrared camera and a wireless signal scanning component.

[0035] It can be understood that since the current detection methods for hidden cameras often rely on professional detection equipment, it is difficult to simultaneously meet the user's privacy protection requirements and application cost requirements. Therefore, step S10 is performed. By integrating a signal scanning component in the intelligent TV, the problem that the user needs to additionally use professional equipment to detect hidden cameras is avoided, and the effect of detecting hidden cameras through the intelligent TV already configured in the room is achieved.

[0036] In addition, it can be understood that in the case where the user has no need to detect surrounding cameras, if the signal scanning component integrated in the intelligent TV is started, it will not only cause meaningless consumption of resources but also cause a certain degree of infringement of the user's privacy. Therefore, step S10 is performed to avoid the signal scanning component integrated in the intelligent TV being called at an inappropriate time, so that the existing hardware of the intelligent TV can directly respond to the detection requirement at an appropriate time, achieving a low-cost and convenient detection method while reducing the threshold of the user's detection operation.

[0037] Exemplarily, the user triggers the "camera detection" function through the remote control of the smart TV, or the supporting mobile phone App, or the voice assistant, generates a detection instruction and sends it to the smart TV system. After receiving the instruction, the system calls the pre-integrated infrared camera (such as a CMOS sensor supporting the near-infrared band) and the wireless signal scanning component (such as a chipset supporting Wi-Fi and Bluetooth spectrum analysis) through the internal API interface, and starts their driver programs. The infrared camera enters the infrared light shooting mode, and the wireless signal scanning component starts full-band scanning (including 2.4GHz / 5GHz Wi-Fi, Bluetooth, and RF signals, etc.), ensuring that the hardware device is initialized and enters the state to be collected. This step directly utilizes the existing hardware resources of the smart TV through the linkage of software instructions and hardware drivers, without the need for external devices, reducing the complexity of user operations.

[0038] Step S20: Collect signal data of the surrounding environment of the smart TV through the signal scanning component to obtain the data to be detected in the surrounding environment;

[0039] It should be noted that the data to be detected refers to the light band data collected by the infrared camera and the source data collected by the wireless signal scanning component, and these data are used to analyze and evaluate whether there are hidden cameras in the surrounding environment.

[0040] Exemplarily, since the lens of a micro camera usually has significant reflection characteristics for infrared light, the infrared camera scans the room from multiple angles through the built-in pan-tilt or wide-angle lens (for example, rotating and shooting horizontally from 0° to 180°), captures the possible reflection of the camera lens in the environment, and generates multiple frames of infrared light band image data (including features such as brightness and reflection point coordinates). At the same time, the surrounding wireless signals can also be scanned in real time through the wireless signal scanning component, record the MAC addresses, signal strengths (RSSI), protocol types (such as Wi-Fi Direct and Bluetooth Low Energy), and packet characteristics (such as periodic broadcast packets) of all active devices, and integrate this information into a source data table. The data collection process lasts for a preset collection duration to ensure coverage of all potential hidden areas and wireless signal bands in the room, and finally generates a data set to be detected containing light band images and wireless signal logs.

[0041] Step S30: Analyze and evaluate the data to be detected to determine the camera detection result of the surrounding environment.

[0042] It should be noted that the camera detection result is a conclusion obtained through comprehensive analysis, and this conclusion can include information such as whether there is a camera, the location information of the camera, the device type, the signal strength, and the confidence score, etc.

[0043] It can be understood that since traditional detection methods rely on manual judgment or a single detection index and are prone to misjudgment due to subjectivity or one-sided data, step S30 is performed to classify and evaluate multi-source data to achieve data-driven automated analysis, improve the objectivity and confidence of detection results, and thus help users quickly locate threats and take protective measures.

[0044] Exemplarily, the detection software built into the smart TV processes and analyzes the data to be detected received. First, the software uses a preset algorithm to filter out known safe light sources and wireless signals, such as the infrared remote control signal of the TV itself and authorized Wi-Fi devices. Then, the software deeply analyzes the remaining unknown light sources and wireless signals to evaluate whether they match the signal characteristics emitted by a known reference camera. If data matching the camera characteristics is detected, the software marks this data and generates a report, finally determining whether there is a camera in the surrounding environment of the smart TV and ultimately generating a structured detection report including confidence level, location, and device type.

[0045] In a feasible implementation manner, the signal scanning component includes an infrared camera and a wireless signal scanning component, and the data to be detected includes optical band data collected by the infrared camera and source data collected by the wireless signal scanning component;

[0046] Step S30 may include steps S31 to S33:

[0047] Step S31, transmitting the optical band data to an infrared light recognition model, and analyzing and evaluating the optical band data through the infrared light recognition model to obtain a first evaluation result;

[0048] It should be noted that the optical band data is environmental infrared light image or video data collected by the infrared camera, usually containing infrared reflection information of specific wavelengths (such as near-infrared: about 750 nm to 1400 nm (400 THz to 214 THz), mid-infrared: about 1400 nm to 3000 nm (214 THz to 100 THz), far-infrared: about 3000 nm to 1 mm (100 THz to 300 GHz)) for capturing the reflective characteristics of the micro-camera lens; the infrared light recognition model is a preset algorithm model based on machine learning or rule engine, and traditional machine learning models such as support vector machines and random forests can be selected, or neural networks can be used to analyze the infrared optical band data and identify the reflective characteristics of the camera lens; the first evaluation result is the analysis output of the infrared light recognition model for the optical band data, usually including whether there are camera reflective characteristics, position coordinates, and camera suspicion degree (i.e., confidence score), etc.

[0049] It can be understood that, in order to specifically and accurately identify the infrared light source, step S31 is performed. By introducing a large model for infrared identification, it is possible to avoid being unable to distinguish the normal infrared light source in the environment from the infrared light emitted by the camera, thereby achieving the effect of accurately identifying potential camera infrared signals.

[0050] In a feasible implementation manner, the step of analyzing and evaluating the optical band data through the infrared light identification model in step S31 to obtain the first evaluation result may include steps S311 to S314:

[0051] Step S311, extracting features from the optical band data through the infrared light identification model to obtain the optical band features to be identified;

[0052] It should be noted that the optical band features to be identified refer to the representative feature information extracted from the optical band data. These features can reflect the specific attributes of the infrared light source, such as intensity, frequency, waveform, etc. These attributes are the key indicators for distinguishing different types of light sources.

[0053] It can be understood that since it is necessary to extract information that can represent the camera features from complex optical band data, step S311 is performed. This can avoid misjudgment problems caused by noise, interference, or complex backgrounds when directly analyzing the original infrared image. By extracting key optical features, the accuracy and robustness of the detection are improved.

[0054] Exemplarily, the infrared light identification model first receives the optical band data collected from the infrared camera. Then, the model processes the data through a series of algorithms, such as principal component analysis or convolutional neural network, to extract key feature parameters, such as peak wavelength, spectral intensity, spectral shape, etc. These feature parameters are marked as the optical band features to be identified and stored in the memory for subsequent classification analysis.

[0055] Step S312, classifying the optical band features to be identified to obtain the optical band category corresponding to the optical band features to be identified;

[0056] It should be noted that the optical band category is the result of classification based on the optical band features to be identified. It usually includes categories such as "camera lens reflection", "non-camera reflection" (such as glass, metal), and "unknown reflection", etc. This classification process is completed through a pre-trained infrared light identification model, and the category label and confidence score (camera suspicion degree) corresponding to each feature are output.

[0057] It can be understood that since it is necessary to compare the extracted features with known light source categories to determine whether it is the light band emitted by the camera, performing step S312 can avoid the problem of blurred or unclear detection results caused by the lack of classification logic after feature extraction, thereby generating a clear classification result, providing the user with clear suspicious area information, and facilitating further investigation and decision-making.

[0058] Exemplarily, the extracted light band features to be recognized are passed through a pre-trained classifier (i.e., an infrared light recognition model). The model calculates the probabilities of belonging to different categories based on the feature vectors of the features (such as "camera lens reflection", "non-camera reflection", "unknown reflection"). For example, when the model detects that the reflection intensity distribution of a certain feature vector matches the height of the camera lens and the shape is a regular circle, it outputs the category of "camera lens reflection" with a confidence level of 95%.

[0059] Step S313: Map the light band category to the corresponding target light sources, and obtain the first camera suspicion degree of each target light source based on the infrared light recognition model;

[0060] It should be noted that the target light source refers to the light source that may represent a camera or other device identified by analyzing the light band data; the first camera suspicion degree is a quantitative indicator, which represents the degree of certainty or credibility of the recognition model regarding whether the identified target light source is truly a camera.

[0061] Step S314: Correlate and combine each target light source and each first camera suspicion degree, and use the combination result as the first evaluation result.

[0062] In this embodiment, by using the infrared light recognition model to extract and classify the features of the light band data, the problems of noise interference and unclear features that may occur when directly analyzing the original light band data are avoided, and the effect of accurately identifying and classifying the camera features from complex light source information is achieved. This method can improve the sensitivity and specificity of the detection system, ensure the accuracy and reliability of camera detection, and thus effectively protect the privacy and security of users.

[0063] Step S32: Transmit the source data to the wireless source recognition model, and analyze and evaluate the source data through the wireless source recognition model to obtain the second evaluation result;

[0064] It should be noted that the source data refers to the wireless signal information collected by the wireless signal scanning component, including the signal source type (such as Wi-Fi, Bluetooth, etc.), signal strength (measured in dBm, indicating the strength of the signal), operating frequency of the signal (such as 2.4 GHz, 5 GHz), etc.; the wireless source identification model is a specially designed algorithm or model, which can select traditional machine learning models such as support vector machines and random forests, or use neural networks to analyze wireless signal data to identify whether there are wireless communication characteristics related to the camera; the second evaluation result refers to the analysis result output by the wireless source identification model, including whether there are camera-related wireless signals, device types, and camera suspicion levels, etc.

[0065] In a feasible implementation manner, the step of analyzing and evaluating the source data through the wireless source identification model in step S32 to obtain the second evaluation result may include steps S321 to S324:

[0066] Step S321, extracting features from the source data through the wireless source identification model to obtain the source features to be identified;

[0067] It should be noted that the source features to be identified refer to the discriminative feature information extracted from the wireless signal data, and these features can reflect the technical attributes of the wireless signal source, such as signal strength change patterns, data packet size and frequency, transmission intervals, signal strength, frequency, modulation methods, transmission protocols, etc. These features are the key factors for identifying different types of wireless devices.

[0068] It can be understood that since it is necessary to extract information that can represent the characteristics of the wireless camera from complex wireless signal data, performing step S321 can avoid the problem of ineffective identification of specific devices due to signal complexity and diversity, and realizes screening out the feature information related to the camera from a large amount of signal data, thus providing an effective and reliable data basis for subsequent classification and analysis.

[0069] Exemplarily, the wireless source identification model processes the data through a series of algorithms (such as fast Fourier transform, wavelet transform, or deep learning network) to extract feature vectors that can represent the characteristics of the signal source, such as signal strength change patterns, data packet size and frequency, transmission intervals, etc. These feature vectors are the source features to be identified.

[0070] Step S322, classifying the source features to be identified to obtain the source category corresponding to the source features to be identified;

[0071] It should be noted that the source category refers to different categories into which signal sources are divided according to wireless signal characteristics. For example, Wi-Fi devices, Bluetooth devices, mobile phone signals, camera signals, etc. Each category has its specific signal characteristics and communication behaviors.

[0072] It can be understood that since the extracted features need to be compared with known wireless signal source categories to determine whether the signal is emitted by a wireless camera, performing step S322 can avoid the problem of being unable to distinguish a wireless camera from other wireless devices and achieve accurate identification of the wireless camera.

[0073] Exemplarily, based on the matching degree between the extracted source features to be identified and predefined source categories (such as ordinary Wi-Fi devices, Bluetooth devices, wireless cameras, etc.) by a wireless source identification model, each feature vector is assigned to the corresponding source category.

[0074] Step S323: Map the source category to the corresponding target sources, and obtain the second camera suspicion degree of each of the target sources based on the wireless source identification model;

[0075] It should be noted that the target source refers to a wireless communication device that may be used to transmit image or video data identified by analyzing wireless signals. These devices may be part of a camera and are used to send the captured image information; the second camera suspicion degree is a quantitative index given for each identified target source according to the matching degree between its signal characteristics and known camera signal characteristics, as well as other attributes of the signal (such as signal stability, transmission rate, transmission mode, etc.). This score reflects the trust level of the smart TV detection system as to whether the identified target source is truly part of a camera and whether the camera is transmitting data. The higher the second camera suspicion degree, the more the system believes that the target source is a working camera.

[0076] Step S324: Correlate and combine each of the target sources and each of the second camera suspicion degrees, and use the combination result as the second evaluation result.

[0077] In this embodiment, by using a wireless source identification model to extract features and classify source data, the problem of being unable to effectively identify specific devices (such as wireless cameras) due to signal complexity and diversity in wireless signal analysis is avoided, and potential camera signals are accurately identified and classified from numerous wireless signals. This method can improve the detection ability of wireless cameras, reduce false alarms and missed detections, thereby effectively protecting user privacy security and enhancing the security of the wireless environment.

[0078] Step S33. Determine the camera detection result of the surrounding environment according to the first evaluation result and the second evaluation result.

[0079] It can be understood that in order to fuse the dual detection results of optical and wireless signals and solve the limitations of a single method, step S33 is performed, which can avoid missed detections caused by relying on a single detection dimension, such as cameras without wireless functions or wireless devices with disguised lenses. Through multi-source data fusion, the detection accuracy and coverage are significantly improved, and a more comprehensive detection result is obtained.

[0080] Exemplarily, the detection software of the smart TV comprehensively analyzes the first evaluation result and the second evaluation result. Extract each target object in the first evaluation result and the second evaluation result, and the camera suspicion degree of each target object. Among them, the target objects include the target light source in the first evaluation result and the target signal source in the second evaluation result. Then, identify the azimuth characteristics of each target light source and target signal source, and fuse the camera suspicion degrees of the target light source and target signal source based on the azimuth characteristics to obtain the comprehensive camera suspicion degree of each target object. Finally, generate the camera detection result of the surrounding environment according to each target object and each comprehensive camera suspicion degree. For example: First, the software will compare the signal sources marked as suspicious in the two evaluation results. If both indicate the presence of a suspicious signal at the same location, that location is highly suspected to be where the camera is located. Then, the software will weight the two results according to the preset weight assignment rule. For example, if the result of the infrared light recognition model is more reliable, its weight will be higher. Finally, the software will combine the weighted evaluation results and determine the final camera detection result through a decision algorithm. If the total score obtained by the decision algorithm exceeds the preset threshold, the system will determine that there is a camera in the environment and display the detection result through the user interface, including the possible location of the camera, the signal strength, and the camera suspicion degree. In this way, users can take corresponding protection measures based on this information to protect personal privacy and security.

[0081] In this embodiment, by transmitting the light band data to the infrared light recognition model and the signal source data to the wireless signal source recognition model, the problems of misjudgment and missed detection that may be caused by a single detection method are avoided, and the analysis and evaluation of two data sources are integrated to improve the accuracy and reliability of camera detection. This method can simultaneously utilize the characteristics of infrared light and wireless signals, ensuring a comprehensive detection of potential cameras in the environment, thereby effectively protecting user privacy and reducing the risk of illegal monitoring.

[0082] In a feasible implementation manner, step S33 may include steps S331 to S334:

[0083] Step S331. Obtain the Media Access Control (MAC) address in the signal source data and transmit the MAC address to the MAC recognition model;

[0084] It should be noted that the MAC recognition model refers to a preset database or algorithm system, which contains the prefix information of the MAC addresses of known device manufacturers and can identify the device manufacturers of devices through the prefixes of the MAC addresses.

[0085] It can be understood that since the MAC address is a unique identifier of a wireless device and the device manufacturer can be traced by identifying the MAC address, performing step S331 can avoid the problem of being unable to determine the identity of the wireless device, thus realizing the auxiliary determination of the device type through the device manufacturer information.

[0086] Step S332: Identify the corresponding device manufacturer according to the MAC address through the MAC recognition model;

[0087] It can be understood that since devices produced by different device manufacturers have different uses and characteristics, performing step S332 can avoid the problem of being unable to use the device manufacturer information to improve the detection accuracy, and realizes the effect of combining the manufacturer information to improve the detection accuracy of the camera.

[0088] Exemplarily, after receiving the MAC address, the MAC recognition model will query its built-in database, which contains the MAC address prefixes of each device manufacturer and their corresponding information. The model determines the device manufacturer corresponding to each MAC address by comparing the organization unique identifier in the MAC address prefix.

[0089] Step S333: Associatively combine the device manufacturer and the third camera suspicion degree of the device manufacturer, and use the combination result as the third evaluation result;

[0090] It should be noted that the third camera suspicion degree is a quantitative index given according to the matching degree between the device manufacturer identified according to the MAC address and the known camera device manufacturer database. This score reflects the trust degree of the smart TV detection system in whether the device manufacturer identified according to the MAC address is likely to produce camera devices. The higher the third camera suspicion degree, the more the system believes that the device manufacturer is associated with the production of camera devices, thus increasing the possibility that the device is a camera; The third evaluation result is an evaluation result of whether the wireless device detected in the environment is likely to be a camera based on the device manufacturer information identified according to the MAC address. This result may indicate that a certain device is produced by a camera manufacturer, thus increasing the possibility that it is a camera.

[0091] Exemplarily, the corresponding camera suspicion degree is evaluated according to whether the manufacturer has relevant operations for producing cameras. This evaluation process can be carried out by querying a preset manufacturer-suspicion degree table, or a preset large model can be called for intelligent evaluation, so that the associated device manufacturer and camera suspicion degree form a structured third evaluation result, which can be embodied as a device manufacturer + probability value.

[0092] Step S334, determine the camera detection result of the surrounding environment according to the first evaluation result, the second evaluation result and the third evaluation result.

[0093] It can be understood that since the judgment is made only based on the infrared detection result and the source feature detection result, there may still be uncertainty. Therefore, step S334 is performed to further improve the comprehensiveness and reliability of camera detection by further enriching the multi-source data.

[0094] Exemplarily, the first evaluation result (the result based on the infrared light data analysis), the second evaluation result (the result based on the wireless signal feature classification), and the third evaluation result (the device manufacturer information based on the MAC address recognition) are comprehensively analyzed, and an algorithm is used to weigh these three results. For example, if at least two results indicate the possibility of the existence of a camera, the system will determine that there is a camera in the environment and display the detection result through the user interface, including the possible location of the camera, the signal strength, the camera suspicion degree, etc.

[0095] In this embodiment, by further additionally considering the corresponding device manufacturer according to the MAC address in the wireless source data and comprehensively considering the three evaluation results to determine the camera detection result, the problems of misjudgment and missed detection that may be caused by the insufficient coverage range of the detection method are avoided, and the effects of improving the accuracy, comprehensiveness and reliability of camera detection are achieved.

[0096] In a feasible embodiment, step S334 may include steps S100 to S600:

[0097] Step S100, obtain each target object in the first evaluation result, the second evaluation result and the third evaluation result, and the camera suspicion degree of each target object, where the target object includes the target light source in the first evaluation result, the target signal source in the second evaluation result, and the device manufacturer in the third evaluation result;

[0098] Step S200, measure the relative angles and relative distances of each target light source, and calculate the first azimuth feature of each target light source based on the relative angles and relative distances;

[0099] It should be noted that the first azimuth feature refers to the position information of the target light source in space, including but not limited to coordinate data such as angles and distances. These features help to determine the specific positions of each target object in the physical space, thereby providing a basis for subsequent camera suspicion degree fusion.

[0100] Exemplarily, for each target light source, the system measures its angle and distance relative to the detection device using triangulation, and obtains the first azimuth feature of each target light source based on these calculation results.

[0101] Step S300: Obtain the signal strength and phase difference in the source data of each target source, and calculate the second azimuth feature of each target source based on the signal strength and phase difference.

[0102] It should be noted that the second azimuth feature refers to the position information of the target source in space, including but not limited to coordinate data such as angles and distances. These features help to determine the specific positions of each target object in the physical space, thereby providing a basis for subsequent camera suspicion degree fusion.

[0103] Exemplarily, for each target source, the system analyzes the signal strength and phase difference in the source data, then estimates the distance of each target source using the signal strength attenuation model, and estimates the direction of each target source through the phase difference. Based on these estimation results, the second azimuth feature of each target source is calculated.

[0104] Step S400: Query the device deployment locations corresponding to each device manufacturer, and map the device deployment locations to the third azimuth feature of each device manufacturer.

[0105] It should be noted that the third azimuth feature refers to the position information of the device manufacturer in space, including but not limited to coordinate data such as angles and distances. These features help to determine the specific positions of each target object in the physical space, thereby providing a basis for subsequent camera suspicion degree fusion.

[0106] Exemplarily, for each device manufacturer, the system may need to rely on the device deployment location to estimate its azimuth. A device database can be established to record the device deployment locations of each device manufacturer and map these deployment locations to the corresponding third azimuth feature, thereby determining the azimuth information of each device manufacturer.

[0107] Step S500: Perform weighted fusion on the camera suspicion degrees of the fusible target light source, the fusible target source, and the fusible device manufacturer to obtain the comprehensive camera suspicion degree of each target object, where the azimuths indicated by the first azimuth feature, the second azimuth feature, and the third azimuth feature of the fusible target light source, the fusible target source, and the fusible device manufacturer are within the same regional range.

[0108] It should be noted that the comprehensive camera suspicion degree refers to a new camera suspicion degree obtained by performing weighted average or other weighted fusion algorithms on the camera suspicion degrees from different evaluation results (target light source, target information source, device manufacturer). This score synthesizes information from multiple dimensions to more accurately evaluate the possibility that the target object is a camera.

[0109] It can be understood that since it is often difficult to smoothly fuse the evaluation results of multiple different dimensions into an evaluation effect for a specific object, performing step S500 can avoid the problem of being unable to effectively fuse the evaluation results of different dimensions of the same object, thereby enhancing the accuracy of camera recognition.

[0110] Exemplarily, the system uses a multi-factor score fusion algorithm to combine the orientation characteristics of each target object with its camera suspicion degree. For example, a comprehensive camera suspicion degree is calculated through weighted average or other machine learning models, and this score more comprehensively reflects the possibility that the target object is a camera.

[0111] Step S600: Correlate and combine each target object and each comprehensive camera suspicion degree, and use the combination result as the camera detection result of the surrounding environment.

[0112] In this embodiment, by performing data fusion and analysis based on the orientation characteristics of the object, the problem of being unable to effectively fuse the evaluation results of different dimensions of the same object is avoided, and accurate detection and positioning of potential cameras in the surrounding environment are achieved. Specifically, by identifying the orientation characteristics of each target object and fusing its camera suspicion degree, the accuracy and reliability of the detection are effectively improved. Finally, the camera detection result is generated according to the comprehensive camera suspicion degree, which not only reduces the false alarm rate but also ensures the comprehensiveness and practicality of the detection result.

[0113] This embodiment provides a method for an intelligent TV to detect surrounding cameras. By integrating a signal scanning component in the intelligent TV, data collection and analysis and evaluation are performed on the surrounding environment where the intelligent TV is located, so as to obtain the camera detection result of the surrounding environment, and the effect of accurately detecting hidden cameras through the intelligent TV configured in the room is achieved.

[0114] In a feasible embodiment, the method for an intelligent TV to detect surrounding cameras further includes steps A01 to A02:

[0115] Step A01: Obtain a light band data annotation set, where the light band data annotation set includes an infrared light data subset;

[0116] It should be noted that the optical band data annotation set refers to a data set containing multiple optical band data and their corresponding annotations (such as light source type, characteristics, etc.), while the infrared light data subset refers to a data set specifically for the infrared band.

[0117] Step A02: Train a preset first machine learning model according to the optical band data annotation set to obtain an infrared recognition model.

[0118] It should be noted that the first machine learning model refers to a general machine learning model framework for processing optical band data. After being trained with a specific data set, this model can be transformed into an infrared recognition model with specific functions.

[0119] It can be understood that due to the possible performance deficiencies or misidentifications when using a general model to process specific tasks, so in step A02, training a dedicated recognition model for the characteristics of infrared light can avoid the performance deficiencies of the general model in data processing, thus achieving efficient recognition and classification of infrared light.

[0120] Exemplarily, first preprocess the optical band data annotation set, including filtering, denoising, etc., to extract useful frequency features. Then extract features such as frequency, amplitude, and phase, which will be used to train the AI model. In addition, divide the data set into a training set and a validation set for model training and performance evaluation. Among them, use the validation set to evaluate indicators such as the accuracy, recall rate, and precision rate of the model.

[0121] In this embodiment, by training a general machine learning model based on optical wave data of different frequencies to train a dedicated recognition model for the characteristics of infrared light, the accuracy of infrared light recognition is improved.

[0122] In a feasible embodiment, the method for the smart TV to detect surrounding cameras further includes steps B01 - B02:

[0123] Step B01: Obtain a source data annotation set, where the source data annotation set includes a wireless camera data annotation subset;

[0124] It should be noted that the source data annotation set refers to a data set containing various wireless signal sources, including signal data of normal devices (such as mobile phones, computers, routers, etc.) and various types of cameras. These data sets have undergone manual or automated annotation processing to mark the source and characteristics of the signals; the wireless camera data annotation subset refers to the part of the source data annotation set specifically for wireless camera signals, which contains the unique information and annotations of wireless camera signals.

[0125] Step B02: Train a preset second machine learning model according to the source data annotation set to obtain a wireless source identification model.

[0126] It should be noted that the second machine learning model refers to a general machine learning model framework that has not been optimized and trained for the wireless signal identification task before performing the task.

[0127] It can be understood that since there may be performance deficiencies or misidentifications when using a general model to process the wireless signal identification task, so in Step B02, a special identification model is trained according to the signal characteristics of the wireless camera, which can avoid the performance deficiencies of the general model in data processing, thus achieving efficient identification and classification of the wireless camera signal.

[0128] Exemplarily, extract useful features from the source data annotation set, such as signal strength change patterns, packet sizes and frequencies, transmission intervals, etc., and then use signal processing techniques to analyze the time-domain and frequency-domain characteristics of the signal, enabling the model to learn to identify the signal characteristics of different devices.

[0129] In this embodiment, by training a general machine learning model based on different types of wireless signal data, a special identification model is trained for the characteristics of the wireless camera, improving the accuracy of identifying the wireless camera.

[0130] In a feasible embodiment, the method for the smart TV to detect surrounding cameras further includes steps C01 to C02:

[0131] Step C01: Obtain a MAC address data annotation set, where the MAC address data annotation set includes MAC addresses, device manufacturers, and the corresponding relationship between MAC and device manufacturers;

[0132] Step C02: Train a preset third machine learning model according to the MAC address data annotation set to obtain a MAC address identification model.

[0133] It should be noted that the third machine learning model refers to a general machine learning model framework that has not been optimized and trained for the task of associating MAC addresses with device manufacturers before performing the task.

[0134] It can be understood that since there may be performance deficiencies or misidentifications when using a general model to process the wireless signal identification task, so in Step C02, a special model for identifying the corresponding relationship between MAC addresses and device manufacturers is trained, which can avoid the performance deficiencies of the general model in data processing, thus achieving the effect of quickly and accurately identifying wireless device manufacturers.

[0135] Exemplarily, a general machine learning model is trained based on a MAC address data annotation set to identify manufacturer information through the organization-unique identifier of the MAC address, and then certain tools and websites are docked to obtain device manufacturer data, so as to indirectly identify whether it is a camera through the MAC address.

[0136] In this embodiment, a general machine learning model is trained based on MAC address data to train a dedicated recognition model for the MAC characteristics of camera manufacturers, improving the accuracy of identifying camera manufacturers.

[0137] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as in the above-mentioned embodiment one can be referred to the above introduction and will not be elaborated hereinafter. On this basis, please refer to Figure 2 , after step S30, the method for the smart TV to detect surrounding cameras further includes steps S40 to S50:

[0138] Step S40, perform data screening on the camera detection results based on the camera suspicion degree of the camera detection results to obtain the target detection results in the camera detection results;

[0139] It should be noted that the camera suspicion degree refers to a quantitative index given by the analysis model when evaluating the camera detection results. This index reflects the confidence degree of the model in the correctness of the detection results. This score is usually based on the consistency degree between the detection results and known patterns or features, as well as the prediction probability of the model for the results; the target detection results refer to the detection results that the system believes are most likely to truly represent the existence of the camera after being screened by the camera suspicion degree. These results are the final conclusions output by the system for reporting to the user or taking further actions.

[0140] It can be understood that due to the complexity of the real environment, some uncertain or low-confidence results may be generated during the detection process, resulting in a large number of detection results with a certain probability but negligible in the final detection results. Therefore, performing step S40 can avoid misreporting these low-probability detection results to the user, thereby reducing the false alarm probability and ensuring that the user receives screened high-confidence information.

[0141] Exemplarily, first set a confidence threshold, and then compare the camera suspicion degree of each camera detection result with this threshold. Only when the camera suspicion degree is higher than or equal to the preset threshold, the corresponding detection result will be considered as the target detection result. Screen out these high-confidence results to form a list of target detection results for further processing or reporting.

[0142] Step S50, output the target detection results.

[0143] Exemplarily, the system pops up a notification window on the TV screen, or displays the location, type, and camera suspicion degree of the target camera on a dedicated security monitoring interface. In addition, the system can also notify the user of the target detection result by means of sound warning, mobile application push notification, or email, etc., to ensure that the user can timely learn about potential security threats and take corresponding protection measures.

[0144] In this embodiment, by quantifying and screening the detection results based on the camera suspicion degree, it is ensured that only the detection results with high confidence are output as the target detection results, avoiding false alarms caused by low-confidence results and unnecessary redundant information display, improving the simplicity and reliability of the detection results, as well as the user's trust in the system detection.

[0145] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar content as in the above-mentioned first and second embodiments can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 3 , after step 30, the method for the smart TV to detect surrounding cameras may further include steps S60 to S70:

[0146] Step S60, output the camera detection result and receive the feedback data corresponding to the camera detection result;

[0147] It should be noted that the feedback data refers to the confirmation information or correction information provided by the user or other systems for the output camera detection result. This data can be the response of the user to confirm whether the detection result is correct, or the false alarm or missed detection situation reported by the user.

[0148] It can be understood that in order to verify the accuracy of the detection result and obtain information in actual use from the user, so step S60 can avoid the problem that the detection result does not match the actual situation, thereby ensuring the continuous improvement of the system and user satisfaction.

[0149] Exemplarily, after the detection system of the smart TV completes the camera detection, it will display the detection result through the user interface, including the location, signal strength, and camera suspicion degree of the camera, etc. At the same time, the system will provide an interaction interface that allows the user to confirm the detection result or raise an objection. The user can provide feedback by clicking the confirmation button, selecting the false alarm / missed detection option, or directly marking the error on the interface. The system records this user feedback data and associates it with the corresponding detection result for subsequent analysis.

[0150] Exemplarily, after determining the camera detection results of the surrounding environment, data screening is performed on the camera detection results based on the camera suspicion degree of the camera detection results to obtain the target detection results in the camera detection results, and the target detection results are output. Then, the feedback data corresponding to the target detection results is received, where the feedback data can be data input by the user according to the interaction interface provided by the system.

[0151] Step S70: Generate training data according to the feedback data, and train a preset recognition model with the training data, where the recognition model is used to perform the step of analyzing and evaluating the data to be detected.

[0152] It should be noted that the training data refers to the labeled information extracted from the feedback data, which is used to guide the recognition model to learn how to more accurately identify the camera. These data usually include correct detection results, cases of false detections, and relevant environmental parameters; the recognition model can include an infrared light recognition model, a wireless signal source recognition model, and a MAC recognition model.

[0153] It can be understood that since the recognition model needs to be continuously updated and learned to adapt to new environments and challenges, performing step S70 can avoid problems such as performance degradation or obsolescence of the model caused by lack of update, thereby improving the generalization ability of the model and ensuring the long-term effectiveness of the model.

[0154] Exemplarily, the system processes the collected user feedback data and converts it into a format that can be used for training the machine learning model. This includes marking the correct detection results confirmed by the user as positive samples, marking the false alarm results marked by the user as negative samples, and extracting the corresponding feature data. Then, the system uses these labeled data sets as training data and inputs them into the recognition model to optimize the parameters of the model through an iterative training process. After training is completed, the updated model will be able to more accurately analyze and evaluate the data to be detected, improving the accuracy and efficiency of camera detection.

[0155] In this embodiment, by adopting the method of user interaction feedback and iterative training of the machine learning model, problems such as long-term accuracy degradation and poor adaptability caused by model solidification and environmental changes are avoided, achieving the effects of continuously optimizing the recognition model, improving detection accuracy, and adapting to different environmental changes. Through this process, the system can not only correct false detections in a timely manner but also continuously improve its own learning ability to ensure long-term efficient camera detection performance.

[0156] Exemplarily, to help understand the implementation process of the method for detecting surrounding cameras of an intelligent TV obtained by combining this embodiment with the above-mentioned first embodiment, please refer to Figure 4 , Figure 4A brief process schematic diagram of a method for an intelligent TV to detect surrounding cameras is provided. Specifically:

[0157] After starting the intelligent TV, select the camera detection mode in the intelligent TV to turn on the wireless signal scanning component in this mode to collect wireless signal sources around the intelligent TV and obtain source data. Then, based on the source strength of each source in the source data, filter the source data. When there is any source in the source data whose source strength is greater than the preset threshold, start scanning components such as an infrared sensor (i.e., an infrared camera) to perform strong light scanning of different wavelengths on the source area range to obtain optical band data. Compress, encrypt, and package the above-obtained source data and optical band data to obtain the data to be detected, and attach the ID of the intelligent TV to this data and send it to the cloud server cluster to reconstruct the data transmitted in this cluster so that the data meets the requirements of the AI structure algorithm. Then, use the AI algorithm in the cloud cluster, that is, call the corresponding infrared recognition model, wireless source recognition model, and MAC address recognition model to analyze the corresponding data to be detected, determine whether there is a suspicious device, and then return the detection result to the intelligent TV to feedback the detection result to the user through the user interface of the intelligent TV. If a suspicious device is found, an alarm is issued and processing suggestions are provided.

[0158] It should be noted that the process of using the AI algorithm, that is, calling the corresponding infrared recognition model, wireless source recognition model, and MAC address recognition model to analyze the corresponding data to be detected, can occur in the cloud server cluster to reduce the hardware cost of the local intelligent TV and improve the recognition performance, or it can occur in the local model of the intelligent TV to protect user privacy and at the same time reduce the requirements for network transmission conditions.

[0159] In addition, it should also be noted that the data to be transmitted can be pre-filtered and screened to streamline the data and improve the efficiency of subsequent data processing, or the optical band data and source data can be collected simultaneously to ensure that all the collected data can be effectively analyzed and evaluated, improving the accuracy and comprehensiveness of the recognition.

[0160] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for an intelligent TV to detect surrounding cameras of this application. Based on this technical concept, more forms of simple transformations are within the protection scope of this application.

[0161] This application also provides an apparatus for an intelligent TV to detect surrounding cameras. Please refer to Figure 5 The apparatus for an intelligent TV to detect surrounding cameras includes:

[0162] A start module 10, configured to receive a detection instruction and call the signal scanning component in the intelligent TV according to the detection instruction;

[0163] The acquisition module 20 is configured to collect signal data of the surrounding environment of the smart TV through the signal scanning component, and obtain the data to be detected of the surrounding environment;

[0164] The detection module 30 is configured to analyze and evaluate the data to be detected, and determine the camera detection result of the surrounding environment.

[0165] Optionally, the signal scanning component includes an infrared camera and a wireless signal scanning component, and the data to be detected includes optical band data and source data. Among them, the optical band data is collected by the infrared camera, and the source data is collected by the wireless signal scanning component;

[0166] The detection module 30 is further configured to:

[0167] Transmit the optical band data to the infrared light recognition model, and analyze and evaluate the optical band data through the infrared light recognition model to obtain a first evaluation result;

[0168] Transmit the source data to the wireless source recognition model, and analyze and evaluate the source data through the wireless source recognition model to obtain a second evaluation result;

[0169] Determine the camera detection result of the surrounding environment according to the first evaluation result and the second evaluation result.

[0170] Optionally, the detection module 30 is further configured to:

[0171] Extract features from the optical band data through the infrared light recognition model to obtain the optical band features to be recognized;

[0172] Classify the optical band features to be recognized to obtain the optical band category corresponding to the optical band features to be recognized;

[0173] Map the optical band category to the corresponding target light sources, and obtain the first camera suspicion degree of the target light sources based on the infrared light recognition model;

[0174] Associate and combine the target light sources and the first camera suspicion degrees, and use the combination result as the first evaluation result.

[0175] Optionally, the detection module 30 is further configured to:

[0176] Extract features from the source data through the wireless source recognition model to obtain the source features to be recognized;

[0177] Classify the source features to be recognized to obtain the source category corresponding to the source features to be recognized;

[0178] Map the source category to the corresponding target sources, and obtain the second camera suspicion degree of each target source based on the wireless source recognition model;

[0179] Associate and combine each target source and each second camera suspicion degree, and use the combination result as the second evaluation result.

[0180] Optionally, the detection module 30 is further configured to:

[0181] Obtain the Media Access Control (MAC) address in the source data, and transmit the MAC address to the MAC recognition model;

[0182] Identify the corresponding device manufacturer according to the MAC address through the MAC recognition model;

[0183] Associate and combine the device manufacturer and the third camera suspicion degree of the device manufacturer, and use the combination result as the third evaluation result;

[0184] Determine the camera detection result of the surrounding environment according to the first evaluation result, the second evaluation result, and the third evaluation result.

[0185] Optionally, the detection module 30 is further configured to:

[0186] Obtain each target object in the first evaluation result, the second evaluation result, and the third evaluation result, and the camera suspicion degree of each target object, where the target object includes the target light source in the first evaluation result, the target source in the second evaluation result, and the device manufacturer in the third evaluation result;

[0187] Measure the relative angles and relative distances of each target light source, and calculate the first azimuth feature of each target light source based on the relative angles and the relative distances;

[0188] Obtain the signal strength and phase difference in the source data of each target source, and calculate the second azimuth feature of each target source based on the signal strength and the phase difference;

[0189] Query the device deployment location corresponding to each device manufacturer, and map the device deployment location to the third azimuth feature of each device manufacturer;

[0190] Weightedly fuse the camera suspicion degrees of the fusible target light source, the fusible target source, and the fusible device manufacturer to obtain the comprehensive camera suspicion degree of each target object, where the azimuths indicated by the first azimuth feature, the second azimuth feature, and the third azimuth feature of the fusible target light source, the fusible target source, and the fusible device manufacturer are within the same regional range;

[0191] Associate and combine each of the target objects with the suspected degrees of each integrated camera, and use the combination result as the camera detection result of the surrounding environment.

[0192] Optionally, the output module 40 in the intelligent TV for detecting surrounding cameras is used for:

[0193] Perform data screening on the camera detection result based on the suspected degree of the camera in the camera detection result to obtain the target detection result in the camera detection result;

[0194] Output the target detection result.

[0195] Optionally, the feedback module 50 in the intelligent TV for detecting surrounding cameras is used for:

[0196] Output the camera detection result and receive the feedback data corresponding to the camera detection result;

[0197] Generate training data according to the feedback data, and train a preset recognition model with the training data, where the recognition model is used to perform the step of analyzing and evaluating the data to be detected.

[0198] The intelligent TV for detecting surrounding cameras provided by this application adopts the intelligent TV for detecting surrounding cameras method in the above embodiment, and can solve the technical problem of how to accurately detect hidden cameras at low cost. Compared with the prior art, the beneficial effects of the intelligent TV for detecting surrounding cameras provided by this application are the same as those of the intelligent TV for detecting surrounding cameras method provided by the above embodiment, and other technical features in the intelligent TV for detecting surrounding cameras are the same as the features disclosed in the method of the above embodiment, which will not be elaborated here.

[0199] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent TV for detecting surrounding cameras method in the first embodiment above.

[0200] Next, refer to Figure 6, which shows a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The illustrated electronic device is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0201] As Figure 6 shown, the electronic device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory 1002 or the program loaded from the storage device 1003 into the random access memory 1004. In the random access memory 1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. The input / output interface 1006 is also connected to the bus. Generally, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the shown systems. Instead, more or fewer systems may be implemented or had.

[0202] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by a processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0203] The electronic device provided by the present application adopts the method for a smart TV to detect surrounding cameras in the above embodiments, and can solve the technical problem of how to accurately detect hidden cameras at low cost. Compared with the prior art, the beneficial effects of the electronic device provided by the present application are the same as those of the method for a smart TV to detect surrounding cameras provided in the above embodiments, and other technical features in the electronic device are the same as those disclosed in the method of the previous embodiment, which will not be elaborated herein.

[0204] It should be understood that each part disclosed in the present application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0205] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0206] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for a smart TV to detect surrounding cameras in the above embodiments.

[0207] The computer-readable storage medium provided by the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0208] The above computer-readable storage medium may be included in an electronic device; or it may exist separately and not be assembled into the electronic device.

[0209] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device is caused to: receive a detection instruction, and call a signal scanning component in a smart TV according to the detection instruction; collect signal data of the surrounding environment of the smart TV through the signal scanning component to obtain data to be detected of the surrounding environment; analyze and evaluate the data to be detected to determine a camera detection result of the surrounding environment.

[0210] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0211] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0212] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0213] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned method for detecting a peripheral camera of a smart TV, and can solve the technical problem of how to accurately detect a hidden camera at low cost. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the method for detecting a peripheral camera of a smart TV provided by the above embodiments, and will not be elaborated here.

[0214] The above are only some embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made under the technical concept of the present application by using the content of the specification and drawings of the present application, or any direct / indirect application in other related technical fields is included in the patent protection scope of the present application.

Claims

1. A method for detecting a peripheral camera of a smart TV, characterized in that, The method for detecting a peripheral camera of a smart TV includes: Receiving a detection instruction and invoking a signal scanning component in the smart TV according to the detection instruction; Collecting signal data of the peripheral environment of the smart TV through the signal scanning component to obtain the data to be detected of the peripheral environment; Analyzing and evaluating the data to be detected to determine the camera detection result of the peripheral environment.

2. The method for detecting a peripheral camera of an intelligent television according to claim 1, wherein, The signal scanning component includes an infrared camera and a wireless signal scanning component, and the data to be detected includes optical band data collected by the infrared camera and source data collected by the wireless signal scanning component; The step of analyzing and evaluating the data to be detected to determine the camera detection result of the peripheral environment includes: Transmitting the optical band data to an infrared light recognition model, and analyzing and evaluating the optical band data through the infrared light recognition model to obtain a first evaluation result; Transmitting the source data to a wireless source recognition model, and analyzing and evaluating the source data through the wireless source recognition model to obtain a second evaluation result; Determining the camera detection result of the peripheral environment according to the first evaluation result and the second evaluation result.

3. The method for detecting a peripheral camera of an intelligent television according to claim 2, wherein The step of analyzing and evaluating the optical band data through the infrared light recognition model to obtain a first evaluation result includes: Extracting features of the optical band data through the infrared light recognition model to obtain the optical band features to be recognized; Classifying the optical band features to be recognized to obtain the optical band category corresponding to the optical band features to be recognized; Mapping the optical band category to the corresponding target light sources, and obtaining the first camera suspicion degree of each target light source based on the infrared light recognition model; Associating and combining each target light source and each first camera suspicion degree, and taking the combination result as the first evaluation result.

4. The method for detecting a peripheral camera of an intelligent television according to claim 2, wherein The step of analyzing and evaluating the source data through the wireless source recognition model to obtain a second evaluation result includes: Extracting features of the source data through the wireless source recognition model to obtain the source features to be recognized; Classifying the source features to be recognized to obtain the source category corresponding to the source features to be recognized; Mapping the source category to the corresponding target sources, and obtaining the second camera suspicion degree of each target source based on the wireless source recognition model; Associating and combining each target source and each second camera suspicion degree, and taking the combination result as the second evaluation result.

5. The method for detecting a peripheral camera of an intelligent television according to claim 2, wherein The step of determining the camera detection result of the peripheral environment according to the first evaluation result and the second evaluation result includes: Obtaining the media access control (MAC) address in the source data, and transmitting the MAC address to a MAC recognition model; Identifying the corresponding device manufacturer according to the MAC address through the MAC recognition model; Associating and combining the device manufacturer and the third camera suspicion degree of the device manufacturer, and taking the combination result as the third evaluation result; Determine the camera detection result of the surrounding environment according to the first evaluation result, the second evaluation result, and the third evaluation result.

6. The method for detecting a peripheral camera of an intelligent television according to claim 5, wherein The step of determining the camera detection result of the surrounding environment according to the first evaluation result, the second evaluation result, and the third evaluation result includes: Obtain each target object in the first evaluation result, the second evaluation result, and the third evaluation result, and the camera suspicion degree of each target object, where the target object includes the target light source in the first evaluation result, the target signal source in the second evaluation result, and the device manufacturer in the third evaluation result; Measure the relative angles and relative distances of each target light source, and calculate the first azimuth feature of each target light source based on the relative angles and the relative distances; Obtain the signal strength and phase difference in the signal source data of each target signal source, and calculate the second azimuth feature of each target signal source based on the signal strength and the phase difference; Query the device deployment location corresponding to each device manufacturer, and map the device deployment location to the third azimuth feature of each device manufacturer; Perform weighted fusion on the camera suspicion degrees of the fusible target light source, the fusible target signal source, and the fusible device manufacturer to obtain the comprehensive camera suspicion degree of each target object, where the azimuths indicated by the first azimuth feature, the second azimuth feature, and the third azimuth feature of the fusible target light source, the fusible target signal source, and the fusible device manufacturer are within the same regional range; Perform associated combination on each target object and each comprehensive camera suspicion degree, and use the combination result as the camera detection result of the surrounding environment.

7. The method for detecting a peripheral camera of an intelligent television according to claim 1, wherein After the step of determining the camera detection result of the surrounding environment, it further includes: Output the camera detection result, and receive the feedback data corresponding to the camera detection result; Generate training data according to the feedback data, and train a preset recognition model through the training data, where the recognition model is used to execute the step of analyzing and evaluating the data to be detected.

8. An intelligent TV peripheral camera detection device, characterized in that The intelligent TV surrounding camera detection device includes: A startup module, configured to receive a detection instruction and call a signal scanning component in the intelligent TV according to the detection instruction; An acquisition module, configured to collect signal data of the surrounding environment of the intelligent TV through the signal scanning component to obtain the data to be detected of the surrounding environment; A detection module, configured to analyze and evaluate the data to be detected to determine the camera detection result of the surrounding environment.

9. An electronic device, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the intelligent TV surrounding camera detection method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the intelligent TV surrounding camera detection method according to any one of claims 1 to 7.

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