A concealed beacon detection method and device, computer equipment and storage medium

Abstract

The application discloses a concealed beacon detection method and device, computer equipment and a storage medium. The detection method comprises the following steps: receiving a beacon detection request; in response to the beacon detection request, entering a radar map detection page of a beacon device near a target terminal; acquiring a broadcast signal emitted by a beacon device near the target terminal, and analyzing and identifying the type of a positioning beacon device and distance information of the positioning beacon device from the target terminal based on the broadcast signal emitted by the beacon device near the target terminal; and displaying the type icon and distance information of the detected positioning beacon device near the target terminal on the radar map detection page. Through the above method, the detailed information of the concealed beacon can be more intuitively and accurately displayed, so that the situation that the terminal device identifies incorrectly due to the fact that the concealed beacon device is disguised as several different beacon devices around the terminal device is avoided, and the network security of the terminal device is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, and particularly relates to a concealed beacon detection method and device, computer equipment and a storage medium. BACKGROUND

[0002] With the development of Internet services, a beacon device can send a broadcast signal to a terminal device, for example, to a smart terminal device and a mobile terminal device within a signal range, so that the terminal device determines the position of the beacon device, and a crowdsourced location network is a common device positioning technology, that is, composed of a beacon device and a terminal device, the beacon device will continuously send broadcast signals to the outside, and the terminal device will report the discovery position of the beacon device after receiving the broadcast signals, thereby realizing the positioning of the beacon device. However, a concealed beacon device is a special beacon device imitated or modified by a malicious user through technical means, and the concealed beacon device can perform concealed tracking on the terminal device. Unlike the foregoing beacon device, the concealed beacon device will use various technical means to disguise the state of the concealed beacon device while maintaining positioning, thereby reducing the network security of the terminal device.

[0003] At present, the terminal device can continuously collect the signals of the corresponding beacon device, and the corresponding beacon device uses a kind of broadcast signal in a preset time period, and uses another broadcast signal in the next time period, so that the terminal device can analyze the frequency and duration of the same signal appearing in the time period to determine whether the broadcast signal is performing malicious tracking and other unsafe network behavior operations. However, the concealed beacon device can stagger the preset time period to perform different broadcast signals, so as to disguise the concealed beacon device as several different beacon devices around the terminal device, and thus the detection accuracy of the concealed beacon device is still reduced, and the network security of the terminal device is still reduced. Therefore, how to improve the detection accuracy of the concealed beacon to improve the network security of the terminal device becomes a problem to be solved. SUMMARY

[0004] This application provides a method and apparatus for detecting concealed beacons, a computer device, and a storage medium. By requesting a beacon detection request, the system accesses a radar chart detection page for beacon devices near the target terminal. This page displays the type icon and distance information of the detected beacon devices near the target terminal. This provides a more intuitive and accurate display of detailed information about the beacons near the target terminal, assisting or guiding the user in identifying concealed beacons. This avoids situations where concealed beacons are disguised as several different beacons around the terminal, leading to incorrect identification by the terminal device, thereby improving the network security of the terminal device.

[0005] In view of this, the first aspect of this application provides a method for detecting a hidden beacon, comprising:

[0006] Receive a beacon detection request, wherein the beacon detection request is used to request the detection of beacon devices near the target terminal;

[0007] In response to the beacon detection request, the system enters the radar image detection page for beacon devices near the target terminal.

[0008] The system acquires broadcast signals emitted by beacon devices near the target terminal, and analyzes and identifies the type of positioning beacon device and the distance information between the positioning beacon device and the target terminal based on the broadcast signals emitted by beacon devices near the target terminal.

[0009] The radar image detection page displays the type icon and distance information of the positioning beacon device near the detected target terminal.

[0010] A second aspect of this application provides a concealed beacon detection device, comprising:

[0011] The receiving module is used to receive beacon detection requests, wherein the beacon detection requests are used to request the detection of beacon devices near the target terminal;

[0012] The response module is used to respond to beacon detection requests and enter the radar image detection page of beacon devices near the target terminal.

[0013] The processing module is used to acquire broadcast signals emitted by beacon devices near the target terminal, and analyze and identify the type of positioning beacon device and the distance information between the positioning beacon device and the target terminal based on the broadcast signals emitted by the beacon devices near the target terminal.

[0014] The display module is used to display the type icon and distance information of the positioning beacon device near the detected target terminal on the radar image detection page.

[0015] In one possible implementation, the radar chart detection page also includes interface switching controls;

[0016] The receiving module is also used to receive trigger requests for the type icon of the positioning beacon device on the radar image detection page, and enter the positioning beacon device details display page corresponding to the type icon to display advanced details of the positioning beacon device;

[0017] The receiving module is also used to receive interface switching requests for the interface switching control, enter the detection analysis results page of the positioning beacon devices detected on the radar map detection page, and display the detection analysis results of all positioning beacon devices on the detection analysis results page to assist or guide the user in identifying hidden beacons.

[0018] In one possible implementation, the processing module is further configured to analyze and identify the Media Access Control (MAC) address of the beacon device and the battery status information of the beacon device based on the broadcast signal emitted by the beacon device near the target terminal.

[0019] The receiving module specifically receives trigger requests for the type icon of the positioning beacon device on the radar image detection page, and enters the positioning beacon device details display page corresponding to the type icon to display the MAC address of the positioning beacon device and the battery status information of the positioning beacon device.

[0020] In one possible implementation, the processing module is specifically used to analyze and identify the first field in the broadcast signal to determine the location network type of the beacon device, wherein different location network types correspond to different load data extraction strategies.

[0021] By using the load data extraction strategy corresponding to the location network type, the target load data field is extracted from the load data field in the broadcast signal, and the beacon device is determined to be the location beacon device based on the target load data;

[0022] The load data field in the broadcast signal is analyzed and identified to obtain the type of positioning beacon device and the broadcast signal strength information of the positioning beacon device;

[0023] Signal strength analysis is performed on the broadcast signal strength information of the positioning beacon device to obtain the distance information between the positioning beacon device and the target terminal.

[0024] In one possible implementation, the processing module is specifically used to analyze and identify the first field in the broadcast signal through the first branch node of the classification decision tree model in order to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different classification decision tree sub-models;

[0025] The processing module is specifically used to analyze and identify the load data fields in the broadcast signal through the classification decision tree sub-model corresponding to the positioning network type, so as to obtain the type of positioning beacon device and the broadcast signal strength information of the positioning beacon device.

[0026] In one possible implementation, the processing module is specifically used to analyze and identify the load data field in the broadcast signal by locating the classification decision tree sub-model corresponding to the network type, so as to obtain the first load data field and the second load data field.

[0027] By analyzing and identifying the first branch sub-node of the classification decision tree sub-model corresponding to the location network type, the broadcast signal strength information of the location beacon device can be obtained.

[0028] By analyzing and identifying the second branch sub-node of the classification decision tree sub-model corresponding to the location network type, the type of the location beacon device can be obtained.

[0029] In one possible implementation, the processing module is specifically used to determine that the second field in the broadcast signal matches the first target field, and to analyze and identify the first field in the broadcast signal to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different second target fields;

[0030] The processing module is specifically used to determine whether the target load data field matches the second target field corresponding to the location network type, and to determine whether the beacon device is a location beacon device.

[0031] In one possible implementation, the processing module is specifically configured to determine, through the root node of the classification decision tree model, whether the second field in the broadcast signal matches the first target field.

[0032] The processing module is specifically used to determine the second target field corresponding to the location network type by using the second branch node of the classification decision tree model.

[0033] In one possible implementation, the covert beacon detection device further includes an acquisition module and a model building module;

[0034] The acquisition module is used to acquire a set of broadcast signal samples, wherein the set of broadcast signal samples includes multiple broadcast signal samples, each broadcast signal sample includes a field feature sample, the field feature sample includes a first field sample, a second field sample, and a payload data field sample, and each broadcast signal sample corresponds to the type of beacon device and the location network type of the beacon device;

[0035] The processing module is also used to segment each broadcast signal sample in the broadcast signal sample set to obtain the field features of each broadcast signal sample, wherein the field features include the first field feature, the second field feature, and the load data field feature;

[0036] The processing module is also used to calculate the information gain of the field features of each broadcast signal sample to obtain the field feature information gain ratio of each broadcast signal sample. The field feature information gain ratio includes the first field feature information gain ratio, the second field feature information gain ratio, and the load data field feature information gain ratio.

[0037] The model building module is used to construct a classification decision tree model based on the field feature information gain ratio of each broadcast signal sample.

[0038] A third aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the methods described in the above aspects.

[0039] A fourth aspect of this application provides a computer device, characterized in that it includes: a memory, a transceiver, a processor, and a bus system; wherein the memory is used to store a program; the processor is used to execute the program in the memory to implement the methods described in the above aspects; and the bus system is used to connect the memory and the processor to enable the memory and the processor to communicate.

[0040] A fifth aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above aspects.

[0041] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0042] This application provides a method for detecting hidden beacons. First, a beacon detection request is received, requesting the detection of beacon devices near a target terminal. In response to the request, a radar chart detection page for detecting beacon devices near the target terminal is accessed. Based on this, the broadcast signals emitted by the beacon devices near the target terminal are acquired, and the type of the location beacon device and its distance from the target terminal are analyzed and identified based on these broadcast signals. Finally, the type icon and distance information of the detected location beacon device near the target terminal are displayed on the radar chart detection page. This method, by accessing the radar chart detection page for detecting beacon devices near the target terminal through a beacon detection request, and displaying the type icon and distance information of the detected location beacon device near the target terminal, provides a more intuitive and accurate display of detailed information about the location beacon devices near the target terminal. This assists or guides users in identifying hidden beacons, preventing situations where hidden beacons are disguised as several different beacon devices around the terminal, leading to incorrect identification by the terminal device, thereby improving the network security of the terminal device. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of a hidden beacon detection method in an embodiment of this application;

[0044] Figure 2 A flowchart illustrating the concealed beacon detection method provided in this application embodiment;

[0045] Figure 3 A schematic diagram of one embodiment of the covert beacon detection method provided in this application;

[0046] Figure 4 A schematic diagram of the radar image detection page provided in the embodiments of this application;

[0047] Figure 5 This is another schematic diagram of the radar image detection page provided in the embodiments of this application;

[0048] Figure 6 A schematic diagram of an interface for displaying details of a location beacon device corresponding to a type icon provided in this application embodiment;

[0049] Figure 7 A schematic diagram of the detection and analysis results page provided in an embodiment of this application;

[0050] Figure 8 This is another schematic diagram of the interface display page for the location beacon device details page corresponding to the type icon provided in the embodiments of this application;

[0051] Figure 9 This is another schematic diagram of the detection and analysis results page provided in the embodiments of this application;

[0052] Figure 10 A schematic flowchart of broadcast signal processing for covert beacon detection provided in an embodiment of this application;

[0053] Figure 11 A flowchart illustrating a method for extracting the broadcast public key of the Apple Find My location network type provided in this application embodiment;

[0054] Figure 12 A flowchart illustrating a method for detecting hidden beacons based on a classification decision tree model, provided in an embodiment of this application.

[0055] Figure 13 This is a schematic diagram of the structure of a concealed beacon detection device provided in an embodiment of this application;

[0056] Figure 14 This is a block diagram of a portion of the structure of a mobile phone associated with a terminal in an embodiment of this application. Detailed Implementation

[0057] This application provides a method and apparatus for detecting concealed beacons, a computer device, and a storage medium. By requesting a beacon detection request, the system accesses a radar chart detection page for beacon devices near the target terminal. This page displays the type icon and distance information of the detected beacon devices near the target terminal. This provides a more intuitive and accurate display of detailed information about the beacons near the target terminal, assisting or guiding the user in identifying concealed beacons. This avoids situations where concealed beacons are disguised as several different beacons around the terminal, leading to incorrect identification by the terminal device, thereby improving the network security of the terminal device.

[0058] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “corresponding to,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0059] With the development of internet services, beacon devices can broadcast signals to terminal devices, such as smart and mobile devices within signal range, enabling these devices to determine the beacon's location. Crowdsourced location networks (CRMs) are a common device location technology, consisting of beacon devices and terminal devices. Beacon devices continuously broadcast signals, and terminal devices, upon receiving these signals, report the beacon's location, thus locating the beacon. However, stealth beacon devices are special types of beacon devices that malicious users forge or modify using technical means. These devices can covertly track terminal devices. Unlike the aforementioned beacon devices, stealth beacon devices not only maintain their location but also employ various techniques to disguise their status. For example, they may frequently modify their identifiers, change their broadcast rotation time unpredictably, or disguise themselves as dozens of ordinary beacon devices, thereby reducing the network security of the terminal devices. Currently, terminal devices can continuously collect signals from corresponding beacon devices. Within a preset time period, the corresponding beacon device uses one broadcast signal, and in the next time period, it uses a different broadcast signal. This allows the terminal device to analyze the frequency and duration of the same signal appearing within a time period to determine whether the broadcast signal is engaging in malicious tracking or other insecure network behaviors. However, stealth beacon devices can stagger their broadcasts within the preset time period, thus disguising themselves as several different beacon devices surrounding the terminal device. This still reduces the accuracy of stealth beacon detection and consequently, the network security of the terminal device. Therefore, improving the accuracy of stealth beacon detection to enhance the network security of the terminal device is a pressing issue. Based on this, this application provides a stealth beacon detection method to improve the network security of terminal devices.

[0060] First, for ease of understanding, some terms or concepts involved in the embodiments of this application will be explained.

[0061] I. Bluetooth Low Energy (BLE) Broadcast

[0062] BLE broadcasting is currently the most important communication method for various beacon devices. Through the BLE protocol, beacon devices can periodically broadcast small segments of data of about tens of bytes with extremely low power consumption.

[0063] II. Advertisement Rotation Time

[0064] To prevent malicious users from tracking the beacon's location by monitoring its broadcast signals, the beacon generates a new signal at regular intervals that is completely unpredictable and untraceable by outsiders. This interval is called the broadcast rotation period.

[0065] III. Classification Tree

[0066] A classification tree is a tree structure commonly used for categorizing data. Each branch node in a classification tree is a dividing point for a data feature, and each branch node corresponds to a specific category. When a program needs to classify a segment of data, it can start from the root node of a classification tree and check each branch node downwards until it matches the category of a given branch node.

[0067] IV. ID3 (Iterative Dichotomiser 3) Algorithm

[0068] The ID3 algorithm is one of the classic algorithms in the field of machine learning. It can calculate the information gain of each feature in a sample dataset and summarize the decision tree (classification tree) structure that exists in it.

[0069] The application system of this application embodiment is described below, and this application embodiment is applied to a BLE broadcast system. Please refer to [link / reference]. Figure 1 , Figure 1 This is a system schematic diagram of the hidden beacon detection method in the embodiments of this application, such as... Figure 1As shown, the video processing system includes a target terminal device and a beacon device. The concealed beacon detection method is specifically executed by the target terminal device. The target terminal device receives a beacon detection request, which requests the detection of beacon devices near the target terminal. In response to the beacon detection request, the target terminal device enters the radar chart detection page for detecting beacon devices near the target terminal. Based on this, the target terminal device obtains the broadcast signals emitted by the beacon devices near the target terminal, and analyzes and identifies the type of the positioning beacon device and the distance information of the positioning beacon device from the target terminal based on the broadcast signals emitted by the beacon devices near the target terminal using the method provided in this application embodiment. Then, the type icon and distance information of the detected positioning beacon device near the target terminal are displayed on the radar chart detection page. The target terminal device can further determine the battery status information of the positioning beacon device, or obtain the medium access control (MAC) address of the positioning beacon device. This allows for a more intuitive and accurate display of detailed information about the positioning beacon devices near the target terminal, assisting or guiding users in identifying hidden beacons. This avoids situations where hidden beacon devices disguise themselves as several different beacon devices around the target terminal device, leading to incorrect identification by the target terminal device, thereby improving the network security of the terminal device.

[0070] It should be noted that the aforementioned target terminal devices may include, but are not limited to, tablet computers, laptops, PDAs, mobile phones, personal computers (PCs), and intelligent voice interaction devices. Furthermore, target terminal devices may also include, but are not limited to, smart home appliances and in-vehicle terminals. The target terminal devices and beacon devices can be directly or indirectly connected via wireless networks, wired networks, or removable storage media. The aforementioned wireless networks use standard communication technologies and / or protocols. The wireless network is typically the Internet, but can also be any network, including but not limited to Bluetooth, Local Area Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), mobile, private networks, or any combination of virtual private networks. In some embodiments, customized or dedicated data communication technologies may be used to replace or supplement the aforementioned data communication technologies. The removable storage media may be a Universal Serial Bus (USB) flash drive, external hard drive, or other removable storage media.

[0071] Secondly, although Figure 1 Only one terminal device and one beacon device are shown in the image, but it should be understood that... Figure 1The examples provided are for understanding this scheme only; the actual number of terminal devices and beacon devices should be flexibly determined based on the actual situation.

[0072] Since this application embodiment requires processing the fields carried in the broadcast signal based on a classification decision tree model, it needs to be implemented based on the field of artificial intelligence. Before introducing the hidden beacon detection method provided in this application embodiment, some basic concepts in the field of artificial intelligence will be introduced first. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain optimal results. In other words, artificial intelligence is a comprehensive technology of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. Artificial intelligence studies the design principles and implementation methods of various intelligent machines, enabling machines to have the functions of perception, reasoning, and decision-making. Artificial intelligence technology is a comprehensive discipline involving a wide range of fields, including both hardware and software technologies. Basic artificial intelligence technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, large hidden beacon detection technology, operating / interaction systems, and mechatronics. Artificial intelligence software technology mainly includes several major areas such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0073] With the advancement of artificial intelligence (AI) technology, research has expanded into various directions. Machine learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory, among others. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of AI and the fundamental way to endow computers with intelligence; its applications span all areas of AI. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and instructional learning.

[0074] For easier understanding, please refer to Figure 2 , Figure 2 Please refer to the flowchart of the covert beacon detection method provided in the embodiments of this application. Figure 2 The stealth beacon detection method consists of two stages: the classification decision tree model construction stage and the stealth beacon detection stage. The functions and processes of each stage will be described below:

[0075] In the classification decision tree model construction stage A1, the BLE sniffing method is first used to collect broadcast signal samples of different positioning network types, different beacon device types, and different battery status information of mobile phones. For example, broadcast signal samples sent by AirTag when fully charged, broadcast signal samples sent by AirPods when fully charged, and broadcast signal samples sent by SmartTag when 50% battery is reached. This results in a broadcast signal sample set, which includes multiple broadcast signal samples. Each broadcast signal sample includes a field feature sample, which includes a first field sample, a second field sample, and a load data field sample. Secondly, based on the BLE broadcast technology standard, each broadcast signal sample in the broadcast signal sample set is segmented. This involves cross-referencing the frequently occurring field features in each broadcast signal sample to obtain the field features of each sample. These field features include the first field feature, the second field feature, and the payload data field feature. Then, the ID3 algorithm is called to further calculate the information gain of the field features of each broadcast signal sample, resulting in the field feature information gain ratio of each sample. This field feature information gain ratio includes the information gain ratios of the first, second, and payload data fields. Based on the field feature information gain ratios of each broadcast signal sample, a classification decision tree model is constructed.

[0076] In the concealed beacon detection phase A2, the target terminal device first receives a beacon detection request, which requests the detection of beacon devices near the target terminal. In response to the beacon detection request, the device enters the radar image detection page for detecting beacon devices near the target terminal and obtains the broadcast signal emitted by the beacon devices near the target terminal. At this time, the broadcast signal emitted by the beacon devices near the target terminal is then determined by the root node of the classification decision tree model constructed in phase A1 to determine whether the second field in the broadcast signal matches the first target field. Then, the first branch node of the classification decision tree model constructed in phase A1 is used to analyze and identify the first field in the broadcast signal to determine the location network type of the beacon device. Different location network types correspond to different load data extraction strategies, different location network types correspond to different second target fields, and different location network types correspond to different classification decision tree sub-models.

[0077] Furthermore, the target terminal device extracts the target load data field from the load data field in the broadcast signal using the load data extraction strategy corresponding to the location network type. It then determines that the target load data field matches the second target field corresponding to the location network type and identifies the beacon device as a location beacon device. Based on this, the load data field in the broadcast signal is analyzed and identified using the classification decision tree sub-model corresponding to the location network type constructed by A1 to obtain the first load data field and the second load data field. The first load data field is then analyzed and identified using the first branch sub-node of the classification decision tree sub-model corresponding to the location network type to obtain the broadcast signal strength information of the location beacon device. Signal strength analysis is then performed on the broadcast signal strength information of the location beacon device to obtain the distance information between the location beacon device and the target terminal. Finally, the second load data field is analyzed and identified using the second branch sub-node of the classification decision tree model corresponding to the location network type to determine the type of the location beacon device.

[0078] Furthermore, the target terminal device can also analyze and identify the Media Access Control (MAC) address and battery status information of the beacon devices based on broadcast signals emitted by beacon devices near the target terminal, using methods similar to those described above. This information is then displayed on the radar chart detection page, showing the type icon, distance information, MAC address, and battery status information of the detected beacon devices near the target terminal. To accommodate different user display needs, the radar chart detection page also includes an interface switching control. Based on this, the target terminal device receives a trigger request for the type icon of the beacon device on the radar chart detection page and enters the beacon device details display page corresponding to the type icon, showcasing advanced details of the beacon device. Alternatively, the target terminal device receives an interface switching request for the interface switching control and enters a page displaying the detection analysis results of the detected beacon devices on the radar chart detection page. This page displays the detection analysis results of all beacon devices to assist or guide the user in identifying hidden beacons. It should be understood that the specific display format of the beacon devices on the interface is not limited here; under specific location network types, the beacon device's public key can also be determined.

[0079] Based on the above introduction, the following section uses a terminal device as the executing entity to describe the hidden beacon detection method in this application. Please refer to [link / reference]. Figure 3 , Figure 3 A schematic diagram of one embodiment of the covert beacon detection method provided in this application is shown below. Figure 3 As shown, the method includes:

[0080] 101. Receive beacon detection request.

[0081] In this embodiment, the target terminal device receives a beacon detection request, which is used to request the detection of beacon devices near the target terminal. Specifically, during the user's use of the target terminal device, the user opens the detection program on the target terminal device and performs a broadcast signal reception operation to generate a beacon detection request for requesting the detection of beacon devices near the target terminal. The target terminal device then receives the beacon detection request, initializes the Bluetooth BLE environment, and waits to receive broadcast signals.

[0082] 102. In response to the beacon detection request, enter the radar image detection page of the beacon equipment near the target terminal.

[0083] In this embodiment, the target terminal device responds to the beacon detection request and enters the radar image detection page for beacon devices near the target terminal. For ease of understanding, Figure 4 This is a schematic diagram of an interface for a radar image detection page provided in an embodiment of this application. Figure 4 The image in (A) shows a radar image detection page presented through the shape of radar radiation. Figure 4 The image in (B) shows a radar image detection page presented in a color-coded manner. It should be understood that... Figure 4 The examples provided are for understanding this solution only. In practical applications, the radar image detection page can present the interface effect not only through the shape of radar radiation, but also through more distances, as well as through a combination of distances and color differences. This is not a limitation.

[0084] 103. Obtain the broadcast signals emitted by beacon devices near the target terminal, and analyze and identify the type of positioning beacon device and the distance information between the positioning beacon device and the target terminal based on the broadcast signals emitted by the beacon devices near the target terminal.

[0085] In this embodiment, the target terminal device acquires broadcast signals emitted by beacon devices near the target terminal. Then, the broadcast signals emitted by the beacon devices near the target terminal are segmented to obtain different fields. Based on these different fields, it is first determined whether the beacon device is a positioning beacon device. Then, based on these different fields, the type of positioning beacon device and its distance from the target terminal are identified. Specifically, the aforementioned type of positioning beacon device may include, but is not limited to, Smart Tag, Air Tag, and other device types.

[0086] Secondly, the distance information between the positioning beacon device and the target terminal is obtained by performing a fixed signal strength analysis on the distance information between the positioning beacon device and the target terminal. For example, in this embodiment, when the broadcast signal strength information is between 0dB and -40dB, the fixed signal strength analysis determines that the distance between the positioning beacon device and the target terminal is very close, equivalent to being right next to the positioning beacon device. When the broadcast signal strength information is between -40dB and -60dB, the fixed signal strength analysis determines that the distance between the positioning beacon device and the target terminal is relatively close, roughly equivalent to being a few seats away on a high-speed train. When the broadcast signal strength information is below -60dB, the fixed signal strength analysis determines that the distance between the positioning beacon device and the target terminal is far, roughly equivalent to being in the same office or the same train carriage. In practical applications, the distance information between the positioning beacon device and the target terminal needs to be determined based on the specific application scenario and the specific broadcast signal strength information; therefore, the aforementioned examples should not be construed as limitations of this solution.

[0087] 104. Display the type icon and distance information of the positioning beacon device near the detected target terminal on the radar image detection page.

[0088] In this embodiment, the target terminal device displays the type icon and distance information of the location beacon device detected near the target terminal on the radar image detection page. This radar image detection page may include interface switching controls and the type icon of the location beacon device. For easier understanding, please refer to... Figure 5 , Figure 5 This is another schematic diagram of the radar image detection page provided in the embodiments of this application, such as... Figure 5 As shown, Figure 5 The image in (A) shows a radar image detection page presented through the shape of radar radiation. Figure 5 The diagram in (B) shows a radar image detection page presented using color difference. Based on this, D1 refers to the type icon of a location beacon device of type Smart Tag, D2 refers to the type icon of a location beacon device of type Air Tag, D3 refers to the type icon of location beacons with other device type information, D4 refers to the type icon of a location beacon device of type Stealth, and D5 refers to the interface switching control. It should be understood that... Figure 4 The examples provided are for illustrative purposes only. In practical applications, the radar chart detection page can not only present the interface using radar radiation shapes, but also display the interface using various distances. Furthermore, the icon types can be adjusted based on actual needs; no restrictions are imposed here.

[0089] This embodiment provides a method for detecting hidden beacons. Using this method, a beacon detection request is made to access a radar chart detection page for beacon devices near the target terminal. This page displays the type icon and distance information of the detected beacon devices near the target terminal. This provides a more intuitive and accurate display of detailed information about the beacons near the target terminal, assisting or guiding the user in identifying hidden beacons. This avoids situations where hidden beacons are disguised as several different beacons around the terminal, leading to incorrect identification by the terminal, thereby improving the network security of the terminal device.

[0090] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the concealed beacon detection method provided in this application, the radar image detection page further includes an interface switching control;

[0091] Hidden beacon detection methods also include:

[0092] Upon receiving a trigger request for the type icon of the positioning beacon device on the radar image detection page, enter the positioning beacon device details display page corresponding to the type icon to display advanced details of the positioning beacon device;

[0093] Upon receiving an interface switching request for the interface switching control, the system proceeds to the detection and analysis results page for the location beacon devices detected on the radar map detection page. The detection and analysis results page displays the detection and analysis results for all location beacon devices to assist or guide the user in identifying hidden beacons.

[0094] In this embodiment, the target terminal device receives a trigger request for the type icon of the positioning beacon device on the radar image detection page, and enters the positioning beacon device details display page corresponding to the type icon to display advanced details of the positioning beacon device. Specifically, because in Figure 2As shown in step 104, the target terminal device displays the type icon and distance information of the nearby positioning beacon devices on the radar image detection page. If the user wishes to use other display methods, such as list display or detailed display, then based on the user's needs, the type icon of the positioning beacon device on the radar image detection page can be manipulated. This causes the target terminal device to receive a trigger request for the type icon of the positioning beacon device on the radar image detection page and enter the detailed display page of the positioning beacon device corresponding to the type icon, so as to display the advanced detailed information of the positioning beacon device. It should be understood that the advanced detailed information includes the type icon of the positioning beacon device and the broadcast signal strength information. In practical applications, the advanced detailed information may also include, but is not limited to, the battery status information of the positioning beacon device and the MAC address of the positioning beacon device.

[0095] For ease of understanding, based on Figure 5 For an example, please see [example]. Figure 6 , Figure 6 This is a schematic diagram of an interface for displaying the details of a location beacon device corresponding to a type icon provided in an embodiment of this application, such as... Figure 6 As shown, G1 refers to the type icon of a location beacon device of type SmartTag, G2 refers to the type icon of a location beacon device of type AirTag, G3 refers to the type icon of a location beacon device with other device type information, and G4 refers to the type icon of a location beacon device of type stealth. Therefore, it can be seen that in... Figure 6 In Figure (A), if a user wants to see more detailed information about a concealed positioning beacon device, they can interact with the type icon G4 to obtain the desired information. Figure 6 Figure (B) Figure 6 In Figure (B), G5 refers to the type icon of the concealed beacon device, G6 refers to the concealed type, and G7 refers to the broadcast signal strength information of the beacon device.

[0096] Furthermore, users can interact with the interface switching controls on the radar chart detection page to request a switch between interfaces on the target terminal device. This request leads to a page displaying the detection and analysis results of the location beacon devices detected on the radar chart. The detection and analysis results page displays the results for all location beacon devices in a list format, showing the type icons and distance information of each device to assist or guide the user in identifying concealed beacons. It should be understood that this detection and analysis results page can also display the battery status information and MAC address of the location beacon devices.

[0097] For ease of understanding, based on Figure 5 For an example, please see [example]. Figure 7 , Figure 7 This is a schematic diagram of an interface for the detection and analysis results page provided in an embodiment of this application, such as... Figure 7 As shown, F1 refers to the type icon of a location beacon device of type Smart Tag, F2 refers to the type icon of a location beacon device of type Air Tag, F3 refers to the type icon of location beacon devices with other device type information, F4 refers to the type icon of a location beacon device of type Stealth, and F5 refers to the interface switching control. Therefore, it can be seen that in... Figure 7 In Figure (A), if a user wants to display the detection and analysis results of the detected location beacon devices in a list format, the user needs to operate the interface switching control F5 to obtain the results. Figure 7 Figure (B) shows the detection analysis results page displaying the detection analysis results of all positioning beacon devices. F5 refers to the interface switching control, F6 refers to the type icon of the concealed positioning beacon device, and F7 refers to the distance information of the positioning beacon device. Users can... Figure 7 You can also re-enter the interface by using the F5 control in Figure (B). Figure 7 The interface shown in Figure (A) will not be described in detail here.

[0098] It should be understood that the diagram and Figure 7 The examples provided are for illustrative purposes only and should not be construed as limitations of this solution. Furthermore, in practical applications, the target terminal device can also display advanced details such as the change curve of the broadcast signal strength information of the beacon device within a preset time period, or show the time of the last scan of the broadcast signal on the detection and analysis results page, etc. These are not exhaustive examples here.

[0099] This embodiment provides another method for detecting concealed beacons. By displaying multi-dimensional information about beacon devices on the radar map detection page, users of the target terminal device can more intuitively learn about the relevant information of surrounding beacon devices. Based on user needs, advanced details of the location beacon devices can be displayed on the location beacon device details display page, or the detection analysis results of all location beacon devices can be displayed on the detection analysis results page. This helps or guides users to more conveniently identify concealed beacons. Therefore, while ensuring the feasibility of concealed beacon detection, the security and flexibility of the terminal device are further improved.

[0100] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the hidden beacon detection method provided in this application, the hidden beacon detection method further includes:

[0101] Based on the broadcast signals emitted by beacon devices near the target terminal, the media access control MAC address of the positioning beacon device and the power status information of the positioning beacon device are analyzed and identified.

[0102] Upon receiving a trigger request for the type icon of the positioning beacon device on the radar image detection page, the system will navigate to the positioning beacon device details display page corresponding to the type icon to show advanced details of the positioning beacon device, specifically including:

[0103] Upon receiving a trigger request for the type icon of the positioning beacon device on the radar image detection page, the system will enter the positioning beacon device details display page corresponding to the type icon to display the MAC address of the positioning beacon device and the battery status information of the positioning beacon device.

[0104] In this embodiment, the target terminal device can also analyze and identify the Media Access Control (MAC) address of the positioning beacon device and its battery status information based on the broadcast signals emitted by beacon devices near the target terminal. For example, the MAC address of positioning beacon device 1 is "32:E9:52:0A:11", and the MAC address of positioning beacon device 2 is "D0:B6:26:AF:51", etc. The battery status information of positioning beacon device 1 is "50%", and the battery status information of positioning beacon device 2 is "100%", etc.

[0105] Based on this, after the target terminal device displays the type icon and distance information of the detected positioning beacon devices near the target terminal on the radar image detection page, it can also receive trigger requests for the type icon of the positioning beacon device on the radar image detection page. This leads to a details display page for the positioning beacon device corresponding to the type icon. In addition to displaying the type icon and distance information, the page further displays the MAC address and battery status information of the positioning beacon device. Similarly, users can also operate the interface switching control on the radar image detection page, causing the target terminal device to receive an interface switching request. This leads to a detection analysis results page displaying the detection results of the positioning beacon devices detected on the radar image detection page. This detection analysis results page displays the type icon, distance information, battery status information, and MAC address of all positioning beacon devices, further assisting or guiding users in identifying hidden beacons. As can be seen from the foregoing embodiments, in practical applications, the target terminal device can also display advanced details information such as the change curve of the broadcast signal strength information of the beacon device within a preset time period, or display information such as the time when the broadcast signal was last scanned on the detection and analysis results page. Specific examples are not exhaustive here.

[0106] For a further understanding of this solution, please refer to [link / reference]. Figure 8 , Figure 8 This is another schematic diagram of the interface display page for the location beacon device details page corresponding to the type icon provided in the embodiments of this application, such as... Figure 8 As shown, H1 refers to the type icon of the concealed beacon device, H2 refers to the concealed type, H3 refers to the broadcast signal strength information of the beacon device, H4 refers to the MAC address of the beacon device, H5 refers to the battery status information of the beacon device, and H6 refers to the change curve of the broadcast signal strength information of the beacon device within a preset time period.

[0107] Secondly, please refer to Figure 9 , Figure 9 This is another schematic diagram of the detection and analysis results page provided in the embodiments of this application, such as... Figure 9 As shown, this is specifically the detection and analysis results page for all positioning beacon devices. E1 refers to the type icon of each positioning beacon device, E2 refers to the MAC address of each device, E3 refers to the battery status information, E4 refers to the distance information, E5 refers to the time the broadcast signal was last detected (15 seconds ago), and E6 refers to the interface switching control. It should be understood that... Figure 8 as well as Figure 9 The examples provided are for understanding this scheme only and should not be construed as limiting the scheme.

[0108] In this embodiment, another method for detecting hidden beacons is provided. By using the above method, information of multiple dimensions is further displayed on different display pages, enabling users to more intuitively learn about the relevant information of surrounding beacon devices. Furthermore, based on the time of the last scan of the broadcast signal and the change curve of the broadcast signal strength information of the beacon device, the method can further assist or guide users to more conveniently determine the hidden beacon, thereby improving the security and flexibility of the terminal device.

[0109] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the covert beacon detection method provided in this application, the type of the positioning beacon device and the distance information between the positioning beacon device and the target terminal are analyzed and identified based on the broadcast signal emitted by the beacon device near the target terminal. Specifically, this includes:

[0110] The first field in the broadcast signal is analyzed and identified to determine the location network type of the beacon device. Different location network types correspond to different load data extraction strategies.

[0111] By using the load data extraction strategy corresponding to the location network type, the target load data field is extracted from the load data field in the broadcast signal, and the beacon device is determined to be the location beacon device based on the target load data;

[0112] The load data field in the broadcast signal is analyzed and identified to obtain the type of positioning beacon device and the broadcast signal strength information of the positioning beacon device;

[0113] Signal strength analysis is performed on the broadcast signal strength information of the positioning beacon device to obtain the distance information between the positioning beacon device and the target terminal.

[0114] This embodiment provides a method for processing broadcast signals used in covert beacon detection. (See also: [link to previous document]). Figure 10 , Figure 10 This application provides a schematic flowchart of broadcast signal processing for covert beacon detection, as shown in the embodiments of the present application. Figure 10 As shown. The target terminal device, based on a similar method to the aforementioned embodiments, receives a beacon detection request and, in response, enters a radar chart detection page for detecting beacon devices near the target terminal. At this time, the target terminal device initializes the Bluetooth BLE environment based on the beacon detection request and waits to receive broadcast signals, thereby obtaining the broadcast signals emitted by beacon devices near the target terminal. There can be one or more beacon devices near the target terminal, which is not limited here. Specifically, the broadcast signal sent by the beacon device includes a first field, a second field, and a payload data field. Specifically, the first field is the company identifier code (CIC), the second field is the field corresponding to the advertisement type (AD Type), and the payload data field is the field corresponding to the data payload.

[0115] Based on this, in step 201, after the target terminal device obtains the broadcast signal sent by the beacon device, since the broadcast signal is usually in binary format, the target terminal device first segments the broadcast signal into a first field, a second field, and a payload data field according to the BLE broadcast technology standard. For example, the following uses an Apple Mac laptop to send a broadcast signal to the terminal device, and the binary format of the broadcast signal sent by the Apple Mac laptop to the target terminal device is "1EFF4C001219006AE80FC7205AC60DE5C54F89236A8AF16303B1D635C20200". For example, after receiving the aforementioned broadcast signal, the terminal device can divide it into four fields according to the BLE broadcast technology standard: “1E”, “FF”, “4C00”, and “1219006AE80FC7205AC60DE5C54F89236A8AF16303B1D635C20200”. The aforementioned field “0xFF” is the second field, the aforementioned field “4C00” is the first field, and the aforementioned field “1219006AE80FC7205AC60DE5C54F89236A8AF16303B1D635C20200” is the load data field. It should be understood that the foregoing example is only for understanding this solution. In practical applications, the broadcast signal can also be segmented into other fields. For example, field "1E" is the third field, and the third field is specifically the field corresponding to the broadcast message length (AD Length). Therefore, the specific field obtained from the segmentation should not be construed as a limitation of this solution. The specific segmentation techniques are processed according to the BLE broadcast technology standard, and therefore will not be elaborated here.

[0116] Based on this, the target terminal device needs to first determine whether the second field in the broadcast signal matches the first target field. If so, the target terminal device analyzes and identifies the first field in the broadcast signal to determine the beacon device's location network type. Different location network types correspond to different load data extraction strategies, and different location network types correspond to different second target fields. Since this scheme is based on BLE broadcast for covert beacon detection, it is known from the broadcast technology standard that broadcast signals sent using BLE broadcast technology must carry "0xFF". Therefore, the first target field in this scheme is specifically the field "0xFF". That is to say, when the second field in the broadcast signal is not the field "0xFF", it indicates that the beacon device is sending the broadcast signal using other broadcast technologies. This scheme does not perform any further processing on such broadcast signals, that is, it determines that the broadcast signal is a signal unrelated to BLE broadcast technology.

[0117] For example, when the second field obtained by the terminal device matches the field "0xFF", it indicates that the beacon device is transmitting the broadcast signal using BLE broadcast technology. Only then will the terminal device further analyze and identify the first field in the broadcast signal to determine the beacon device's location network type. This means that different location network types correspond to different first fields (Manufacturer Identification Code CICI). For instance, if the first field is "4C00", then the beacon device's location network type can be determined to be the Apple Find My location network type. Secondly, if the first field is "7500", then the beacon device's location network type can be determined to be the Samsung Galaxy Find location network type. Thirdly, if the first field is a field different from the aforementioned two, then the beacon device's location network type can be determined to be the Tile location network type or other location network types. It should be understood that since there are many location network types in actual applications, and different location network types have different location type fields set based on the needs of manufacturers or suppliers, they are not exhaustively listed here, and the aforementioned examples should not be construed as limitations of this solution.

[0118] In step 202, since different location network types correspond to different load data extraction strategies, the target terminal device will extract the target load data field from the load data field in the broadcast signal using the load data extraction strategy corresponding to the location network type determined in step 201, and determine the beacon device as a location beacon device based on the target load data. For example, if the determined beacon device's location network type is the Apple Find My location network type, then based on the Apple Find My location network... The load data extraction strategy corresponding to the network type of My is [data: 2], which means extracting the first two fields from the load data field. If we continue to take the example of Apple's Mac laptop sending a broadcast signal to the target terminal device, and the binary format of the broadcast signal sent by Apple's Mac laptop to the target terminal device is "1EFF4C001219006AE80FC7205AC60DE5C54F89236A8AF16303B1D635C20200", we know from the above example that the second field is the field "0xFF", the first field is the field "4C00", and the field "1219006AE80FC7205AC60DE5C54F89236A8AF16303B1D635C20200" is the load data field. If we extract the first two fields from the aforementioned load data field, we can get "1219", so the target load data field is "1219".

[0119] Furthermore, in step 202, since different positioning network types correspond to different second target fields as described above, when the target payload data field matches the second target field corresponding to the positioning network type, the target terminal device determines that the beacon device is a positioning beacon device. For example, if the determined positioning network type of the beacon device is the Apple Find My positioning network type, and the determined target payload data field is "1219", then for the Apple Find My positioning network type, the second target field corresponding to the Apple Find My positioning network type is "1219", and the second target field is specifically used to indicate that the broadcast signal is used for positioning. Therefore, it can be determined that the beacon device sending the broadcast signal is a positioning beacon device. At this time, it is known that the target payload data field "1219" matches the second target field "1219" corresponding to the Apple Find My positioning network type, and only then will the terminal device further process the payload data field. Conversely, if the target payload data field does not match the second target field corresponding to the positioning network type, it is determined that the broadcast signal is a signal unrelated to positioning within the positioning network type, meaning that the beacon device sending the broadcast signal is not a positioning beacon device. It should be understood that the specific target load data fields need to be determined based on the specific load data extraction strategy corresponding to the location network type and the obtained load data fields. Therefore, the above example should not be construed as a limitation of this solution.

[0120] In step 203, the target terminal device analyzes and identifies the load data field in the broadcast signal to obtain the type of the positioning beacon device and its broadcast signal strength information. Then, in step 204, the target terminal device performs signal strength analysis on the broadcast signal strength information of the positioning beacon device to obtain the distance information between the positioning beacon device and the target terminal. For example, if the broadcast signal strength information is between 0 dB and -40 dB, the target terminal device can determine that the distance between the positioning beacon device and the target terminal is very close, equivalent to being right next to it. If the broadcast signal strength information is between -40 and -60 dB, the target terminal device can determine that the distance between the positioning beacon device and the target terminal is relatively close, approximately equivalent to being a few seats away on a high-speed train. And if the broadcast signal strength information is below -60 dB, the target terminal device can determine that the distance between the positioning beacon device and the target terminal is far, approximately equivalent to being in the same office or the same train carriage. In practical applications, the distance between the positioning beacon device and the target terminal needs to be determined based on the specific application scenario and the specific broadcast signal strength information. Therefore, the aforementioned examples should not be construed as limitations of this solution.

[0121] It should be understood that, in different application scenarios, terminal devices can also process load data fields to determine the power status information of beacon devices, and under specific location network types, they can also determine the public key of beacon devices, that is, to find deep patterns under certain location network types through manual analysis of signal samples. For example, how to extract the broadcast public key of beacon devices under the Apple Find My location network type. Different location network types have different special information, which will not be described in detail here. The aforementioned examples should not be construed as limitations of this solution.

[0122] This embodiment provides a method for processing broadcast signals for covert beacon detection. Using this method, the location network type is determined through a first field and a second field in the broadcast signal. Then, a target load data field is extracted from the load data field using a load data extraction strategy corresponding to the location network type. Based on the load data field, the type of beacon device and the broadcast signal strength information are determined. This enables more accurate covert beacon detection to obtain detailed information about the beacon device, preventing the beacon device from disguising itself as several different beacon devices around the terminal device, thus avoiding incorrect identification by the terminal device and improving the network security of the terminal device.

[0123] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the covert beacon detection method provided in this application, the first field in the broadcast signal is analyzed and identified to determine the location network type of the beacon device, specifically including:

[0124] The first field in the broadcast signal is analyzed and identified by the first branch node of the classification decision tree model to determine the positioning network type of the beacon device. Different positioning network types correspond to different classification decision tree sub-models.

[0125] The load data fields in the broadcast signal are analyzed and identified to determine the type of positioning beacon device and its broadcast signal strength information, specifically including:

[0126] By using a classification decision tree sub-model corresponding to the location network type, the load data field in the broadcast signal is analyzed and identified to obtain the type of location beacon device and the broadcast signal strength information of the location beacon device.

[0127] This embodiment provides a method for occult beacon detection based on a classification decision tree model. The target terminal device analyzes and identifies the first field in the broadcast signal through the first branch node of the classification decision tree model. Specifically, the first branch node of the classification decision tree model matches the first field in the broadcast signal with the CIC of different location network types. Based on the matching result, the location network type of the beacon device is determined. At this time, different location network types correspond to different classification decision tree sub-models. For example, the CIC of the Apple Find My location network type is "4C00", and the CIC of the Samsung Galaxy Find location network type is "7500". If the second field is "4C00", it can be determined that the location network type of the beacon device is the Apple Find My location network type.

[0128] Furthermore, since different positioning network types correspond to different classification decision tree sub-models, the target terminal device further analyzes and identifies the load data fields in the broadcast signal using the classification decision tree sub-model corresponding to the positioning network type, in order to obtain the type of positioning beacon device and the broadcast signal strength information of the positioning beacon device. The following section will detail the method for analyzing and identifying the load data fields in the broadcast signal using the classification decision tree model corresponding to the positioning network type.

[0129] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the covert beacon detection method provided in this application, the payload data field in the broadcast signal is analyzed and identified through a classification decision tree sub-model corresponding to the location network type to obtain the type of the location beacon device and the broadcast signal strength information of the location beacon device, specifically including:

[0130] By locating the classification decision tree sub-model corresponding to the network type, the load data field in the broadcast signal is analyzed and identified to obtain the first load data field and the second load data field.

[0131] By analyzing and identifying the first branch sub-node of the classification decision tree sub-model corresponding to the location network type, the broadcast signal strength information of the location beacon device can be obtained.

[0132] By analyzing and identifying the second branch sub-node of the classification decision tree sub-model corresponding to the location network type, the type of the location beacon device can be obtained.

[0133] In this embodiment, the target terminal device processes the load data field using a classification decision tree sub-model corresponding to the location network type to obtain a first load data field and a second load data field. Then, it analyzes and identifies the first load data field using the first branch sub-node of the classification decision tree sub-model corresponding to the location network type to obtain the broadcast signal strength information of the location beacon device. Specifically, the broadcast signal strength information is the received signal strength indication (RSSI). Similarly, it analyzes and identifies the second load data field using the second branch sub-node of the classification decision tree model corresponding to the location network type to obtain the type of location beacon device. Furthermore, the target terminal device can also process the load data field using the classification decision tree model corresponding to the location network type to obtain a third candidate load data field. It then analyzes and identifies the third load data field using the third branch sub-node of the classification decision tree model corresponding to the location network type to obtain the power status information of the location beacon device.

[0134] For example, taking the classification decision tree sub-model corresponding to the Apple Find My location network type as an example, the first branch sub-node of the classification decision tree sub-model determines the broadcast signal strength information as -45, the second branch sub-node determines the type as AirTag, and the first branch sub-node determines the battery status information as 75%. Therefore, through the classification decision tree model corresponding to the Apple Find My location network type, the beacon device can be identified as an AirTag with a broadcast signal strength of -45 and a battery status of 75%. It should be understood that there is no hierarchical order of classification execution among the first, second, and third branch sub-nodes; this order needs to be determined based on the obtained classification decision tree sub-model. Furthermore, in the classification decision tree model corresponding to the Apple Find My location network type, device type information can also include AirPods, and battery status information can also include 100%, 50%, 25%, etc. Therefore, the aforementioned example should not be construed as a limitation of this application.

[0135] Furthermore, as demonstrated by the foregoing embodiments, in practical applications, under specific location network types, the target terminal device can also determine the public key of the beacon device, that is, by manually analyzing signal samples to find certain deep patterns under specific location network types. For ease of understanding, the example of how to extract the broadcast public key of the beacon device under the Apple Find My location network type will be used for illustration. Please refer to [link to relevant documentation]. Figure 11 ,Figure 11 This is a flowchart illustrating a method for extracting the broadcast public key of the Apple Find My location network type, as provided in an embodiment of this application. Figure 11 As shown, in step C1, the terminal device uses the fourth branch sub-node of the classification decision tree sub-model corresponding to the Apple Find My location network type to determine whether the second-to-last byte of the payload data field is "0". If so, step C2 is executed. Therefore, in step C2, the terminal device calculates the first 48 bits of the public key based on the medium access control (MAC) address of the beacon device. Otherwise, if not, step C3 is executed. In step C3, the terminal device calculates the first 48 bits of the public key based on the MAC address and the second-to-last byte of the beacon device. Then, in step C4, the terminal device extracts the last 176 bits of the public key from the payload data field. Finally, in step C5, the terminal device combines the first 48 bits of the public key calculated in step C2 or C3 with the last 176 bits of the public key extracted in step C4 to obtain a 224-bit public key. It should be understood that the above example is only for understanding this scheme and should not be construed as a limitation of this scheme.

[0136] In this embodiment, another method for detecting hidden beacons is provided. Using the above method, more detailed information about the beacon device is obtained through the classification decision tree sub-model corresponding to the positioning network type. Since different positioning network types correspond to different classification decision tree sub-models, different discrete feature information is classified through the sub-nodes of each branch in the classification decision tree model. This ensures the accuracy of hidden beacon detection under the positioning network type, while also ensuring the efficiency of hidden beacon detection.

[0137] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the covert beacon detection method provided in this application, the first field in the broadcast signal is analyzed and identified to determine the location network type of the beacon device, specifically including:

[0138] The second field in the broadcast signal is determined to match the first target field, and the first field in the broadcast signal is analyzed and identified to determine the positioning network type of the beacon device. Different positioning network types correspond to different second target fields.

[0139] The beacon device was identified as a location beacon device based on the target load data, specifically including:

[0140] The target load data field is determined to match the second target field corresponding to the location network type, and the beacon device is determined to be a location beacon device.

[0141] In this embodiment, after the target terminal device segments the broadcast signal into a first field, a second field, and a payload data field according to the BLE broadcast technology standard, it needs to determine whether the second field matches the first target field. If so, the terminal device determines that the second field matches the first target field, i.e., it determines that the broadcast signal is a signal generated based on BLE broadcast technology, and executes subsequent steps. Conversely, if not, the terminal device determines that the first field does not match the first target field, i.e., it determines that the broadcast signal is a signal unrelated to BLE broadcast technology. Based on this, after the target terminal device determines that the second field matches the first target field, the terminal device will analyze and identify the first field in the broadcast signal to determine the positioning network type of the beacon device. At this time, different positioning network types correspond to different second target fields. That is, based on the previous embodiment, the first field in the broadcast signal is matched with the CIC of different positioning network types through the first branch node of the classification decision tree model, and the positioning network type of the beacon device is determined based on the matching result. For example, the CIC for Apple's Find My location network type is "4C00", and the CIC for Samsung's Galaxy Find location network type is "7500". If the second field is "4C00", it can be determined that the beacon device's location network type is the Apple Find My location network type.

[0142] Since different positioning network types correspond to different second target fields, the target terminal device needs to further determine whether the target payload data field matches the second target field corresponding to the positioning network type. If it does, the terminal device determines that it has matched, thus confirming that the broadcast signal is a positioning signal within the positioning network type, and therefore confirming that the beacon device is a positioning beacon device. Conversely, if it does not, the terminal device determines that it has not matched, i.e., it determines that the broadcast signal is a positioning signal unrelated to positioning within the positioning network type. The following section will specifically introduce how to perform the aforementioned judgment based on a classification decision tree model.

[0143] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the covert beacon detection method provided in this application, determining that the second field in the broadcast signal hits the first target field specifically includes:

[0144] By using the root node of the classification decision tree model, it is determined that the second field in the broadcast signal matches the first target field;

[0145] Determine if the target load data field matches the second target field corresponding to the location network type, specifically including:

[0146] By using the second branch node of the classification decision tree model, we can determine the second target field that matches the location network type of the target load data field.

[0147] In this embodiment, the target terminal device determines whether the second field in the broadcast signal matches the first target field through the root node of the classification decision tree model. That is, it needs to determine whether the second field matches the first target field through the root node of the classification decision tree model. If yes, the terminal device determines that the second field matches the first target field, i.e., it determines that the broadcast signal is a signal generated based on BLE broadcast technology, and executes subsequent steps. Conversely, if no, the terminal device determines that the first field does not match the first target field, i.e., it determines that the broadcast signal is a signal unrelated to BLE broadcast technology. Based on this, after the target terminal device determines that the second field in the broadcast signal matches the first target field through the root node of the classification decision tree model, the target terminal device will analyze and identify the first field in the broadcast signal to determine the positioning network type of the beacon device. At this time, different positioning network types correspond to different second target fields. That is, based on the aforementioned embodiment, the first field in the broadcast signal is matched with the CIC of different positioning network types through the first branch node of the classification decision tree model, and the positioning network type of the beacon device is determined based on the matching result. For example, the CIC for Apple's Find My location network type is "4C00", and the CIC for Samsung's Galaxy Find location network type is "7500". If the second field is "4C00", it can be determined that the beacon device's location network type is the Apple Find My location network type.

[0148] Since different positioning network types correspond to different second target fields, the target terminal device needs to further determine whether the target payload data field matches the second target field corresponding to the positioning network type. This is done by using the second branch node of the classification decision tree model to determine if the target payload data field matches the second target field corresponding to the positioning network type. If yes, the terminal device determines that it has matched the target field, thus confirming that the broadcast signal is a positioning signal within the positioning network type, and therefore identifying the beacon device as a positioning beacon device. Conversely, if no, the terminal device determines that it has not matched the target field, meaning the broadcast signal is an irrelevant positioning signal within the positioning network type.

[0149] To facilitate understanding, we will again use the example of "1219" as the second target field corresponding to the network type in Apple's Find My location service. Please refer to [link / reference]. Figure 12 , Figure 12 This is a flowchart illustrating a method for hidden beacon detection based on a classification decision tree model, as provided in an embodiment of this application. Figure 12As shown, in step B1, the terminal device determines whether the second field matches "0xFF" through the root node of the classification decision tree model. If yes, step B2 is executed; otherwise, the broadcast signal is determined to be unrelated to BLE broadcast technology. In step B2, the first branch node of the classification decision tree model determines whether the second field is "4C00". If yes, the location network type is the Apple Find My location network type, and based on the load data extraction strategy corresponding to the Apple Find My location network type, the target load data field is extracted from the load data field, and then step B3 is executed. If no, the second field needs to be matched with the CIC of different location network types. If the second field is "7500", the location network type is the Samsung Galaxy Find location network type; if it is another field, the location network type may be another location network type, which is not exhaustively listed here.

[0150] Further, in step B3, the terminal device uses the second branch node of the classification decision tree model to determine whether the target load data field matches the second target field corresponding to the location network type. If yes, it executes B4, which is to process the load data field through the classification decision tree sub-model corresponding to the location network type of Apple Find My to determine the type of beacon device and the broadcast signal strength information. If no, the terminal device determines that the broadcast signal is a signal unrelated to location in the location network type. Figure 12 The examples provided are only for understanding the specific process of hidden beacon detection based on the classification decision tree model in this scheme, and should not be construed as limitations of this scheme.

[0151] In this embodiment, another method for hidden beacon detection based on a classification decision tree model is provided. Specifically, the relevant information of the beacon device is obtained through the classification decision tree model. The classification decision tree model is often used to classify data and can classify relatively discrete feature information. It does not require encoding the discrete feature information into sparse encoded vectors and then fitting the model. Therefore, the classification decision tree model can improve the efficiency of hidden beacon detection and ensure the accuracy of hidden beacon detection.

[0152] Optionally, in the above Figure 3 Based on the corresponding embodiments, in an optional embodiment of the hidden beacon detection method provided in this application, the hidden beacon detection method further includes:

[0153] Obtain a set of broadcast signal samples, wherein the set of broadcast signal samples includes multiple broadcast signal samples, each broadcast signal sample includes a field feature sample, the field feature sample includes a first field sample, a second field sample, and a payload data field sample, and each broadcast signal sample corresponds to the type of beacon device and the location network type of the beacon device;

[0154] Each broadcast signal sample in the broadcast signal sample set is segmented to obtain the field features of each broadcast signal sample. The field features include the first field feature, the second field feature, and the load data field feature.

[0155] Information gain is calculated for the field features of each broadcast signal sample to obtain the field feature information gain ratio of each broadcast signal sample. The field feature information gain ratio includes the first field feature information gain ratio, the second field feature information gain ratio, and the load data field feature information gain ratio.

[0156] A classification decision tree model is constructed based on the field feature information gain ratio of each broadcast signal sample.

[0157] In this embodiment, a training method for a classification decision tree model for covert beacon detection is provided. The terminal device first uses the BLE sniffing method to collect broadcast signal samples of different positioning network types, types, and different battery status information of the mobile phone. For example, the broadcast signal sample set includes broadcast signals sent by AirTag when fully charged (100%), broadcast signals sent by AirPods when fully charged (100%), broadcast signals sent by SmartTag when 50% battery, etc. The specifics are not limited here, so that the terminal device can obtain a broadcast signal sample set. The broadcast signal sample set includes multiple broadcast signal samples, each broadcast signal sample includes field feature samples, including a first field sample, a second field sample, and a load data field sample. Each broadcast signal sample corresponds to the type of beacon device, the positioning network type of the beacon device, and the broadcast signal strength information and battery status information of the beacon device.

[0158] Based on this, the terminal device segments each broadcast signal sample in the broadcast signal sample set by cross-comparing the frequently occurring field features in each broadcast signal sample, obtaining the field features of each broadcast signal sample. These field features include a first field feature, a second field feature, and a load data field feature. The first field feature is the field feature corresponding to the first field, the second field feature is the field feature corresponding to the second field, and the load data field feature is the field feature corresponding to the load data field. It should be understood that in practical applications, the field features may also include a third field feature, specifically the field feature corresponding to the AD Length field.

[0159] Furthermore, the terminal device uses the ID3 algorithm to calculate the information gain of the field features of each broadcast signal sample, obtaining the field feature information gain ratio for each broadcast signal sample. This field feature information gain ratio includes the information gain ratio of the first field feature, the information gain ratio of the second field feature, and the information gain ratio of the payload data field feature. It should be understood that in practical applications, if the field features may also include a third field feature, then the field feature information gain ratio may also include the information gain ratio of the third field feature. Therefore, the terminal device constructs a classification decision tree model based on the field feature information gain ratio of each broadcast signal sample, that is, sorting the different field feature information gain ratios from high to low. Based on the sorted field feature information gain ratios from high to low, a complete classification decision tree model is derived, and the obtained classification decision tree model is used for the aforementioned hidden beacon detection.

[0160] In this embodiment, a training method for a classification decision tree model for hidden beacon detection is provided. Using the above method, broadcast signal samples can correspond to different types, positioning network types, and power status information, etc., making the sources of broadcast signal samples in the broadcast signal sample set more extensive. Therefore, the classification decision tree model learned based on the ID3 algorithm is more complete, and the results obtained by identifying broadcast signals are more complete and accurate.

[0161] Figure 13 This is a schematic diagram of the structure of a concealed beacon detection device provided in an embodiment of this application, as shown below. Figure 13 As shown, the concealed beacon detection device 1300 includes:

[0162] The receiving module 1301 is used to receive a beacon detection request, wherein the beacon detection request is used to request the detection of beacon devices near the target terminal;

[0163] Response module 1302 is used to respond to a beacon detection request and enter the radar image detection page of beacon devices near the target terminal;

[0164] The processing module 1303 is used to acquire the broadcast signal emitted by the beacon device near the target terminal, and analyze and identify the type of the positioning beacon device and the distance information between the positioning beacon device and the target terminal based on the broadcast signal emitted by the beacon device near the target terminal.

[0165] Display module 1304 is used to display the type icon and distance information of the positioning beacon device near the detected target terminal on the radar image detection page.

[0166] Optionally, in the above Figure 13Based on the corresponding embodiments, in another embodiment of the concealed beacon detection device 1300 provided in this application, the radar image detection page further includes an interface switching control;

[0167] The receiving module 1301 is also used to receive a trigger request for the type icon of the positioning beacon device on the radar image detection page, and enter the positioning beacon device details display page corresponding to the type icon to display advanced details of the positioning beacon device;

[0168] The receiving module 1301 is also used to receive an interface switching request for the interface switching control, enter the detection analysis result page of the positioning beacon devices detected on the radar image detection page, and display the detection analysis results of all positioning beacon devices on the detection analysis result page to assist or guide the user in identifying hidden beacons.

[0169] Optionally, in the above Figure 13 Based on the corresponding embodiments, in another embodiment of the concealed beacon detection device 1300 provided in this application, the processing module 1303 is further used to analyze and identify the media access control MAC address of the positioning beacon device and the power status information of the positioning beacon device based on the broadcast signal emitted by the beacon device near the target terminal.

[0170] The receiving module 1301 is specifically used to receive trigger requests for the type icon of the positioning beacon device on the radar image detection page, and enter the positioning beacon device details display page corresponding to the type icon to display the MAC address of the positioning beacon device and the battery status information of the positioning beacon device.

[0171] Optionally, in the above Figure 13 Based on the corresponding embodiments, in another embodiment of the concealed beacon detection device 1300 provided in this application, the processing module 1303 is specifically used to analyze and identify the first field in the broadcast signal to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different load data extraction strategies.

[0172] By using the load data extraction strategy corresponding to the location network type, the target load data field is extracted from the load data field in the broadcast signal, and the beacon device is determined to be the location beacon device based on the target load data;

[0173] The load data field in the broadcast signal is analyzed and identified to obtain the type of positioning beacon device and the broadcast signal strength information of the positioning beacon device;

[0174] Signal strength analysis is performed on the broadcast signal strength information of the positioning beacon device to obtain the distance information between the positioning beacon device and the target terminal.

[0175] Optionally, in the above Figure 13 Based on the corresponding embodiments, in another embodiment of the hidden beacon detection device 1300 provided in this application, the processing module 1303 is specifically used to analyze and identify the first field in the broadcast signal through the first branch node of the classification decision tree model in order to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different classification decision tree sub-models;

[0176] The processing module 1303 is specifically used to analyze and identify the load data field in the broadcast signal through the classification decision tree sub-model corresponding to the positioning network type, so as to obtain the type of positioning beacon device and the broadcast signal strength information of the positioning beacon device.

[0177] Optionally, in the above Figure 13 Based on the corresponding embodiments, in another embodiment of the hidden beacon detection device 1300 provided in this application, the processing module 1303 is specifically used to analyze and identify the load data field in the broadcast signal by using the classification decision tree sub-model corresponding to the positioning network type, so as to obtain the first load data field and the second load data field.

[0178] By analyzing and identifying the first branch sub-node of the classification decision tree sub-model corresponding to the location network type, the broadcast signal strength information of the location beacon device can be obtained.

[0179] By analyzing and identifying the second branch sub-node of the classification decision tree sub-model corresponding to the location network type, the type of the location beacon device can be obtained.

[0180] Optionally, in the above Figure 13 Based on the corresponding embodiments, in another embodiment of the concealed beacon detection device 1300 provided in this application, the processing module 1303 is specifically used to determine that the second field in the broadcast signal hits the first target field, and to analyze and identify the first field in the broadcast signal to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different second target fields;

[0181] The processing module 1303 is specifically used to determine that the target load data field matches the second target field corresponding to the positioning network type, and to determine that the beacon device is a positioning beacon device.

[0182] Optionally, in the above Figure 13 Based on the corresponding embodiments, in another embodiment of the hidden beacon detection device 1300 provided in this application, the processing module 1303 is specifically used to determine whether the second field in the broadcast signal hits the first target field through the root node of the classification decision tree model;

[0183] The processing module 1303 is specifically used to determine the second target field corresponding to the location network type by using the second branch node of the classification decision tree model.

[0184] Optionally, in the above Figure 13 Based on the corresponding embodiments, in another embodiment of the concealed beacon detection device 1303 provided in this application, the concealed beacon detection device 1303 further includes an acquisition module 1305 and a model building module 1306;

[0185] The acquisition module 1305 is used to acquire a set of broadcast signal samples, wherein the set of broadcast signal samples includes multiple broadcast signal samples, each broadcast signal sample includes a field feature sample, the field feature sample includes a first field sample, a second field sample, and a load data field sample, and each broadcast signal sample corresponds to the type of beacon device and the location network type of the beacon device.

[0186] The processing module 1303 is also used to segment each broadcast signal sample in the broadcast signal sample set to obtain the field features of each broadcast signal sample, wherein the field features include the first field feature, the second field feature and the load data field feature;

[0187] The processing module 1303 is also used to calculate the information gain of the field features of each broadcast signal sample to obtain the field feature information gain ratio of each broadcast signal sample, wherein the field feature information gain ratio includes the first field feature information gain ratio, the second field feature information gain ratio, and the load data field feature information gain ratio.

[0188] Model building module 1306 is used to build a classification decision tree model based on the field feature information gain ratio of each broadcast signal sample.

[0189] This application also provides another covert beacon detection device, all of which are deployed on terminal devices, such as... Figure 14 As shown, for ease of explanation, only the parts related to the embodiments of this application are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of this application. The following explanation uses a mobile phone as an example of a terminal device:

[0190] Figure 14 This is a block diagram illustrating a portion of the structure of a mobile phone related to the terminal provided in the embodiments of this application. (Reference) Figure 14The mobile phone includes components such as a radio frequency (RF) circuit 1110, a memory 1120, an input unit 1130, a display unit 1140, a sensor 1150, an audio circuit 1160, a wireless fidelity (WiFi) module 1170, a processor 1180, and a power supply 1190. Those skilled in the art will understand that... Figure 14 The mobile phone structure shown does not constitute a limitation on the mobile phone and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0191] The following is combined Figure 14 A detailed introduction to each component of a mobile phone:

[0192] RF circuit 1110 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink information from the base station and processes it with processor 1180; additionally, it transmits uplink data to the base station. Typically, RF circuit 1110 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc. Furthermore, RF circuit 1110 can also communicate wirelessly with networks and other devices. The aforementioned wireless communication can use any communication standard or protocol, including but not limited to Global System for Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0193] The memory 1120 can be used to store software programs and modules. The processor 1180 executes various mobile phone functions and data processing by running the software programs and modules stored in the memory 1120. The memory 1120 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 1120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0194] The input unit 1130 can be used to receive input numerical or character information, and to generate key signal inputs related to the object settings and function control of the mobile phone. Specifically, the input unit 1130 may include a touch panel 1131 and other input devices 1132. The touch panel 1131, also known as a touch screen, can collect touch operations on or near the object (such as operations performed by the object using a finger, stylus, or any suitable object or accessory on or near the touch panel 1131), and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 1131 may include two parts: a touch detection device and a touch controller. The touch detection device detects the touch position of the object and detects the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 1180, and can also receive and execute commands sent by the processor 1180. In addition, the touch panel 1131 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 1131, the input unit 1130 may also include other input devices 1132. Specifically, other input devices 1132 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0195] Display unit 1140 can be used to display information input by an object or information provided to an object, as well as various menus of the mobile phone. Display unit 1140 may include display panel 1141, optionally configured as a Liquid Crystal Display (LCD), Organic Light-Emitting Diode (OLED), or similar display panel 1141. Further, touch panel 1131 may cover display panel 1141. When touch panel 1131 detects a touch operation on or near it, it transmits the information to processor 1180 to determine the type of touch event. Subsequently, processor 1180 provides corresponding visual output on display panel 1141 based on the type of touch event. Although in Figure 14 In this embodiment, the touch panel 1131 and the display panel 1141 are two separate components to realize the input and output functions of the mobile phone. However, in some embodiments, the touch panel 1131 and the display panel 1141 can be integrated to realize the input and output functions of the mobile phone.

[0196] The mobile phone may also include at least one sensor 1150, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 1141 according to the ambient light level, and the proximity sensor can turn off the display panel 1141 and / or the backlight when the phone is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used for applications that recognize the phone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition-related functions (such as pedometer, taps), etc. Other sensors that may be configured in the mobile phone, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, are not described in detail here.

[0197] Audio circuit 1160, speaker 1161, and microphone 1162 provide an audio interface between the device and the mobile phone. Audio circuit 1160 converts received audio data into electrical signals and transmits them to speaker 1161, where speaker 1161 converts them into sound signals for output. On the other hand, microphone 1162 converts collected sound signals into electrical signals, which are received by audio circuit 1160, converted into audio data, and then processed by processor 1180 before being transmitted via RF circuit 1110 to, for example, another mobile phone, or the audio data can be output to memory 1120 for further processing.

[0198] WiFi is a short-range wireless transmission technology. A mobile phone using the WiFi module 1170 can help users send and receive emails, browse web pages, and access streaming media, providing them with wireless broadband internet access. Although Figure 14 WiFi module 1170 is shown, but it is understood that it is not an essential component of a mobile phone.

[0199] The processor 1180 is the control center of the mobile phone, connecting various parts of the phone through various interfaces and lines. It executes various functions and processes data by running or executing software programs and / or modules stored in the memory 1120, and by calling data stored in the memory 1120. Optionally, the processor 1180 may include one or more processing units; preferably, the processor 1180 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 1180.

[0200] The mobile phone also includes a power supply 1190 (such as a battery) that supplies power to various components. Preferably, the power supply can be logically connected to the processor 1180 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0201] Although not shown, mobile phones may also include a camera, Bluetooth module, etc., which will not be elaborated here.

[0202] In this embodiment of the application, the processor 1180 included in the terminal is used to perform, for example... Figure 3 The illustrated embodiments and Figure 3 The corresponding embodiments.

[0203] This application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to perform the aforementioned actions. Figure 3 The steps performed by the target terminal device in the method described in the illustrated embodiment.

[0204] This application also provides a computer program product including a program, which, when run on a computer, causes the computer to perform the aforementioned actions. Figure 3 The steps performed by the target terminal device in the method described in the illustrated embodiment.

[0205] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0206] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, at least two units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0207] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across at least two network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0208] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0209] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0210] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for detecting concealed beacons, characterized in that, include: Receive a beacon detection request, wherein the beacon detection request is used to request the detection of beacon devices near the target terminal; In response to the beacon detection request, the system enters the radar image detection page for detecting beacon devices near the target terminal; The system acquires broadcast signals emitted by beacon devices near the target terminal, and analyzes and identifies the type of positioning beacon device and the distance information between the positioning beacon device and the target terminal based on the broadcast signals emitted by the beacon devices near the target terminal. The radar image detection page displays the type icon and distance information of the positioning beacon device near the detected target terminal. The type icon is used to distinguish whether the positioning beacon device is of the concealed type.

2. The detection method according to claim 1, characterized in that, The radar image detection page also includes interface switching controls; The method further includes: Upon receiving a trigger request for the type icon of the positioning beacon device on the radar image detection page, the system enters the positioning beacon device details display page corresponding to the type icon to display advanced details of the positioning beacon device; Upon receiving an interface switching request for the interface switching control, the system enters a page displaying the detection and analysis results of the positioning beacon devices detected on the radar image detection page. The detection and analysis results of all positioning beacon devices are displayed on the detection and analysis results page to assist or guide the user in identifying concealed beacons.

3. The detection method according to claim 2, characterized in that, The method further includes: Based on the broadcast signal emitted by the beacon device near the target terminal, the media access control MAC address of the positioning beacon device and the power status information of the positioning beacon device are analyzed and identified. The step of receiving a trigger request for a type icon of a positioning beacon device on the radar image detection page, and entering the positioning beacon device details display page corresponding to the type icon, to display advanced details of the positioning beacon device, including: Upon receiving a trigger request for the type icon of the positioning beacon device on the radar image detection page, the system enters the positioning beacon device details display page corresponding to the type icon to display the MAC address of the positioning beacon device and the battery status information of the positioning beacon device.

4. The detection method according to claim 1, characterized in that, The method of analyzing and identifying the type of positioning beacon device and the distance information between the positioning beacon device and the target terminal based on the broadcast signal emitted by the beacon device near the target terminal includes: The first field in the broadcast signal is analyzed and identified to determine the location network type of the beacon device, wherein different location network types correspond to different load data extraction strategies; Using the load data extraction strategy corresponding to the location network type, the target load data field is extracted from the load data field in the broadcast signal, and the beacon device is determined to be the location beacon device based on the target load data; The load data field in the broadcast signal is analyzed and identified to obtain the type of the positioning beacon device and the broadcast signal strength information of the positioning beacon device; Signal strength analysis is performed on the broadcast signal strength information of the positioning beacon device to obtain the distance information between the positioning beacon device and the target terminal.

5. The detection method according to claim 4, characterized in that, Analyzing and identifying the first field in the broadcast signal to determine the location network type of the beacon device includes: The first field in the broadcast signal is analyzed and identified by the first branch node of the classification decision tree model to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different classification decision tree sub-models; The step of analyzing and identifying the load data field in the broadcast signal to obtain the type of the positioning beacon device and the broadcast signal strength information of the positioning beacon device includes: By using the classification decision tree sub-model corresponding to the positioning network type, the load data field in the broadcast signal is analyzed and identified to obtain the type of the positioning beacon device and the broadcast signal strength information of the positioning beacon device.

6. The detection method according to claim 5, characterized in that, The step of analyzing and identifying the load data field in the broadcast signal using a classification decision tree sub-model corresponding to the positioning network type to obtain the type of the positioning beacon device and the broadcast signal strength information of the positioning beacon device includes: The load data field in the broadcast signal is analyzed and identified using the classification decision tree sub-model corresponding to the positioning network type to obtain the first load data field and the second load data field. The first load data field is analyzed and identified through the first branch sub-node of the classification decision tree sub-model corresponding to the positioning network type to obtain the broadcast signal strength information of the positioning beacon device. By analyzing and identifying the second load data field through the second branch sub-node of the classification decision tree sub-model corresponding to the positioning network type, the type of the positioning beacon device can be obtained.

7. The detection method according to claim 4, characterized in that, The step of analyzing and identifying the first field in the broadcast signal to determine the location network type of the beacon device includes: The second field in the broadcast signal is determined to match the first target field, and the first field in the broadcast signal is analyzed and identified to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different second target fields; The step of determining the beacon device as the positioning beacon device based on the target load data includes: The target load data field is determined to match the second target field corresponding to the location network type, and the beacon device is determined to be the location beacon device.

8. The detection method according to claim 7, characterized in that, Determining that the second field in the broadcast signal matches the first target field includes: By using the root node of the classification decision tree model, it is determined that the second field in the broadcast signal matches the first target field; The step of determining that the target load data field matches the second target field corresponding to the location network type includes: By using the second branch node of the classification decision tree model, it is determined that the target load data field matches the second target field corresponding to the location network type.

9. The detection method according to claim 5, characterized in that, The method further includes: Obtain a set of broadcast signal samples, wherein the set of broadcast signal samples includes multiple broadcast signal samples, each broadcast signal sample includes a field feature sample, the field feature sample includes a first field sample, a second field sample, and a payload data field sample, and each broadcast signal sample corresponds to the type of beacon device and the location network type of the beacon device; Each broadcast signal sample in the broadcast signal sample set is segmented to obtain the field features of each broadcast signal sample, wherein the field features include a first field feature, a second field feature, and a load data field feature; Information gain is calculated for the field features of each broadcast signal sample to obtain the field feature information gain ratio of each broadcast signal sample, wherein the field feature information gain ratio includes the first field feature information gain ratio, the second field feature information gain ratio, and the load data field feature information gain ratio; The classification decision tree model is constructed based on the field feature information gain ratio of each broadcast signal sample.

10. A concealed beacon detection device, characterized in that, The concealed beacon detection device includes: A receiving module is used to receive a beacon detection request, wherein the beacon detection request is used to request the detection of beacon devices near the target terminal; The response module is used to respond to the beacon detection request and enter the radar image detection page for detecting beacon devices near the target terminal; The processing module is used to acquire broadcast signals emitted by beacon devices near the target terminal, and analyze and identify the type of positioning beacon device and the distance information of the positioning beacon device from the target terminal based on the broadcast signals emitted by the beacon devices near the target terminal. The display module is used to display the type icon and distance information of the positioning beacon device near the detected target terminal on the radar image detection page. The type icon is used to distinguish whether the positioning beacon device is of the concealed type.

11. The detection device according to claim 10, characterized in that, The radar image detection page also includes interface switching controls; The receiving module is also configured to receive a trigger request for the type icon of the positioning beacon device on the radar image detection page, and enter the positioning beacon device details display page corresponding to the type icon to display advanced details information of the positioning beacon device; The receiving module is also used to receive an interface switching request for the interface switching control, enter the detection analysis result page of the positioning beacon devices detected on the radar image detection page, and display the detection analysis result page of all positioning beacon devices to assist or guide the user in identifying hidden beacons.

12. The detection device according to claim 11, characterized in that, The processing module is also used to analyze and identify the Media Access Control (MAC) address of the positioning beacon device and the battery status information of the positioning beacon device based on the broadcast signal emitted by the beacon device near the target terminal. The receiving module is specifically used to receive a trigger request for the type icon of the positioning beacon device on the radar image detection page, and enter the positioning beacon device details display page corresponding to the type icon to display the MAC address of the positioning beacon device and the battery status information of the positioning beacon device.

13. The detection device according to claim 10, characterized in that, The processing module is specifically used to analyze and identify the first field in the broadcast signal to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different load data extraction strategies; Using the load data extraction strategy corresponding to the location network type, the target load data field is extracted from the load data field in the broadcast signal, and the beacon device is determined to be the location beacon device based on the target load data; The load data field in the broadcast signal is analyzed and identified to obtain the type of the positioning beacon device and the broadcast signal strength information of the positioning beacon device; Signal strength analysis is performed on the broadcast signal strength information of the positioning beacon device to obtain the distance information between the positioning beacon device and the target terminal.

14. The detection device according to claim 13, characterized in that, The processing module is specifically used to analyze and identify the first field in the broadcast signal through the first branch node of the classification decision tree model in order to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different classification decision tree sub-models; The processing module is specifically used to analyze and identify the load data field in the broadcast signal through the classification decision tree sub-model corresponding to the positioning network type, so as to obtain the type of the positioning beacon device and the broadcast signal strength information of the positioning beacon device.

15. The detection device according to claim 14, characterized in that, The processing module is specifically used to analyze and identify the load data field in the broadcast signal through the classification decision tree sub-model corresponding to the positioning network type, so as to obtain the first load data field and the second load data field. The first load data field is analyzed and identified through the first branch sub-node of the classification decision tree sub-model corresponding to the positioning network type to obtain the broadcast signal strength information of the positioning beacon device. By analyzing and identifying the second load data field through the second branch sub-node of the classification decision tree sub-model corresponding to the positioning network type, the type of the positioning beacon device can be obtained.

16. The detection device according to claim 13, characterized in that, The processing module is specifically used to determine that the second field in the broadcast signal hits the first target field, and to analyze and identify the first field in the broadcast signal to determine the positioning network type of the beacon device, wherein different positioning network types correspond to different second target fields; The processing module is specifically used to determine that the target load data field matches the second target field corresponding to the location network type, and to determine that the beacon device is the location beacon device.

17. The detection device according to claim 16, characterized in that, The processing module is specifically used to determine, through the root node of the classification decision tree model, whether the second field in the broadcast signal matches the first target field. The processing module is specifically used to determine, through the second branch node of the classification decision tree model, whether the target load data field matches the second target field corresponding to the positioning network type.

18. The detection device according to claim 14, characterized in that, The detection device also includes an acquisition module and a model building module; The acquisition module is used to acquire a set of broadcast signal samples, wherein the set of broadcast signal samples includes multiple broadcast signal samples, each broadcast signal sample includes a field feature sample, the field feature sample includes a first field sample, a second field sample, and a load data field sample, and each broadcast signal sample corresponds to the type of beacon device and the location network type of the beacon device; The processing module is further configured to segment each broadcast signal sample in the broadcast signal sample set to obtain field features of each broadcast signal sample, wherein the field features include a first field feature, a second field feature, and a load data field feature; The processing module is further configured to perform information gain calculation on the field features of each broadcast signal sample to obtain the field feature information gain ratio of each broadcast signal sample, wherein the field feature information gain ratio includes the first field feature information gain ratio, the second field feature information gain ratio, and the load data field feature information gain ratio. The model building module is used to build the classification decision tree model based on the field feature information gain ratio of each broadcast signal sample.

19. A computer device, characterized in that, include: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is used to execute the program in the memory to implement the method of any one of claims 1 to 9; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

20. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method as claimed in any one of claims 1 to 9.

21. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method as described in any one of claims 1 to 9.

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