Method and device for detecting abnormal signal and readable storage medium

Abstract

The embodiment of the invention discloses a method and equipment for detecting an abnormal signal and a readable storage medium. The method comprises the following steps: when the communication equipment is disconnected with access network equipment, the communication equipment acquires M frequency points supported by the communication equipment, and M is a positive integer; the communication equipment respectively measures the RSSIs on the M frequency points to obtain N RSSI values, and N is an integer greater than or equal to M; and according to the N RSSI values, whether an abnormal signal exists in the environment is determined, and the RSSI value of the frequency point where the abnormal signal is located is greater than or equal to a preset threshold value. According to the embodiment of the invention, the method can achieve the efficient detection of the abnormal signal while saving the cost.

Description

Technical Field

[0001] The present application relates to the field of signal detection technology, and in particular to a method, device and readable storage medium for detecting abnormal signals. Background Art

[0002] With the increasing popularity of wireless communication technology, various wireless devices have emerged one after another, including a large number of hidden cameras, eavesdropping devices and positioning tracking devices. These devices are often small in size, highly disguised, and difficult to detect, posing a serious threat to people's privacy and security.

[0003] Hidden wireless devices (also referred to as hidden devices) emit a large amount of radio signals during use. For example, hidden cameras and eavesdropping devices emit a large amount of radio signals for a long time when uploading audio or image data. At present, the characteristic of hidden cameras emitting infrared light can be used to find hidden devices. Infrared rays are mainly used for video recording in low-light environments. If the hidden camera turns off this function, this method cannot detect hidden devices. At the same time, this method cannot detect hidden eavesdropping devices. Professional wireless detection equipment is usually expensive, large in size and inconvenient to carry.

[0004] Therefore, how to achieve efficient and accurate detection of hidden equipment while saving costs remains to be explored. Summary of the invention

[0005] The embodiments of the present application provide a method, device and readable storage medium for detecting abnormal signals, which can achieve efficient and accurate concealed device detection while saving costs.

[0006] In a first aspect, the present application provides a method for detecting abnormal signals, which is applied to a communication device, comprising: when the communication device is disconnected from the access network device, obtaining M frequency points supported by the communication device. Measure the received signal strength indicator (RSSI) at the M frequency points respectively to obtain N RSSI values, and measure at least one RSSI value at one frequency point. According to the N RSSI values, determine whether there is an abnormal signal in the environment, and the RSSI value at the frequency point where the abnormal signal is located is greater than or equal to a preset threshold value. Wherein, M is a positive integer, and N is an integer greater than or equal to M.

[0007] It can be understood that if the communication device determines that there is an abnormal signal in the environment, it means that there is a hidden wireless device in the environment; if the communication device determines that there is no abnormal signal in the environment, it means that there is no hidden wireless device in the environment.

[0008] Exemplarily, the communication device may be a terminal device with wireless transceiver function, such as a mobile phone, a notebook with a mobile communication module, a tablet, etc. In addition, the communication device also has a radio power measurement function, that is, has the ability to measure the RSSI of the signal.

[0009] Exemplarily, in the embodiment of the present application, after the communication device is disconnected from the access network device, the control module in the communication device can control the communication device to send or receive signals through an attention (AT) command.

[0010] The embodiment of the present application uses existing communication equipment to measure the RSSI at multiple frequency points supported by itself, and analyzes the measured RSSI to determine whether there are abnormal signals in the environment, and then determine whether there are hidden wireless devices in the environment. No additional hardware equipment and software development are required, while saving costs, it can efficiently and accurately detect whether there are hidden wireless devices in the environment, protecting the privacy and security of users.

[0011] In combination with the first aspect, in a possible implementation manner, obtaining M frequency points supported by the communication device includes:

[0012] Obtain a frequency band list corresponding to at least one communication standard supported by the communication device; determine M frequency points from the frequency band list corresponding to the at least one communication standard, wherein the frequency interval between two adjacent frequency points is a fixed value. Exemplarily, the frequency interval between two adjacent frequency points may be 200 kHz.

[0013] Generally, a communication device can support one or more communication standards. Under one communication standard, the communication device can support communication on multiple frequency bands. A frequency band can include multiple frequency points, and there is a fixed frequency interval between adjacent frequency points. Exemplarily, the communication standard can include 4G or 5G or the next generation communication standard, such as 6G.

[0014] The present application discovers hidden wireless devices by performing wireless power scanning on all frequency bands under all communication formats supported by the communication device, thereby improving the accuracy of detection.

[0015] In combination with the first aspect, in a possible implementation manner, respectively measuring the RSSIs at the M frequency points to obtain N RSSI values ​​includes:

[0016] Measuring multiple times at each of the M frequency points to obtain the maximum RSSI value of the signal received at each frequency point. Determining whether there is an abnormal signal in the environment based on the N RSSI values ​​includes: determining whether there is an abnormal signal based on the maximum RSSI value of the signal received at each frequency point.

[0017] It can be understood that since the time when the hidden wireless device sends the radio signal is unknown, the present application measures multiple times at each frequency point to increase the possibility of detecting abnormal signals, thereby improving the accuracy of the measurement and reducing the possibility of missing certain frequencies; and by analyzing the maximum RSSI measured at each frequency point, it is determined whether there are hidden wireless devices in the environment, which can improve the accuracy of detection.

[0018] In combination with the first aspect, in a possible implementation manner, determining whether there is an abnormal signal according to the maximum RSSI value of the signal received at each frequency point includes:

[0019] The maximum RSSI value of the signal received at each frequency point is compared with the preset threshold value; when the maximum RSSI value of the signal received at a frequency point among the M frequency points is greater than or equal to the preset threshold value, it is determined that there is an abnormal signal in the environment, that is, there is a hidden wireless device in the environment. Conversely, when the maximum RSSI values ​​of the signals received at the M frequency points are all less than the preset threshold, it is determined that there is no abnormal signal in the environment, that is, there is no hidden wireless device in the environment.

[0020] Exemplarily, the above-mentioned preset threshold value may be pre-set in the communication device.

[0021] In combination with the first aspect, in a possible implementation manner, determining whether there is an abnormal signal in the environment according to N RSSI values ​​includes:

[0022] The N RSSI values ​​and the frequency points corresponding to the N RSSI values ​​are input into the trained machine learning model for processing to obtain the output result of the machine learning model, which includes the probability of the existence of an abnormal signal at each of the M frequency points. According to the output result of the machine learning model, it is determined whether there is an abnormal signal in the environment.

[0023] Exemplarily, when the probability of an abnormal signal existing at a certain frequency point among the M frequency points is greater than or equal to a threshold value, such as 0.8, it is determined that an abnormal signal exists in the environment, that is, a hidden wireless device exists in the environment. Conversely, when the probability of an abnormal signal existing at the M frequency points is less than the threshold value, it is determined that there is no abnormal signal in the environment, that is, there is no hidden wireless device in the environment.

[0024] Exemplarily, the above-mentioned machine learning model may include a decision tree, a random forest, a neural network or other models.

[0025] This application uses a machine learning model to analyze whether there are abnormal signals in the environment, which can quickly eliminate common interference signals and further improve the accuracy of hidden device detection.

[0026] In combination with the first aspect, in a possible implementation manner, the method further includes:

[0027] If there is an abnormal signal in the environment, determine the target frequency point where the abnormal signal is located; measure the RSSI at the target frequency point at multiple positions respectively, and obtain RSSI values ​​corresponding to the multiple positions, wherein one position corresponds to at least one RSSI value; based on the RSSI values ​​corresponding to the multiple positions, determine the position closest to the hidden device, which is the device that sends the abnormal signal, and the RSSI value corresponding to the position closest to the hidden device is the largest.

[0028] It can be understood that when there are abnormal signals in the environment, it means that there may be hidden devices in the environment, and the target frequency point where the abnormal signal is located can be further determined. This application locks the target frequency point where the abnormal signal is located, measures the RSSI of the received signal at multiple locations, and finds the location with the largest RSSI value, that is, the location closest to the hidden device. This can help users find the location of the hidden device and realize the positioning and discovery of the hidden device.

[0029] In combination with the first aspect, in a possible implementation, before obtaining the M frequency points supported by the communication device, the method also includes: in response to a user operation or receiving an attention (AT) command, disconnecting the connection with the access network device, and controlling the communication device to send or receive signals through the AT command.

[0030] Exemplarily, the user's operation may include: unplugging a subscriber identity module (SIM) card of the communication device, or switching the communication device to an airplane mode.

[0031] In a second aspect, an embodiment of the present application provides a communication device, the communication device comprising:

[0032] The acquisition module is used to obtain M frequency points supported by the communication device when the communication device is disconnected from the access network device, where M is a positive integer; the measurement module is used to measure the RSSI at the M frequency points respectively to obtain N RSSI values, where N is an integer greater than or equal to M; the determination module is used to determine whether there is an abnormal signal in the environment based on the N RSSI values, and the RSSI value at the frequency point where the abnormal signal is located is greater than or equal to a preset threshold value.

[0033] In combination with the second aspect, in a possible implementation method, the above-mentioned acquisition module is specifically used to: obtain a frequency band list corresponding to at least one communication standard supported by the communication device; determine M frequency points from the frequency band list corresponding to at least one communication standard, where the frequency interval between two adjacent frequency points is a fixed value.

[0034] In conjunction with the second aspect, in a possible implementation, the measurement module is specifically used to: measure multiple times at each of the M frequency points to obtain the maximum RSSI value of the signal received at each frequency point. The determination module is specifically used to: determine whether there is an abnormal signal based on the maximum RSSI value of the signal received at each frequency point.

[0035] In combination with the second aspect, in a possible implementation method, the above-mentioned determination module is also specifically used to: compare the maximum RSSI value of the signal received at each frequency point with the preset threshold value; when the maximum RSSI value of the signal received at a frequency point among the above-mentioned M frequency points is greater than or equal to the preset threshold, it is determined that there is an abnormal signal in the environment.

[0036] In combination with the second aspect, in a possible implementation method, the above-mentioned determination module is specifically used to: input N RSSI values ​​and the frequency points corresponding to the N RSSI values ​​into a trained machine learning model for processing, and obtain the output result of the machine learning model, the output result including the probability of the existence of an abnormal signal at each frequency point among the M frequency points; according to the output result of the machine learning model, determine whether there is an abnormal signal in the environment.

[0037] In combination with the second aspect, in a possible implementation, the above-mentioned determination module is also used to determine the target frequency point where the abnormal signal is located. The above-mentioned measurement module is also used to measure the RSSI at the target frequency point at multiple positions respectively, and obtain RSSI values ​​corresponding to multiple positions, wherein one position corresponds to at least one RSSI value. The above-mentioned determination module is also used to determine the position closest to the hidden device based on the RSSI values ​​corresponding to the multiple positions. The hidden device is the device that sends the above-mentioned abnormal signal, and the RSSI value corresponding to the position closest to the hidden device is the largest.

[0038] In combination with the second aspect, in a possible implementation manner, the above-mentioned communication device also includes a response module, and the response module is used to disconnect the connection with the access network device in response to the user's operation or receiving an AT command.

[0039] In a third aspect, an embodiment of the present application provides a communication device, which includes: a processor, a memory, and a communication interface. The processor is connected to the memory and the communication interface, wherein the communication interface is used to provide a data communication function, the memory is used to store a computer program, and the processor is used to call the computer program, so that the communication device executes the method for detecting abnormal signals provided by the first aspect or any possible implementation of the first aspect, and can also achieve the beneficial effects of the method for detecting abnormal signals provided by the first aspect.

[0040] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a terminal device, the communication device executes the method for detecting abnormal signals provided by the first aspect or any feasible implementation method of the first aspect, and can also achieve the beneficial effects of the method for detecting abnormal signals provided by the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0042] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0043] Figure 2 It is a flow chart of a method for detecting abnormal signals provided in an embodiment of the present application;

[0044] Figure 3 This is a schematic diagram of RSSI measurement results at multiple frequency points provided in an embodiment of the present application;

[0045] Figure 4 It is a structural schematic diagram of a communication device provided in an embodiment of the present application;

[0046] Figure 5 This is another structural diagram of the communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In the description of the present application, "first" and "second" etc. are only used to distinguish different objects, rather than to describe a specific order. In addition, unless otherwise specified, " / " means "or", for example, A / B can mean A or B. "And / or" in this article is only a kind of association relationship describing associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, "at least one" means one or more, and "multiple" means two or more. "The following one (or more)" or similar expressions refer to any combination of these items, including any combination of single or plural items (individuals). For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a and b and c. Among them, a, b, c can be single or multiple.

[0048] The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0049] In this application, the words "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described in this application as "exemplary", "for example" or "for example" should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the words "exemplary", "for example" or "for example" is intended to present the related concepts in a concrete way.

[0050] In the present application, "when", "if" and "if" all mean that the device will take corresponding actions under certain objective circumstances. It does not limit the time, nor does it require that the device must make a judgment when it is implemented, nor does it mean that there are other limitations.

[0051] Elements used in the singular herein are intended to mean "one or more" rather than "one and only one" unless specifically stated otherwise.

[0052] In the present application, determining information D according to information C includes determining information D only according to information C, and also includes determining information D according to information C and other information. In addition, information C is used to determine information D, and it can also include indirect determination, for example, information D is determined according to information E, and information E is determined according to information C.

[0053] The technical solution provided by this application can be applied to the detection of hidden devices in scenes such as hotels, offices, and airplanes. With the development of science and technology, various hidden devices emerge in an endless stream, such as eavesdropping devices and hidden cameras. The existence of these hidden devices seriously infringes on personal privacy and may endanger the security of society and even the country. By detecting abnormal signals in the environment, it can be determined whether there are hidden devices. Please refer to Figure 1 , Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present application. Figure 1As shown, the hidden device 100 can transmit data to the terminal device 101 or the access network device 102 via radio waves. The radio waves can be detected by other communication devices in the environment, such as the terminal device 103 in the figure. In addition to receiving radio waves from the hidden device 100, the terminal device 103 can also receive radio waves from other devices, such as radio waves from a television, a router, a base station, etc. The terminal device 103 can detect abnormal signals by detecting and analyzing the received radio waves, so as to further detect the hidden device that transmits the abnormal signal.

[0054] It can be understood that the concealed device in the present application can be a hidden wireless device, which refers to a device that uses radio waves to transmit information / data.

[0055] Among them, terminal equipment, also known as user equipment (UE), mobile station (MS), mobile terminal (MT), etc., can refer to equipment that provides voice and / or data connectivity to users. For example, handheld devices with wireless connection functions, vehicle-mounted devices, etc. At present, some examples of terminal equipment are: mobile phones, tablet computers, laptops, PDAs, mobile internet devices (MID), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc.

[0056] The following is a brief introduction to the relevant terms involved in this application.

[0057] 1. Received signal strength indicator (RSSI)

[0058] RSSI is an indicator for measuring signal strength in wireless communication, and its unit is usually expressed in dBm (decibel milliwatt). RSSI measurement is usually completed by the receiving end network card and provided to the upper layer through the PMD_RSSI.indicate interface. In wireless communication networks, the size of the RSSI value can be used to measure the strength of the signal.

[0059] RSSI is a relative measurement value. After a communication device receives a signal, it can calculate the RSSI of the signal based on the signal power. RSSI values ​​are usually negative. The larger the value (i.e., the closer to 0), the stronger the signal. The calculation formula of RSSI is: Among them, P 接收 Indicates the power of the signal received by the communication device.

[0060] 2. Communication Standard

[0061] A communication standard refers to a set of signal transmission methods, protocols, and standards in a communication system. Different communication standards are suitable for different application scenarios and requirements. The communication standards in this application may include second generation (2G), third generation (3G), fourth generation (4G), or fifth generation (5G) mobile communication standards. Under different communication standards, communication devices can communicate on different frequency bands. Under a communication standard, a communication device supports one or more frequency bands, and a frequency band includes multiple frequency points.

[0062] Exemplarily, 5G frequency bands may include n1, n3, n28, n41, n77, n78 and n79. Taking n1 frequency band as an example, the frequency range of uplink communication is: 1920MHz-1980MHz, and the frequency range of downlink communication is: 211MHz-2170MHz. According to the fixed frequency interval, the frequency band can be divided into multiple frequency points.

[0063] In this application, unless otherwise specified, the same or similar parts between various embodiments or implementations can refer to each other. In each embodiment of this application, and each implementation method / implementation method / implementation method in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments and each implementation method / implementation method / implementation method in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and each implementation method / implementation method / implementation method in each embodiment can be combined to form new embodiments, implementation methods, implementation methods, or implementation methods according to their inherent logical relationships. The implementation methods of this application described below do not constitute a limitation on the scope of protection of this application.

[0064] In a possible implementation, the communication device in the present application may be a terminal device with a mobile communication function. Figure 1 The terminal device 103 in.

[0065] See also Figure 2 , Figure 2 1 is a flow chart of a method for detecting abnormal signals provided in an embodiment of the present application. Figure 2 As shown, the method for detecting abnormal signals includes but is not limited to the following steps:

[0066] S101, the communication device is disconnected from the access network device, and the sending or receiving of signals is controlled by AT commands.

[0067] In a possible implementation, the user can remove the SIM card in the communication device or set the communication device to flight mode. In response to the user's operation, the communication device can enter a non-signaling mode, that is, the communication device is disconnected from the access network device, and the sending or receiving of signals is controlled by AT commands. In other words, in the embodiment of the present application, after the communication device is disconnected from the access network device, the subsequent actions / steps of the communication device are all controlled by commands.

[0068] In another possible implementation, the communication device sends an AT command to the mobile communication module, and the mobile communication module disconnects from the access network device after receiving the AT command. The communication device controls the mobile communication module to send or receive signals through the AT command.

[0069] S102, when the communication device is disconnected from the access network device, obtain M frequency points supported by the communication device, where M is a positive integer.

[0070] In a possible implementation, after the communication device is disconnected from the access network device, the communication device can obtain M frequency points supported by itself. These M frequency points may be frequency points included in all frequency bands supported by the communication device, or may be frequency points included in some frequency bands supported by the communication device, which is not limited in the embodiments of the present application. Exemplarily, the communication device supports one or more communication standards, and the communication standard may include one or more of 2G, 3G, 4G, or 5G. Among them, under a communication standard, the communication device supports one or more frequency bands. A frequency band may include one or more frequency points, and the frequency interval between two adjacent frequency points is a fixed value, such as 200kHz. The above-mentioned M frequency points may be frequency points under a certain communication standard, or the M frequency points are frequency points under all communication standards supported by the communication device. This embodiment of the present application is not limited.

[0071] It is understandable that when a manufacturer produces a communication device, it will pre-configure the communication standards supported by the communication device and a list of frequency bands corresponding to the communication standards, and pre-store them in the communication device.

[0072] In one possible implementation, the communication device can use a command to search for a locally stored communication standard and a frequency band list corresponding to the communication standard. One of the frequency band lists includes one or more frequency bands. Exemplarily, the communication device can send command 1 to the mobile communication module to obtain one or more communication standards supported by the communication device. Then, the communication device can send command 2 to the mobile communication module, where command 2 includes a certain communication standard for obtaining a frequency band list corresponding to the communication standard. The communication device can send multiple commands 2 to the mobile communication module to obtain a frequency band list corresponding to multiple communication standards. Under different communication standards, the frequency band lists supported by the communication device are not exactly the same. For example, under the 5G communication standard, the frequency bands supported by the communication device mainly include: n1, n3, n28, n41, n77, n78 and n79 bands.

[0073] After the communication device obtains the frequency band list corresponding to one or more communication standards, M frequency points can be determined from the frequency band list corresponding to the one or more communication standards. The frequency interval between two adjacent frequency points is a fixed value. For example, the communication device can divide the frequency bands included in the frequency band list corresponding to the one or more communication standards into multiple frequency points according to the fixed frequency interval, and obtain a total of M frequency points.

[0074] S103, respectively measure the RSSI at M frequency points to obtain N RSSI values, where N is an integer greater than or equal to M.

[0075] In a possible implementation, the communication device can measure the RSSI values ​​at the above-mentioned M frequency points respectively. Exemplarily, the communication device can measure once at a frequency point to obtain M RSSI values. Or, the communication device measures multiple times at a frequency point to obtain N RSSI values, where N is greater than or equal to M. It can be understood that in order to reduce power consumption, the communication device will not continuously scan the signal at the frequency point. In other words, the communication device can intermittently scan the signal at the frequency point according to a pre-set scanning period. Since the period of the hidden device transmitting the signal is unknown and is not synchronized with the scanning period of the communication device in advance. Therefore, if the communication device measures once at a certain frequency point, the RSSI value may not be measured. In other words, even if there is an abnormal signal, the communication device may not be able to scan the abnormal signal at all time points. Then the communication device of the embodiment of the present application measures multiple times at a frequency point intermittently, which has increased the probability of measuring the RSSI value, thereby increasing the probability of detecting abnormal signals and improving the accuracy of hidden device detection.

[0076] When a communication device measures multiple times at one frequency point, the RSSI values ​​measured at different time points may be different, so the communication device of the embodiment of the present application can save the maximum RSSI value at the frequency point for subsequent analysis.

[0077] S104: Determine whether there is an abnormal signal in the environment according to the N RSSI values ​​measured at the M frequency points.

[0078] In one possible implementation, the communication device can compare the size of N RSSI values ​​with a preset threshold value. When there is an RSSI value greater than or equal to the preset threshold value among the N RSSI values, the communication device determines that there is an abnormal signal in the environment, that is, there is a hidden device in the environment. When the N RSSI values ​​are all less than the preset threshold value, the communication device determines that there is no abnormal signal in the environment, that is, there is no hidden device in the environment. It can be understood that when there are multiple RSSI values ​​greater than or equal to the preset threshold value, it means that there are multiple abnormal signals in the environment, but these multiple abnormal signals may belong to the same hidden device. Among them, the preset threshold value can be pre-set in the communication device.

[0079] In a possible implementation, after step S103, the method of the embodiment of the present application may further include: the communication device draws and outputs a measurement result graph according to the M frequency points and the RSSI values ​​measured at the M frequency points. The horizontal axis of the measurement result graph is the frequency point / frequency, and the vertical axis is the RSSI value.

[0080] In a possible implementation, if the above-mentioned communication device measures multiple times at each of the M frequency points to obtain the maximum RSSI value of the signal received at each frequency point, the communication device can compare the maximum RSSI value of the signal received at each frequency point with the preset threshold value. If the maximum RSSI value of the signal received at a frequency point among the M frequency points is greater than or equal to the preset threshold value, it is determined that there is an abnormal signal in the environment, that is, there is a hidden device in the environment. When the maximum RSSI values ​​of the signals received at the M frequency points are all less than the preset threshold value, it is determined that there is no abnormal signal in the environment, that is, there is no hidden device in the environment. The embodiment of the present application compares the maximum RSSI value obtained by measuring at each frequency point with the preset threshold value, which can improve the accuracy of detection.

[0081] In a possible implementation, the embodiment of the present application may also use a machine learning model to analyze whether there are abnormal signals or hidden devices in the environment. The machine learning model here may be pre-trained. The data used in the training of the machine learning model may include: multiple pre-set frequency points, and RSSI values ​​of various signals (including signals emitted by hidden devices, signals emitted by non-hidden devices, and / or environmental noise) collected at these multiple frequency points. In other words, the training phase of the machine learning model can take the RSSI value and the frequency point at which the RSSI value is measured as input, and take the label 0 or 1 as output. Among them, the label of the frequency point where the abnormal signal (such as the signal emitted by the hidden device) is 1, and the label of the frequency point where other signals (such as mobile phones, base stations, etc., signals emitted by non-hidden devices, environmental noise, etc.) are located is 0. After the machine learning model is trained, the N RSSI values ​​collected by the communication device and the frequency points corresponding to these N RSSI values ​​can be input into the machine learning model for processing to obtain the output result of the machine learning model. The output result includes the probability of the existence of an abnormal signal at each of the M frequency points. In other words, the output result includes the probability that each frequency point is labeled "1". The communication device can determine whether there are abnormal signals in the environment based on the output results of the machine learning model. For example, if the probability of an abnormal signal existing at a certain frequency point in the output results of the machine learning model is greater than a threshold, such as 0.8, it is determined that there are abnormal signals in the environment, that is, there are hidden devices in the environment. When the probability of abnormal signals existing at these M frequency points in the output results of the machine learning model are all less than the threshold, it is determined that there are no abnormal signals in the environment, that is, there are no hidden devices in the environment.

[0082] In order to improve accuracy, the abnormal signal can be further analyzed in combination with the preset threshold value. When the probability of an abnormal signal existing at a certain frequency point in the output result of the machine learning model is greater than the threshold, the RSSI value measured at the frequency point is obtained, and the RSSI value is compared with the preset threshold value. If the RSSI value is greater than or equal to the preset threshold value, it is determined that there is an abnormal signal in the environment, that is, there is a hidden device in the environment.

[0083] Among them, the above-mentioned machine learning model may include models such as decision tree, random forest, or neural network.

[0084] The embodiment of the present application uses a machine learning model to analyze whether there are abnormal signals in the environment, which can quickly eliminate common interference signals and further improve the accuracy of hidden device detection.

[0085] In a possible implementation, the communication device can also determine whether there is an abnormal signal in the environment based on whether the measured RSSI value has a significant jump. Exemplarily, the average value of the RSSI values ​​measured at M frequency points is calculated, and then the average value is compared with the RSSI value at each frequency point. If the RSSI value at a certain frequency point is much larger than the average value (for example, the difference between the RSSI value at this frequency point and the average value is greater than a certain value), it means that a significant jump has occurred. Therefore, it can be determined that there is an abnormal signal in the environment, that is, there is a hidden device in the environment.

[0086] It can be understood that although there is a frequency band in mobile communication where the transmission and reception are not on the same frequency, it can be measured by receiving channels of different frequency bands or different standards. Even if the frequency band measured by the communication equipment does not cover the frequency point where the abnormal signal actually exists, the RSSI of the out-of-band noise of the abnormal signal can be measured.

[0087] In a possible implementation, when the communication device determines that there is an abnormal signal / hidden device in the environment, it can further search for the location of the hidden device. Exemplarily, the method of the embodiment of the present application can also include the following steps S105 and S106.

[0088] S105, if there is an abnormal signal in the environment, measure the RSSI at the frequency point where the abnormal signal is located at multiple locations respectively to obtain RSSI values ​​corresponding to the multiple locations, wherein one location corresponds to at least one RSSI value.

[0089] In one possible implementation, if there is an abnormal signal in the environment, that is, there is a hidden device in the environment, the communication device can further determine the target frequency point where the abnormal signal is located. The user can hold the communication device and move it indoors. The communication device can measure the RSSI at the target frequency point at multiple locations respectively, and obtain the RSSI values ​​corresponding to the multiple locations. It can be understood that in order to improve the accuracy of the measurement, the target frequency point can be measured multiple times at one location, and the maximum RSSI value corresponding to each location can be saved.

[0090] S106, determining the position closest to the hidden device according to the RSSI values ​​corresponding to the multiple positions, wherein the hidden device is a device that sends abnormal signals, and the RSSI value corresponding to the position closest to the hidden device is the largest.

[0091] In a possible implementation, the communication device may determine the position closest to the hidden device according to the RSSI values ​​corresponding to the multiple positions. The hidden device is the device that sends the abnormal signal, and the RSSI value corresponding to the position closest to the hidden device is the largest.

[0092] For example, in a test space (such as a room or a car), remove the SIM card of a communication device (such as a mobile phone) with two antennas (i.e. switch to airplane mode). At this time, the communication device is disconnected from the access network device (such as a base station). The user can use AT commands to set the scanning frequency band of the communication device to 800MHz to 5.8GHz and the scanning interval to 100 milliseconds. To reduce interference, the user can turn off other known communication devices in the test space (such as mobile phones, wireless routers, or Bluetooth headsets, etc.). See Figure 3 , Figure 3 Schematic diagram of RSSI measurement results at multiple frequency points provided by an embodiment of the present application. Figure 3 As shown in the figure, the RSSI of the signals received by antenna 1 (i.e. Ant1) and antenna 2 (i.e. Ant2) both show obvious jumps around 1.847GHz. Therefore, it can be determined that the signal with a measured RSSI of -50dBm is an abnormal signal, and the frequency point where the abnormal signal is located is 1.847GHz. Based on this information, the scanning frequency can be locked to 1.847GHz, and then a single frequency point continuous scan can be performed at different locations in the test space to determine the position with the largest RSSI. The position with the largest RSSI is the position closest to the hidden device.

[0093] The embodiment of the present application uses existing communication equipment to measure the RSSI at multiple frequency points supported by itself, and analyzes the measured RSSI to determine whether there are abnormal signals in the environment, and then determine whether there are hidden wireless devices in the environment. No additional hardware equipment and software development are required, while saving costs, it can efficiently and accurately detect whether there are hidden wireless devices in the environment, protecting the privacy and security of users.

[0094] The method of the embodiment of the present application is described in detail above, and the device of the embodiment of the present application is provided below.

[0095] See also Figure 4 , Figure 4 1 is a schematic diagram of a structure of a communication device provided in an embodiment of the present application. The communication device may include:

[0096] The acquisition module 401 is used to acquire M frequency points supported by the communication device when the communication device is disconnected from the access network device, where M is a positive integer;

[0097] The measuring module 402 is used to measure the RSSI at M frequency points respectively to obtain N RSSI values, where N is an integer greater than or equal to M;

[0098] The determination module 403 is used to determine whether there is an abnormal signal in the environment according to the N RSSI values, and the RSSI value at the frequency point where the abnormal signal is located is greater than or equal to a preset threshold value.

[0099] In a possible implementation, the acquisition module 401 is specifically used to: obtain a frequency band list corresponding to at least one communication standard supported by the communication device; determine M frequency points from the frequency band list corresponding to at least one communication standard, where the frequency interval between two adjacent frequency points is a fixed value.

[0100] In a possible implementation, the measurement module 402 is specifically used to: perform multiple measurements on each of the M frequency points to obtain the maximum RSSI of the signal received on each frequency point. The determination module 403 is specifically used to: determine whether there is an abnormal signal based on the maximum RSSI of the signal received on each frequency point.

[0101] In a possible implementation, the above-mentioned determination module 403 is also specifically used to: compare the maximum RSSI of the signal received at each frequency point with the above-mentioned preset threshold value; when the maximum RSSI of the signal received at a frequency point among the above-mentioned M frequency points is greater than or equal to the preset threshold value, it is determined that there is an abnormal signal in the environment.

[0102] In one possible implementation, the above-mentioned determination module 403 is specifically used to: input N RSSI values ​​and the frequency points corresponding to the N RSSI values ​​into a trained machine learning model for processing, and obtain the output result of the machine learning model, the output result including the probability of the existence of an abnormal signal at each frequency point among the M frequency points; according to the output result of the machine learning model, determine whether there is an abnormal signal in the environment.

[0103] In a possible implementation, the determination module 403 is further used to determine the target frequency point where the abnormal signal is located. The measurement module 402 is further used to measure the RSSI at the target frequency point at multiple positions respectively to obtain RSSI values ​​corresponding to the multiple positions, wherein one position corresponds to at least one RSSI value. The determination module 403 is further used to determine the position closest to the hidden device based on the RSSI values ​​corresponding to the multiple positions, the hidden device is the device that sends the abnormal signal, and the RSSI value corresponding to the position closest to the hidden device is the largest.

[0104] In a possible implementation, the communication device further includes a response module 404. The response module 404 is configured to disconnect the access network device in response to a user operation or receiving an attention AT command.

[0105] In a specific implementation, the above communication device can execute the above modules Figure 2The steps or methods executed by the communication device in the embodiment of the present invention implement the functions implemented in the above method embodiment. For details, please refer to the above Figure 2 The corresponding descriptions provided in each step of the method embodiment are not repeated here.

[0106] In the embodiment of the present application, when the communication device is disconnected from the access network device, the acquisition module 401 acquires the M frequency points supported by the communication device; the measurement module 402 measures the RSSI at the M frequency points respectively to obtain N RSSI values; the determination module 403 determines whether there is an abnormal signal in the environment based on the N RSSI values, and the RSSI value at the frequency point where the abnormal signal is located is greater than or equal to the preset threshold value. While saving costs, it can efficiently and accurately detect whether there are hidden wireless devices in the environment and protect the privacy and security of users.

[0107] See also Figure 5 , Figure 5 is another structural diagram of the communication device provided in the embodiment of the present application. Figure 5 As shown, the communication device provided in the embodiment of the present application includes: at least one communication bus 501, at least one processor 502, such as a CPU, a memory 503, a user interface 504 and at least one communication interface 505. Among them, the communication bus 501 is used to realize the connection and communication between these components. Among them, the user interface 504 may include a display screen (Display), and the communication interface 505 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface). The memory 503 may be a high-speed RAM memory, or it may be a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 503 may optionally also be at least one storage device located away from the aforementioned processor 502. As Figure 5 As shown, the memory 503 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.

[0108] exist Figure 5 In the terminal device shown, the communication interface 505 can provide network communication functions; and the user interface 504 is mainly used to provide an input interface for the user; Figure 5 In the communication device shown, the processor 502 may be used to load the program instructions stored in the memory 503, and specifically perform the following operations:

[0109] When the communication device is disconnected from the access network device, obtain M frequency points supported by the communication device, where M is a positive integer; measure the RSSI at the M frequency points respectively to obtain N RSSI values, where N is an integer greater than or equal to M; determine whether there is an abnormal signal in the environment based on the N RSSI values, and the RSSI value at the frequency point where the abnormal signal is located is greater than or equal to the preset threshold value.

[0110] It can be understood that the communication device described in the embodiments of the present application can be used to perform the above Figure 2 The method of the embodiment shown in the figure is described in detail, and the beneficial effects of the same method are not described in detail.

[0111] The embodiment of the present application also provides a computer storage medium, which is used to store a computer program. When the computer program is run on a terminal device, the communication device can execute the above Figure 2 The method or steps of the illustrated embodiment.

[0112] The embodiment of the present application also provides a computer program product or a computer program, the computer program product or the computer program includes a computer instruction, the computer instruction is stored in a computer-readable storage medium. The processor of the communication device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the communication device can execute the above Figure 2 The method of the illustrated embodiment will not be described in detail here.

[0113] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0114] In short, the above is only a preferred embodiment of the technical solution of this application, and is not intended to limit the protection scope of this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application should be included in the protection scope of this application.

Claims

1. A method for detecting abnormal signals, characterized in that: Applied to communication equipment, including: When the communication device is disconnected from the access network device, obtaining M frequency points supported by the communication device, where M is a positive integer; Measuring received signal strength indicators RSSI at the M frequency points respectively to obtain N RSSI values, where N is an integer greater than or equal to M; It is determined whether there is an abnormal signal in the environment according to the N RSSI values, and the RSSI value at the frequency point where the abnormal signal is located is greater than or equal to a preset threshold value.

2. The method according to claim 1, characterized in that The acquiring the M frequency points supported by the communication device includes: Obtain a frequency band list corresponding to at least one communication standard supported by the communication device; M frequency points are determined from a frequency band list corresponding to the at least one communication standard, wherein a frequency interval between two adjacent frequency points is a fixed value.

3. The method according to claim 1 or 2, characterized in that: The step of respectively measuring the received signal strength indication RSSI at the M frequency points to obtain N RSSI values ​​includes: Measuring multiple times at each of the M frequency points to obtain a maximum RSSI value of a signal received at each frequency point; Determining whether there is an abnormal signal in the environment according to the N RSSI values ​​includes: Whether there is an abnormal signal is determined according to the maximum RSSI value of the signal received at each frequency point.

4. The method according to claim 3, characterized in that The determining whether there is an abnormal signal according to the maximum RSSI value of the signal received at each frequency point includes: Compare the maximum RSSI value of the signal received at each frequency point with the preset threshold value; When the maximum RSSI value of the signal received at a frequency point among the M frequency points is greater than or equal to a preset threshold value, it is determined that an abnormal signal exists in the environment.

5. The method according to claim 1 or 2, characterized in that: The determining whether there is an abnormal signal in the environment according to the N RSSI values ​​includes: Input the N RSSI values ​​and the frequency points corresponding to the N RSSI values ​​into a trained machine learning model for processing, and obtain an output result of the machine learning model, wherein the output result includes a probability that an abnormal signal exists at each of the M frequency points; Based on the output results of the machine learning model, determine whether there are abnormal signals in the environment.

6. The method according to claim 1 or 2, characterized in that: The method further comprises: If there is an abnormal signal in the environment, determine the target frequency point where the abnormal signal is located; Measuring the RSSI at the target frequency point at multiple positions respectively to obtain RSSI values ​​corresponding to the multiple positions, wherein one position corresponds to at least one RSSI value; According to the RSSI values ​​corresponding to the multiple positions, the position closest to the hidden device is determined. The hidden device is the device that sends the abnormal signal, and the RSSI value corresponding to the position closest to the hidden device is the largest.

7. The method according to claim 1 or 2, characterized in that: Before acquiring the M frequency points supported by the communication device, the method further includes: In response to a user operation or receiving an attention AT command, the connection with the access network device is disconnected.

8. A communication device, characterized in that: include: An acquisition module, configured to acquire M frequency points supported by the communication device when the communication device is disconnected from the access network device, where M is a positive integer; A measurement module, used to measure the received signal strength indication RSSI at the M frequency points respectively to obtain N RSSI values, where N is an integer greater than or equal to M; The determination module is used to determine whether there is an abnormal signal in the environment according to the N RSSI values, and the RSSI value at the frequency point where the abnormal signal is located is greater than or equal to a preset threshold value.

9. A communication device, characterized in that: include: Processor, memory and communication interface; The processor is connected to the memory and the communication interface, wherein the communication interface is used to provide a data communication function, the memory is used to store a computer program, and the processor is used to call the computer program so that the communication device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is loaded and executed by a processor, so that a communication device having the processor executes the method according to any one of claims 1 to 7.

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