Signal interference determination method and device, storage medium and electronic equipment

Abstract

The invention discloses a signal interference determination method and device, a storage medium and electronic equipment, and relates to the technical field of smart home. The signal interference determination method comprises the following steps: determining a power spectral density map of equipment according to channel state information acquired from a wireless fidelity signal connected with the equipment; performing inverse Fourier transform on frequency data in a preset frequency range in the power spectral density map to obtain a convolution kernel function; and performing convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and determining that the device is subjected to signal interference under the condition that the energy intensity of the convolution result exceeds a first threshold value. Through adoption of the technical scheme, the problem of power consumption caused by signal interference detection through a radar in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of smart home technology, and more specifically, to a method and device for determining signal interference, a storage medium, and an electronic device. Background Art

[0002] In modern smart door lock systems, radar detection technology is widely used to sense changes in the surrounding environment, especially to detect whether a living organism (such as a person) is close to the door lock area. Radar sensors determine the distance and motion state of the target object by emitting electromagnetic waves and receiving reflected signals. However, this continuous detection method has obvious power consumption problems.

[0003] In the related technology, there is no effective solution to the problem of power consumption caused by radar detection signal interference. Summary of the invention

[0004] The embodiments of the present application provide a method and device for determining signal interference, a storage medium, and an electronic device to at least solve the problem of power consumption caused by detecting signal interference through radar in the related art.

[0005] According to an embodiment of the present application, a method for determining signal interference is provided, including: determining a power spectrum density diagram of the device based on channel state information obtained from a wireless fidelity signal to which the device is connected; performing an inverse Fourier transform on frequency data within a preset frequency range in the power spectrum density diagram to obtain a convolution kernel function; performing a convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and determining that the device is subject to signal interference when an energy intensity of the convolution result exceeds a first threshold.

[0006] In an optional embodiment, a power spectrum density diagram of the device is determined based on channel state information obtained from a wireless fidelity signal connected to the device, including: when a preset number of channel state information is obtained, arranging the channel state information in chronological order to form a time series; converting the time series from the time domain to the frequency domain through Fourier transform to obtain frequency domain information; and determining the power spectrum density diagram based on the frequency domain information.

[0007] In an optional embodiment, before determining the power spectral density diagram of the device based on the channel state information obtained from the wireless fidelity signal to which the device is connected, the method further includes: when the wireless fidelity signal is periodically started, sending an empty data packet to a router, and determining whether a confirmation frame returned by the router based on the empty data packet is received; when it is determined that a confirmation frame returned by the router based on the empty data packet is received, calculating the received signal strength indication of the wireless fidelity signal and the channel state information.

[0008] In an optional embodiment, after calculating the received signal strength indication of the wireless fidelity signal and the channel state information, the method further includes: obtaining a first received signal strength indication within a preset time from the received signal strength indication, and performing a linear regression analysis on the first received signal strength indication to obtain an analysis result; performing a linear fitting on the analysis result by a least squares method to obtain a linear function; and when the slope of the linear function exceeds a second threshold, capturing an image within the field of view of the device by a camera of the device.

[0009] In an optional embodiment, after determining that the device is subject to signal interference, the method further includes: starting a camera on the device, and capturing an image within the field of view of the device through the camera; when a human figure appears in the image, analyzing facial features of the human figure through a deep learning model to determine whether the human figure is stored in a database of the device; when it is determined that the human figure is not stored in the database of the device, transmitting the image to a mobile device of the target object, and issuing a security warning to the mobile device of the target object.

[0010] In an optional embodiment, after determining that the device is subject to signal interference, the method further includes: when a black screen appears in the image, transmitting the image to the target object's mobile device, and issuing a security warning to the target object's mobile device.

[0011] In an optional embodiment, transmitting the image to a mobile device of a target object includes: determining a compressed image of the image; converting the format of the compressed image into a target format compatible with a network transmission protocol, wherein the network transmission protocol defines rules for transmitting the image in a network; symmetrically encrypting the compressed image in the target format to obtain an encrypted image; and transmitting the encrypted image to the mobile device of the target object via the network transmission protocol.

[0012] According to another aspect of the present invention, a device for determining signal interference is also provided, including: a first determination module, used to determine a power spectrum density diagram of the device based on channel state information obtained from a wireless fidelity signal connected to the device; a calculation module, used to perform an inverse Fourier transform on frequency data within a preset frequency range in the power spectrum density diagram to obtain a convolution kernel function; a second determination module, used to perform a convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and determine that the device is subject to signal interference when the energy intensity of the convolution result exceeds a first threshold.

[0013] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining signal interference when running.

[0014] According to another aspect of an embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the signal interference determination method through the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method for determining the signal interference is implemented.

[0016] In an embodiment of the present application, a power spectrum density diagram of a device is determined based on channel state information, wherein the channel state information is obtained from a wireless fidelity signal to which the device is connected; frequency data within a preset frequency range in the power spectrum density diagram is determined, and the frequency data is subjected to an inverse Fourier transform to obtain a convolution kernel function; the convolution function and the channel state information are convolved to obtain a corresponding convolution result, and when the energy intensity of the convolution result exceeds a first threshold, it is determined that the device is subject to signal interference. The above technical solution solves the problem of power consumption caused by detecting signal interference through radar in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 It is a hardware environment diagram of a method for determining signal interference in an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for determining signal interference according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a method for determining signal interference according to an embodiment of the present application;

[0022] Figure 4It is a structural block diagram of a device for determining signal interference according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to one aspect of an embodiment of the present application, a method for determining signal interference is provided. The method for determining signal interference is widely used in smart home (SmartHome), smart home, smart home device ecology, smart house (IntelligenceHouse) ecology and other whole-house intelligent digital control application scenarios. Optionally, in this embodiment, the above-mentioned method for determining signal interference can be applied to Figure 1 In the hardware environment composed of the smart device 102 and the router 104 shown in FIG. Figure 1 As shown, the smart device 102 is connected to the router 104 via a network, and can be used to provide services (such as application services, etc.) for the smart device 102 or a client installed on the smart device 102. A database can be set on the smart device 102 or independently of the smart device 102 to provide data storage services for the smart device 102 and the router 104. Cloud computing and / or edge computing services can be configured on the smart device 102 or independently of the smart device 102 to provide data computing services for the smart device 102 and the router 104.

[0026] The network may include but is not limited to at least one of the following: wired network, wireless network. The wired network may include but is not limited to at least one of the following: wide area network, metropolitan area network, local area network, and the wireless network may include but is not limited to at least one of the following: WIFI (Wireless Fidelity), Bluetooth. The smart device 102 and the router 104 may be but are not limited to PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projection equipment, smart TV, smart clothes drying rack, smart curtain, smart audio and video, smart socket, smart audio, smart speaker, smart fresh air equipment, smart kitchen and bathroom equipment, smart bathroom equipment, smart sweeping robot, smart window cleaning robot, smart mopping robot, smart air purification equipment, smart steamer, smart microwave oven, smart kitchen treasure, smart purifier, smart water dispenser, system platform, etc.

[0027] In this embodiment, a method for determining signal interference is provided, which is applied to the above-mentioned smart device. Figure 2 : is a flow chart of a method for determining signal interference according to an embodiment of the present application, the process comprising the following steps:

[0028] Step S202, determining a power spectrum density diagram of the device according to channel state information obtained from a wireless fidelity signal to which the device is connected;

[0029] Step S204, performing inverse Fourier transform on the frequency data within a preset frequency range in the power spectrum density graph to obtain a convolution kernel function;

[0030] Step S206: performing a convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result. When the energy intensity of the convolution result exceeds a first threshold, it is determined that the device is subject to signal interference.

[0031] In an embodiment of the present application, a power spectrum density diagram of the device is determined based on the channel state information obtained from the wireless fidelity signal connected to the device; the frequency data within a preset frequency range in the power spectrum density diagram is subjected to an inverse Fourier transform to obtain a convolution kernel function; a convolution operation is performed on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and when the energy intensity of the convolution result exceeds a first threshold, it is determined that the device is subject to signal interference. The above technical solution solves the problem of power consumption caused by detecting signal interference through radar in the related art.

[0032] In an optional embodiment, a power spectrum density diagram of the device is determined based on channel state information obtained from a wireless fidelity signal connected to the device, including: when a preset number of channel state information is obtained, arranging the channel state information in chronological order to form a time series; converting the time series from the time domain to the frequency domain through Fourier transform to obtain frequency domain information; and determining the power spectrum density diagram based on the frequency domain information.

[0033] Smart devices (such as smart door locks) periodically receive CSI data from routers during Wi-Fi communication. For example, assume that the smart door lock receives CSI data from the router every 300ms, and each received CSI data contains the strength of the WiFi signal at different frequencies. Within a period of time, such as 3 seconds, at least 9 data packets are collected to form a preliminary CSI data set. After collecting a preset number of CSI data, these CSI data are arranged in the order of time received to form a time series. For example, the collected 9 data packets are arranged in time order, and the CSI data in each data packet forms a time series: [CSI1(t1), CSI2(t2),..., CSIn(tn)]. The above time series is Fourier transformed to convert the time series from the time domain to the frequency domain to obtain the frequency domain information of the WiFi signal. According to the frequency domain information, the power spectrum density diagram is determined, wherein the power spectrum density diagram provides the probability distribution of the WiFi signal energy at different frequencies. By analyzing the power spectrum density diagram, it can be determined whether there is energy change in a specific frequency range in the WiFi signal, and then infer whether there is human activity in the environment. For example, suppose the power spectrum density graph shows that the WiFi signal energy changes significantly in the frequency range of 100Hz to 200Hz, and the preset frequency range matches the WiFi signal change caused by human activity. This means that there are objects (such as people) in the environment moving at a specific frequency, which causes the change in WiFi signal strength. By setting a first threshold to determine whether the change in WiFi signal energy in the preset frequency band is large enough, it can be determined whether there is WiFi signal interference, that is, whether there is human activity.

[0034] In an optional embodiment, before determining the power spectral density diagram of the device based on the channel state information obtained from the wireless fidelity signal to which the device is connected, the method further includes: when the wireless fidelity signal is periodically started, sending an empty data packet to a router, and determining whether a confirmation frame returned by the router based on the empty data packet is received; when it is determined that a confirmation frame returned by the router based on the empty data packet is received, calculating the received signal strength indication of the wireless fidelity signal and the channel state information.

[0035] The Wi-Fi module in the smart door lock will start periodically to maintain a wireless connection with the router. This startup cycle is usually between hundreds of milliseconds and a few seconds, depending on the power consumption management and communication requirements of the device. In this embodiment, the Wi-Fi module automatically starts every 300ms. When the Wi-Fi module starts, an empty data packet is sent to the router. The empty data packet is a special Wi-Fi data packet, in which the amount of data transmitted by the empty data packet is zero, and is mainly used to trigger the response of the router so that the smart door lock can measure the channel state. For example, when the Wi-Fi module of the smart door lock is started, the smart door lock will send an empty data packet to the router and wait for the confirmation frame returned by the router based on the empty data packet. The confirmation frame is the response of the router to the empty data packet, indicating that the router has successfully received the empty data packet and confirmed the stability of the communication link. For example, after the router receives the empty data packet, it will send a confirmation frame to the smart door lock. The smart door lock confirms that the confirmation frame has been received, and the hardware function of the Wi-Fi module can be used to calculate RSSI (i.e., received signal strength indication) and CSI (i.e., channel state information). For example, after receiving the confirmation frame, the smart door lock starts measuring the RSSI value, and the baseband processor of the Wi-Fi module starts calculating the CSI data. This calculation process involves sampling and analyzing the signal strength of multiple frequency subcarriers to obtain detailed channel status information.

[0036] In an optional embodiment, after calculating the received signal strength indication of the wireless fidelity signal and the channel state information, the method further includes: obtaining a first received signal strength indication within a preset time from the received signal strength indication, and performing a linear regression analysis on the first received signal strength indication to obtain an analysis result; performing a linear fitting on the analysis result by a least squares method to obtain a linear function; and when the slope of the linear function exceeds a second threshold, capturing an image within the field of view of the device by a camera of the device.

[0037] Optionally, assume that the Wi-Fi module of the smart door lock starts at a frequency of once every 300ms and sends an empty data packet to the router at home. After receiving it, the router returns a confirmation frame. In this process, the smart door lock can obtain RSSI and CSI for analyzing the trend of signal changes. The smart door lock records the RSSI value each time a confirmation frame is received in the last 10 seconds. In these 10 seconds, the smart door lock records 20 groups of RSSI values, for example: 1st second: RSSI = -65dBm, 2nd second: RSSI = -64.5dBm, 3rd second: RSSI = -64dBm... 10th second: RSSI = -60dBm. The smart door lock performs linear regression analysis on these 20 groups of RSSI values. Linear regression analysis attempts to find a linear relationship between RSSI values ​​and time. If the RSSI value gradually increases over time, it means that someone is slowly approaching the smart door lock, causing the signal strength to gradually increase.

[0038] After analyzing the relationship between the RSSI value and time, the smart door lock uses the least squares method to fit the linear regression results to obtain an optimal linear function. Assuming that the analysis results show that the RSSI value increases linearly over time, the function obtained by linear fitting may be RSSI = 0.5 × time - 70 (unit: dBm / s). The smart door lock checks whether the slope of the linear function (i.e., the rate of change of RSSI over time) exceeds the preset second threshold. Assuming that the second threshold is set to 0.3dBm / s, if the slope of the linear function is 0.5dBm / s and exceeds the second threshold, the smart door lock can determine that someone is approaching slowly. The smart door lock immediately activates its built-in camera to capture an image in front of the door to confirm the situation and perform further identity authentication or security response. For example, if the image captured by the camera shows that someone is standing in front of the door, the smart door lock can send a notification to the target object's mobile device, allowing the target object to remotely view and decide whether to unlock the door lock.

[0039] In an optional embodiment, after determining that the device is subject to signal interference, the method further includes: starting a camera on the device, and capturing an image within the field of view of the device through the camera; when a human figure appears in the image, analyzing facial features of the human figure through a deep learning model to determine whether the human figure is stored in a database of the device; when it is determined that the human figure is not stored in the database of the device, transmitting the image to a mobile device of the target object, and issuing a security warning to the mobile device of the target object.

[0040] After detecting that the RSSI value continues to increase in a short period of time, the smart door lock activates the camera and takes a high-definition image in front of the door. The captured image is transmitted to the deep learning model of the smart door lock in real time for analysis, trying to match it with the authorized faces in the database. If the facial features of the character image do not match the features of any authorized member in the database, the smart door lock will determine that this is an unauthorized approach. After determining that the character image is not stored in the database, the smart door lock will immediately transmit the captured image to the target object's mobile device and issue a security warning to the target object's mobile device. This warning usually includes image details, timestamps, and possible threat levels, allowing the target object to quickly understand the situation in front of the door and take appropriate measures. For example, the smart door lock sent a warning notification to the target object's mobile phone, with an image of a strange visitor and a timestamp: "Warning: At 14:30 on September 12, 2023, an unauthorized person appeared in front of the door, please pay attention to safety."

[0041] In an optional embodiment, after determining that the device is subject to signal interference, the method further includes: when a black screen appears in the image, transmitting the image to the target object's mobile device, and issuing a security warning to the target object's mobile device.

[0042] Suppose the image captured by the camera is completely black, or the image quality is extremely degraded and no objects or people can be identified. This may be because the camera is blocked (such as someone covering the lens with an object). After the smart door lock detects the black screen image, it immediately sends this abnormal information to the user's mobile phone through the Wi-Fi network. The user's mobile phone will receive a push notification, indicating that there is an abnormality in the camera in front of the door and needs immediate attention. For example: "Emergency Notice: At 22:00 on September 12, 2023, the camera image is abnormal, the screen is completely black, and there may be a security threat. Please check it immediately."

[0043] In an optional embodiment, transmitting the image to a mobile device of a target object includes: determining a compressed image of the image; converting the format of the compressed image into a target format compatible with a network transmission protocol, wherein the network transmission protocol defines rules for transmitting the image in a network; symmetrically encrypting the compressed image in the target format to obtain an encrypted image; and transmitting the encrypted image to the mobile device of the target object via the network transmission protocol.

[0044] When the camera of the smart door lock captures the image in front of the door, the image processing module of the smart door lock will first compress the high-resolution original image into a compressed image with a lower resolution to reduce the size of the image file and improve the transmission speed and efficiency. The compression format includes but is not limited to JPEG, PNG or H.264. The compressed image needs to be further converted into a format compatible with the network transmission protocol. Different network environments or mobile devices may support different image formats, so the smart door lock will convert the compressed image into a format suitable for network transmission. For example, if the target object's mobile device (such as a mobile phone) prefers to receive images in PNG format, the smart door lock will convert the compressed image in JPEG format into PNG format. After the image format conversion is completed, the smart door lock will encrypt the converted compressed image to ensure the security and privacy of the image during transmission and prevent unauthorized access or data leakage. It should be clear that this embodiment does not limit the specific encryption method. The encrypted image is transmitted to the target mobile device via a network transmission protocol suitable for a home network environment, wherein the network transmission protocol includes but is not limited to HTTP, HTTPS or MQTT. This embodiment follows the rules of the network transmission protocol to ensure that the image can reach the target object's mobile device safely and efficiently.

[0045] In order to better understand the process of the above-mentioned method for determining signal interference, the process of the above-mentioned method for determining signal interference is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0046] Figure 3 Schematic diagram of a method for determining signal interference according to an embodiment of the present invention. Figure 3 As shown, the specific steps include:

[0047] The WiFi module in the device will be automatically activated periodically to exchange data with the home router. In this embodiment, when the WiFi module is activated, it not only processes network data, but also captures the WiFi signal strength information reflected back to the door lock by the human body. This signal strength information includes RSSI (received signal strength indication) and CSI (channel state information).

[0048] When the WiFi module is started periodically, it sends an empty data packet to the router. When the device receives the confirmation frame returned by the router based on the empty data packet, the device calculates the RSSI and CSI values ​​in the WiFi signal through the baseband processor of the chip. These RSSI and CSI values ​​reflect the attenuation of the WiFi signal during transmission, and the change in the attenuation pattern is related to human body movement.

[0049] In order to analyze this information more accurately, at least 9 data packets are required to collect CSI data every 3 seconds. These data packets are arranged in chronological order to form a time series, and then the sequence is Fourier transformed and converted into a signal representation in the frequency domain to obtain a power spectrum density diagram. The power spectrum density diagram reveals the energy distribution of different frequency components in the signal. Check whether the energy in the specified frequency range in the power spectrum density diagram is significantly increased, because human activities will produce obvious signal changes in a specific low-frequency range. This frequency range is defined by the manufacturer and is used to determine whether there is human activity around the device.

[0050] In order to adapt to different human approach speeds, two trigger mechanisms are set. For the case of slow approach, a linear regression analysis is performed on the RSSI value. If the slope of the linear function exceeds the second threshold, it is considered that someone is approaching slowly. At this time, the door lock will wake up the camera to capture and analyze the image. If the camera captures someone or detects an abnormal situation, such as a black screen in the monitoring area, the user will be notified through the network.

[0051] For the case of rapid approach, the power spectrum density of the device is determined based on the CSI, and the frequency data in the preset frequency range in the power spectrum density diagram is inversely Fourier transformed to obtain a convolution kernel function. Optionally, in this embodiment, the above-mentioned preset frequency range is set to the range of frequencies F1 to F2. This convolution kernel function is processed with the time domain information extracted from the CSI data by wavelet transform. If the energy intensity of the processed data exceeds the first threshold, it will also trigger the camera to automatically wake up, capture and analyze the image, and notify the user through the network.

[0052] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, an operating terminal, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0053] Figure 4 is a structural block diagram of a device for determining signal interference according to an embodiment of the present application; Figure 4 As shown, including:

[0054] A first determination module 40, configured to determine a power spectrum density diagram of the device according to channel state information obtained from a wireless fidelity signal to which the device is connected;

[0055] A calculation module 42, configured to perform an inverse Fourier transform on the frequency data within a preset frequency range in the power spectrum density graph to obtain a convolution kernel function;

[0056] The second determination module 44 is used to perform a convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and determine that the device is subject to signal interference when the energy intensity of the convolution result exceeds a first threshold.

[0057] In an embodiment of the present application, a power spectrum density diagram of the device is determined based on the channel state information obtained from the wireless fidelity signal connected to the device; the frequency data within a preset frequency range in the power spectrum density diagram is subjected to an inverse Fourier transform to obtain a convolution kernel function; a convolution operation is performed on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and when the energy intensity of the convolution result exceeds a first threshold, it is determined that the device is subject to signal interference. The above technical solution solves the problem of power consumption caused by detecting signal interference through radar in the related art.

[0058] In an optional embodiment, the first determination module 40 is further used to arrange the channel state information in chronological order to form a time series when a preset amount of the channel state information is obtained; convert the time series from the time domain to the frequency domain through Fourier transform to obtain frequency domain information; and determine the power spectrum density diagram based on the frequency domain information.

[0059] In an optional embodiment, the first determination module 40 is further used to send an empty data packet to the router when the wireless fidelity signal is periodically started, and to determine whether a confirmation frame returned by the router based on the empty data packet is received; when it is determined that a confirmation frame returned by the router based on the empty data packet is received, calculate the received signal strength indication of the wireless fidelity signal and the channel state information.

[0060] In an optional embodiment, the first determination module 40 is further used to obtain a first received signal strength indication within a preset time from the received signal strength indication, and perform linear regression analysis on the first received signal strength indication to obtain an analysis result; perform linear fitting on the analysis result by the least squares method to obtain a linear function; and when the slope of the linear function exceeds a second threshold, capture an image within the field of view of the device by the camera of the device.

[0061] In an optional embodiment, the second determination module 44 is also used to start the camera on the device and capture an image within the field of view of the device through the camera; when a human image appears in the image, the facial features of the human image are analyzed through a deep learning model to determine whether the human image is stored in the database of the device; when it is determined that the human image is not stored in the database of the device, the image is transmitted to the target object's mobile device, and a security warning is issued to the target object's mobile device.

[0062] In an optional embodiment, the second determining module 44 is further configured to transmit the image to the target object's mobile device and issue a safety warning to the target object's mobile device when a black screen appears in the image.

[0063] In an optional embodiment, the second determination module 44 is further used to determine a compressed image of the image; convert the format of the compressed image into a target format compatible with a network transmission protocol, wherein the network transmission protocol defines rules for transmitting the image in a network; symmetrically encrypt the compressed image in the target format to obtain an encrypted image; and transmit the encrypted image to a mobile device of the target object via the network transmission protocol.

[0064] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0065] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0066] S1, determining a power spectrum density diagram of the device according to channel state information obtained from a wireless fidelity signal connected to the device;

[0067] S2, performing an inverse Fourier transform on the frequency data within a preset frequency range in the power spectrum density graph to obtain a convolution kernel function;

[0068] S3, performing a convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and determining that the device is subject to signal interference when an energy intensity of the convolution result exceeds a first threshold.

[0069] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0070] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0071] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0072] S1, determining a power spectrum density diagram of the device according to channel state information obtained from a wireless fidelity signal connected to the device;

[0073] S2, performing an inverse Fourier transform on the frequency data within a preset frequency range in the power spectrum density graph to obtain a convolution kernel function;

[0074] S3, performing a convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and determining that the device is subject to signal interference when an energy intensity of the convolution result exceeds a first threshold.

[0075] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0076] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0077] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0078] The embodiments of the present application also provide a computer program, which includes computer instructions, which are stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0079] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0080] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0081] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining signal interference, characterized in that: include: Determining a power spectrum density graph of the device based on channel state information obtained from a wireless fidelity signal to which the device is connected; Performing an inverse Fourier transform on the frequency data within a preset frequency range in the power spectrum density graph to obtain a convolution kernel function; A convolution operation is performed on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and when energy intensity of the convolution result exceeds a first threshold, it is determined that the device is subject to signal interference.

2. The method for determining signal interference according to claim 1, characterized in that: Determining a power spectrum density diagram of the device according to channel state information obtained from a wireless fidelity signal connected to the device, including: When a preset number of the channel state information is acquired, arranging the channel state information in chronological order to form a time series; Converting the time series from the time domain to the frequency domain by Fourier transform to obtain frequency domain information; The power spectrum density diagram is determined according to the frequency domain information.

3. The method for determining signal interference according to claim 1, characterized in that: Before determining the power spectrum density graph of the device according to the channel state information obtained from the wireless fidelity signal connected to the device, the method further includes: When the wireless fidelity signal is periodically started, sending an empty data packet to a router, and determining whether a confirmation frame returned by the router based on the empty data packet is received; When it is determined that a confirmation frame returned by the router based on the null data packet is received, a received signal strength indicator of the Wi-Fi signal and the channel state information are calculated.

4. The method for determining signal interference according to claim 3, characterized in that: After calculating the received signal strength indicator of the wireless fidelity signal and the channel state information, the method further includes: Acquire a first received signal strength indication within a preset time from the received signal strength indication, and perform a linear regression analysis on the first received signal strength indication to obtain an analysis result; Performing linear fitting on the analysis results by the least square method to obtain a linear function; When the slope of the linear function exceeds a second threshold, an image within the field of view of the device is captured by a camera of the device.

5. The method for determining signal interference according to claim 1, characterized in that: After determining that the device is subject to signal interference, the method further includes: Starting a camera on the device, and capturing an image within a field of view of the device through the camera; In the case where a human figure appears in the image, analyzing facial features of the human figure by a deep learning model to determine whether the human figure is stored in a database of the device; When it is determined that the character image is not stored in the database of the device, the image is transmitted to the mobile device of the target object, and a safety warning is issued to the mobile device of the target object.

6. The method for determining signal interference according to claim 5, characterized in that: After determining that the device is subject to signal interference, the method further includes: In the case where a black screen appears in the image, the image is transmitted to a mobile device of the target object, and a safety warning is issued to the mobile device of the target object.

7. The method for determining signal interference according to claim 5, characterized in that: Transmitting the image to a target subject's mobile device, comprising: determining a compressed image of the image; Converting the format of the compressed image into a target format compatible with a network transmission protocol, wherein the network transmission protocol defines rules for transmitting the image in a network; Symmetrically encrypting the compressed image in the target format to obtain an encrypted image; The encrypted image is transmitted to the target object's mobile device via the network transmission protocol.

8. A device for determining signal interference, characterized in that: include: A first determination module, configured to determine a power spectrum density graph of the device according to channel state information obtained from a wireless fidelity signal connected to the device; A calculation module, used for performing an inverse Fourier transform on the frequency data within a preset frequency range in the power spectrum density graph to obtain a convolution kernel function; The second determination module is used to perform a convolution operation on the convolution kernel function and the channel state information to obtain a corresponding convolution result, and determine that the device is subject to signal interference when the energy intensity of the convolution result exceeds a first threshold.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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