Global navigation satellite system safety monitoring method, device, equipment and medium

Abstract

The invention discloses a global navigation satellite system safety monitoring method, device and equipment and a medium, and relates to the technical field of satellite positioning. Comprising the following steps: acquiring an original global navigation satellite system signal and corresponding NMEA protocol data, and carrying out signal processing operation on the original global navigation satellite system signal; if the signal strength is continuously or suddenly weakened, indicating that the to-be-monitored global navigation satellite receiving equipment has an abnormal condition, and sending corresponding alarm information to a central processing node; and if the to-be-monitored global navigation satellite receiving equipment does not have the abnormal condition, executing an abnormal detection process based on a multi-navigation system cross validation technology and an abnormal detection machine learning algorithm, and sending an obtained abnormal detection result to a central processing node, so that the central processing node executes a corresponding abnormal processing process. According to the invention, under the condition that the power consumption of the receiving equipment is limited, the GNSS signal abnormity can be accurately detected in a fixed scene and a mobile scene at the same time.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a global navigation satellite system security monitoring method, device, equipment and medium. Background Art

[0002] The Global Navigation Satellite System (GNSS) is a system that uses satellite signals for positioning, navigation and timing. GNSS systems include but are not limited to the following four major satellite systems: GPS (Global Positioning System of the United States), BeiDou (Global Navigation Satellite System of China), Galileo (Global Navigation Satellite System of Europe), and GLONASS (Global Navigation Satellite System of Russia). GNSS signals are widely used in many fields such as transportation, military, and urban management. The reliability and accuracy of their data are crucial to the safety and efficiency of these systems.

[0003] With the development of satellite positioning technology, GNSS is increasingly used in digital cities, traffic management, logistics, military and other fields. However, the reliability and security of GNSS signals face many threats, such as GNSS spoofing attacks: misleading receiving devices by sending false satellite signals, resulting in positioning errors. GNSS interference: interference from other signal sources may cause GNSS signal quality to deteriorate or even fail. In fixed scenarios, the position and environment of GNSS receiving devices are relatively stable, and anomalies can be identified by comparing the received GNSS signals with known preset location data. In mobile scenarios, the location of the device is constantly changing, and traditional GNSS anomaly detection methods are difficult to adapt to the complex signal environment during movement, and high-power devices cannot meet the needs of mobile devices. In the prior art, many GNSS anomaly detection schemes require high-performance, dedicated receiving devices, which are usually large in size, high in power consumption, and expensive, and cannot be widely used in small mobile devices. Someone proposed a low-cost GPS spoofing detection technology for Android phones, but this method is only applicable to specific devices and is limited to GPS spoofing detection, and cannot be widely used in other GNSS systems or mobile scenarios.

[0004] As can be seen from the above, how to support anomaly detection in mobile and fixed scenarios with limited device power consumption and ensure that the device can still accurately detect anomalies in GNSS data during movement is an urgent problem to be solved. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a global navigation satellite system security monitoring method, device, equipment and medium, which can support anomaly detection in mobile scenarios and fixed scenarios when the device power consumption is limited, and ensure that the device can still accurately detect anomalies in GNSS data during movement. The specific scheme is as follows:

[0006] In a first aspect, the present application provides a global navigation satellite system security monitoring method, which is applied to an edge computing node, including:

[0007] Acquiring an original global navigation satellite system signal and corresponding NMEA protocol data, and performing a signal processing operation on the original global navigation satellite system signal to obtain a signal characteristic and a key protocol field of the original global navigation satellite system signal; the original global navigation satellite system signal is a signal sent by several target navigation systems;

[0008] Based on the signal strength of the original global navigation satellite system signal, it is determined whether the global navigation satellite receiving device to be monitored has an abnormality caused by a hardware problem of the device itself or an environmental factor; if the signal strength continuously or suddenly weakens, it indicates that the global navigation satellite receiving device to be monitored has the abnormality, and a corresponding alarm message is sent to a central processing node so that the central processing node executes a corresponding abnormality handling process based on the alarm message;

[0009] If the global navigation satellite receiving device to be monitored does not have the abnormal situation, executing the corresponding abnormality detection process according to the type of the edge computing node to obtain the abnormality detection result; the type of the edge computing node includes a fixed edge computing node and a mobile edge computing node;

[0010] The abnormality detection result is sent to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the abnormality detection result.

[0011] Optionally, the edge computing node is used to perform anomaly detection of global navigation satellite system data in the global navigation satellite receiving device to be monitored; the central processing node is used to collect detection results from the edge computing node, and perform statistical analysis and result display based on the detection results;

[0012] Among them, the mobile edge computing node can independently perform statistical analysis and display results.

[0013] Optionally, performing a signal processing operation on the original global navigation satellite system signal to obtain a signal characteristic and a protocol key field of the original global navigation satellite system signal includes:

[0014] Using a low-pass filter and a noise suppression algorithm to remove high-frequency noise and interference in the original global navigation satellite system signal, and extracting signal features of the original global navigation satellite system signal;

[0015] The original global navigation satellite system signal is parsed based on the NMEA protocol data, and key fields are extracted to obtain the protocol key fields.

[0016] Optionally, sending the abnormality detection result to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the abnormality detection result includes:

[0017] sending the abnormal detection result characterized as normal data and the reference time of the navigation system to the central processing node so that the central processing node stores the original global navigation satellite system signal into a preset storage space;

[0018] Or, the abnormal detection result characterized as data abnormality and the corresponding abnormal data content are sent to the central processing node, so that the central processing node records the abnormal data content and displays the abnormal location, time and satellite information.

[0019] Optionally, executing a corresponding anomaly detection process for the fixed edge computing node to obtain an anomaly detection result includes:

[0020] Determining whether the position of the current global navigation satellite receiving device to be monitored is consistent with a preset fixed reference position based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol;

[0021] If the position of the global navigation satellite receiving device to be monitored is consistent with the preset fixed reference position, an abnormality detection result indicating that the data is normal is obtained;

[0022] If the current position of the global navigation satellite receiving device to be monitored is inconsistent with the preset fixed reference position, an abnormality detection result characterized as data abnormality is obtained.

[0023] Optionally, executing a corresponding anomaly detection process for the mobile edge computing node to obtain an anomaly detection result includes:

[0024] Performing a data consistency check based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol to obtain a data consistency check result; the data consistency check includes a signal-to-noise ratio check, a satellite status check, a signal status check, and a change rate and change trend check;

[0025] Based on the data consistency check result, a multi-navigation system cross-validation is performed on the original global navigation satellite system signal to obtain a verification result of the multi-navigation system cross-validation; the multi-navigation system cross-validation is to compare and analyze the time and position data of the several target navigation systems;

[0026] The target navigation systems are classified according to the verification results of the multi-navigation system cross-validation, and anomaly detection is performed on the global navigation satellite system signals corresponding to the classified target navigation systems using an anomaly detection machine learning algorithm model to obtain the anomaly detection result.

[0027] Optionally, the central processing node trains the anomaly detection machine learning algorithm model according to the received anomaly detection results and corresponding anomaly data, and updates the trained model parameters to the mobile edge computing node.

[0028] In a second aspect, the present application provides a global navigation satellite system security monitoring device, which is applied to an edge computing node, including:

[0029] an original GNSS signal acquisition module, used for acquiring an original GNSS signal and corresponding NMEA protocol data, and performing a signal processing operation on the original GNSS signal to obtain a signal characteristic and a key protocol field of the original GNSS signal; the original GNSS signal is a signal sent by a plurality of target navigation systems;

[0030] a global navigation satellite receiving device abnormality detection module, used to determine whether the global navigation satellite receiving device to be monitored has an abnormality caused by a hardware problem of the device itself or an environmental factor based on the signal strength of the original global navigation satellite system signal; if the signal strength is continuously or suddenly weakened, it indicates that the global navigation satellite receiving device to be monitored has the abnormality, and sends corresponding alarm information to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the alarm information;

[0031] The original global navigation satellite system signal anomaly detection module is used to execute a corresponding anomaly detection process according to the type of the edge computing node to obtain an anomaly detection result if the global navigation satellite receiving device to be monitored does not have the abnormal situation; the type of the edge computing node includes a fixed edge computing node and a mobile edge computing node;

[0032] The abnormality detection result sending module is used to send the abnormality detection result to the central processing node so that the central processing node executes the corresponding abnormality processing process based on the abnormality detection result.

[0033] In a third aspect, the present application provides an electronic device, including:

[0034] Memory, used to store computer programs;

[0035] The processor is used to execute the computer program to implement the aforementioned global navigation satellite system safety monitoring method.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program implements the aforementioned global navigation satellite system safety monitoring method when executed by a processor.

[0037] The present application provides a method for safety monitoring of a global navigation satellite system. First, an original global navigation satellite system signal and corresponding NMEA protocol data are obtained, and a signal processing operation is performed on the original global navigation satellite system signal to obtain signal characteristics and protocol key fields of the original global navigation satellite system signal; the original global navigation satellite system signal is a signal sent by several target navigation systems; then, based on the signal strength of the original global navigation satellite system signal, it is determined whether there is an abnormality in a global navigation satellite receiving device to be monitored; if the signal strength is continuously or suddenly weakened, it indicates that there is an abnormality in the global navigation satellite receiving device to be monitored, and a corresponding alarm information is sent to a central processing node so that the central processing node executes a corresponding abnormality handling process based on the alarm information; then, if there is no abnormality in the global navigation satellite receiving device to be monitored, a corresponding abnormality detection process is executed according to the type of the edge computing node to obtain an abnormality detection result; the type of the edge computing node includes a fixed edge computing node and a mobile edge computing node; finally, the abnormality detection result is sent to the central processing node so that the central processing node executes a corresponding abnormality handling process based on the abnormality detection result.

[0038] As can be seen from the above, the present application completes the GNSS spoofing detection process through the edge computing node and the central processing node, which reduces the burden of the central processing node, improves the response speed of the system, and reduces the power consumption of the device; and adopts different anomaly detection processes according to the motion state of the global navigation satellite device. That is, the dynamic behavior (such as acceleration, speed) of the global navigation satellite device is combined with the changing characteristics of the GNSS signal to improve the accuracy and robustness of GNSS spoofing attack detection. In this way, it can support anomaly detection in mobile scenarios and fixed scenarios with limited device power consumption, ensuring that the device can still accurately detect anomalies in GNSS data during movement. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention 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, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0040] Figure 1 A flow chart of a global navigation satellite system security monitoring method applied to edge computing nodes disclosed in this application;

[0041] Figure 2 This is an architecture diagram of a global navigation satellite system safety monitoring system disclosed in this application;

[0042] Figure 3 A schematic diagram of the internal structure of an edge computing node disclosed in this application;

[0043] Figure 4 A calculation flow chart of a central processing node disclosed in this application;

[0044] Figure 5 A flowchart of a specific global navigation satellite system security monitoring method applied to edge computing nodes disclosed in this application;

[0045] Figure 6 A schematic diagram of a global navigation satellite system security monitoring device applied to an edge computing node disclosed in the present application;

[0046] Figure 7 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] With the development of satellite positioning technology, GNSS is increasingly used in digital cities, traffic management, logistics, military and other fields. However, the reliability and security of GNSS signals face many threats, such as GNSS spoofing attacks: misleading receiving devices by sending false satellite signals, resulting in positioning errors. GNSS interference: interference from other signal sources may cause the quality of GNSS signals to deteriorate or even fail. In fixed scenarios, the position and environment of GNSS receiving devices are relatively stable, and anomalies can be identified by comparing the received GNSS signals with known preset location data. In mobile scenarios, the location of the device is constantly changing, and traditional GNSS anomaly detection methods are difficult to adapt to the complex signal environment during movement, and high-power devices cannot meet the needs of mobile devices. In the prior art, many GNSS anomaly detection schemes require high-performance, dedicated receiving devices, which are usually large in size, high in power consumption, and expensive, and cannot be widely used in small mobile devices. To this end, the present application provides a global navigation satellite system security monitoring solution that can support anomaly detection in mobile scenarios and fixed scenarios with limited device power consumption, ensuring that the device can still accurately detect anomalies in GNSS data during movement.

[0049] See also Figure 1 As shown, the embodiment of the present application discloses a global navigation satellite system security monitoring method, which is applied to an edge computing node, including:

[0050] Step S11: acquiring an original global navigation satellite system signal and corresponding NMEA protocol data, and performing a signal processing operation on the original global navigation satellite system signal to obtain signal characteristics and protocol key fields of the original global navigation satellite system signal.

[0051] In this embodiment, the original global navigation satellite system signal is a signal sent by several target navigation systems, and the several target navigation systems include but are not limited to GPS, Beidou, Galileo and GLONASS.

[0052] See also Figure 2As shown, the edge computing node is used to perform anomaly detection of global navigation satellite system data in the global navigation satellite receiving device to be monitored; that is, it is used to perform anomaly detection of GNSS data in a local device (such as a mobile terminal), which is divided into a fixed scene mode and a mobile scene mode. Among them, the mobile edge computing node can independently perform statistical analysis and result display, and can operate independently without interacting with the central processing node. The central processing node is used to collect the detection results from the edge computing node, and perform statistical analysis and result display based on the detection results; that is, collect the detection results from the edge computing node, perform statistical analysis and result display. Among them, if the central processing node cannot process the detection result of the edge computing node, the edge computing node can output the detection result independently.

[0053] See also Figure 3 As shown, the edge computing node obtains the original global navigation satellite system signal and the corresponding NMEA protocol data through a multi-mode GNSS antenna, and processes the system signal and the corresponding NMEA protocol data. Specifically, the signal processing operation is performed on the original global navigation satellite system signal to obtain the signal characteristics and protocol key fields of the original global navigation satellite system signal, which may include: using a low-pass filter and a noise suppression algorithm to remove high-frequency noise and interference in the original global navigation satellite system signal, and extracting the signal characteristics of the original global navigation satellite system signal; performing protocol parsing on the original global navigation satellite system signal based on the NMEA protocol data, and extracting key fields to obtain the protocol key fields. Among them, the key fields of the protocol include but are not limited to timestamp information (time tag of each signal), location coordinates (current latitude, longitude and altitude information of the GNSS device), satellite status (satellite operation status, such as signal quality indication) and signal quality (received signal strength, such as C / N0 value); the signal characteristics include but are not limited to spectrum distribution, main frequency, bandwidth, harmonic components, noise power density, spectrum sharpness, signal energy concentration, instantaneous frequency change trend, phase characteristics, signal in-band interference characteristics and other signal characteristics. It is worth mentioning that the specific method of extracting the signal characteristics of the original global navigation satellite system signal includes but is not limited to extracting the signal characteristics of the original global navigation satellite system signal by Fourier transform, wavelet transform and other methods.

[0054] Step S12: based on the signal strength of the original global navigation satellite system signal, determine whether the global navigation satellite receiving device to be monitored has an abnormality caused by a hardware problem of the device itself or environmental factors; if the signal strength weakens continuously or suddenly, it indicates that the global navigation satellite receiving device to be monitored has the abnormality, and sends a corresponding alarm message to the central processing node so that the central processing node executes a corresponding abnormality handling process based on the alarm message.

[0055] In this embodiment, it is determined whether there is an abnormality in the monitored global navigation satellite receiving device based on the signal strength of the original global navigation satellite system signal. If the signal strength continues to weaken or suddenly weakens (SNR decreases and the amplitude becomes smaller), an alarm message representing the abnormality caused by a failure of the GNSS receiving device itself, weather or environmental obstruction is output to the central processing node.

[0056] Step S13: If the global navigation satellite receiving device to be monitored does not have the abnormal situation, a corresponding abnormality detection process is executed according to the type of the edge computing node to obtain an abnormality detection result.

[0057] In this embodiment, the types of edge computing nodes include fixed edge computing nodes and mobile edge computing nodes, and accordingly, the anomaly detection process includes an anomaly detection process for fixed edge computing nodes and an anomaly detection process for mobile edge computing nodes. Specifically, executing the corresponding anomaly detection process for the fixed edge computing node to obtain an anomaly detection result may include: judging whether the position of the current global navigation satellite receiving device to be monitored is consistent with the preset fixed reference position based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol; if the position of the current global navigation satellite receiving device to be monitored is consistent with the preset fixed reference position, an anomaly detection result characterized as normal data is obtained; if the position of the current global navigation satellite receiving device to be monitored is inconsistent with the preset fixed reference position, an anomaly detection result characterized as abnormal data is obtained. That is, whether the data is normal is judged by comparing the receiving time and position coordinates with the preset fixed reference position.

[0058] Correspondingly, executing the corresponding anomaly detection process for the mobile edge computing node to obtain the anomaly detection result may include: performing data consistency verification based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol to obtain the data consistency verification result; the data consistency verification includes signal-to-noise ratio verification, satellite status verification, signal status verification, and change rate and change trend verification; performing multi-navigation system cross-validation on the original global navigation satellite system signal based on the data consistency verification result to obtain the verification result of the multi-navigation system cross-validation; the multi-navigation system cross-validation is to compare and analyze the time and position data of the several target navigation systems; that is, to analyze whether the timestamps and position coordinates provided by the four navigation systems are consistent; classifying the several target navigation systems according to the verification result of the multi-navigation system cross-validation, that is, classifying the navigation systems with data deviation at the same time, and using the anomaly detection machine learning algorithm model to perform anomaly detection on the global navigation satellite system signals corresponding to the classified target navigation systems to obtain the anomaly detection result. Among them, the signal-to-noise ratio check is to determine whether the signal reception is abnormal based on the fluctuation of the real-time signal-to-noise ratio (C / N0) data; the satellite status check is to detect whether the received satellite trajectory matches the navigation ephemeris data; the signal status check is to check the signal characteristics obtained after Fourier transform; the change rate and change trend check is to analyze the change trend and distribution characteristics of the signal to identify potential anomalies.

[0059] In a specific implementation, the use of anomaly detection machine learning algorithm model to perform anomaly detection on the global navigation satellite system signal corresponding to the classified target navigation system to obtain the anomaly detection result may include: inputting the multi-dimensional features of the protocol and the original signal, the multi-dimensional features of the protocol and the original signal including but not limited to time, location information consistency features, signal-to-noise ratio and signal strength features, Fourier spectrum features (main frequency, harmonics, interference frequency, etc.), satellite trajectory consistency features; using anomaly detection machine learning algorithm combined with time domain and frequency domain features, and using machine learning models (such as inverse reinforcement learning or neural networks) to perform anomaly detection and classification. That is, the possibility of outputting anomaly signals, and classifying and identifying anomaly types (such as interference signals, deceptive signals, equipment failures, etc.).

[0060] It should be noted that the central processing node trains the anomaly detection machine learning algorithm model according to the received anomaly detection results and the corresponding anomaly data, and updates the trained model parameters to the mobile edge computing node. That is, the edge computing node regularly receives the latest algorithm model parameters pushed by the central processing node, and automatically loads the new model parameters to optimize the adaptability to the dynamic interference environment.

[0061] Step S14: Send the abnormality detection result to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the abnormality detection result.

[0062] In this embodiment, see Figure 4 As shown, the central processing node receives GNSS abnormal alarm data from the edge computing node and further processes the data. Specifically, the abnormal detection result is sent to the central processing node so that the central processing node performs the corresponding abnormal processing process based on the abnormal detection result, which may include: sending the abnormal detection result characterized as normal data and the reference time of the navigation system to the central processing node so that the central processing node stores the original global navigation satellite system signal in a preset storage space; or, sending the abnormal detection result characterized as abnormal data and the corresponding abnormal data content to the central processing node so that the central processing node records the abnormal data content and displays the abnormal position, time and satellite information. That is, if the edge computing node detects an abnormality, the central processing node will record the information and display the abnormal position, time and satellite information; otherwise, record the normal data flow.

[0063] It is worth mentioning that the central processing node trains the anomaly detection machine learning algorithm model according to the received anomaly detection results and the corresponding anomaly data, and updates the trained model parameters to the mobile edge computing node. That is, the model is trained according to the new data set, and the updated model parameters are passed to the edge computing node, so that the edge computing node regularly receives the latest algorithm model parameters pushed by the central processing node, and automatically loads the new model parameters to optimize the adaptability to the dynamic interference environment.

[0064] As can be seen from the above, the embodiment of the present application completes the GNSS spoofing detection process through the edge computing node and the central processing node, reduces the burden of the central processing node, improves the response speed of the system, and reduces the power consumption of the device; and adopts different abnormality detection processes according to the motion state of the global navigation satellite device. That is, the dynamic behavior (such as acceleration, speed) of the global navigation satellite device is combined with the changing characteristics of the GNSS signal to improve the accuracy and robustness of GNSS spoofing attack detection. By receiving a variety of GNSS signal sources, the system's fault tolerance and detection accuracy for signal anomalies are significantly improved. An alarm mechanism based on real-time detection is provided, and multiple alarm methods (SMS, email, APP push) are supported to ensure that when a GNSS spoofing attack is detected, the user can be notified in time to take countermeasures. In this way, it is possible to support abnormality detection in mobile and fixed scenarios under limited device power consumption, ensuring that the device can still accurately detect abnormalities in GNSS data during movement.

[0065] See also Figure 5 As shown, the embodiment of the present application discloses a global navigation satellite system security monitoring method, which is applied to an edge computing node, including:

[0066] In this embodiment, NMEA protocol data and original GNSS signals are received by receiving modules of GPS, Beidou, Galileo and GLONASS. The decoded navigation message is parsed according to the NMEA protocol, and the NMEA protocol data is extracted, wherein the NMEA protocol data includes but is not limited to the following key fields: timestamp information, used to obtain the time tag of each signal; location coordinates, used to extract the current latitude, longitude and altitude information of the GNSS device; satellite status, used to obtain the satellite operation status (such as signal quality indication); signal quality, used to analyze the received signal strength (such as C / N0 value). The original digital signal is subjected to signal denoising and filtering. That is, a low-pass filter and a noise suppression algorithm are used to remove high-frequency noise and interference in the signal. And the signal is analyzed in the frequency domain by Fourier transform to extract signal features. Among them, the signal features include but are not limited to spectrum distribution, main frequency, bandwidth, harmonic components, noise power density, spectrum sharpness, signal energy concentration, instantaneous frequency change trend, phase characteristics, signal in-band interference characteristics, etc.

[0067] In this embodiment, it is determined whether there is an abnormality in the global navigation satellite receiving device to be monitored based on the signal strength of the original GNSS signal. If the signal strength continues to weaken or suddenly weakens (SNR decreases and the amplitude becomes smaller), an alarm message representing the abnormality caused by a failure of the GNSS receiving device itself, weather or environmental obstruction is output to the central processing node.

[0068] Further, determine whether the device is a fixed-position scene edge node. If the device is a fixed-position scene edge node, compare the received time and location coordinates with the preset fixed reference position to determine whether they are consistent. If the device is a mobile location scene edge node, perform preliminary anomaly detection on the time, location information, signal strength, etc. of the protocol data and the original signal characteristics, which may include determining whether the signal reception is abnormal based on the fluctuation of the real-time signal-to-noise ratio (C / N0) data; detecting whether the received satellite trajectory matches the navigation ephemeris data; analyzing the change trend and distribution characteristics of the signal to identify potential anomalies; and detecting whether the above features obtained after Fourier transform are abnormal. Compare and analyze the time and location data from the four major navigation systems of GPS, Beidou, Galileo and GLONASS. That is, analyze whether the timestamps and location coordinates provided by the four navigation systems are consistent. And classify the navigation systems with data deviations at the same time. Apply anomaly detection machine learning algorithms to the classified navigation system data. Specifically, it may include the multi-dimensional features of the input protocol and the original signal; the multi-dimensional features of the protocol and the original signal include but are not limited to the consistency features of time and location information; signal-to-noise ratio and signal strength features; Fourier spectrum features (main frequency, harmonics, interference frequency, etc.); satellite trajectory consistency features. The algorithm combines time domain and frequency domain features, uses machine learning models (such as inverse reinforcement learning or neural networks) to output the possibility of abnormal signals, and classifies and identifies abnormal types (such as interference signals, deception signals, equipment failures, etc.). It should be noted that it is necessary to regularly receive and load the latest algorithm model parameters pushed by the central node to optimize the adaptability to dynamic interference environments.

[0069] In this embodiment, if an abnormal GNSS signal is detected, an abnormal alarm message is output and detailed abnormal data content (such as abnormal satellite number, abnormal signal time period, etc.) is provided. If the detected signal is normal, reliable navigation information is output and the navigation system time is adjusted to the reference time to improve the system time synchronization accuracy.

[0070] As can be seen from the above, the embodiment of the present application significantly improves the system's fault tolerance and detection accuracy for signal anomalies by receiving multiple GNSS signal sources. Combining the dynamic behavior of the device (such as acceleration, speed) with the changing characteristics of the GNSS signal, it can perform effective anomaly detection in both static and dynamic scenarios, and is suitable for high-speed mobile scenarios such as drones and vehicle-mounted equipment, which improves the accuracy and robustness of GNSS spoofing attack detection. An efficient computing process design is provided, and the design of the edge computing node can reduce the burden of the central node, improve the response speed of the system, and accurately detect GNSS data anomalies without increasing the power consumption of the device. An alarm mechanism based on real-time detection is provided, and multiple alarm methods (SMS, email, APP push) are supported to ensure that when a GNSS spoofing attack is detected, the user can be notified in time to take countermeasures. Thereby, under the condition of limited power consumption of the device, anomaly detection in mobile scenarios and fixed scenarios is supported, ensuring that the device can still accurately detect anomalies in GNSS data during movement.

[0071] Accordingly, see Figure 6 As shown, an embodiment of the present application provides a global navigation satellite system security monitoring device, which is applied to an edge computing node, including:

[0072] The original GNSS signal acquisition module 11 is used to acquire the original GNSS signal and the corresponding NMEA protocol data, and perform signal processing operations on the original GNSS signal to obtain the signal characteristics and protocol key fields of the original GNSS signal; the original GNSS signal is a signal sent by several target navigation systems;

[0073] The global navigation satellite receiving device abnormality detection module 12 is used to determine whether the global navigation satellite receiving device to be monitored has an abnormality caused by a hardware problem of the device itself or an environmental factor based on the signal strength of the original global navigation satellite system signal. If the signal strength is continuously or suddenly weakened, it indicates that the global navigation satellite receiving device to be monitored has the abnormality, and sends corresponding alarm information to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the alarm information;

[0074] The original global navigation satellite system signal anomaly detection module 13 is used to execute the corresponding anomaly detection process according to the type of the edge computing node to obtain an anomaly detection result if the global navigation satellite receiving device to be monitored does not have the abnormal situation; the type of the edge computing node includes a fixed edge computing node and a mobile edge computing node;

[0075] The abnormality detection result sending module 14 is used to send the abnormality detection result to the central processing node so that the central processing node executes the corresponding abnormality processing process based on the abnormality detection result;

[0076] Among them, the mobile edge computing node can independently perform statistical analysis and result display; the central processing node trains the anomaly detection machine learning algorithm model according to the received anomaly detection results and corresponding anomaly data, and updates the trained model parameters to the mobile edge computing node.

[0077] As can be seen from the above, the embodiment of the present application completes the GNSS spoofing detection process through the edge computing node and the central processing node, which reduces the burden of the central processing node, improves the response speed of the system, and reduces the power consumption of the device; and adopts different anomaly detection processes according to the motion state of the global navigation satellite device. That is, the dynamic behavior of the global navigation satellite device and the changing characteristics of the GNSS signal are combined to improve the accuracy and robustness of GNSS spoofing attack detection. Thereby, it is possible to support anomaly detection in mobile scenarios and fixed scenarios under the condition of limited device power consumption, ensuring that the device can still accurately detect anomalies in GNSS data during movement.

[0078] In some specific implementations, the original global navigation satellite system signal acquisition module 11 may specifically include:

[0079] a signal feature extraction unit, configured to remove high-frequency noise and interference in the original global navigation satellite system signal by using a low-pass filter and a noise suppression algorithm, and to extract signal features of the original global navigation satellite system signal;

[0080] The protocol key field extraction unit is used to perform protocol parsing on the original global navigation satellite system signal based on NMEA protocol data, and extract key fields to obtain the protocol key fields.

[0081] In some specific implementations, the original GNSS signal anomaly detection module 13 may specifically include:

[0082] A fixed edge computing node anomaly detection submodule, configured to execute a corresponding anomaly detection process for the fixed edge computing node to obtain an anomaly detection result;

[0083] The mobile edge computing node anomaly detection submodule is used to execute the corresponding anomaly detection process for the mobile edge computing node to obtain an anomaly detection result.

[0084] Furthermore, the fixed edge computing node anomaly detection submodule may specifically include:

[0085] A position information determination unit, configured to determine whether the position of the current global navigation satellite receiving device to be monitored is consistent with a preset fixed reference position based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol;

[0086] A first abnormality detection result obtaining unit, configured to obtain an abnormality detection result indicating that the data is normal if the position of the global navigation satellite receiving device to be monitored is consistent with the preset fixed reference position;

[0087] The second abnormality detection result obtaining unit is used to obtain an abnormality detection result characterized as a data abnormality if the current position of the global navigation satellite receiving device to be monitored is inconsistent with the preset fixed reference position.

[0088] Furthermore, the mobile edge computing node anomaly detection submodule may specifically include:

[0089] A data consistency check unit, configured to perform a data consistency check based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol to obtain a data consistency check result; the data consistency check includes a signal-to-noise ratio check, a satellite status check, a signal status check, and a change rate and change trend check;

[0090] a multi-navigation system cross-validation unit, configured to perform multi-navigation system cross-validation on the original global navigation satellite system signal based on the data consistency check result to obtain a verification result of the multi-navigation system cross-validation; the multi-navigation system cross-validation is to perform a comparative analysis on the time and position data of the several target navigation systems;

[0091] The third anomaly detection result acquisition unit is used to classify the several target navigation systems according to the verification results of the multi-navigation system cross-validation, and use the anomaly detection machine learning algorithm model to perform anomaly detection on the global navigation satellite system signals corresponding to the classified target navigation systems to obtain the anomaly detection results.

[0092] In some specific implementations, the abnormality detection result sending module 14 may specifically include:

[0093] a first abnormality detection result processing unit, configured to send the abnormality detection result characterized as normal data and the reference time of the navigation system to the central processing node, so that the central processing node stores the original global navigation satellite system signal into a preset storage space;

[0094] The second anomaly detection result processing unit is used to send the anomaly detection result characterized as data anomaly and the corresponding abnormal data content to the central processing node, so that the central processing node records the abnormal data content and displays the abnormal position, time and satellite information.

[0095] In some specific implementations, the global navigation satellite system safety monitoring device may further include:

[0096] An edge computing unit is used to perform anomaly detection of global navigation satellite system data in the global navigation satellite receiving device to be monitored;

[0097] The central processing unit is used to collect the detection results from the edge computing nodes and perform statistical analysis and result display based on the detection results.

[0098] Furthermore, the present application also discloses an electronic device. Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the content in the figure cannot be regarded as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the global navigation satellite system security monitoring method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0099] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0100] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0101] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and may be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the global navigation satellite system security monitoring method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 may further include computer programs that can be used to complete other specific tasks.

[0102] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the aforementioned disclosed method for monitoring the safety of a global navigation satellite system. The specific steps of the method may refer to the corresponding contents disclosed in the aforementioned embodiments, and will not be described in detail here.

[0103] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0104] Professionals may further 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 the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may 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.

[0105] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0106] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0107] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A global navigation satellite system security monitoring method, characterized in that: Applied to edge computing nodes, including: Acquiring an original global navigation satellite system signal and corresponding NMEA protocol data, and performing a signal processing operation on the original global navigation satellite system signal to obtain a signal characteristic and a key protocol field of the original global navigation satellite system signal; the original global navigation satellite system signal is a signal sent by several target navigation systems; Based on the signal strength of the original global navigation satellite system signal, it is determined whether the global navigation satellite receiving device to be monitored has an abnormality caused by a hardware problem of the device itself or an environmental factor; if the signal strength continuously or suddenly weakens, it indicates that the global navigation satellite receiving device to be monitored has the abnormality, and a corresponding alarm message is sent to a central processing node so that the central processing node executes a corresponding abnormality handling process based on the alarm message; If the global navigation satellite receiving device to be monitored does not have the abnormal situation, executing the corresponding abnormality detection process according to the type of the edge computing node to obtain the abnormality detection result; the type of the edge computing node includes a fixed edge computing node and a mobile edge computing node; The abnormality detection result is sent to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the abnormality detection result.

2. The method for monitoring the safety of a global navigation satellite system according to claim 1, characterized in that: The edge computing node is used to perform anomaly detection of global navigation satellite system data in the global navigation satellite receiving device to be monitored; the central processing node is used to collect detection results from the edge computing node, and perform statistical analysis and result display based on the detection results; Among them, the mobile edge computing node can independently perform statistical analysis and display results.

3. The method for monitoring the safety of a global navigation satellite system according to claim 1, characterized in that: The performing a signal processing operation on the original global navigation satellite system signal to obtain a signal characteristic and a protocol key field of the original global navigation satellite system signal includes: Using a low-pass filter and a noise suppression algorithm to remove high-frequency noise and interference in the original global navigation satellite system signal, and extracting signal features of the original global navigation satellite system signal; The original global navigation satellite system signal is parsed based on the NMEA protocol data, and key fields are extracted to obtain the protocol key fields.

4. The method for monitoring the safety of a global navigation satellite system according to claim 1, characterized in that: The sending the abnormality detection result to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the abnormality detection result includes: sending the abnormal detection result characterized as normal data and the reference time of the navigation system to the central processing node so that the central processing node stores the original global navigation satellite system signal into a preset storage space; Or, the abnormal detection result characterized as data abnormality and the corresponding abnormal data content are sent to the central processing node, so that the central processing node records the abnormal data content and displays the abnormal location, time and satellite information.

5. The method for monitoring the safety of a global navigation satellite system according to claim 1, characterized in that: Executing a corresponding anomaly detection process for the fixed edge computing node to obtain an anomaly detection result includes: Determining whether the position of the current global navigation satellite receiving device to be monitored is consistent with a preset fixed reference position based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol; If the position of the global navigation satellite receiving device to be monitored is consistent with the preset fixed reference position, an abnormality detection result indicating that the data is normal is obtained; If the current position of the global navigation satellite receiving device to be monitored is inconsistent with the preset fixed reference position, an abnormality detection result characterized as data abnormality is obtained.

6. The method for monitoring the safety of a global navigation satellite system according to any one of claims 1 to 5, characterized in that: Executing a corresponding anomaly detection process for the mobile edge computing node to obtain an anomaly detection result, including: Performing a data consistency check based on the signal characteristics of the original global navigation satellite system signal and the key fields of the protocol to obtain a data consistency check result; the data consistency check includes a signal-to-noise ratio check, a satellite status check, a signal status check, and a change rate and change trend check; Based on the data consistency check result, a multi-navigation system cross-validation is performed on the original global navigation satellite system signal to obtain a verification result of the multi-navigation system cross-validation; the multi-navigation system cross-validation is to compare and analyze the time and position data of the several target navigation systems; The target navigation systems are classified according to the verification results of the multi-navigation system cross-validation, and anomaly detection is performed on the global navigation satellite system signals corresponding to the classified target navigation systems using an anomaly detection machine learning algorithm model to obtain the anomaly detection result.

7. The method for monitoring the safety of a global navigation satellite system according to claim 6, characterized in that: The central processing node trains the anomaly detection machine learning algorithm model according to the received anomaly detection results and corresponding anomaly data, and updates the trained model parameters to the mobile edge computing node.

8. A global navigation satellite system safety monitoring device, characterized in that: Applied to edge computing nodes, including: an original GNSS signal acquisition module, used for acquiring an original GNSS signal and corresponding NMEA protocol data, and performing a signal processing operation on the original GNSS signal to obtain a signal characteristic and a key protocol field of the original GNSS signal; the original GNSS signal is a signal sent by a plurality of target navigation systems; a global navigation satellite receiving device abnormality detection module, used to determine whether the global navigation satellite receiving device to be monitored has an abnormality caused by a hardware problem of the device itself or an environmental factor based on the signal strength of the original global navigation satellite system signal; if the signal strength is continuously or suddenly weakened, it indicates that the global navigation satellite receiving device to be monitored has the abnormality, and sends corresponding alarm information to the central processing node so that the central processing node executes a corresponding abnormality processing process based on the alarm information; The original global navigation satellite system signal anomaly detection module is used to execute a corresponding anomaly detection process according to the type of the edge computing node to obtain an anomaly detection result if the global navigation satellite receiving device to be monitored does not have the abnormal situation; the type of the edge computing node includes a fixed edge computing node and a mobile edge computing node; The abnormality detection result sending module is used to send the abnormality detection result to the central processing node so that the central processing node executes the corresponding abnormality processing process based on the abnormality detection result.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the global navigation satellite system safety monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the global navigation satellite system security monitoring method according to any one of claims 1 to 7 is implemented.

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