GNSS interference monitoring method and system based on navigation electronic map platform

Abstract

The invention relates to the technical field of signal monitoring, in particular to a GNSS interference monitoring method and system based on a navigation electronic map platform, and the method comprises the steps: obtaining corresponding GNSS signal data through a plurality of GNSS receivers distributed in a network, uploading the GNSS signal data, calling a pre-trained GNSS signal analysis model, and carrying out the calculation of the GNSS signal analysis model. Obtaining a corresponding GNSS signal analysis result; calling a corresponding GNSS signal type based on the GNSS signal analysis result, judging whether the GNSS signal type is in an abnormal mode, if so, determining the GNSS signal as a GNSS interference signal, and identifying a corresponding interference source position; and updating and displaying the detected interference source position and the corresponding interference influence range on the electronic map platform in real time, generating a corresponding suggested avoidance path on the electronic map platform based on the interference source position and the interference influence range, and sending the suggested avoidance path to a user on the electronic map platform. According to the invention, the accuracy of GNSS interference monitoring can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of signal monitoring, and in particular to a GNSS interference monitoring method and system based on a navigation electronic map platform. Background Art

[0002] With the widespread use of global navigation satellite systems, the reliability and security of GNSS signals have become extremely important. However, in some specific environments, such as between high-rise buildings in cities, in mountainous areas, tunnels and other areas with complex terrain, GNSS signals are easily interrupted or interfered by various factors. In addition, in some cases, human interference can also pose a serious threat to GNSS signals.

[0003] There are already some technologies for monitoring GNSS interference on the market, but most of these technologies have the following shortcomings: the monitoring range is limited and cannot cover a large area; the response speed is slow and interference cannot be detected in time; the positioning of the interference source is not accurate enough.

[0004] From the above, it can be seen that how to improve the accuracy of GNSS interference monitoring still needs to be solved. Summary of the invention

[0005] In order to improve the accuracy of GNSS interference monitoring, the present application provides a GNSS interference monitoring method and system based on a navigation electronic map platform.

[0006] In the first aspect, the present application provides a GNSS interference monitoring method based on a navigation electronic map platform, which adopts the following technical solution: A GNSS interference monitoring method based on a navigation electronic map platform comprises: obtaining corresponding GNSS signal data through multiple GNSS receivers distributed in a network, uploading the GNSS signal data, calling a pre-trained GNSS signal analysis model, and obtaining corresponding GNSS signal analysis results; calling the corresponding GNSS signal type based on the GNSS signal analysis results, judging whether the GNSS signal type is in an abnormal mode, and if so, determining it as a GNSS interference signal, and identifying the corresponding interference source position; updating and displaying the detected interference source position and the corresponding interference influence range on the electronic map platform in real time, generating a corresponding recommended avoidance path on the electronic map platform based on the interference source position and the interference influence range, and sending the recommended avoidance path to a user on the electronic map platform.

[0007] By adopting the above technical solution, signal data is collected through multiple GNSS receivers distributed in the network, and the data is processed using a pre-trained signal analysis model to identify interference signals and their sources in abnormal modes, so that the location of the interference source and its impact range can be updated and displayed in real time on the electronic map platform. In addition, a recommended avoidance path is generated based on the interference situation and sent to the user, thereby achieving accurate monitoring and timely response to GNSS interference, significantly improving the accuracy of monitoring and the efficiency of user response.

[0008] Optionally, the method also includes: continuously acquiring a corresponding GNSS signal data set based on a time series; determining a corresponding signal change trend based on the GNSS signal data set; if the signal change trend is a slow change, determining it to be a natural environment change; if the signal change trend is a drastic change, determining it to be human interference.

[0009] By adopting the above technical solution, by judging whether the signal changes are slow or drastic, it is possible to distinguish between natural environmental changes and human interference, thereby further improving the accuracy and reliability of GNSS interference monitoring, being able to more accurately identify the type of interference, and helping the system to more effectively respond to different types of interference events.

[0010] Optionally, the method also includes: obtaining environmental data corresponding to different regions, wherein the environmental data includes terrain data and weather data; retrieving a pre-set mapping relationship table of environmental data and monitoring parameters, wherein the monitoring parameters include signal thresholds and filter coefficients; determining corresponding target monitoring parameters based on the environmental data and the mapping relationship table, and adjusting the parameters of the monitoring system based on the target monitoring parameters.

[0011] By adopting the above technical solution, the corresponding target monitoring parameters (such as signal threshold, filter coefficient) are determined through the pre-set mapping relationship table of environmental data and monitoring parameters, so as to dynamically adjust the parameters of the monitoring system. This can optimize the monitoring parameters according to different geographical environment characteristics and improve the accuracy and adaptability of GNSS interference monitoring.

[0012] Optionally, the method also includes: retrieving corresponding historical interference data, wherein the historical interference data includes signal strength, frequency offset, timestamp, and geographic location; determining corresponding periodic interference signals and non-periodic interference signals based on the historical interference data; obtaining expected interference signals corresponding to future time periods based on the periodic interference signals, and generating a report to send to the user.

[0013] By adopting the above technical solution, periodic interference signals and non-periodic interference signals are identified by analyzing historical interference data, and the expected interference situation in the future period is predicted based on the periodic interference signal, and a report is generated and sent to the user. This method can help users understand the potential interference risks in advance, so as to take preventive measures, improve the system's early warning capabilities and the user's response efficiency.

[0014] Optionally, after determining that the GNSS signal type is a GNSS interference signal, the method also includes: determining an impact range corresponding to the GNSS interference signal based on the GNSS signal type and the corresponding abnormal pattern, wherein the impact range includes the number of affected users and the size of the geographical area; obtaining duration data corresponding to the GNSS interference signal, and determining a corresponding interference level based on the duration data and the impact range; determining whether the interference level is a major impact level, and if so, notifying the initiation of an emergency response procedure.

[0015] By adopting the above technical solution, the interference level is determined by analyzing the impact range of the interference signal (including the number of affected users and the size of the geographical area) and combining the duration data of the interference signal. If the interference level is judged to reach a significant impact level, the emergency response procedure will be notified to start; the impact of the interference can be accurately assessed, and emergency measures can be quickly initiated when necessary, thereby effectively mitigating the negative impact of the interference.

[0016] Optionally, after determining that it is a GNSS interference signal, the method also includes: collecting signal interference data related to the GNSS interference signal published by the public on social media platforms, extracting corresponding interference key data from the signal interference data, wherein the interference key data includes geographic location information and timestamp; verifying the interference impact range corresponding to the interference key data and the GNSS interference signal to obtain a corresponding credibility score; calling a pre-set credibility threshold to determine whether the credibility score is greater than the credibility threshold, and then determining it as a signal interference event.

[0017] By adopting the above technical solutions, the credibility of interference detection is enhanced by utilizing data reported by the public, and the accuracy and reliability of the monitoring system are improved.

[0018] In the second aspect, the present application provides a GNSS interference monitoring device based on a navigation electronic map platform, which adopts the following technical solution: A GNSS interference monitoring device based on a navigation electronic map platform, comprising: A signal analysis result acquisition module, which acquires corresponding GNSS signal data through multiple GNSS receivers distributed in the network, uploads the GNSS signal data, and retrieves a pre-trained GNSS signal analysis model to acquire corresponding GNSS signal analysis results; An interference source location identification module retrieves the corresponding GNSS signal type based on the GNSS signal analysis result, determines whether the GNSS signal type is in an abnormal mode, and if so, determines it to be a GNSS interference signal, for identifying the corresponding interference source location; The recommended avoidance path generation module updates and displays the detected interference source location and the corresponding interference impact range in real time on the electronic map platform, and is used to generate a corresponding recommended avoidance path on the electronic map platform based on the interference source location and the interference impact range, and sends the recommended avoidance path to the user on the electronic map platform.

[0019] In a third aspect, the present application provides a GNSS interference monitoring system based on a navigation electronic map platform, which adopts the following technical solutions: A GNSS interference monitoring method based on a navigation electronic map platform comprises a processor, wherein the processor runs a program of any one of the above-mentioned GNSS interference monitoring methods based on a navigation electronic map platform.

[0020] In a fourth aspect, the present application provides a storage medium, which adopts the following technical solution: A storage medium stores a program of any one of the above-mentioned GNSS interference monitoring methods based on a navigation electronic map platform.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: by comprehensively utilizing multi-point GNSS signal data, pre-trained signal analysis models, time series analysis, environmental data mapping, historical interference data, and social media public reports and other means, it achieves accurate monitoring of GNSS interference, real-time updating and display of the location of the interference source and its impact range, and generates recommended avoidance paths and sends them to users, which significantly improves the accuracy of monitoring and the user's response efficiency, while enhancing the system's reliability and early warning capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a GNSS interference monitoring method based on a navigation electronic map platform according to an exemplary embodiment.

[0023] Figure 2 It is a structural block diagram of a GNSS interference monitoring method and device based on a navigation electronic map platform according to an exemplary embodiment. DETAILED DESCRIPTION

[0024] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0025] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0026] The present application embodiment discloses a GNSS interference monitoring method based on a navigation electronic map platform, referring to Figure 1 ,include: S100, obtaining corresponding GNSS signal data through multiple GNSS receivers distributed in the network, uploading the GNSS signal data, calling a pre-trained GNSS signal analysis model, and obtaining corresponding GNSS signal analysis results.

[0027] Among them, multiple GNSS receivers distributed in different geographical locations are used to collect GNSS signal data to ensure data diversity and coverage, and then the collected GNSS signal data is uploaded to a central processing server or cloud platform for centralized processing and analysis; point collection of data can cover a wider range and improve the coverage of the monitoring system, and multi-point data can help to verify each other, reduce the impact of single point failures, and enhance data reliability.

[0028] In addition, the pre-trained GNSS signal analysis model is loaded. The GNSS signal analysis model can identify the characteristics of different types of GNSS signals. The uploaded GNSS signal data is analyzed using the GNSS signal analysis model to extract the key features of the signal and generate GNSS signal analysis results. The GNSS signal analysis model can be used to identify the type of signal, such as normal signal, multipath signal, interference signal, etc.

[0029] It should be pointed out here that the training process of the GNSS signal analysis model includes: first, data collection, obtaining a large amount of normal and abnormal GNSS signal data from multiple GNSS receivers, and preprocessing it, including data cleaning and feature extraction. Then, these labeled data sets are used to train machine learning or deep learning models, and the model parameters are optimized through repeated iterations so that it can accurately distinguish normal signals from interference signals, and finally a GNSS signal analysis model that can effectively identify GNSS interference is obtained.

[0030] The GNSS signal analysis model trained in this way can identify different types of interference signals and has high accuracy and robustness, so that it can effectively monitor and locate GNSS interference sources in practical applications and improve the monitoring performance of the system.

[0031] S110, based on the GNSS signal analysis result, retrieve the corresponding GNSS signal type, determine whether the GNSS signal type is in an abnormal mode, and if so, determine it to be a GNSS interference signal, and identify the corresponding interference source location.

[0032] Among them, the signal type is determined according to the analysis results, and it can be checked whether it meets the standards of the abnormal mode. If the signal type meets the definition of the abnormal mode, it is considered to be an interference signal, and the position of the interference source can be located using multi-point data and signal characteristics.

[0033] By determining whether the signal type is in an abnormal mode, normal signals and interference signals can be quickly distinguished; and by locating the location of the interference source through multi-point data and signal characteristics, the location of the interference source can be accurately identified and located.

[0034] S120, updating and displaying the detected interference source location and the corresponding interference impact range on the electronic map platform in real time, generating a corresponding suggested avoidance path on the electronic map platform based on the interference source location and the interference impact range, and sending the suggested avoidance path to the user on the electronic map platform.

[0035] Once the interference source is detected and its location and interference impact range are determined, the location and impact range of the interference source are immediately updated on the electronic map platform, the location of the interference source is marked on the map, and the area affected by the interference is marked with a specific color or icon; the system automatically calculates alternative paths to bypass the interference area, recommends these paths to users, and pushes the recommended avoidance paths to users who are using navigation services in the form of notifications. This allows users to be informed of the interference risks in a timely manner and provide reasonable avoidance solutions to ensure that the user's navigation experience is not affected.

[0036] In an embodiment of the application, the method further includes: S111, continuously acquiring a corresponding GNSS signal data set based on a time series.

[0037] Among them, the GNSS receiver will not only collect signal data at a certain moment, but also continuously collect data over a period of time to form a time series data set; every certain time interval (for example, every second or every minute), the receiver will record parameters such as signal strength, frequency, and arrival time. Over time, these data will be accumulated to form a time series database containing historical data, which can be realized through cloud storage or other forms of data warehouses.

[0038] S112: Determine a corresponding signal change trend based on the GNSS signal data set.

[0039] Among them, before analysis, some preprocessing operations may be performed on the original data, such as noise removal, smoothing, missing value filling, etc.; statistical or time series analysis methods (such as moving average method, ARIMA model, etc.) can be used to identify the trend of signal changes over time. The key is to identify whether the signal strength, frequency or other key parameters show a consistent directional change (increase or decrease) or periodic fluctuations.

[0040] It should be noted here that the criteria for defining "slow changes" and "dramatic changes" are usually based on experience and threshold settings. For example, if the signal strength gradually decreases by 5% within an hour, it may be considered a slow change; if the signal strength suddenly decreases by 20% within a minute, it may be considered a drastic change.

[0041] By analyzing the time series of signal data, we can distinguish between slow signal changes caused by natural factors (such as weather changes) and drastic signal changes caused by human factors (such as malicious interference), thereby more accurately determining the nature of the interference source.

[0042] S113, if the signal change trend is a slow change, it is determined to be a natural environment change; if the signal change trend is a drastic change, it is determined to be human interference.

[0043] Among them, according to the trend analysis results of S112, evaluate whether the signal change meets the previously set standards. If the signal change is determined to be a slow change, it is assumed that the change is caused by natural environmental factors, such as weather changes or terrain obstruction. If the signal changes very drastically, it is suspected that it is due to human factors, such as deliberate interference equipment. In addition, in some cases, it may be necessary to combine other data sources (such as meteorological data, ground facility layout, etc.) for cross-validation to confirm the initial hypothesis. By distinguishing between natural environmental changes and human interference, different response measures can be taken in a targeted manner. For natural changes, users may only need to understand the current situation; for human interference, further technical means (such as anti-interference technology) or legal means may be required to resolve it.

[0044] By executing steps S111 to S113 and introducing time series analysis, the ability to understand GNSS signal changes is enhanced, allowing the system to more intelligently distinguish different types of signal changes, thereby making more reasonable judgments and responses.

[0045] In addition, in the embodiment of the present application, the impact of different regions is also taken into consideration, so the method further includes: S114, obtaining environmental data corresponding to different regions.

[0046] Among them, terrain data acquisition: obtain terrain information of different regions through geographic information system (GIS) or other professional map services, including altitude, slope, landform type, etc. These data can be obtained from public terrain databases or specialized terrain mapping service providers. Weather data acquisition: obtain real-time weather conditions through meteorological departments or third-party weather forecast services, including temperature, humidity, wind speed, rainfall, etc. These data can be obtained from meteorological service websites or applications through API interfaces.

[0047] The purpose of acquiring environmental data is to better understand and explain the reasons for changes in GNSS signals, because different terrain and weather conditions will have different effects on GNSS signals. For example, tall buildings or mountains may block the signal, while bad weather may cause signal attenuation or refraction.

[0048] S115, retrieve a pre-set mapping relationship table between environmental data and monitoring parameters.

[0049] Among them, the monitoring parameters include signal threshold and filter coefficient; mapping relationship table establishment: during the system design phase, a mapping relationship table between environmental data (topography and weather) and monitoring parameters is established based on historical data and experimental test results. For example, in a mountainous environment, a higher signal strength threshold may be required to ensure signal quality; and on rainy days, the filter parameters may need to be adjusted to reduce signal noise; the mapping relationship table provides consistency guidance between environmental conditions and monitoring parameters, so that the system can automatically adjust the monitoring parameters according to changes in the actual environment, store the mapping relationship table in the system's database, and ensure that the monitoring system can access this data at any time.

[0050] S116, determining corresponding target monitoring parameters based on the environmental data and the mapping relationship table, and adjusting parameters of the monitoring system based on the target monitoring parameters.

[0051] Among them, after the system obtains the environmental data of the current area, it finds the monitoring parameters that match the current environment according to the mapping relationship table; adjusts the configuration of the GNSS signal monitoring system according to the matched monitoring parameters, such as adjusting the signal strength threshold, changing the filter coefficient, etc.; as the environmental conditions change, the system will re-acquire the environmental data regularly or as needed, and adjust the monitoring parameters accordingly to maintain the optimal performance of the monitoring system.

[0052] By dynamically adjusting the monitoring parameters, it is possible to ensure that the system can still effectively detect GNSS signals under different environmental conditions and minimize false alarms or missed alarms caused by environmental changes, which can not only improve the accuracy of monitoring, but also enhance the adaptability and flexibility of the system. In summary, by introducing environmental data and adjusting the monitoring parameters based on these data, the intelligence level of the GNSS interference monitoring system can be improved, enabling it to work more effectively in complex and changing actual environments.

[0053] After determining that the GNSS signal type is a GNSS interference signal, the method further includes: S117: Determine an impact range corresponding to the GNSS interference signal based on the GNSS signal type and the corresponding abnormal mode.

[0054] Among them, signal type identification: after confirming that the signal is a GNSS interference signal, the system will further analyze the characteristics of the interference signal, such as interference frequency, intensity, duration, etc.; impact range assessment: the system will determine the number of affected GNSS receivers based on the received signal anomaly data, and calculate the size of the affected geographic area in combination with the location information of these receivers; user impact statistics: by associating receivers with user devices, the number of affected users is counted. For example, if multiple vehicles or mobile phones in a certain area report the same interference signal, it can be inferred that there are a large number of affected users in the area.

[0055] Assessing the impact scope helps to clarify the scale of the impact of the interference event, provide data support for subsequent emergency response, and facilitate the effective allocation of resources.

[0056] S118, obtaining duration data corresponding to the GNSS interference signal, and determining a corresponding interference level based on the duration data and the impact range.

[0057] The system records the time when the interference signal first appears, and continues to monitor until the interference signal returns to normal, so as to calculate the duration of the interference. According to the duration of the interference and the scope of influence, the level of this interference event is determined with reference to the preset interference level standard. The interference level standard may include but is not limited to the following situations: if the interference lasts for a short time (for example, less than 5 minutes) and the scope of influence is small (such as only a few receivers are affected), it can be regarded as minor interference; if the interference lasts for a long time (for example, more than 30 minutes) and the scope of influence is large (such as affecting multiple cities or regions), it may be judged as major interference.

[0058] It should be pointed out here that the standards for interference levels are usually formulated based on historical data analysis and can be adjusted and optimized in the system.

[0059] S119, determining whether the interference level is a major impact level, and if so, initiating an emergency response procedure.

[0060] The system determines whether the interference level determined in S118 has reached the preset major impact level standard. If the interference level is determined to be a major impact level, the system will automatically trigger the emergency response procedure and send a notification to relevant personnel. The purpose of initiating the emergency response procedure is to take prompt action to reduce the impact of interference on users and society and to ensure the stability and security of navigation services.

[0061] Emergency response procedures may include, but are not limited to: sending alert information to users in the affected area to remind them to pay attention to safety and provide alternative navigation solutions; activating backup navigation systems or services to ensure the continuity of critical infrastructure and services; notifying relevant management departments and technical support teams so that they can take quick measures to solve the problem; and recording relevant information of the interference incident to provide a basis for subsequent investigations.

[0062] Based on the execution of steps S117 to S119, a detailed assessment and response mechanism is used to ensure timely and effective response in the face of GNSS interference, minimizing the impact of interference on users and society.

[0063] After determining that it is a GNSS interference signal, in order to further improve the accuracy, the method further includes: S121, collecting signal interference data related to GNSS interference signals published by the public from social media platforms, and extracting corresponding interference key data from the signal interference data.

[0064] Among them, determine the social media platforms that need to be monitored, such as Weibo, WeChat Moments, Twitter, Facebook, etc. These platforms are usually places where the public shares daily life and emergencies. By setting specific keywords or topic tags (hashtags), such as "GPS failure", "navigation error", "satellite signal loss", etc., they are used to automatically crawl related posts. Crawler technology or the API interface provided by the social media platform can be used to collect content containing these keywords, including text descriptions, pictures, videos, etc.

[0065] Using social media as an information source can quickly capture GNSS interference situations experienced by the public, providing a complementary data source to help verify and confirm interference events.

[0066] Natural language processing is performed on the collected social media posts to extract geographic location information (such as through location tags in the posts or address information in the text description) and timestamps (posting time). Invalid or irrelevant posts are then removed to ensure that the extracted data is about the current disturbance event.

[0067] S122, verifying the interference impact range corresponding to the interference key data and the GNSS interference signal to obtain a corresponding credibility score.

[0068] The geographic location information and timestamp extracted from social media are compared with the interference location and time reported by the GNSS receiver to check whether the interference location reported by social media coincides with the interference location detected by the GNSS receiver and whether they are close in time. The credibility score is calculated based on the comparison results. For example, if multiple social media reports point to the same time and place, the credibility score is higher. By comparing data from different sources, the reliability of interference event confirmation can be increased to ensure that only verified information is adopted as part of the interference event.

[0069] S123, calling a preset credibility threshold, and determining whether the credibility score is greater than the credibility threshold, and then determining it as a signal interference event.

[0070] Among them, a credibility threshold is set based on historical data and experience. This threshold represents the minimum score required for the system to consider an event credible, and the credibility score obtained is compared with the preset threshold. If the credibility score is higher than the threshold, the system confirms that the event is a real signal interference event and includes it in the monitoring system for tracking and processing. By setting a credibility threshold, inaccurate or irrelevant information can be filtered out, ensuring that only interference events with high credibility are officially recorded and measures are taken.

[0071] Based on executing steps S121 to S123, collecting public feedback through social media and verifying the authenticity of these feedbacks by combining technical means, this method not only improves the sensitivity and accuracy of the GNSS interference monitoring system, but also increases the transparency of the system.

[0072] Finally, the solution in the embodiment of the present application also has a certain predictive effect, so the method also includes: S124, retrieve corresponding historical interference data.

[0073] Among them, historical interference data includes detailed information of each interference event, such as signal strength, frequency offset, timestamp, and geographic location.

[0074] S125, determining corresponding periodic interference signals and non-periodic interference signals based on historical interference data.

[0075] Among them, the historical interference data is preprocessed, including cleaning and formatting, to ensure the consistency and availability of the data. Then, time series analysis methods (such as Fourier transform, autocorrelation function, etc.) are used to identify periodic patterns in the data. For example, if the signal strength decreases repeatedly in the same time period every week, it can be considered a periodic interference. Interference events without obvious patterns are classified as non-periodic interference. By distinguishing between periodic and non-periodic interference signals, the system can more accurately predict future interference events and provide users with more precise services.

[0076] S126, obtaining an expected interference signal corresponding to a future time period based on the periodic interference signal, and generating a report to send to a user.

[0077] Based on the historical data of periodic interference signals, the GNSS signal analysis model is used to predict the interference situation in the future period, and predict the interference signal strength, frequency offset and other information in a certain period in the future. A detailed report is generated based on the prediction results, including the expected interference period, strength, frequency offset and possible geographical location, and the generated report is sent to potentially affected users or related institutions via email, SMS, application push, etc.

[0078] By leveraging the periodic patterns in historical data to predict future interference events and notifying users of the prediction results in the form of reports, it has the following functions: allowing users to adjust plans in advance to avoid areas or time periods that may be affected; helping relevant departments or institutions to rationally arrange resources to deal with possible interference events; improving the scientific nature and accuracy of decision-making through historical data-driven analysis methods; reducing navigation interruptions caused by unforeseen interference events and improving users' trust and satisfaction with the service.

[0079] The present application embodiment discloses a GNSS interference monitoring device based on a navigation electronic map platform, referring to Figure 2 , the devices include but are not limited to: The signal analysis result acquisition module 200 acquires corresponding GNSS signal data through multiple GNSS receivers distributed in the network, uploads the GNSS signal data, and retrieves a pre-trained GNSS signal analysis model to acquire corresponding GNSS signal analysis results; The interference source location identification module 210 retrieves the corresponding GNSS signal type based on the GNSS signal analysis result, determines whether the GNSS signal type is in an abnormal mode, and if so, determines it to be a GNSS interference signal, which is used to identify the corresponding interference source location; The recommended avoidance path generation module 220 updates and displays the detected interference source location and the corresponding interference impact range in real time on the electronic map platform, generates a corresponding recommended avoidance path based on the interference source location and the interference impact range on the electronic map platform, and sends the recommended avoidance path to the user on the electronic map platform.

[0080] Furthermore, the device includes but is not limited to: A GNSS signal data set acquisition module, used to continuously acquire corresponding GNSS signal data sets based on a time series; A GNSS signal data set determination module, used to determine a corresponding signal change trend based on the GNSS signal data set; The change determination module is used to determine that if the signal change trend is a slow change, it is a natural environment change; if the signal change trend is a drastic change, it is used to determine that it is human interference.

[0081] Furthermore, the device includes but is not limited to: An environmental data acquisition module is used to acquire environmental data corresponding to different regions, wherein the environmental data includes terrain data and weather data; A mapping relationship table retrieval module is used to retrieve a pre-set mapping relationship table between environmental data and monitoring parameters, wherein the monitoring parameters include signal thresholds and filter coefficients; The target monitoring parameter determination module is used to determine the corresponding target monitoring parameters based on the environmental data and the mapping relationship table, and adjust the parameters of the monitoring system based on the target monitoring parameters.

[0082] Furthermore, the device includes but is not limited to: A historical interference data retrieval module is used to retrieve corresponding historical interference data, wherein the historical interference data includes signal strength, frequency offset, timestamp, and geographic location; A periodicity determination module, used to determine corresponding periodic interference signals and non-periodic interference signals based on historical interference data; The expected interference signal acquisition module is used to acquire the expected interference signal corresponding to the future time period based on the periodic interference signal, and generate a report to send to the user.

[0083] Furthermore, the device includes but is not limited to: An impact range determination module is used to determine the impact range corresponding to the GNSS interference signal based on the GNSS signal type and the corresponding abnormal mode, wherein the impact range includes the number of affected users and the size of the geographical area; The interference level determination module obtains the duration data corresponding to the GNSS interference signal, and determines the corresponding interference level based on the duration data and the impact range; The level judgment module is used to judge whether the interference level is a major impact level. If so, it notifies the emergency response procedure to start.

[0084] Furthermore, the device includes but is not limited to: An interference key data extraction module collects signal interference data related to GNSS interference signals published by the public on social media platforms, and is used to extract corresponding interference key data from the signal interference data, wherein the interference key data includes geographic location information and timestamp; A credibility score acquisition module is used to obtain a corresponding credibility score based on verification of the interference impact range corresponding to the interference key data and the GNSS interference signal; The signal interference event determination module retrieves a preset credibility threshold and determines whether the credibility score is greater than the credibility threshold, which is used to determine it as a signal interference event.

[0085] In summary, the solutions in the embodiments of the present application have the following beneficial effects: Through a distributed GNSS receiver network, data diversity and coverage are ensured, the coverage of the monitoring system is improved, and data reliability is enhanced; using pre-trained GNSS signal analysis models, signal types are automatically identified, normal signals are distinguished from interference signals, and the accuracy and efficiency of monitoring are improved; the location and impact range of interference sources are updated in real time on the electronic map platform, and avoidance paths are generated, and users are notified in a timely manner to ensure that their navigation experience is not affected.

[0086] Through time series analysis, we can identify slow and drastic changes in signals, distinguish natural environmental changes from human interference, and improve the accuracy of interference event judgment; introducing time series analysis can enhance the system's ability to understand signal changes, so that the system can more intelligently distinguish different types of signal changes and make reasonable judgments and responses.

[0087] Dynamically adjust monitoring parameters based on environmental data to ensure that the system can still effectively detect GNSS signals in different environments, thereby improving the accuracy and adaptability of monitoring; determine the interference level based on the duration and impact range of the interference to facilitate systematic management and response to various interference events; and automatically trigger emergency response procedures to take prompt action to reduce the impact of interference on users and society and ensure the stability and security of navigation services.

[0088] Collect public feedback through social media to increase the reliability of interference event confirmation and improve the sensitivity and accuracy of the system; use historical interference data to predict future interference events, notify users in advance, help them adjust their plans, and reduce the risk of navigation interruptions; use historical data-driven analysis methods to reduce problems caused by unforeseen interference events and improve user trust and satisfaction with the service.

[0089] An embodiment of the present application further discloses a GNSS interference monitoring system based on a navigation electronic map platform, comprising a processor, wherein the program of any one of the above-mentioned GNSS interference monitoring methods based on a navigation electronic map platform is running on the processor.

[0090] The embodiment of the present application further discloses a storage medium storing a program of the GNSS interference monitoring method based on the navigation electronic map platform as described in any one of the above.

[0091] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A GNSS interference monitoring method based on a navigation electronic map platform, characterized in that: include: Acquire corresponding GNSS signal data through multiple GNSS receivers distributed in the network, upload the GNSS signal data, retrieve a pre-trained GNSS signal analysis model, and acquire corresponding GNSS signal analysis results; Retrieving the corresponding GNSS signal type based on the GNSS signal analysis result, determining whether the GNSS signal type is in an abnormal mode, and if so, determining it to be a GNSS interference signal, and identifying the corresponding interference source position; The detected interference source location and the corresponding interference impact range are updated and displayed in real time on the electronic map platform, a corresponding suggested avoidance path is generated on the electronic map platform based on the interference source location and the interference impact range, and the suggested avoidance path is sent to the user on the electronic map platform.

2. The GNSS interference monitoring method based on the navigation electronic map platform according to claim 1 is characterized in that: The method also includes: Continuously acquire corresponding GNSS signal data sets based on time series; Determining a corresponding signal change trend based on the GNSS signal data set; If the signal change trend is slow, it is determined to be a natural environment change; if the signal change trend is drastic, it is determined to be human interference.

3. The GNSS interference monitoring method based on the navigation electronic map platform according to claim 2 is characterized in that: The method also includes: Acquire environmental data corresponding to different regions, wherein the environmental data includes terrain data and weather data; Retrieving a pre-set mapping relationship table of environmental data and monitoring parameters, wherein the monitoring parameters include signal thresholds and filter coefficients; Based on the environmental data and the mapping relationship table, corresponding target monitoring parameters are determined, and based on the target monitoring parameters, parameters of the monitoring system are adjusted.

4. The GNSS interference monitoring method based on the navigation electronic map platform according to claim 1 is characterized in that: The method also includes: Retrieving corresponding historical interference data, wherein the historical interference data includes signal strength, frequency offset, timestamp, and geographic location; Determining corresponding periodic interference signals and non-periodic interference signals based on the historical interference data; An expected interference signal corresponding to a future time period is acquired based on the periodic interference signal, and a report is generated and sent to a user.

5. The GNSS interference monitoring method based on the navigation electronic map platform according to claim 1 is characterized in that: After determining that the type of the GNSS signal is a GNSS interference signal, the method further includes: Determine the impact range corresponding to the GNSS interference signal based on the GNSS signal type and the corresponding abnormal mode, wherein the impact range includes the number of affected users and the size of the geographical area; Acquire duration data corresponding to the GNSS interference signal, and determine a corresponding interference level based on the duration data and the impact range; Determine whether the interference level is a major impact level. If so, notify and initiate the emergency response procedure.

6. The GNSS interference monitoring method based on the navigation electronic map platform according to claim 1 is characterized in that: After determining that the signal is a GNSS interference signal, the method further includes: Collect signal interference data related to GNSS interference signals published by the public from social media platforms, and extract corresponding interference key data from the signal interference data, wherein the interference key data includes geographic location information and a timestamp; Verify the interference impact range corresponding to the interference key data and the GNSS interference signal to obtain a corresponding credibility score; The preset credibility threshold is retrieved to determine whether the credibility score is greater than the credibility threshold, and then it is determined to be a signal interference event.

7. A GNSS interference monitoring device based on a navigation electronic map platform, characterized in that: include: A signal analysis result acquisition module, which acquires corresponding GNSS signal data through multiple GNSS receivers distributed in the network, uploads the GNSS signal data, and retrieves a pre-trained GNSS signal analysis model to acquire corresponding GNSS signal analysis results; An interference source location identification module retrieves the corresponding GNSS signal type based on the GNSS signal analysis result, determines whether the GNSS signal type is in an abnormal mode, and if so, determines it to be a GNSS interference signal, for identifying the corresponding interference source location; The recommended avoidance path generation module updates and displays the detected interference source location and the corresponding interference impact range in real time on the electronic map platform, and is used to generate a corresponding recommended avoidance path on the electronic map platform based on the interference source location and the interference impact range, and sends the recommended avoidance path to the user on the electronic map platform.

8. A GNSS interference monitoring system based on a navigation electronic map platform, characterized in that: It includes a processor, in which runs a program of the GNSS interference monitoring method based on the navigation electronic map platform as described in any one of claims 1-6.

9. A storage medium, characterized in that: A program storing the GNSS interference monitoring method based on the navigation electronic map platform as described in any one of claims 1-6.

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