Satellite navigation method and system for all-terrain multifunctional vehicle

Abstract

The invention discloses an all-terrain multifunctional vehicle satellite navigation method and system, relates to the field of all-terrain multifunctional vehicle satellite navigation, is used for improving the navigation reliability and safety of an all-terrain multifunctional vehicle in a complex terrain, and comprises the following steps: obtaining the current position of the vehicle and the actual signal characteristics of a satellite signal received by the vehicle; calculating an expected signal characteristic of the satellite signal according to the current position of the vehicle and preset known environment data; according to the actual signal characteristics and the expected signal characteristics, determining whether signal abnormity exists or not; under the condition that the signal is abnormal, determining whether an abnormal area exists in a preset path going to the destination or not; and under the condition that the abnormal area exists in the preset path, generating an alternative path for avoiding the abnormal area so as to go to the destination.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation for all-terrain multi-functional vehicles, and more particularly to a satellite navigation method and system for all-terrain multi-functional vehicles. Background Technology

[0002] Traditional satellite navigation methods for all-terrain multi-functional vehicles (ATVs) suffer from significant limitations when facing remote, rugged, and environmentally variable mountainous and forested terrain. These limitations stem from outdated pre-set map information, limited local environmental perception, and frequent dynamic environmental changes, severely impacting navigation accuracy, continuity, and autonomous decision-making capabilities. Consequently, they struggle to meet the demands of high-precision operations or multi-vehicle collaborative tasks. Without addressing these issues, ATVs performing tasks such as geological exploration, field search and rescue, and environmental monitoring may face route disruptions, mission delays, and even personnel safety risks. Summary of the Invention

[0003] This application discloses a satellite navigation method and system for all-terrain multi-functional vehicles, aiming to improve the navigation reliability and safety of all-terrain multi-functional vehicles in complex terrain.

[0004] In a first aspect, this application discloses a satellite navigation method for an all-terrain multi-functional vehicle, comprising the following steps:

[0005] Obtain the vehicle's current location and the actual signal characteristics of the satellite signals received by the vehicle;

[0006] Based on the vehicle's current location and pre-set known environmental data, calculate the expected signal characteristics of the satellite signal;

[0007] Based on the actual signal characteristics and the expected signal characteristics, determine whether there is a signal anomaly;

[0008] In the event of signal anomalies, determine whether there are any abnormal areas in the preset route to the destination;

[0009] If there are abnormal areas in the preset path, an alternative path that avoids the abnormal areas will be generated to reach the destination.

[0010] Optionally, based on the actual signal characteristics and the expected signal characteristics, determine whether there is a signal anomaly, including:

[0011] For satellite signals from multiple satellite signals, determine the signal strength difference, signal-to-noise ratio difference, and geometric distribution difference of the satellite signals; the signal strength difference is the difference between the actual signal strength and the expected signal strength, the signal-to-noise ratio difference is the difference between the actual signal-to-noise ratio and the expected signal-to-noise ratio, and the geometric distribution difference is the difference between the actual geometric distribution value and the expected geometric distribution value;

[0012] If the signal strength difference is less than the preset strength difference, the signal-to-noise ratio difference is greater than the preset signal-to-noise ratio difference, and the geometric distribution value is greater than the preset geometric distribution value, then a signal anomaly is determined to exist.

[0013] Optionally, the actual geometric distribution value includes one or more of the following: horizontal precision factor, vertical precision factor, and positional precision factor.

[0014] Optionally, determine whether there are any abnormal areas in the preset path to the destination, including:

[0015] Determine whether the abnormal satellite signals are from the same direction;

[0016] When satellite signals with abnormal signals are in the same direction, the system combines the vehicle's current location, direction of travel, and preset path to determine whether there are abnormal areas in the preset path to the destination using geometric reverse tracing.

[0017] Optionally, in the event of signal anomalies, determine whether there are abnormal areas in the preset path to the destination, including:

[0018] Identify geological creep characteristic signals in the presence of signal anomalies;

[0019] The creep activity index is calculated based on the decay rate, phase drift rate, and duration of geological creep characteristic signals; the creep activity index is used to reflect the deformation rate and activity level of the subsurface medium.

[0020] If the creep activity index is greater than a preset threshold, an abnormal area is determined to exist in the preset path to the destination.

[0021] Optionally, the method also includes:

[0022] For any given satellite, compare the actual signal characteristics with the expected signal characteristics;

[0023] When the actual signal characteristics show a continuous downward trend compared to the expected signal characteristics, or when the carrier phase change rate shows a gradual cumulative drift, the actual signal characteristics are determined to be geological creep characteristic signals.

[0024] Optionally, the creep activity index satisfies the following relationship:

[0025] C_index=w1×|d1 / dt|_avg+w2×|d2 / dt|_avg+w3×Duration

[0026] C_index is the creep activity index, w1, w2 and w3 are preset coefficients, |d1 / dt|_avg is the average rate of decrease of the signal-to-noise ratio difference, |d2 / dt|_avg is the average rate of change of the carrier phase of the satellite signal, and Duration is the duration of the creep characteristic signal.

[0027] Optionally, in the presence of signal anomalies, geological creep characteristic signals can be identified, including:

[0028] In the presence of signal anomalies, determine whether the characteristic patterns of the signal anomalies match the known influence patterns of specific environmental factors;

[0029] If the characteristic pattern of a signal anomaly matches the known influence pattern of a specific environmental factor, then the signal anomaly is attributed to that environmental factor.

[0030] If the characteristic pattern of the signal anomaly does not match the known influence pattern of a specific environmental factor, then the geological creep characteristic signal is identified.

[0031] Optionally, determine whether the characteristic patterns of the signal anomalies match known influence patterns of specific environmental factors, including:

[0032] Acquire real-time meteorological data, ionospheric activity data, and environmental data;

[0033] By combining real-time meteorological data, ionospheric activity data, and environmental data, we can determine whether the characteristic patterns of signal anomalies match the known influence patterns of specific environmental factors.

[0034] Secondly, this application also discloses a satellite navigation system for an all-terrain multi-functional vehicle, the system comprising:

[0035] The signal receiving module is used to obtain the vehicle's current location and the actual signal characteristics of the satellite signals received by the vehicle;

[0036] The calculation module is used to calculate the expected signal characteristics of the satellite signal based on the vehicle's current location and preset known environmental data;

[0037] The determination module is used to determine whether there is a signal anomaly based on the actual signal characteristics and the expected signal characteristics;

[0038] The determination module is also used to determine whether there are abnormal areas in the preset path to the destination in the event of signal abnormalities.

[0039] The route generation and provision module is used to generate an alternative route that avoids the abnormal area in the preset route, so as to reach the destination.

[0040] Beneficial effects

[0041] This application discloses a satellite navigation method for all-terrain multi-functional vehicles. It acquires the vehicle's current position and the actual signal characteristics of the satellite signal, calculates the expected signal characteristics of the satellite signal based on known environmental data, and then determines whether there is a signal anomaly based on the difference between the actual and expected signal characteristics. After confirming a signal anomaly, the method further determines whether there is an abnormal region in the preset path, and if an abnormal region exists, generates an alternative path to avoid that region. This method effectively solves the problems of insufficient navigation accuracy and continuity caused by outdated map information, limited local environmental perception capabilities, and frequent dynamic environmental changes in existing technologies. By actively identifying satellite signal anomalies and combining them with environmental data to determine abnormal regions in the path, this application can promptly detect and avoid potential risks, such as road interruptions or dangerous areas caused by geological creep or landslides, thereby significantly improving the navigation reliability and safety of all-terrain multi-functional vehicles in complex terrain, ensuring the smooth execution of missions and personnel safety. Attached Figure Description

[0042] Figure 1 This is a schematic flowchart of a satellite navigation method for an all-terrain multi-functional vehicle provided in an embodiment of the present invention;

[0043] Figure 2 This is a schematic flowchart of another all-terrain multi-functional vehicle satellite navigation method provided in this embodiment of the invention;

[0044] Figure 3 This is a schematic diagram of the structure of a satellite navigation system for an all-terrain multi-functional vehicle provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0046] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0047] First, let's introduce the terminology used in this application.

[0048] The vehicle's current location refers to the geographical coordinates of the all-terrain multi-functional vehicle at a certain moment, which is usually calculated by the signals received from the Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), or other satellite navigation systems.

[0049] The actual signal characteristics of a satellite signal refer to the various physical parameters of the satellite signal received by the vehicle, such as signal strength, signal-to-noise ratio, carrier phase, pseudorange, etc. These parameters directly reflect the impact on the signal during propagation.

[0050] The expected signal characteristics of a satellite signal refer to the parameters that a satellite signal should have under ideal or known conditions, calculated based on the vehicle's current location and preset known environmental data (such as terrain models, ionospheric models, tropospheric models, etc.).

[0051] Signal anomalies refer to significant differences between actual signal characteristics and expected signal characteristics. These differences may be caused by a variety of factors, such as signal blockage, multipath effects, interference, ionospheric disturbances, or geological activities.

[0052] An abnormal area refers to a region in the preset path that may affect the safe passage of vehicles or the accuracy of navigation, such as landslides, mudslides, areas with geological creep, or areas with strong interference sources.

[0053] The preset route refers to the initial driving route planned before the mission begins, based on the destination and known map information.

[0054] Alternative routes are new routes that are replanned after an abnormal area is found in the preset route, which can avoid the abnormal area and guide the vehicle to the destination.

[0055] The following specific embodiments will provide a detailed description and explanation of the satellite navigation method for an all-terrain multi-functional vehicle provided in this application.

[0056] Reference Figure 1 This invention provides a satellite navigation method for all-terrain multi-functional vehicles, comprising the following steps:

[0057] S1, obtain the vehicle's current location and the actual signal characteristics of the satellite signals received by the vehicle.

[0058] The vehicle's current position can be obtained in several ways. For example, an onboard GNSS receiver can receive signals from multiple satellites in real time and perform calculations using techniques such as differential positioning, RTK (real-time kinematic) positioning, or PPP (precise point positioning) to obtain high-precision three-dimensional position information. Alternatively, data from an inertial navigation system (INS) can be combined with a navigation algorithm to calculate the position when GNSS signals are interrupted or their accuracy degrades, ensuring the continuity and reliability of the position information. Obtaining the actual signal characteristics of the satellite signals received by the vehicle involves capturing, tracking, and demodulating the raw satellite signals. This is typically accomplished by the RF front-end and baseband processor within the GNSS receiver, extracting a series of raw observations including signal strength (C / N0), signal-to-noise ratio, carrier phase, pseudorange, and Doppler shift. These observations form the basis for subsequent analysis of signal anomalies.

[0059] S2. Based on the vehicle's current location and preset known environmental data, calculate the expected signal characteristics of the satellite signal.

[0060] Calculating the expected signal characteristics is a crucial reference for identifying signal anomalies. Pre-defined known environmental data can include a high-precision digital elevation model (DEM), land cover type data, ionospheric models, tropospheric models, and known interference source distribution information. For example, based on the vehicle's current location and DEM data, terrain obstruction along the satellite signal propagation path can be calculated, thereby predicting signal strength and the number of visible satellites. Combining ionospheric and tropospheric models, the delay and refraction effects experienced by the signal during propagation can be predicted. Using this pre-defined data, a theoretical signal propagation model can be constructed, allowing calculation of parameters such as signal strength, signal-to-noise ratio, and carrier phase that each visible satellite signal should possess at the current location and under known conditions.

[0061] S3. Based on the actual signal characteristics and the expected signal characteristics, determine whether there is a signal anomaly.

[0062] For example, the difference between the actual signal strength and the expected signal strength, the difference between the actual signal-to-noise ratio and the expected signal-to-noise ratio, and the difference between the actual carrier phase and the expected carrier phase can be calculated. When these differences exceed preset thresholds, a signal anomaly can be identified. The threshold setting needs to comprehensively consider receiver performance, environmental noise levels, and acceptable positioning accuracy requirements.

[0063] Specifically, for satellite signals among multiple satellite signals, the signal strength difference, signal-to-noise ratio difference, and geometric distribution difference of the satellite signals are determined; the signal strength difference is the difference between the actual signal strength and the expected signal strength, the signal-to-noise ratio difference is the difference between the actual signal-to-noise ratio and the expected signal-to-noise ratio, and the geometric distribution difference is the difference between the actual geometric distribution value and the expected geometric distribution value.

[0064] If the signal strength difference is less than the preset strength difference, the signal-to-noise ratio difference is greater than the preset signal-to-noise ratio difference, and the geometric distribution value is greater than the preset geometric distribution value, then a signal anomaly is determined to exist.

[0065] Thus, by comprehensively considering three key indicators—signal strength difference, signal-to-noise ratio difference, and geometric distribution difference—and setting corresponding thresholds for judgment, the inaccuracy that might arise from judging based on a single indicator is resolved. Signal strength difference reflects the attenuation or enhancement of the signal during propagation; signal-to-noise ratio difference reflects the degree of noise interference affecting signal quality; and geometric distribution difference is related to the impact of satellite signal spatial configuration on positioning accuracy. When these three indicators simultaneously meet specific anomaly conditions—namely, signal strength difference less than a preset strength difference, signal-to-noise ratio difference less than a preset signal-to-noise ratio difference, and geometric distribution difference greater than a preset geometric distribution value—it indicates a significant and multi-dimensional anomaly in the quality and availability of the satellite signal, rather than simple random fluctuations. This multi-dimensional, multi-threshold judgment mechanism can more comprehensively and accurately identify genuine signal anomalies, effectively avoiding misjudgments or omissions.

[0066] Specifically, the aforementioned actual geometric distribution values ​​may include one or more of the following: horizontal precision factor, vertical precision factor, and positional precision factor.

[0067] The Horizontal Precision Factor (HDOP) measures the impact of the horizontal geometric distribution of satellites on positioning accuracy. A wider horizontal distribution of satellites results in a smaller HDOP value and higher horizontal positioning accuracy. The Vertical Precision Factor (VDOP) measures the impact of the vertical geometric distribution of satellites on positioning accuracy. A wider vertical distribution of satellites results in a smaller VDOP value and higher vertical positioning accuracy. The Position Precision Factor (PDOP) is a combination of HDOP and VDOP, measuring the impact of the three-dimensional geometric distribution of satellites on positioning accuracy. A smaller PDOP value indicates higher overall positioning accuracy. By introducing these specific precision factors, the geometric distribution of satellite signals can be quantified and evaluated in greater detail.

[0068] In the process of determining whether a signal anomaly exists, the geometric distribution difference is a key indicator when comparing the actual signal characteristics with the expected signal characteristics. This application further clarifies that the actual geometric distribution value can specifically include one or more of the horizontal precision factor, vertical precision factor, and position precision factor. When the geometric distribution of a satellite signal deteriorates due to certain anomalies (such as obstruction, multipath effects, or interference), the values ​​of these precision factors (HDOP, VDOP, PDOP) will increase accordingly. By monitoring the changes in these specific precision factors, signal anomalies caused by poor satellite geometric distribution can be identified more accurately. For example, if the HDOP or PDOP value suddenly increases and exceeds a preset threshold, it indicates that the geometric distribution of the satellite signal may have been interfered with, resulting in a decrease in positioning accuracy, and thus being judged as a signal anomaly. This refinement helps to distinguish different types of signal anomalies. For example, a simple decrease in signal strength may be caused by obstruction, while the deterioration of the geometric precision factor more directly points to problems with the satellite signal receiving environment or the quality of the signal itself.

[0069] S4. In the event of signal anomalies, determine whether there are any abnormal areas in the preset path to the destination.

[0070] Specifically, it can determine whether the abnormal satellite signals are in the same direction; if the abnormal satellite signals are in the same direction, combined with the vehicle's current position, direction of travel, and preset path, the geometric reverse tracing method can be used to determine whether there are abnormal areas in the preset path to the destination.

[0071] It should be noted that determining whether the satellite signals with signal anomalies are in the same direction refers to analyzing all detected satellite signals with signal anomalies to determine whether the source directions of these anomalies are concentrated within a specific azimuth or elevation angle range. The purpose is to preliminarily determine whether the signal anomalies are caused by localized regional factors, such as obstruction, interference sources, or geological activity in a specific direction. The geometric reverse tracing method can be understood as a method that uses geometric principles to analyze the vehicle's current position, direction of travel, and preset path, combined with the directional information of the satellite signals with signal anomalies, to reverse-calculate the geographical location or area that may cause these signal anomalies. In practical applications, this method can, for example, construct virtual rays from the vehicle to the source of the anomaly and analyze the intersection points or dense areas of these rays on the preset path to accurately locate the anomaly area. Its purpose is to transform abstract signal anomaly data into concrete geospatial information to facilitate route planning.

[0072] Thus, by first determining whether the satellite signals exhibiting anomalies are in the same direction, signal anomalies caused by localized factors can be effectively filtered out, avoiding misjudging widespread interference as path anomalies. It is precisely this directional judgment that allows the subsequent geometric reverse tracing method to work more focusedly and accurately. By combining the vehicle's current position, direction of travel, and preset path, the geometric reverse tracing method can integrate the directional information of multiple anomalous signals, reversely calculating the common source area of ​​these anomalous signals, thereby accurately identifying potential anomalous areas on the preset path. This method effectively solves the problem that it is difficult to accurately correlate signal anomalies with specific areas on the path, improving the accuracy and reliability of anomalous area identification.

[0073] S5. If there is an abnormal area in the preset path, generate an alternative path to avoid the abnormal area and reach the destination.

[0074] Alternative paths can be generated using various path planning algorithms, such as the A× algorithm, Dijkstra's algorithm, or RRT (Rapid Exploratory Random Tree) algorithm. During the planning process, abnormal areas need to be marked as impassable or high-risk areas, and factors such as vehicle kinematic constraints, terrain slope, and road width need to be considered to generate a safe, efficient new path that avoids abnormal areas. After generating the alternative path, the system sends new navigation instructions to the vehicle, guiding the driver or autonomous driving system along the alternative path to the destination.

[0075] The all-terrain multi-functional vehicle satellite navigation method proposed in this application dynamically identifies satellite signal anomalies by acquiring the vehicle's current position and actual satellite signal characteristics in real time, and calculating expected signal characteristics in conjunction with preset known environmental data. The core innovation of this method lies in the fact that it goes beyond simply detecting signal anomalies; it further associates signal anomalies with potential anomaly areas in the preset path and ultimately generates alternative paths that avoid these anomaly areas.

[0076] Compared with traditional navigation methods, this application has significant advantages. Traditional navigation systems mainly rely on preset digital maps and local environmental perception systems. However, as mentioned in the background, preset maps often have a lag and cannot reflect real-time environmental changes, such as sudden landslides or new obstacles formed by floods. While local environmental perception systems (such as lidar, millimeter-wave radar, and high-resolution cameras) can detect obstacles at close range, their detection range is limited, and their performance is greatly reduced in complex environments (such as dense vegetation or severe weather), making it difficult to predict path obstructions at long distances.

[0077] This application, by comparing the actual characteristics of satellite signals with their expected characteristics, enables the perception of environmental changes from a more macroscopic and real-time perspective. For example, when a vehicle approaches an area where the terrain has undergone minor deformation due to geological creep, traditional maps may not show any anomalies, and local sensors may fail to detect them due to the subtle deformation. However, geological creep can cause changes in the medium along the satellite signal propagation path, resulting in anomalies in signal strength, signal-to-noise ratio, or carrier phase. The method in this application can capture these subtle signal anomalies and, combined with the vehicle's position and a preset path, infer the presence of potentially anomalous areas such as those caused by geological creep within the preset path using techniques such as reverse tracing.

[0078] Furthermore, this application can promptly generate alternative routes to avoid abnormal areas upon detection, thereby preventing vehicles from entering dangerous areas or encountering route interruptions. This not only improves the reliability and safety of navigation but also significantly enhances mission execution efficiency. For example, in wilderness search and rescue missions, the ability to promptly avoid landslide areas is crucial for the safety of rescue personnel. In environmental monitoring missions, the ability to dynamically adjust routes to avoid areas with strong interference sources ensures the accuracy of monitoring data.

[0079] In summary, this application effectively compensates for the shortcomings of traditional navigation methods in complex dynamic environments by introducing real-time comparison of satellite signal features and intelligent identification mechanisms for abnormal areas. It provides a more intelligent, reliable and safe navigation solution for all-terrain multi-functional vehicles, greatly enhancing their operational capabilities in various complex mission scenarios.

[0080] Traditional satellite navigation methods for all-terrain multi-functional vehicles, while capable of identifying signal problems when anomalies are detected, may struggle to accurately distinguish between signal anomalies caused by general environmental interference (such as weather or building obstruction) and those caused by actual geological risks (such as geological creep) when determining whether abnormal areas exist along the preset path to the destination. Failure to address this issue could lead to vehicles continuing to travel in areas with potential geological risks, increasing driving risks, or generating unnecessary alternative routes due to misjudgment, reducing navigation efficiency. To address this, this application proposes a more refined anomaly area identification mechanism. By identifying geological creep characteristic signals and calculating a creep activity index, it can more accurately determine whether abnormal areas caused by geological activity exist along the preset path.

[0081] In some embodiments of this application, such as Figure 2 As shown, the above-mentioned method of determining whether there are abnormal areas in the preset path to the destination when there are signal anomalies specifically includes:

[0082] S101. Identify geological creep characteristic signals when signal anomalies are present.

[0083] Specifically, for any given satellite, the actual signal characteristics are compared with the expected signal characteristics;

[0084] When the actual signal characteristics show a continuous downward trend compared to the expected signal characteristics, or when the carrier phase change rate shows a gradual cumulative drift, the actual signal characteristics are determined to be geological creep characteristic signals.

[0085] Among these, a sustained downward trend refers to a continuous, non-random decrease in parameters such as actual signal strength or signal-to-noise ratio relative to expected values ​​over a period of time. The carrier phase change rate refers to the instantaneous rate of change of the satellite signal carrier phase, while gradual cumulative drift indicates a slow but continuous cumulative deviation in the carrier phase over time. These specific signal change patterns are considered typical manifestations of the impact of geological creep on satellite signal propagation paths.

[0086] Thus, by monitoring the differences between the actual and expected signal characteristics of satellite signals, and paying particular attention to whether these differences exhibit a continuous decreasing trend or a gradual cumulative drift in the carrier phase, it is possible to distinguish specific signal anomalies caused by geological creep from other types of signal interference. Geological creep typically leads to slow deformation of the Earth's surface medium, thereby affecting the propagation path and medium properties of satellite signals. This effect often manifests as long-term, gradual changes in the signal, rather than sudden or random interference. Therefore, identifying these specific time-series patterns can effectively indicate the presence of geological creep.

[0087] S102. Calculate the creep activity index based on the decay rate, phase drift rate, and duration of the geological creep characteristic signal.

[0088] The creep activity index is used to reflect the deformation rate and activity level of the underground medium.

[0089] C_index=w1×|d1 / dt|_avg+w2×|d2 / dt|_avg+w3×Duration;

[0090] C_index is the creep activity index, w1, w2 and w3 are preset coefficients, |d1 / dt|_avg is the average rate of decrease of the signal-to-noise ratio difference, |d2 / dt|_avg is the average rate of change of the carrier phase of the satellite signal, and Duration is the duration of the creep characteristic signal.

[0091] Understandably, a larger creep activity index indicates a higher deformation rate and activity level of the subsurface medium. |d1 / dt|_avg refers to the average rate of change of the signal-to-noise ratio difference of the satellite signal over a period of time; its average rate of decrease reflects the trend of signal quality deterioration due to creep activity. |d2 / dt|_avg refers to the average rate of change of the carrier phase of the satellite signal over time; its average value can reflect the changes in the signal propagation path caused by subsurface medium deformation. Duration refers to the duration of the characteristic geological creep signal; a longer duration generally indicates more significant creep activity.

[0092] S103. If the creep activity index is greater than a preset threshold, determine that there is an abnormal area in the preset path to the destination.

[0093] This application's solution, upon detecting signal anomalies, further identifies geological creep characteristic signals, thereby distinguishing general signal anomalies from specific risks caused by geological activity. By quantifying the decay rate, phase drift rate, and duration of the geological creep characteristic signals and calculating the creep activity index, an objective and quantitative indicator can be provided to assess the intensity and potential risks of geological creep activity. It is precisely this refined identification and quantification mechanism that enables the system to more accurately determine whether there are anomalous areas caused by geological creep in the preset path, avoiding the blindness of path avoidance based solely on general signal anomalies, thus improving the reliability and safety of navigation.

[0094] Through the above technical solution, this application effectively solves the problem of insufficient accuracy in identifying abnormal areas in traditional methods. By introducing the identification of geological creep characteristic signals and the calculation of creep activity index, the system can more accurately determine whether there are potentially dangerous areas caused by geological activity in the preset path. This not only improves the safety of all-terrain multi-functional vehicle navigation and avoids vehicles entering geologically unstable areas, but also reduces misjudgments caused by non-geological risks by distinguishing different types of signal anomalies, thereby improving the efficiency and reliability of navigation.

[0095] In some preferred embodiments, suppose an all-terrain multi-purpose vehicle is navigating in a mountainous area, and its preset route passes through a region known to be geologically active. When the vehicle approaches this area, the actual signal characteristics of the satellite signals acquired by the signal receiving module show anomalies. For example, the signal strength of multiple satellite signals continuously decreases, and the carrier phase change rate exhibits a gradual cumulative drift. The calculation module calculates the expected signal characteristics of the satellite signals based on the vehicle's current location and preset known environmental data, and compares them with the actual signal characteristics to determine that a signal anomaly exists. Further, in the presence of a signal anomaly, the determination module initiates the process of identifying geological creep characteristic signals. The system analyzes the patterns of these anomalous signals and finds that they conform to the continuous attenuation and phase drift patterns unique to geological creep, thus identifying these anomalous signals as geological creep characteristic signals. Subsequently, the system extracts the attenuation rate, phase drift rate, and duration of these geological creep characteristic signals. For example, the signal strength continuously decreases at a rate of 0.5 dB per minute, the carrier phase continuously drifts at a rate of 0.1 radians per second, and this phenomenon has lasted for 10 minutes. Based on these parameters, the system calculates the creep activity index. Assuming the calculated creep activity index is 0.8, and the preset threshold is 0.5, the system determines that an abnormal area caused by geological creep exists in the preset path to the destination because the creep activity index of 0.8 is greater than the preset threshold of 0.5. The path generation and provision module then generates an alternative path that avoids this abnormal area and provides it to the vehicle to ensure that the vehicle can safely reach its destination.

[0096] In some embodiments described above, geological creep characteristic signals are directly identified when signal anomalies are present. However, in practical applications, satellite signal anomalies can be caused by various factors, such as severe weather and ionospheric activity. Failure to effectively distinguish signal anomalies caused by these known environmental factors may lead to misjudgments of geological creep characteristic signals, thereby affecting the accuracy of anomaly area identification.

[0097] In some embodiments, identifying geological creep characteristic signals in the presence of signal anomalies includes:

[0098] S201. In the presence of signal anomalies, determine whether the characteristic pattern of the signal anomaly matches the known influence pattern of a specific environmental factor.

[0099] Specifically, the characteristic patterns of signal anomalies refer to the comprehensive manifestations of changes in parameters such as signal strength, signal-to-noise ratio, carrier phase, and frequency drift observed after comparing actual signal characteristics with expected signal characteristics. Examples include a continuous decrease in signal strength, a sudden drop in signal-to-noise ratio, and periodic or non-periodic carrier phase drift. Known influence patterns of specific environmental factors refer to pre-stored satellite signal anomaly patterns related to specific environmental factors (such as heavy rainfall, dense fog, ionospheric scintillation, solar flares, and upper-level wind shear). These patterns can be obtained through historical data analysis, simulation, or field testing and are stored in known environmental data.

[0100] S202. If the characteristic pattern of the signal anomaly matches the known influence pattern of a specific environmental factor, then the signal anomaly is attributed to that environmental factor.

[0101] Specifically, the detected signal anomaly pattern can be compared with the influence patterns of various known environmental factors stored in the database. The comparison process can employ pattern recognition algorithms, machine learning models, or rule-based expert systems. If the comparison results show that the current signal anomaly pattern is highly similar to the influence pattern of a known environmental factor or conforms to a preset matching rule, then the two are considered a match.

[0102] If the characteristic pattern of a signal anomaly matches the known influence pattern of a specific environmental factor, it indicates that the current signal anomaly is likely caused by that specific environmental factor. In this case, the signal anomaly will be attributed to that environmental factor, and appropriate measures can be taken, such as adjusting the weights of the navigation algorithm, alerting the driver to environmental changes, or temporarily not considering it a geological creep signal.

[0103] S203. If the characteristic pattern of the abnormal signal does not match the known influence pattern of a specific environmental factor, then identify the geological creep characteristic signal.

[0104] The proposed solution effectively improves the accuracy of geological creep characteristic signal identification by introducing a pre-judgment step before identifying such signals. Specifically, when a satellite signal anomaly is detected, the system does not immediately attribute it to geological creep. Instead, it analyzes the characteristic patterns of the signal anomaly and compares them with known influence patterns of pre-defined specific environmental factors. This comparison process distinguishes signal anomalies caused by known environmental factors (such as meteorological conditions and ionospheric activity) from potential geological creep signals. By attributing matching signal anomalies to corresponding environmental factors, misclassifying non-geological creep-related signal anomalies as geological creep characteristic signals avoids this misjudgment, thus reducing the false alarm rate. Only when the characteristic patterns of the signal anomaly do not match the influence patterns of any known environmental factors is the geological creep characteristic signal further identified. This ensures that subsequent geological creep assessments are based on purer and more indicative signal data, making the identification of anomaly areas more accurate and reliable.

[0105] Through the above technical solution, this application can effectively distinguish signal anomalies caused by geological creep from those caused by other environmental factors, significantly improving the accuracy and reliability of geological creep characteristic signal identification. This avoids invalid path planning or unnecessary alarms due to misjudgment, enabling all-terrain multi-functional vehicles to more accurately identify potential abnormal areas when using satellite navigation in complex environments, thereby improving the overall performance and safety of the navigation system.

[0106] In some preferred embodiments, suppose an all-terrain multi-purpose vehicle is driving in a certain area when its satellite navigation system detects an anomaly in the satellite signal, manifested as a sudden and significant drop in signal strength accompanied by rapid carrier phase drift. The system first analyzes the characteristic patterns of this signal anomaly. For example, through real-time monitoring of meteorological data, it is discovered that the area is experiencing a severe thunderstorm, and ionospheric activity data also shows intense ionospheric scintillation. The system compares the current characteristic patterns of the signal anomaly (sudden drop in signal strength, rapid phase drift) with preset patterns of the impact of severe thunderstorms and ionospheric scintillation on satellite signals. If the comparison results show that the current anomaly pattern highly matches the impact patterns of severe thunderstorms and ionospheric scintillation, the system will attribute this signal anomaly to these environmental factors and will not immediately identify it as a geological creep characteristic signal. Conversely, if the system detects a signal anomaly, but real-time meteorological data and ionospheric activity data show normality, and the characteristic pattern of the signal anomaly (e.g., signal strength continuously and slowly decaying, carrier phase showing gradual cumulative drift) does not match the influence pattern of any known environmental factors, the system will further identify the signal as a geological creep characteristic signal and initiate subsequent creep activity index calculation and anomaly area determination processes.

[0107] In some of the embodiments described above in this application, it is proposed to identify geological creep characteristic signals in the presence of signal anomalies. However, in its implementation, it is necessary to consider in more detail the influence of various environmental factors on satellite signal anomalies in order to avoid misjudging signal anomalies caused by non-geological creep factors as geological creep characteristic signals.

[0108] In this regard, this application further proposes whether the characteristic patterns used to determine signal anomalies match known influence patterns of specific environmental factors, including:

[0109] S301. Acquire real-time meteorological data, ionospheric activity data, and environmental data.

[0110] Specifically, real-time meteorological data can be understood as information on weather conditions at the current moment or in the near future, such as temperature, humidity, air pressure, rainfall, and cloud cover. This data can affect the propagation path and intensity of satellite signals. Ionospheric activity data refers to information such as ionospheric electron density and scintillation index. Changes in the ionosphere can significantly affect the propagation of satellite signals, leading to signal delay, phase drift, or intensity attenuation. Environmental data refers to other environmental information besides meteorology and the ionosphere, such as topography, vegetation cover, building distribution, and electromagnetic interference sources. These factors can also have a local impact on satellite signal reception.

[0111] S302. Combining real-time meteorological data, ionospheric activity data, and environmental data, determine whether the characteristic patterns of signal anomalies match the known influence patterns of specific environmental factors.

[0112] In practical applications, when determining whether the characteristic patterns of signal anomalies match known influence patterns of specific environmental factors, it is first necessary to acquire the aforementioned real-time meteorological data, ionospheric activity data, and environmental data. This data can be obtained through various means, such as vehicle-mounted sensors, external data interfaces, network services, or pre-set databases. Subsequently, this acquired data is compared and analyzed with known influence patterns caused by specific environmental factors. For example, heavy rainfall may cause signal attenuation, ionospheric scintillation may cause signal phase drift, and tall buildings or mountains may cause signal obstruction or multipath effects. By combining this real-time environmental information, it is possible to more accurately determine whether the currently observed signal anomalies are caused by these known environmental factors.

[0113] This application's solution, by acquiring and combining real-time meteorological data, ionospheric activity data, and environmental data, enables a more comprehensive and detailed analysis of the causes of satellite signal anomalies. Because satellite signal propagation is highly susceptible to the influence of the atmosphere (including the troposphere and ionosphere) and the ground environment, these factors can lead to various anomalies such as decreased signal strength, reduced signal-to-noise ratio, phase drift, or multipath effects. By introducing real-time meteorological data, the impact of the troposphere on signal propagation can be assessed; by introducing ionospheric activity data, the impact of the ionosphere on signal propagation can be assessed; and by introducing environmental data, the impact of local topography, obstacles, etc., on signal propagation can be assessed. Therefore, when signal anomalies occur, these multi-dimensional data can be compared with pre-established influence patterns caused by known environmental factors. If the characteristic pattern of the signal anomaly highly matches the known influence pattern of a certain environmental factor, the signal anomaly can be reasonably attributed to that environmental factor, thus avoiding misjudgment as a geological creep characteristic signal. Conversely, if the characteristic pattern of the signal anomaly does not match any known influence patterns of environmental factors, it can be more reliably identified as a geological creep characteristic signal.

[0114] The above technical solutions significantly improve the accuracy of identifying the causes of satellite signal anomalies. By comprehensively considering real-time meteorological, ionospheric activity, and environmental data, signal anomalies caused by non-geological creep factors can be more effectively eliminated, thus avoiding misinterpreting routine environmental interference as geological creep characteristic signals. This makes the identification of geological creep characteristic signals more accurate, reduces the false alarm rate, and consequently enhances the path planning and safety assurance capabilities of the all-terrain multi-functional vehicle satellite navigation system in complex environments.

[0115] like Figure 3 As shown, this embodiment of the invention also provides a satellite navigation system for an all-terrain multi-functional vehicle. The system includes:

[0116] The signal receiving module is used to obtain the vehicle's current location and the actual signal characteristics of the satellite signals received by the vehicle;

[0117] The calculation module is used to calculate the expected signal characteristics of the satellite signal based on the vehicle's current location and preset known environmental data;

[0118] The determination module is used to determine whether there is a signal anomaly based on the actual signal characteristics and the expected signal characteristics;

[0119] The determination module is also used to determine whether there are abnormal areas in the preset path to the destination in the event of signal abnormalities.

[0120] The route generation and provision module is used to generate an alternative route that avoids the abnormal area in the preset route, so as to reach the destination.

[0121] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0123] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0124] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A satellite navigation method for an all-terrain multi-functional vehicle, characterized in that, include: Obtain the vehicle's current location and the actual signal characteristics of the satellite signals received by the vehicle; Based on the vehicle's current location and preset known environmental data, the expected signal characteristics of the satellite signal are calculated; Based on the actual signal characteristics and the expected signal characteristics, determine whether there is a signal anomaly; In the event of signal anomalies, determine whether there are any abnormal areas in the preset route to the destination; If the abnormal area exists in the preset path, an alternative path is generated to avoid the abnormal area and proceed to the destination.

2. The satellite navigation method for an all-terrain multi-functional vehicle according to claim 1, characterized in that, The step of determining whether there is a signal anomaly based on the actual signal characteristics and the expected signal characteristics includes: For a satellite signal among multiple satellite signals, determine the signal strength difference, signal-to-noise ratio difference, and geometric distribution difference of the satellite signals; the signal strength difference is the difference between the actual signal strength and the expected signal strength, the signal-to-noise ratio difference is the difference between the actual signal-to-noise ratio and the expected signal-to-noise ratio, and the geometric distribution difference is the difference between the actual geometric distribution value and the expected geometric distribution value; If the signal strength difference is less than a preset strength difference, the signal-to-noise ratio difference is greater than a preset signal-to-noise ratio difference, and the geometric distribution value is greater than a preset geometric distribution value, then a signal anomaly is determined to exist.

3. The satellite navigation method for an all-terrain multi-functional vehicle according to claim 2, characterized in that, The actual geometric distribution value includes one or more of the following: horizontal precision factor, vertical precision factor, and positional precision factor.

4. The satellite navigation method for an all-terrain multi-functional vehicle according to claim 1, characterized in that, Determining whether there are abnormal areas in the preset path to the destination includes: Determine whether the abnormal satellite signals are from the same direction; When satellite signals with abnormal signals are in the same direction, the system combines the vehicle's current position, direction of travel, and the preset path to determine whether there is an abnormal area in the preset path to the destination using a geometric reverse tracing method.

5. The satellite navigation method for an all-terrain multi-functional vehicle according to claim 2, characterized in that, The step of determining whether there is an abnormal area in the preset path to the destination in the event of signal anomalies includes: Identify geological creep characteristic signals in the presence of signal anomalies; The creep activity index is calculated based on the decay rate, phase drift rate, and duration of the geological creep characteristic signal; the creep activity index is used to reflect the deformation rate and activity level of the subsurface medium. If the creep activity index is greater than a preset threshold, an abnormal area is determined to exist in the preset path to the destination.

6. The satellite navigation method for an all-terrain multi-functional vehicle according to claim 5, characterized in that, The method further includes: For any given satellite, compare the actual signal characteristics with the expected signal characteristics; If the actual signal characteristics show a continuous downward trend compared to the expected signal characteristics, or if the carrier phase change rate shows a gradual cumulative drift, the actual signal characteristics are determined to be the geological creep characteristic signal.

7. A satellite navigation method for an all-terrain multi-functional vehicle according to claim 5, characterized in that, The creep activity index satisfies the following relationship: C_index=w1×|d1 / dt|_avg+w2×|d2 / dt|_avg+w3×Duration; C_index is the creep activity index, w1, w2 and w3 are preset coefficients, |d1 / dt|_avg is the average rate of decrease of the signal-to-noise ratio difference, |d2 / dt|_avg is the average rate of change of the carrier phase of the satellite signal, and Duration is the duration of the creep characteristic signal.

8. A satellite navigation method for an all-terrain multi-functional vehicle according to claim 5, characterized in that, The method of identifying geological creep characteristic signals in the presence of signal anomalies includes: In the presence of signal anomalies, determine whether the characteristic pattern of the signal anomaly matches the known influence pattern of a specific environmental factor; If the characteristic pattern of the signal anomaly matches the known influence pattern of a specific environmental factor, then the signal anomaly is attributed to that environmental factor. If the characteristic pattern of the signal anomaly does not match the known influence pattern of a specific environmental factor, then a geological creep characteristic signal is identified.

9. A satellite navigation method for an all-terrain multi-functional vehicle according to claim 8, characterized in that, Determining whether the characteristic pattern of the signal anomaly matches the known influence pattern of a specific environmental factor includes: Acquire real-time meteorological data, ionospheric activity data, and environmental data; By combining the real-time meteorological data, the ionospheric activity data, and the environmental data, it is determined whether the characteristic pattern of the signal anomaly matches the known influence pattern of a specific environmental factor.

10. A satellite navigation system for an all-terrain multi-functional vehicle, characterized in that, The system includes: The signal receiving module is used to obtain the vehicle's current location and the actual signal characteristics of the satellite signals received by the vehicle; The calculation module is used to calculate the expected signal characteristics of the satellite signal based on the current position of the vehicle and preset known environmental data; The determination module is used to determine whether there is a signal anomaly based on the actual signal characteristics and the expected signal characteristics; The determining module is also used to determine whether there is an abnormal area in the preset path to the destination in the event of signal abnormality. The path generation and provision module is used to generate an alternative path that avoids the abnormal area in the preset path, so as to reach the destination, when the abnormal area exists in the preset path.

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