Riding navigation blind area track correction method and system based on stopwatch inertial navigation

Abstract

The invention provides a riding navigation blind area track correction method and system based on stopwatch inertial navigation, electronic equipment and a storage medium, and the method comprises the steps: obtaining the angular velocity and acceleration information of a stopwatch and the direction information of an earth magnetic field, and carrying out the calculation to obtain a riding course angle and a real-time riding speed; when it is detected that a satellite positioning signal is interrupted, the last credible satellite positioning coordinate before interruption serves as an estimated coordinate point of a first operation period, and in each operation period, a relative displacement vector is calculated by combining the course angle of the current period, the real-time riding speed and the time interval of the operation period; iteratively determining the estimated coordinate point of the current period by taking the estimated coordinate point of the previous period as a reference, and forming a continuous extension track; and finally, matching the extended continuous coordinate points with a road network of a preset digital map, calculating and accumulating the distance between adjacent coordinate points based on the matched real road coordinates, and completing accurate correction of the track and the distance.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of positioning and navigation technology, in particular to a riding navigation blind area trajectory correction method and system based on code table inertial navigation. BACKGROUND

[0002] In the field of cycling, accurate trajectory recording and distance calculation have become core requirements. Whether it is professional cycling training or outdoor leisure cycling, users need to rely on a code table to obtain continuous and reliable sports data to analyze the cycling state. Currently, mainstream code tables generally use GPS (Global Positioning System) positioning technology to receive satellite signals to obtain real-time coordinates and then calculate the riding trajectory and distance. This technology can meet the basic use requirements in open environments and has been widely used in various outdoor sports equipment.

[0003] However, GPS technology relies on satellite signal transmission, and in signal shielding scenarios such as tunnels, dense forests, and urban high-rise areas, signal loss or interruption is likely to occur. At this time, the code table cannot obtain valid coordinate data, which will cause the riding trajectory record to be broken and the distance calculation to be smaller, seriously affecting the integrity and accuracy of the data. In existing technologies, some solutions attempt to use IMU (Inertial Measurement Unit) for short-term trajectory calculation, but pure inertial navigation calculation will accumulate errors due to the integration of angular velocity and acceleration, causing the trajectory to deviate from the true path and cannot be used reliably for a long time.

[0004] In summary, existing technologies cannot simultaneously solve the three core problems of trajectory continuity, trajectory accuracy, and inertial navigation cumulative error in GPS signal shielding environments, and cannot meet the user's demand for accurate riding data in all scenarios. SUMMARY

[0005] Embodiments of the present application provide a riding navigation blind area trajectory correction method and system based on code table inertial navigation to solve the technical problems of code table trajectory recording interruption in GPS signal shielding scenarios, pure inertial navigation calculation with cumulative errors causing trajectory deviation from the true path, and inability to balance GPS blind area trajectory continuity and accuracy.

[0006] In a first aspect, embodiments of the present application provide a riding navigation blind area trajectory correction method based on code table inertial navigation, comprising: S1, obtaining angular velocity information and acceleration information of the code table, determining a riding heading angle based on the angular velocity information and earth magnetic field direction information, and determining a real-time riding speed based on the acceleration information; S2. If it is determined that the satellite positioning signal is interrupted, the relative displacement vector is determined based on the riding heading angle, the real-time riding speed and the time interval of the current operation cycle. The estimated coordinate point of the current operation cycle is determined based on the estimated coordinate point of the previous cycle and the relative displacement vector. The above steps are repeated to form extended continuous coordinate points. The estimated coordinate point of the first operation cycle is determined based on the last satellite positioning signal before the satellite positioning signal was interrupted. S3. Match the extended continuous coordinate points with the road network of the preset digital map, calculate the distance between adjacent coordinate points based on the matched road coordinates and accumulate them to obtain the corrected riding distance, thus completing the trajectory correction.

[0007] Preferably, in step S1, obtaining the angular velocity and acceleration information of the code table includes: Angular velocity and acceleration information are acquired based on the inertial measurement unit (IMU) built into the codebook; The angular velocity information is filtered to remove high-frequency vibration noise during riding. The filtering process uses either a Kalman filter algorithm or a moving average filter algorithm. Based on the preset installation attitude angle of the code table, the influence component caused by Earth's gravity in the acceleration information is removed by vector decomposition.

[0008] Preferably, S2 further includes: The direction information of the Earth's magnetic field is obtained based on the magnetometer built into the codebook, and the frequency at which the magnetometer collects the direction information of the Earth's magnetic field is consistent with the acquisition frequency of the IMU.

[0009] Preferably, the calculation cycle is the time difference between the estimated coordinate points in steps S1 to S2 that determine the current calculation cycle.

[0010] Preferably, in step S2, determining the estimated coordinate point for the current calculation cycle based on the estimated coordinate point of the previous cycle and the relative displacement vector includes: The relative displacement vector is decomposed into azimuth and straight-line distance. Using the geodetic inverse calculation model corresponding to the WGS-84 geodetic coordinate system, the azimuth, the straight-line distance and the estimated coordinate points of the previous calculation cycle are transformed to obtain the latitude and longitude data of the estimated coordinate points of the next calculation cycle.

[0011] As a preferred option, it also includes: S4. If it is determined that the satellite positioning signal has been received again and stabilized, then switch to recording the cycling trajectory based on the satellite positioning signal; wherein, the judgment condition for the satellite positioning signal to stabilize is: the satellite positioning signal strength is greater than -120dBm for 3-5 consecutive operation cycles, and the satellite positioning of adjacent operation cycles is stable.

[0012] Preferably, in step S4, after switching to recording the cycling trajectory based on the satellite positioning signal, the method further includes: The corrected cycling distance obtained in S3 is fused with the distance data after the satellite positioning signal is recovered to compensate for the distance error during the satellite positioning signal interruption.

[0013] Secondly, embodiments of the present invention provide a cycling navigation blind spot trajectory correction system based on a bike table inertial navigation system, comprising: The data acquisition and parameter calculation module is used to acquire the angular velocity and acceleration information of the bike table, determine the riding heading angle based on the angular velocity information and the direction information of the Earth's magnetic field, and determine the real-time riding speed based on the acceleration information. The trajectory extension module is used to determine the relative displacement vector based on the riding heading angle, the real-time riding speed, and the time interval of the current operation cycle if the satellite positioning signal is interrupted. It then determines the estimated coordinate point of the current operation cycle based on the estimated coordinate point of the previous cycle and the relative displacement vector, and repeats the above steps to form extended continuous coordinate points. The estimated coordinate point of the first operation cycle is determined based on the last satellite positioning signal before the satellite positioning signal was interrupted. The trajectory correction module is used to match the extended continuous coordinate points with the road network of the preset digital map, calculate the distance between adjacent coordinate points based on the matched road coordinates and accumulate them to obtain the corrected cycling distance, thus completing the trajectory correction.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the cycling navigation blind spot trajectory correction method based on a code table inertial navigation as described in the first aspect of the present invention.

[0015] Fourthly, embodiments of the present invention provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the cycling navigation blind spot trajectory correction method based on code table inertial navigation as described in the first aspect of the present invention.

[0016] This invention provides a method, system, electronic device, and storage medium for correcting blind spot trajectories in cycling navigation based on a bike table and inertial navigation system. It is based on data acquisition and parameter calculation, with trajectory extension as the core and trajectory correction as the guarantee. The method involves acquiring the angular velocity, acceleration, and Earth's magnetic field direction information from the bike table to calculate the cycling heading angle and real-time cycling speed, providing key parameter support for trajectory estimation. When a satellite positioning signal interruption is detected, the last reliable satellite positioning coordinates before the interruption are used as the estimated coordinate point for the first calculation cycle. Within each calculation cycle, the relative displacement vector is calculated by combining the current cycle's heading angle, real-time cycling speed, and the calculation cycle time interval. The estimated coordinate point of the previous cycle is used as a reference to iteratively determine the estimated coordinate point of the current cycle, forming a continuous extended trajectory. Finally, the extended continuous coordinate points are matched with the road network of a preset digital map. Based on the matched real road coordinates, the distance between adjacent coordinate points is calculated and accumulated to complete the accurate correction of the trajectory and distance. It can continuously record cycling tracks in satellite positioning signal blind spots (such as tunnels and dense forests), avoiding track interruptions. At the same time, by combining iterative calculation of inertial navigation parameters with digital map matching, it effectively suppresses the cumulative error of simple inertial navigation calculation, ensuring the consistency between the extended track and the actual road path, greatly improving the accuracy of cycling distance measurement in blind spot environments, and providing users with complete and reliable cycling navigation data in all scenarios, taking into account both track continuity and measurement accuracy. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a cycling navigation blind spot trajectory correction method based on a code table inertial navigation according to an embodiment of the present invention; Figure 2 This is a schematic diagram of inertial navigation calculation according to an embodiment of the present invention; Figure 3 This is a trajectory extension effect diagram according to an embodiment of the present invention; Figure 4 This is a trajectory correction effect diagram according to an embodiment of the present invention; Figure 5 This is a distance compensation effect diagram according to an embodiment of the present invention; Figure 6 This is a structural block diagram of a cycling navigation blind spot trajectory correction system based on a code table inertial navigation according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the physical structure according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] This invention provides a method for correcting blind spot trajectories in cycling navigation based on a bike table inertial navigation system, such as... Figure 1 As shown, it includes: S1. Obtain the angular velocity and acceleration information from the bike table, determine the riding heading angle based on the angular velocity information and the direction information of the Earth's magnetic field, and determine the real-time riding speed based on the acceleration information.

[0023] Among them, angular velocity information is obtained by sensing the rate of change of angle of the rider's steering and body tilt through the gyroscope, such as how quickly the rider turns their direction when cornering and how quickly the body tilts when climbing hills; it reflects the speed of change of riding direction. Acceleration information is the amount of change in riding speed collected, corresponding to riding states such as starting, accelerating, decelerating, and braking. Earth's magnetic field direction information is data related to the Earth's magnetic field sensed by the magnetometer built into the bike computer (environmental interference needs to be eliminated), providing a reference for determining absolute direction. Riding heading angle is the direction of riding forward calculated based on the above angular velocity information and Earth's magnetic field direction information, clarifying the rider's direction of travel along the geographic coordinate system. Real-time riding speed is the current riding speed obtained by integrating the acceleration information after removing the gravity component.

[0024] In this embodiment, core sensor data is first collected through the collaborative acquisition of an IMU integrated with a bike table and a magnetometer. After filtering and denoising the angular velocity information, the precise cycling heading angle is calculated by combining it with the Earth's magnetic field direction information. Simultaneously, the acceleration information is processed by removing the gravity component and then integrated to obtain the real-time cycling speed, forming the core motion parameters required for trajectory calculation. This provides two key foundational data points—direction and velocity—for trajectory extension in GPS blind spots, solving the problem of lacking effective motion parameters in blind spots when relying solely on satellite positioning, and ensuring reliable initial data support for subsequent trajectory calculation.

[0025] S2. If it is determined that the satellite positioning signal is interrupted, the relative displacement vector is determined based on the riding heading angle, the real-time riding speed and the time interval of the current operation cycle. The estimated coordinate point of the current operation cycle is determined based on the estimated coordinate point of the previous cycle and the relative displacement vector. The above steps are repeated to form extended continuous coordinate points. The estimated coordinate point of the first operation cycle is determined based on the last satellite positioning signal before the satellite positioning signal was interrupted.

[0026] Among them, satellite positioning signal interruption refers to the insufficient strength or complete loss of GPS and other satellite signals received by the bike meter when cycling through tunnels, dense forests, or areas with high-rise buildings, making it impossible to obtain valid positioning coordinates; the calculation cycle time interval is a fixed time unit (e.g., 10ms-100ms) for the bike meter system to perform trajectory calculations twice consecutively, balancing trajectory real-time performance with hardware computing power consumption; the relative displacement vector is calculated by combining the current cycling direction, speed, and calculation cycle time interval, including a combination of cycling distance and direction of travel parameters; the estimated coordinate point is the cycling position coordinate calculated by the displacement vector and the previous position reference point, and the estimated coordinate point of the first calculation cycle is the last reliable GPS positioning coordinate before the satellite signal interruption; the continuous coordinate point is a coherent position sequence formed by multi-cycle iterative calculation.

[0027] In this embodiment, when the bike meter detects an interruption in the satellite positioning signal, it uses the last reliable GPS coordinates before the interruption as the initial reference. Within each calculation cycle, it calculates the relative displacement vector within that cycle using the cycling heading angle calculated by S1, the real-time cycling speed, and the preset calculation cycle time interval. Then, using the estimated coordinate point of the previous cycle as a reference, it superimposes the displacement vector to obtain the estimated coordinate point of the current cycle. Through repeated iterations, it generates extended continuous coordinate points. This achieves continuous recording of cycling tracks in GPS blind spots, completely solving the problem of track breakage caused by satellite signal interruption in existing technologies, ensuring the integrity of cycling data, and simultaneously ensuring the continuity and real-time performance of the track through periodic iterative calculations.

[0028] S3. Match the extended continuous coordinate points with the road network of the preset digital map, calculate the distance between adjacent coordinate points based on the matched road coordinates and accumulate them to obtain the corrected riding distance, thus completing the trajectory correction.

[0029] The preset digital map road network is an electronic map data built into the bike computer that includes information such as road centerline coordinates, road curvature, and node connection relationships, covering common cycling routes such as highways and tunnels; matching refers to the process of corresponding and associating the estimated continuous coordinate points generated by the trajectory extension module with the actual road paths in the built-in digital map through a specific algorithm; the corrected cycling distance is the accurate cycling mileage obtained by calculating the actual distance between adjacent coordinate points and accumulating them based on the matched real road coordinates.

[0030] In this embodiment, to address the issue that the extended continuous coordinate points generated by S2 may deviate from the actual road due to accumulated errors in inertial navigation integration, these coordinate points are algorithmically matched with the preset digital map road network built into the bike computer. This anchors the ambiguous calculated trajectory to the closest actual road path. Then, the inaccurate inertial navigation integration distance data is discarded. Based on the matched real road coordinates, the distances between adjacent coordinate points are calculated and accumulated, ultimately yielding a precise corrected riding distance. This effectively eliminates the accumulated errors caused by simple inertial navigation calculations, solves the problems of inertial navigation trajectories deviating from the actual path and inaccurate distance calculations in existing technologies, ensures the accuracy of riding trajectory and distance data in GPS blind spots, and achieves precise anchoring of the calculated trajectory to the real geographic space.

[0031] Based on the above embodiments, as a preferred implementation, step S1, obtaining the angular velocity information and acceleration information of the code table, includes: Angular velocity and acceleration information are acquired based on the inertial measurement unit (IMU) built into the codebook.

[0032] The Inertial Measurement Unit (IMU) is the core sensing module integrated inside the cycling computer. It typically includes a 3-axis gyroscope and a 3-axis accelerometer, and is designed specifically to capture motion data during cycling.

[0033] In this embodiment, the IMU module built into the cycling computer is used to collect raw angular velocity and acceleration data in real time and synchronously throughout the entire cycling process, without relying on external sensing devices. This ensures the compatibility and immediacy of data acquisition with the cycling computer. It provides direct raw data support for subsequent angular velocity filtering, acceleration gravity component stripping, and heading angle and real-time velocity calculations, avoiding latency or compatibility issues associated with data collection from external devices, and guaranteeing the stability and real-time performance of the entire trajectory correction scheme's data source.

[0034] The angular velocity information is filtered to remove high-frequency vibration noise during riding. The filtering process employs either a Kalman filter or a moving average filter algorithm. High-frequency vibration noise is an irregular interference signal generated during riding due to road bumps, vibrations of the bike computer's mounting components, and mechanical vibrations of the vehicle itself. This noise superimposed on the actual angular velocity data, causing distortion in the rate of angle change detection. The Kalman filter algorithm is an algorithm that suppresses noise through iterative estimation and correction, dynamically adapting to random noise changes during riding. The moving average filter algorithm smooths noise by averaging angular velocity data over multiple consecutive periods; it has low computational complexity and is well-suited to the bike computer's hardware capabilities. In this embodiment, to address the high-frequency vibration noise mixed in the raw angular velocity data acquired by the IMU, the Kalman filter or moving average filter algorithm is selected based on the bike computer's hardware performance and actual noise reduction requirements. This filters out interference signals unrelated to riding direction and tilt, retaining only the effective angular velocity data reflecting the actual changes in riding direction. It significantly improves the purity and accuracy of angular velocity information, avoids deviations in subsequent heading angle calculations caused by high-frequency vibration noise, ensures that the heading angle can truly reflect the direction of cycling, and provides a reliable directional parameter basis for GPS blind zone trajectory extension.

[0035] Based on the preset installation attitude angle of the code table, the influence component caused by Earth's gravity in the acceleration information is removed by vector decomposition.

[0036] The preset installation angle of the cycling computer refers to the standard installation angle of the computer on the handlebars of a bicycle (such as horizontal installation, installation at a 5° angle to the horizontal, etc.), which is pre-stored in the computer system before leaving the factory and is compatible with the installation habits of most cyclists. Vector decomposition decomposes the spatial vector of acceleration information into two components parallel to the riding direction (horizontal direction) and perpendicular to the riding direction (gravity direction) according to the preset installation angle. The influence component caused by Earth's gravity refers to the vertical acceleration component in the raw data collected by the accelerometer due to the effect of Earth's gravity, which is unrelated to the speed change of the rider. In this embodiment, the preset installation angle parameters of the computer are first called, and then vector decomposition technology is used to decompose the raw acceleration data collected by the IMU into components, accurately removing the interference components caused by Earth's gravity, and retaining only the horizontal acceleration data directly related to the riding motion. By eliminating the interference of gravity on acceleration information, pure acceleration data that can truly reflect changes in cycling speed is obtained, which provides a guarantee for accurate estimation of real-time cycling speed through subsequent integral calculations. This avoids the problem of speed calculation being too high or too low due to gravity components, and ensures the accuracy of speed parameters calculated from GPS blind spot tracks.

[0037] Based on the above embodiments, as a preferred implementation, step S2 further includes: The direction information of the Earth's magnetic field is obtained based on the magnetometer built into the codebook, and the frequency at which the magnetometer collects the direction information of the Earth's magnetic field is consistent with the acquisition frequency of the IMU.

[0038] The built-in magnetometer in the cycling computer is a sensing component integrated with the IMU (Integrated Measurement Unit). It is specifically designed to sense the Earth's magnetic field, avoiding the inconvenience and compatibility issues of installing external sensing devices. It also reduces interference from metal parts of the bicycle handlebars and surrounding electronic devices on magnetic field detection. The Earth's magnetic field direction information is the vector data (including direction and intensity) of the Earth's magnetic field in space sensed by the magnetometer. Its core function is to provide an absolute geographical direction reference (such as pointing towards the geomagnetic north pole) for calculating the cycling heading angle, compensating for the drift error that can easily occur when the gyroscope calculates the heading solely by integrating angular velocity. Maintaining a consistent acquisition frequency means that the time interval for the magnetometer to acquire the Earth's magnetic field direction information is the same as the time interval for the IMU to acquire angular velocity and acceleration information (such as 10ms / time or 100ms / time), ensuring that different sensor data correspond accurately in the time dimension.

[0039] In this embodiment, before and during the interruption of satellite positioning signals, the magnetometer built into the bike meter collects the Earth's magnetic field direction information in real time. Simultaneously, its acquisition frequency is strictly controlled to be synchronized with the IMU's acquisition frequency, ensuring that the Earth's magnetic field direction information matches the angular velocity information output by the IMU at the same time point. This provides synchronous data support for the continuous calculation of the cycling heading angle in S2. This solves the direction drift problem that easily occurs when relying solely on IMU angular velocity integration to calculate the heading angle. The synchronously collected Earth's magnetic field direction information provides an absolute reference for the heading angle, ensuring the accuracy of the heading angle in each calculation cycle even in GPS blind spots. Furthermore, frequency synchronization avoids deviations in heading angle calculation caused by data time differences, further guaranteeing the accuracy of relative displacement vector calculation and estimated coordinate point iteration, providing crucial directional data support for the reliability of trajectory extension.

[0040] Based on the above embodiments, as a preferred implementation, the calculation cycle is the time difference of the process of determining the estimated coordinate point of the current calculation cycle in steps S1 to S2.

[0041] The computation cycle is a fixed time unit for the bike meter to complete one closed loop of "data processing-track calculation" when performing track extension in the GPS blind zone. It corresponds to the time interval between each update of the estimated riding position by the system during the ride. That is, from the IMU collecting angular velocity / acceleration and the magnetometer collecting the direction information of the Earth's magnetic field in the S1 stage, to filtering the angular velocity, removing the gravity component from the acceleration, calculating the riding heading angle and real-time riding speed, and then to the S2 stage calculating the relative displacement vector based on the above parameters and finally determining the estimated coordinate point of the current computation cycle, the complete sequence of operations covers all the core actions required for a track position update. The time difference refers to the difference between the start time of two adjacent starts of this complete process. For example, if the current process starts from 0ms and the next process starts from 50ms, the 50ms interval is the computation cycle. In this embodiment, the scope of the calculation cycle is clearly defined: it is based on the entire process from raw data acquisition to outputting the estimated coordinates of the current cycle, rather than the time of a single data acquisition or calculation stage; at the same time, according to the hardware computing power of the bike computer in the cycling scenario (such as low-power chips needing to control the computing load) and the trajectory accuracy requirements (such as high-speed cycling requiring shorter cycles to avoid trajectory lag), a fixed time difference is set so that the system can repeatedly execute the "data processing-coordinate calculation" process at a uniform and stable rhythm, ensuring that the complete estimated coordinate points can be output in each calculation cycle. Its technical effect is to solve the problem of chaotic trajectory calculation rhythm caused by the ambiguity of the calculation cycle definition, and to avoid incomplete cycle and coordinate update gaps caused by only calculating the time of some links (such as only calculating the time of displacement vector calculation). The fixed time difference also balances the real-time performance of the trajectory and the performance of the device. It prevents the estimated coordinates from lagging behind the actual riding position due to the cycle being too long (such as the trajectory deviating from the real path when turning under a long cycle), and also prevents the cycle from increasing the computing power and power consumption burden of the bike computer due to the cycle being too short. It ensures that a continuous extended trajectory can be generated in the GPS blind spot, while also taking into account the battery life of the bike computer, which meets the actual use needs of riders for accurate recording and long battery life.

[0042] Based on the above embodiments, as a preferred implementation, in step S2, determining the estimated coordinate point of the current calculation cycle based on the estimated coordinate point of the previous cycle and the relative displacement vector includes: The relative displacement vector is decomposed into azimuth and straight-line distance. Using the geodetic inverse calculation model corresponding to the WGS-84 geodetic coordinate system, the azimuth, the straight-line distance and the estimated coordinate points of the previous calculation cycle are transformed to obtain the latitude and longitude data of the estimated coordinate points of the next calculation cycle.

[0043] The relative displacement vector is a composite parameter calculated in S2 based on the current cycling heading angle, real-time cycling speed, and the time interval of the current calculation cycle. It contains both the "direction of travel" and the "distance traveled" of the cyclist within that cycle. For example, if a cyclist rides at 30 km / h in the northeast direction for 0.1 seconds, the corresponding relative displacement vector contains the combination of "northeast direction" and "0.83 meters in length". The azimuth angle is the geographic direction parameter obtained after decomposing the relative displacement vector. It refers to the angle rotated clockwise from due north to the cycling direction (e.g., 45° azimuth angle corresponds to the northeast direction), which clarifies the cyclist's specific direction of travel in the geographic coordinate system. The straight-line distance is the actual distance traveled after decomposing the relative displacement vector. It is calculated by "real-time cycling speed × time interval of the calculation cycle" and directly reflects the spatial span of the cyclist's movement within that cycle (e.g., at a speed of 20 km / h and a cycle of 50 ms, the straight-line distance is approximately 0.28 meters). In this embodiment, taking advantage of the characteristic that the relative displacement vector has both direction and length, it is first decomposed into independent azimuth angles (clearly indicating "which way to go") and straight-line distances (clearly indicating "how far to go"), so that the two parameters can be adapted to the input requirements of subsequent coordinate transformation calculations, avoiding calculation deviations caused by incompatible vector parameter formats. This structured decomposition of relative displacement information eliminates heading deviations caused by non-standard direction representations (such as not corresponding to a geographic coordinate system) when directly using composite vectors for coordinate calculations. It ensures that both the "direction" and "distance" parameters accurately reflect the actual riding state during subsequent coordinate transformations, laying the foundation for the accuracy of the estimated geographic positioning of coordinate points.

[0044] Furthermore, the WGS-84 geodetic coordinate system is a globally universal geographic coordinate system (and also the standard coordinate system for GPS positioning). Its latitude and longitude data (such as 30°15′N, 120°30′E) can accurately correspond to the actual location on the Earth's surface, adapting to the unified coordinate requirements of the entire process of GPS positioning, blind spot trajectory extension, and map matching in cycling scenarios. The geodetic inverse calculation model is a professional geographic calculation model that calculates the "end point coordinates" based on "known starting point coordinates, azimuth between two points, and straight-line distance between two points" (different from the forward calculation model that calculates azimuth / distance from known two point coordinates). It specifically solves the problem of converting motion parameters to geographic coordinates in trajectory extrapolation. Latitude and longitude data is the core representation of location under the WGS-84 coordinate system and is the key information carrier for the bike to record the trajectory and match the road with the built-in digital map (built based on the WGS-84 coordinate system).

[0045] In this embodiment, the reference system for coordinate transformation is first determined to be the WGS-84 geodetic coordinate system (ensuring consistency with the coordinate system before GPS interruption to avoid track disconnection caused by cross-coordinate system). Then, the geodetic inverse calculation model is invoked, using the latitude and longitude of the estimated coordinate point from the previous calculation cycle (as the starting point), the decomposed azimuth (direction of travel), and the straight-line distance (travel length) as input parameters. The model calculates and outputs the latitude and longitude data of the estimated coordinate point for the current calculation cycle, completing one iteration of geographic coordinates from "previous position to current position". This solves the problem of accurate conversion of motion parameters to geographic coordinates, avoiding positional offsets caused by simple coordinate superposition (such as directly adding or subtracting distances in a plane coordinate system) (especially in long-distance cycling or non-straight road scenarios, such as curves in tunnels), ensuring that the estimated coordinate point accurately corresponds to the actual position on the Earth's surface; at the same time, because it adopts the same system as GPS and built-in digital geodetic coordinates, it can accurately convert the motion parameters into geographic coordinates. Figure 1 The WGS-84 coordinate system enables seamless coordinate connection between "normal GPS positioning - blind zone trajectory extension - subsequent map matching", completely eliminating trajectory jumps or matching failures caused by cross-coordinate systems, and further ensuring the continuity and geographical relevance of trajectories in GPS blind zones.

[0046] Furthermore, such as Figure 2 As shown, when determining the relative displacement vector, the riding direction of the previous calculation cycle is used as the reference direction, and the riding deviation angle is calculated using the inertial navigation module; the riding distance of the calculation cycle is inferred based on the speed and acceleration, and the estimated coordinates are obtained, taking into account a certain error range.

[0047] Based on the above embodiments, as a preferred implementation, it further includes: S4. If it is determined that the satellite positioning signal has been received again and stabilized, then switch to recording the cycling trajectory based on the satellite positioning signal; wherein, the judgment condition for the satellite positioning signal to stabilize is: the satellite positioning signal strength is greater than -120dBm for 3-5 consecutive operation cycles, and the satellite positioning of adjacent operation cycles is stable.

[0048] Among them, "re-receiving satellite positioning signal and restoring stability" means that after the cyclist leaves GPS blind spots such as tunnels, dense forests, and areas obstructed by tall buildings, the bike meter switches from a state where it cannot receive effective satellite signals (or the signal is intermittent or weak) to continuously receiving signals that meet the positioning accuracy requirements, rather than receiving signals briefly or sporadically. -120dBm is the industry-recognized critical threshold for stable satellite positioning. A value greater than this means that the signal quality is sufficient to support accurate positioning (e.g., the signal improves from -130dBm to -110dBm after leaving a tunnel, which meets the standard). 3-5 consecutive operation cycles refer to the fixed time unit (e.g., 10ms-100ms / cycle, consistent with the operation cycle in S2) used as the counting unit for the bike meter to perform data acquisition and coordinate calculation. Multiple consecutive cycles meet the signal conditions to avoid instantaneous signal interference. The fluctuation range of satellite positioning coordinates between adjacent operation cycles refers to the difference in latitude and longitude output of satellite positioning within two consecutive operation cycles, reflecting the stability of the positioning result. Small fluctuations indicate that the signal has no jumps.

[0049] In this embodiment, while performing S2 trajectory extension and S3 trajectory correction, the bike table simultaneously monitors the satellite positioning signal status in real time. It first determines whether the signal strength is greater than -120dBm for 3-5 consecutive operation cycles (ensuring signal strength meets the standard), and then checks whether the fluctuation amplitude of satellite positioning coordinates in adjacent cycles is less than 5m (ensuring stable positioning results). When both conditions are met, the satellite positioning signal is determined to have stabilized, and the S2 trajectory extension and S3 trajectory correction are stopped. The system then switches to directly recording the cycling trajectory based on the satellite positioning signal, achieving a seamless transition from inertial navigation calculation to satellite positioning. This avoids erroneous switching caused by instantaneous, weak satellite signals (such as brief signal reception at tunnel entrances), preventing trajectory jumps or disconnections. The dual-condition judgment of strength and fluctuation ensures accurate and stable data when switching back to satellite positioning, meeting the cyclist's need for positioning accuracy after leaving blind spots. Simultaneously, it achieves a smooth connection between the inertial navigation calculated trajectory and the satellite positioning trajectory, ensuring the continuity and integrity of trajectory recording throughout the entire cycling process, and solving the trajectory connection discontinuity problem that easily occurs during signal recovery in existing technologies.

[0050] Based on the above embodiments, as a preferred implementation, in step S4, after switching to recording the cycling trajectory based on the satellite positioning signal, the method further includes: The corrected cycling distance obtained in S3 is fused with the distance data after the satellite positioning signal is recovered to compensate for the distance error during the satellite positioning signal interruption.

[0051] The corrected cycling distance in S3 is calculated and accumulated based on real road coordinates by matching continuous coordinate points extended within GPS blind spots (such as tunnels and dense forests) with the built-in digital map road network. This eliminates the accumulated error of simple inertial navigation calculations and represents the most accurate mileage data within blind spots. The distance data after satellite positioning signal recovery is the cycling distance calculated in real time by the bike computer based on satellite positioning (such as GPS) after the satellite signal stabilizes. It corresponds to the road mileage after leaving the blind spot (such as the distance from the tunnel exit to the destination) and has the characteristic of no accumulated error. Data fusion is not a simple addition of the two types of distances, but rather a precise connection processing of the two types of data according to the cycling timeline. It clearly defines the corrected distance for blind spots and the satellite distance for signal recovery, or performs weighted optimization based on the accuracy of the two types of data (such as fine-tuning the slight deviation between the corrected distance and the satellite distance at the connection point through algorithms), ensuring that the mileage record is continuous and without gaps. Figure 3 As shown in the figure, the cycling trajectory is obtained by relying solely on GPS signals, and the trajectory is obtained by extending and inferring the trajectory in the case of GPS signal loss.

[0052] In this embodiment, after switching to satellite positioning to record the trajectory, since satellite positioning cannot compensate for the cycling distance during the signal interruption period (within the blind zone) (there is no positioning data in the blind zone), and the corrected cycling distance obtained in S3 is the only accurate mileage data in the blind zone, the system first extracts the corrected cycling distance corresponding to the blind zone period in S3, and then connects it with the real-time distance data recorded from the end point of the blind zone (i.e., the position when the satellite signal is restored) after satellite positioning is restored, according to the cycling time sequence. Based on the satellite positioning coordinates of the end point of the blind zone, the end point of the corrected distance is aligned with the starting point of the satellite distance. If there is a slight overlap or gap due to positioning accuracy, the deviation is eliminated by algorithm fine-tuning (such as taking the average of the two), and finally a complete cycling distance record covering the entire period from before the blind zone to inside the blind zone to after the blind zone is formed. This solution addresses the issues of inaccurate distance replenishment during blind spot periods after satellite positioning recovery and the inaccuracies in total cycling mileage caused by relying on a single data source (e.g., using only satellite distance results in missing blind spot mileage, while using only corrected distance leads to no mileage update after recovery). It ensures that the distance data throughout the entire cycling process possesses both the high accuracy of the corrected distance within the blind spot and the high reliability of satellite positioning after recovery. This aligns with cyclists' practical needs for recording total mileage, calculating average speed, and planning routes. It eliminates the impact of accumulated errors from inertial navigation on distance accumulation, ensuring that the final output cycling distance data is complete and accurate, and preventing mileage deviations from affecting the usability of the cycling data. Figure 4 The image shown is a trajectory correction diagram, demonstrating how cycling trajectory correction is achieved through trajectory extension and map matching in a tunnel environment where GPS signal is interrupted. Figure 5 The diagram shows the effect of distance compensation, illustrating the difference between the compensation distance obtained from the corrected trajectory and the compensation distance recovered from positioning. The error is larger in non-linear tunnels.

[0053] Secondly, embodiments of the present invention provide a cycling navigation blind spot trajectory correction system based on a bike table inertial navigation system, based on the cycling navigation blind spot trajectory correction methods based on the bike table inertial navigation systems described in the above embodiments, such as... Figure 6 As shown, it includes: The data acquisition and parameter calculation module 610 is used to acquire the angular velocity information and acceleration information of the bike table, determine the riding heading angle based on the angular velocity information and the direction information of the Earth's magnetic field, and determine the real-time riding speed based on the acceleration information. The trajectory extension module 620 is used to determine the relative displacement vector based on the riding heading angle, the real-time riding speed, and the time interval of the current operation cycle if the satellite positioning signal is determined to be interrupted. It then determines the estimated coordinate point of the current operation cycle based on the estimated coordinate point of the previous cycle and the relative displacement vector, and repeats the above steps to form extended continuous coordinate points. The estimated coordinate point of the first operation cycle is determined based on the last satellite positioning signal before the satellite positioning signal was interrupted. The trajectory correction module 630 is used to match the extended continuous coordinate points with the road network of the preset digital map, calculate the distance between adjacent coordinate points based on the matched road coordinates and accumulate them to obtain the corrected riding distance, thus completing the trajectory correction.

[0054] Based on the same concept, this invention also provides a schematic diagram of a physical structure, such as... Figure 7 As shown, the server may include a processor 710, a communications interface 720, a memory 730, and a communication bus 740. The processor 710, communications interface 720, and memory 730 communicate with each other via the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute the steps of the cycling navigation blind spot trajectory correction method based on code table inertial navigation as described in the above embodiments.

[0055] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] Based on the same concept, embodiments of the present invention also provide a non-transitory computer-readable storage medium storing a computer program containing at least one piece of code that can be executed by a master control device to control the master control device to implement the steps of the cycling navigation blind spot trajectory correction method based on the code table inertial navigation described in the above embodiments.

[0057] Based on the same technical concept, this application also provides a computer program, which, when executed by a main control device, is used to implement the above-described method embodiments.

[0058] The program may be stored, in whole or in part, on a storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.

[0059] Based on the same technical concept, this application also provides a processor for implementing the above-described method embodiments. The processor can be a chip.

[0060] The various embodiments of the present invention can be combined arbitrarily to achieve different technical effects.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for correcting blind spot trajectories in cycling navigation based on a bike table inertial navigation system, characterized in that, include: S1. Obtain the angular velocity and acceleration information from the bike table, determine the riding heading angle based on the angular velocity information and the direction information of the Earth's magnetic field, and determine the real-time riding speed based on the acceleration information; S2. If it is determined that the satellite positioning signal is interrupted, the relative displacement vector is determined based on the riding heading angle, the real-time riding speed and the time interval of the current operation cycle. The estimated coordinate point of the current operation cycle is determined based on the estimated coordinate point of the previous cycle and the relative displacement vector. The above steps are repeated to form extended continuous coordinate points. The estimated coordinate point of the first operation cycle is determined based on the last satellite positioning signal before the satellite positioning signal was interrupted. S3. Match the extended continuous coordinate points with the road network of the preset digital map, calculate the distance between adjacent coordinate points based on the matched road coordinates and accumulate them to obtain the corrected riding distance, thus completing the trajectory correction.

2. The method for correcting blind spot trajectories in cycling navigation based on a codebook inertial navigation according to claim 1, characterized in that, In step S1, the acquisition of angular velocity and acceleration information from the code table includes: Angular velocity and acceleration information are acquired based on the inertial measurement unit (IMU) built into the codebook; The angular velocity information is filtered to remove high-frequency vibration noise during riding. The filtering process uses either a Kalman filter algorithm or a moving average filter algorithm. Based on the preset installation attitude angle of the code table, the influence component caused by Earth's gravity in the acceleration information is removed by vector decomposition.

3. The method for correcting blind spot trajectories in cycling navigation based on a bike table inertial navigation according to claim 2, characterized in that, The S2 further includes: The direction information of the Earth's magnetic field is obtained based on the magnetometer built into the codebook, and the frequency at which the magnetometer collects the direction information of the Earth's magnetic field is consistent with the acquisition frequency of the IMU.

4. The method for correcting blind spot trajectories in cycling navigation based on a bike table inertial navigation according to claim 1, characterized in that, The calculation cycle is the time difference between the estimated coordinate points in steps S1 to S2 that determine the current calculation cycle.

5. The method for correcting blind spot trajectories in cycling navigation based on a bike table inertial navigation according to claim 1, characterized in that, In step S2, determining the estimated coordinate point for the current calculation cycle based on the estimated coordinate point of the previous cycle and the relative displacement vector includes: The relative displacement vector is decomposed into azimuth and straight-line distance. Using the geodetic inverse calculation model corresponding to the WGS-84 geodetic coordinate system, the azimuth, the straight-line distance and the estimated coordinate points of the previous calculation cycle are transformed to obtain the latitude and longitude data of the estimated coordinate points of the next calculation cycle.

6. The method for correcting blind spot trajectory in cycling navigation based on a bike table inertial navigation according to claim 1, characterized in that, Also includes: S4. If it is determined that the satellite positioning signal has been received again and stabilized, then switch to recording the cycling trajectory based on the satellite positioning signal; wherein, the judgment condition for the satellite positioning signal to stabilize is: the satellite positioning signal strength is greater than -120dBm for 3-5 consecutive operation cycles, and the fluctuation range of satellite positioning coordinates in adjacent operation cycles is less than 5m.

7. The method for correcting blind spot trajectories in cycling navigation based on a codebook inertial navigation according to claim 6, characterized in that, In step S4, after switching to recording the cycling trajectory based on the satellite positioning signal, the method further includes: The corrected cycling distance obtained in S3 is fused with the distance data after the satellite positioning signal is recovered to compensate for the distance error during the satellite positioning signal interruption.

8. A cycling navigation blind spot trajectory correction system based on a bike table inertial navigation system, characterized in that, include: The data acquisition and parameter calculation module is used to acquire the angular velocity and acceleration information of the bike table, determine the riding heading angle based on the angular velocity information and the direction information of the Earth's magnetic field, and determine the real-time riding speed based on the acceleration information. The trajectory extension module is used to determine the relative displacement vector based on the riding heading angle, the real-time riding speed, and the time interval of the current operation cycle if the satellite positioning signal is interrupted. It then determines the estimated coordinate point of the current operation cycle based on the estimated coordinate point of the previous cycle and the relative displacement vector, and repeats the above steps to form extended continuous coordinate points. The estimated coordinate point of the first operation cycle is determined based on the last satellite positioning signal before the satellite positioning signal was interrupted. The trajectory correction module is used to match the extended continuous coordinate points with the road network of the preset digital map, calculate the distance between adjacent coordinate points based on the matched road coordinates and accumulate them to obtain the corrected cycling distance, thus completing the trajectory correction.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the cycling navigation blind spot trajectory correction method based on the code table inertial navigation as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the cycling navigation blind spot trajectory correction method based on a code table inertial navigation as described in any one of claims 1 to 7.

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