A map navigation method and system based on integrated navigation

Abstract

The present invention discloses a map navigation method and system based on integrated navigation, and specifically relates to the field of integrated navigation, including compass data collection, magnetic declination correction, map integration, navigation system data collection, navigation system data analysis and integrated navigation. The present invention significantly improves the accuracy and robustness of the navigation system, can dynamically adapt to different environments, and reduce the impact of errors; by pre-setting electronic maps and POI tags, it provides rich geographic information support; the real-time navigation service provides intuitive road condition information and path planning, and enhances the user experience. Overall, the present invention provides users with more accurate, reliable and convenient navigation services.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated navigation, and more specifically, to a map navigation method and system based on integrated navigation. Background Art

[0002] The map navigation system in the prior art usually follows a fixed operating process. First, the system receives the location information of the device through the built-in GPS or other satellite navigation system. Then, the system matches the location information with the pre-stored electronic map, determines the current location of the device, and marks it on the map. Subsequently, the system plans the route according to the user's travel needs and current traffic conditions to provide the user with the best travel plan. During the navigation process, the system will also update the location information in real time and adjust the planned route as needed to ensure that the user can reach the destination smoothly.

[0003] However, the map navigation system of the prior art still has some shortcomings. On the one hand, since satellite signals are easily affected by factors such as weather and building obstructions, the acquisition of location information is not accurate and stable enough. On the other hand, the existing system still has shortcomings in data fusion and path planning, and it is difficult to make full use of multi-source navigation data to improve navigation accuracy and robustness. In addition, some systems also have shortcomings in real-time navigation services, such as insufficient update frequency and inaccurate road conditions, which affect the user experience. Therefore, it is necessary to improve and optimize the existing map navigation system to meet the growing needs of users. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a map navigation method and system based on integrated navigation, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0005] To achieve the above object, the present invention provides the following technical solution: a map navigation method based on integrated navigation, comprising:

[0006] Step 1: Compass data acquisition: Obtain raw geomagnetic data through the magnetic sensor to provide input for step 2 and provide direction reference for step 6;

[0007] Step 2: Magnetic declination correction: used to receive the raw data from step 1, perform correction using the magnetic declination data stored in the ROM, and output the true geographic direction data for the integrated navigation in step 6;

[0008] Step 3: Map integration: As a spatial reference framework for navigation, preset electronic maps and create POI markers to provide geographic information support for step 6, and also provide geographic reference for data collection in step 4;

[0009] Step 4: Navigation system data collection: Expand the data scope of step 1 for analysis in step 5 and integrated navigation in step 6;

[0010] Step 5: Navigation system data analysis: used to analyze and process the multi-source data collected in step 4, evaluate the system accuracy and reliability, and use the analysis results to optimize the navigation effect of step 6;

[0011] Step 6: Integrated navigation: Use the directional data corrected in step 2, combine it with the map information in step 3, fuse the multi-source data collected in step 4, and optimize it according to the analysis results of step 5 to finally achieve integrated navigation.

[0012] Preferably, the step 1 collects geomagnetic field data in real time through a magnetic sensor, then collects acceleration sensor data for attitude compensation, and records sampling timestamp and frequency information, and finally performs preliminary digital filtering and noise reduction processing.

[0013] Preferably, the step 1 first accesses the internal register of the sensor through the I2C or SPI communication interface, the sampling rate of the magnetic sensor includes 50Hz and 100Hz, and the range of ±8 Gauss and the 16-bit ADC resolution are configured at the same time. The acceleration sensor is configured to the ±2g range and the low-pass filter is enabled to ensure synchronization with the magnetic sensor sampling. Finally, a 2048-byte FIFO circular buffer is established for data temporary storage, and the corresponding overflow processing mechanism and interrupt trigger condition are set; the original ADC values ​​of the magnetic field of the X, Y, and Z axes are read from the sensor register and converted into μT units, the three-axis acceleration data are synchronously collected and converted into m / s² units, and the sampling sequence number and microsecond timestamp are recorded each time the data is read; the data preprocessing stage uses an 8-16-point sliding window to implement moving average filtering, which effectively removes random noise while maintaining the fast response characteristics of the signal, and identifies mutation points and outliers by setting a detection threshold of ±3 times the standard deviation, and detects The measured abnormal data is corrected using the interpolation method, and the sensor temperature value is read. The compensation coefficient is obtained by looking up the table according to the temperature-performance curve, and the temperature linear compensation calculation is performed; the attitude compensation processing first uses the acceleration data to calculate the pitch angle and roll angle of the device through the inverse tangent function, establishes a quaternion that describes the current attitude, and then establishes the local coordinate system of the device. The sensor data is converted from the device coordinate system to the geographic coordinate system through the rotation matrix, and finally the projection of the magnetic field component on the horizontal plane is calculated to eliminate the influence of gravity on the magnetic field measurement and update the compensated magnetic field vector; in the data output formatting process, the processed magnetic field data is converted to polar coordinate representation, the magnetic field azimuth is calculated and normalized, and the angle resolution is quantified according to actual needs. Then the data is encapsulated into a standard format, frame synchronization words and status information are added, and a checksum is generated. Finally, the information is output to the data queue through a predefined data structure, and the corresponding update flag is set for use by the upper-level application.

[0014] Preferably, the sampling rate of the magnetic sensor is 50 Hz indoors and 100 Hz outdoors, and the measuring range can be adjusted to ±4 Gauss or ±12 Gauss according to actual conditions.

[0015] Preferably, step 2 reads the magnetic declination database from the ROM, then queries the local magnetic declination value based on the current position, compensates for hard iron and soft iron interference, then performs tilt compensation processing, and finally outputs the corrected geographic direction data.

[0016] Preferably, the step 2 is based on the WMM model, and a magnetic declination database is constructed in the ROM. The database uses a latitude and longitude grid with an interval of 0.5 degrees to store the magnetic declination value and the annual change rate. Each data point contains four key parameters: longitude, latitude, magnetic declination and annual change rate. The database updates the basic data every five years, and uses the annual change rate for daily dynamic correction to ensure the timeliness of the data. For positions outside the grid points, a bilinear interpolation algorithm is used to calculate the accurate magnetic declination value by considering the weighted influence of the four adjacent grid points. The real-time correction process first obtains the geographic location coordinates through GPS, and when the GPS signal is unavailable, network positioning is used as an alternative. The acquired location data is smoothed by Kalman filtering to eliminate jumps and noise, and then Based on the current position, the magnetic declination data of the surrounding grid points is queried, and the precise magnetic declination value of the position is obtained through interpolation operation. The magnetic declination value is combined with the real-time read magnetic sensor data to obtain the direction angle based on true north, and the output range is 0 to 359.99 degrees; the error compensation processing includes three steps. The first is the hard iron effect compensation. By recording the data during the 360-degree rotation of the sensor, the eccentricity of the magnetic field intensity ellipse is calculated, and a compensation matrix is ​​established to eliminate the fixed offset. The second is the soft iron effect correction. By measuring the magnetic field distortion in different directions, a 3×3 correction matrix is ​​established to correct the sensor data in real time. Finally, the tilt compensation is performed. The three-axis data provided by the acceleration sensor is used to calculate the inclination and roll angle of the device, and real-time attitude compensation is performed through the rotation matrix.

[0017] Preferably, the step 3 first loads the preset electronic map data, then establishes a POI marking system, constructs a road network topology, implements map projection and coordinate conversion, and finally establishes a map cache mechanism.

[0018] Preferably, in step 3, the vector data in Shape and GeoJSON format are first parsed through the GDAL / OGR library, and the parsed geographic elements are stored in the spatial data table. A tree structure is used to manage different layers during data storage, each layer has a unique identifier, and a dependency relationship is established between layers to achieve on-demand data loading and an incremental update mechanism. The POI tagging system includes data storage and retrieval. The PostGIS spatial database is used for data storage, and a POI classification system and attribute fields are designed, including name, address, and contact information, and a spatial index is added. The retrieval function needs to integrate the Elasticsearch full-text retrieval engine to achieve fuzzy matching and pinyin retrieval functions, and range query is implemented based on the R tree to support multi-condition combination screening. The road network topology construction includes the design of data structure and index optimization, using an adjacency list to store the connection relationship of the road network, establishing a mapping relationship between road sections and nodes, and recording turn restriction information and road Road grade and traffic condition attributes, in terms of index optimization, implement quadtree spatial index structure, and establish a fast lookup table for road sections; coordinate conversion includes two levels: geographic coordinate conversion and screen mapping. In terms of geographic coordinate conversion, use PROJ library to realize conversion between different coordinate systems, support common geographic coordinate systems, implement Mercator projection, and handle cross-zone conversion issues. In terms of screen mapping, it is necessary to accurately calculate the scale, handle map rotation transformation, optimize pixel coordinate accuracy, and implement dynamic resolution adaptation; map cache mechanism uses LRU algorithm to manage tile data at the memory level. When the memory cache reaches the preset upper limit, the least accessed data is automatically cleared. The system predicts the tiles that may be needed based on the user's browsing behavior, loads data in advance, and reduces waiting time. At the disk level, establish persistent tile storage, keep local cache for frequently accessed areas, avoid repeated network requests, and implement incremental update mechanism. When the server data changes, only the changed tiles are updated.

[0019] Preferably, step 4 is used to collect inertial navigation basic data, GNSS raw data and combined navigation data for analysis in step 5.

[0020] Preferably, the inertial navigation basic data include gyroscope bias drift rate, accelerometer scale factor stability, gyroscope random walk error and accelerometer bias instability, which are marked as GBD, ASF, ARW and ASI respectively; the GNSS raw data include carrier phase measurement noise, multipath delay spread parameter, ionospheric delay gradient and satellite geometric precision factor, which are marked as CPM, MDS, IDG and PDOP respectively; the integrated navigation data include attitude angle error accumulation rate, velocity drift coefficient, position error growth rate and heading angle deviation standard deviation, which are marked as ARE, VDC, PEG and HAD respectively.

[0021] Preferably, the gyroscope zero bias drift rate (° / h) is measured by collecting the gyroscope output for 8 consecutive hours under static conditions, sampling at 100 Hz per second, and calculating the rate of change of the zero-position output over time. The accelerometer scale factor stability (ppm) is determined by performing a ±1g flip test on a three-axis turntable and comparing the slope change of the input-output characteristic curve. The gyroscope random walk error (° / √h) is measured by Allen variance analysis, collecting 4 hours of data in a constant temperature environment with a sampling rate of 200 Hz, and calculating the angle increment sequence. The accelerometer zero bias instability (μg) is measured by standing in a constant temperature room, collecting data continuously for 24 hours with a sampling rate of 100 Hz, and analyzing the statistical characteristics of the zero-position output.

[0022] Preferably, the carrier phase measurement noise (mm) uses a high-precision antenna, a fixed point for 30 minutes, and a sampling rate of 1Hz to record the L1 / L2 dual-frequency carrier phase observation value. The multipath delay spread parameter (ns) uses the RF front end to collect the original IF signal, with a sampling rate of 10MHz, and analyzes the signal power delay profile. The ionospheric delay gradient (TECU / km) is synchronously observed using a dual-frequency receiver network, sampled at 5-second intervals, and the regional TEC gradient change is calculated. The satellite geometric precision factor records the visible satellite position information, with an update rate of 1Hz, and calculates the satellite spatial distribution characteristic values ​​in real time.

[0023] Preferably, the attitude angle error accumulation rate (mrad / h) is dynamically tested using an inertial navigation / GNSS combined system, with 10Hz sampling to record the attitude solution error growth process, the speed drift coefficient (m / s / h) is tested by uniform motion of a straight segment, with a sampling rate of 50Hz, to analyze the drift characteristics of the speed solution result, the position error growth rate (m / h) is tested by a circular motion trajectory, with a sampling frequency of 20Hz, to record the position solution deviation changing with time, the heading angle deviation standard deviation (arcmin) is tested by turntable azimuth rotation, with a sampling rate of 100Hz, to statistically analyze the fluctuation characteristics of the heading angle solution result.

[0024] Preferably, step 5 includes inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation, which are used to optimize the navigation effect of step 6.

[0025] Preferably, the inertial navigation basic data analysis specifically includes:

[0026] Bias Drift Assessment: ;

[0027] Scaling factor modeling: ;

[0028] Random error analysis: ;

[0029] Bias instability: ;

[0030] δθ(t) represents the gyroscope output angle error, t represents the time variable (h), ε represents the random white noise term, S(t) represents the actual output ratio of the accelerometer, α(t) represents the time-varying coefficient, σ²(t) represents the gyroscope angle random walk variance, B(t) represents the accelerometer zero bias value, and τ represents the zero bias change characteristic time constant.

[0031] Preferably, the GNSS data analysis specifically includes:

[0032] Phase measurement: ;

[0033] Latency estimation: ;

[0034] Accuracy assessment: ;

[0035] R(t) represents the total error of carrier phase measurement, D(t) represents the total delay error, φ represents the satellite elevation angle, σp represents the position accuracy estimate, and σUERE represents the user equivalent ranging error.

[0036] Preferably, the integrated navigation performance analysis specifically includes:

[0037] Attitude error: ;

[0038] Speed ​​error: ;

[0039] Position error: ;

[0040] Heading accuracy: ;

[0041] θe(t) represents the cumulative attitude error, θ0 represents the initial attitude error, v(t) represents the velocity error, p(t) represents the position error, σH represents the standard deviation of the heading angle, and k represents the time correlation coefficient.

[0042] Preferably, the overall performance evaluation of the system is specifically expressed as:

[0043] ;

[0044] P represents the comprehensive performance index of the system, w1 represents the weight coefficient of inertial navigation data, w2 represents the weight coefficient of GNSS data, and w3 represents the weight coefficient of integrated navigation data.

[0045] Preferably, step 5 divides the navigation system performance into excellent, good, medium and poor by setting thresholds P1, P2 and P3, sends a warning signal to step 6 when the navigation system performance is poor, analyzes the contribution of each sub-item, and corrects or compensates for the subsystem with the worst performance.

[0046] Preferably, step 6 includes a data fusion preprocessing link, a multi-source data fusion link, a navigation solution and output link, an adaptive optimization mechanism and a real-time navigation service link.

[0047] Preferably, the data fusion preprocessing link realizes data collection by constructing a data receiving buffer with a sampling frequency of 100 Hz at the hardware level, and simultaneously processes the magnetic compass data using a second-order Butterworth low-pass filter with a cutoff frequency of 5 Hz. In terms of GNSS data processing, the system performs a PDOP value check and sets the threshold to 6, cooperates with a data synchronization mechanism based on the UTC time reference to ensure the timing consistency of the data, and identifies and eliminates abnormal data through a preset integrity check algorithm.

[0048] Preferably, the multi-source data fusion link adopts a multi-level composite filtering strategy, and the core implementation includes an extended Kalman filter, which processes the state vector including position, velocity and attitude angle through a 15th-order error state model, and integrates GNSS position, velocity and magnetic compass heading data as observation vectors. In terms of heading data processing, a complementary filter with a cutoff frequency of 0.1 Hz is used to fuse the low-frequency data of the magnetic compass and the high-frequency integral value of the gyroscope, and combined with a temperature compensation mechanism. Map matching is based on the principle of minimum distance from point to curve, and position correction is performed within a 50-meter search radius in combination with road section connectivity constraints and driving direction information.

[0049] Preferably, the navigation solution and output link adopts a dual GPS antenna solution to achieve high-precision positioning, and combines odometer data to realize the Dead Reckoning function, and smoothes the motion trajectory through a particle filtering algorithm. In terms of path planning, the optimal path is calculated based on the improved A* algorithm, and the planning strategy can be dynamically adjusted according to real-time road conditions information to achieve multi-level path planning.

[0050] Preferably, in an open environment, the adaptive optimization mechanism has a GNSS weight of 0.7, an inertial navigation weight of 0.2, and a magnetic compass weight of 0.1; in an urban canyon environment, the GNSS weight is adjusted to 0.3, the inertial navigation weight is 0.5, and the magnetic compass weight is 0.2; while in a tunnel environment, it mainly relies on inertial navigation and magnetic compass, with weights of 0.7 and 0.3 respectively, and establishes a sensor error compensation mechanism, including a gyroscope zero bias model, accelerometer calibration compensation, and magnetic compass soft iron compensation, and maintains accuracy through regular online calibration.

[0051] Preferably, the real-time navigation service updates location information at a frequency of 10 Hz, provides early warning prompts when approaching critical intersections, and regularly updates arrival time predictions based on real-time traffic conditions. The navigation interface supports 2D / 3D display mode switching, displays traffic information through color coding, provides lane-level navigation guidance, and automatically adjusts the display ratio at critical intersections.

[0052] Preferably, a map navigation system based on integrated navigation comprises:

[0053] Compass data acquisition module: collects geomagnetic field data in real time through magnetic sensors, then collects acceleration sensor data for attitude compensation, records sampling timestamp and frequency information, and finally performs preliminary digital filtering and noise reduction processing;

[0054] Magnetic declination correction module: reads the magnetic declination database from the ROM, then queries the local magnetic declination value based on the current position, compensates for hard iron and soft iron interference, then performs tilt compensation processing, and finally outputs the corrected geographic direction data;

[0055] Map integration module: first load the preset electronic map data, then establish the POI marking system, build the road network topology, realize map projection and coordinate conversion, and finally establish the map cache mechanism;

[0056] Navigation system data acquisition module: used to collect inertial navigation basic data, GNSS raw data and integrated navigation data;

[0057] Navigation system data analysis module: including inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation;

[0058] Integrated navigation module: including data fusion preprocessing, multi-source data fusion, navigation solution and output, adaptive optimization mechanism and real-time navigation service.

[0059] Technical effects and advantages of the present invention:

[0060] The compass data acquisition can accurately obtain the current attitude of the device by collecting geomagnetic field data and acceleration sensor data in real time for attitude compensation, and provide accurate basic data for subsequent magnetic declination correction and integrated navigation. The moving average filtering and abnormal data correction in the data preprocessing link effectively remove random noise and ensure the stability and reliability of the data. The attitude compensation processing eliminates the influence of gravity on the magnetic field measurement and improves the accuracy of the magnetic field measurement. The magnetic declination correction can correct the magnetic declination of the current position in real time through the magnetic declination database stored in the ROM, output the real geographic direction data, and provide accurate direction reference for integrated navigation. The error compensation processing includes hard iron effect compensation, soft iron effect correction and tilt compensation, which further improves the accuracy of magnetic declination correction. The WMM model is used to construct the magnetic declination database, and the basic data is updated every five years to ensure the timeliness and accuracy of the data. The map integration provides a clear spatial reference framework and geographic information support for navigation by presetting the electronic map and establishing the POI marking system. The construction of the road network topology relationship and the realization of map projection and coordinate conversion ensure the accuracy and availability of the map data. The use of the map cache mechanism It improves the loading speed and response performance of map data and reduces the waiting time for users. The navigation system data acquisition provides a rich data source for the subsequent navigation system data analysis by collecting inertial navigation basic data, GNSS raw data and integrated navigation data. The collection methods and standards of various data are clear to ensure the accuracy and reliability of the data. The navigation system data analysis collects inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation, comprehensively evaluates the accuracy and reliability of the navigation system, and divides the navigation system performance into excellent, good, medium and poor by setting thresholds and weight coefficients, providing optimization direction for subsequent integrated navigation. When the navigation system performance is poor, an early warning signal is issued, and the contribution of each sub-item is analyzed, which helps to timely discover and solve problems. The integrated navigation data fusion preprocessing link ensures the real-time, integrity and quality of multi-source data, providing a reliable foundation for subsequent data fusion. The multi-source data fusion link adopts a multi-level composite filtering strategy to achieve high-precision estimation of position, speed and attitude, and improves the reliability and robustness of the navigation system. The navigation solution and output link adopts dual GPS antennas and Dead Reckoning technology improves the accuracy and reliability of positioning. At the same time, the improved A* algorithm can quickly plan the optimal path and dynamically adjust it according to road conditions. The adaptive optimization mechanism can dynamically adjust the sensor weights according to different environments, reducing the error impact of various sensors and ensuring the long-term stability of system performance. The real-time navigation service provides high-frequency location updates, timely warning prompts, diverse map display modes and intuitive road condition signs, providing users with comprehensive and convenient navigation services. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the structure of the method of the present invention.

[0062] Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] refer to Figure 1 A map navigation method based on integrated navigation is shown, and the specific steps include:

[0065] Step 1: Compass data acquisition: Obtain raw geomagnetic data through the magnetic sensor to provide input for step 2 and provide direction reference for step 6.

[0066] The step 1 collects geomagnetic field data in real time through a magnetic sensor, then collects acceleration sensor data for attitude compensation, records sampling timestamp and frequency information, and finally performs preliminary digital filtering and noise reduction processing.

[0067] The step 1 first accesses the sensor internal register through the I2C or SPI communication interface. The magnetic sensor sampling rate includes 50Hz and 100Hz. At the same time, the range of ±8 Gauss and the 16-bit ADC resolution are configured. The acceleration sensor is configured to the ±2g range and the low-pass filter is enabled to ensure synchronization with the magnetic sensor sampling. Finally, a 2048-byte FIFO circular buffer is established for data temporary storage, and the corresponding overflow processing mechanism and interrupt trigger conditions are set; the original ADC values ​​of the magnetic field of the X, Y, and Z axes are read from the sensor register and converted into μT units, the three-axis acceleration data are synchronously collected and converted into m / s² units, and the sampling sequence number and microsecond timestamp are recorded each time the data is read; the data preprocessing stage uses an 8-16-point sliding window to implement moving average filtering, effectively remove random noise while maintaining the fast response characteristics of the signal, and identify mutation points and outliers by setting a detection threshold of ±3 times the standard deviation. The abnormal data is corrected using the interpolation method, and the sensor temperature value is read. The compensation coefficient is obtained by looking up the table according to the temperature-performance curve, and the temperature linear compensation calculation is performed; the attitude compensation processing first uses the acceleration data to calculate the pitch angle and roll angle of the device through the inverse tangent function, establishes a quaternion that describes the current attitude, and then establishes the local coordinate system of the device. The sensor data is converted from the device coordinate system to the geographic coordinate system through the rotation matrix, and finally the projection of the magnetic field component on the horizontal plane is calculated to eliminate the influence of gravity on the magnetic field measurement and update the compensated magnetic field vector; in the data output formatting process, the processed magnetic field data is converted to polar coordinate representation, the magnetic field azimuth is calculated and normalized, and the angle resolution is quantified according to actual needs. Then the data is encapsulated into a standard format, frame synchronization words and status information are added, and a checksum is generated. Finally, the information is output to the data queue through a predefined data structure, and the corresponding update flag is set for use by the upper-level application.

[0068] It should be specifically explained in this embodiment that the sampling rate of the magnetic sensor is 50 Hz indoors and 100 Hz outdoors, and the measuring range can be adjusted to ±4 Gauss or ±12 Gauss according to actual conditions.

[0069] Step 2: Magnetic declination correction: used to receive the raw data from step 1, correct it through the magnetic declination data stored in ROM, and output the true geographic direction data for the integrated navigation in step 6.

[0070] The step 2 reads the magnetic declination database from the ROM, then queries the local magnetic declination value based on the current position, compensates for hard iron and soft iron interference, then performs tilt compensation processing, and finally outputs the corrected geographic direction data.

[0071] The step 2 is based on the WMM model and constructs a magnetic declination database in the ROM. The database uses a longitude and latitude grid with a 0.5 degree interval to store the magnetic declination value and the annual change rate. Each data point contains four key parameters: longitude, latitude, magnetic declination and annual change rate. The database updates the basic data every five years and uses the annual change rate to perform daily dynamic corrections to ensure data timeliness. For locations outside the grid points, a bilinear interpolation algorithm is used to calculate the accurate magnetic declination value by considering the weighted influence of the four adjacent grid points. The real-time correction process first obtains the geographic location coordinates through GPS. When the GPS signal is unavailable, network positioning is used as an alternative. The acquired location data is smoothed by Kalman filtering to eliminate jumps and noise. Then, based on The magnetic declination data of the surrounding grid points is queried at the current position, and the precise magnetic declination value of the position is obtained through interpolation operation. The magnetic declination value is combined with the magnetic sensor data read in real time to obtain the direction angle based on true north, and the output range is 0 to 359.99 degrees; the error compensation processing includes three steps. The first is the hard iron effect compensation. By recording the data during the 360-degree rotation of the sensor, the eccentricity of the magnetic field intensity ellipse is calculated, and a compensation matrix is ​​established to eliminate the fixed offset. The second is the soft iron effect correction. By measuring the magnetic field distortion in different directions, a 3×3 correction matrix is ​​established to correct the sensor data in real time. Finally, the tilt compensation is performed. The three-axis data provided by the acceleration sensor is used to calculate the inclination and roll angle of the device, and real-time attitude compensation is performed through the rotation matrix.

[0072] Step 3: Map integration: As a spatial reference framework for navigation, pre-set electronic maps and create POI markers to provide geographic information support for step 6, and also provide geographic reference for data collection in step 4.

[0073] The step 3 first loads the preset electronic map data, then establishes a POI marking system, constructs a road network topology, implements map projection and coordinate conversion, and finally establishes a map cache mechanism.

[0074] The step 3 first parses the vector data in Shape and GeoJSON format through the GDAL / OGR library, stores the parsed geographic elements in the spatial data table, and uses a tree structure to manage different layers during data storage. Each layer has a unique identifier, and a dependency relationship is established between layers to achieve on-demand data loading and an incremental update mechanism. The POI tagging system includes data storage and retrieval. The PostGIS spatial database is used for data storage, and a POI classification system and attribute fields are designed, including name, address, and contact information, and a spatial index is added. The retrieval function needs to integrate the Elasticsearch full-text retrieval engine to achieve fuzzy matching and pinyin retrieval functions, and implement range queries based on R-trees to support multi-condition combination screening. The road network topology construction includes the design of data structures and index optimization, using adjacency lists to store the connection relationship of the road network, establishing a mapping relationship between road sections and nodes, and recording turn restriction information and roads, etc. Level and traffic condition attributes. In terms of index optimization, a quadtree spatial index structure is implemented, and a road section fast lookup table is established; coordinate conversion includes two levels: geographic coordinate conversion and screen mapping. In terms of geographic coordinate conversion, the PROJ library is used to implement conversion between different coordinate systems, support common geographic coordinate systems, implement Mercator projection, and handle cross-zone conversion issues. In terms of screen mapping, it is necessary to accurately calculate the scale, handle map rotation transformation, optimize pixel coordinate accuracy, and implement dynamic resolution adaptation; at the memory level, the map cache mechanism uses the LRU algorithm to manage tile data. When the memory cache reaches the preset upper limit, the least accessed data is automatically cleared. The system predicts the tiles that may be needed based on the user's browsing behavior, loads data in advance, and reduces waiting time. At the disk level, persistent tile storage is established, local caches are maintained for frequently visited areas, and repeated network requests are avoided. At the same time, an incremental update mechanism is implemented. When the server data changes, only the changed tiles are updated.

[0075] What needs to be specifically explained in this embodiment is that the tile is a basic data organization method in the map system that divides large-scale map data into small blocks for management and display. In the specific implementation, the system divides the entire map into regular small blocks of a fixed size (usually 256×256 pixels or 512×512 pixels), and each small block is a tile.

[0076] Step 4: Navigation system data collection: Expand the data scope of step 1 for analysis in step 5 and integrated navigation in step 6.

[0077] The step 4 is used to collect inertial navigation basic data, GNSS raw data and integrated navigation data for analysis in step 5.

[0078] The inertial navigation basic data include gyroscope bias drift rate, accelerometer scale factor stability, gyroscope random walk error and accelerometer bias instability, which are marked as GBD, ASF, ARW and ASI respectively. The GNSS raw data include carrier phase measurement noise, multipath delay spread parameter, ionospheric delay gradient and satellite geometric precision factor, which are marked as CPM, MDS, IDG and PDOP respectively. The integrated navigation data include attitude angle error accumulation rate, velocity drift coefficient, position error growth rate and heading angle deviation standard deviation, which are marked as ARE, VDC, PEG and HAD respectively.

[0079] The gyroscope zero bias drift rate (° / h) is measured by collecting the gyroscope output for 8 consecutive hours under static conditions, sampling at 100 Hz per second, and calculating the rate of change of the zero-position output over time. The accelerometer scale factor stability (ppm) is determined by performing a ±1g flip test using a three-axis turntable and comparing the slope change of the input-output characteristic curve. The gyroscope random walk error (° / √h) is measured using Allen variance analysis by collecting 4 hours of data in a constant temperature environment with a sampling rate of 200 Hz to calculate the angle increment sequence. The accelerometer zero bias instability (μg) is measured by standing in a constant temperature room, collecting data continuously for 24 hours with a sampling rate of 100 Hz, and analyzing the statistical characteristics of the zero-position output.

[0080] The carrier phase measurement noise (mm) uses a high-precision antenna, a fixed point for 30 minutes, and a 1Hz sampling rate to record the L1 / L2 dual-frequency carrier phase observation value. The multipath delay spread parameter (ns) uses the RF front end to collect the original IF signal, with a sampling rate of 10MHz, and analyzes the signal power delay profile. The ionospheric delay gradient (TECU / km) is synchronously observed using a dual-frequency receiver network, sampled at 5-second intervals, and the regional TEC gradient change is calculated. The satellite geometric precision factor records the visible satellite position information, with a 1Hz update rate, and calculates the satellite spatial distribution characteristic values ​​in real time.

[0081] The attitude angle error accumulation rate (mrad / h) is dynamically tested using an inertial navigation / GNSS combined system with 10Hz sampling to record the attitude solution error growth process. The speed drift coefficient (m / s / h) is tested by a straight line segment uniform motion test with a 50Hz sampling rate to analyze the drift characteristics of the speed solution result. The position error growth rate (m / h) is tested by a circular motion trajectory with a 20Hz sampling frequency to record the position solution deviation over time. The heading angle deviation standard deviation (arcmin) is tested by a turntable azimuth rotation test with a 100Hz sampling rate to statistically analyze the fluctuation characteristics of the heading angle solution result.

[0082] Step 5: Navigation system data analysis: used to analyze and process the multi-source data collected in step 4, evaluate the system accuracy and reliability, and use the analysis results to optimize the navigation effect of step 6.

[0083] The step 5 includes inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation, which are used to optimize the navigation effect of step 6.

[0084] The inertial navigation basic data analysis specifically includes:

[0085] Bias Drift Assessment: ;

[0086] Scaling factor modeling: ;

[0087] Random error analysis: ;

[0088] Bias instability: ;

[0089] δθ(t) represents the gyroscope output angle error, t represents the time variable (h), ε represents the random white noise term, S(t) represents the actual output ratio of the accelerometer, α(t) represents the time-varying coefficient, σ²(t) represents the gyroscope angle random walk variance, B(t) represents the accelerometer zero bias value, and τ represents the zero bias change characteristic time constant.

[0090] The GNSS data analysis specifically includes:

[0091] Phase measurement: ;

[0092] Latency estimation: ;

[0093] Accuracy assessment: ;

[0094] R(t) represents the total error of carrier phase measurement, D(t) represents the total delay error, φ represents the satellite elevation angle, σp represents the position accuracy estimate, and σUERE represents the user equivalent ranging error.

[0095] The integrated navigation performance analysis specifically includes:

[0096] Attitude error: ;

[0097] Speed ​​error: ;

[0098] Position error: ;

[0099] Heading accuracy: ;

[0100] θe(t) represents the cumulative attitude error, θ0 represents the initial attitude error, v(t) represents the velocity error, p(t) represents the position error, σH represents the standard deviation of the heading angle, and k represents the time correlation coefficient.

[0101] The overall performance evaluation of the system is specifically expressed as follows:

[0102] ;

[0103] P represents the comprehensive performance index of the system, w1 represents the weight coefficient of inertial navigation data, w2 represents the weight coefficient of GNSS data, and w3 represents the weight coefficient of integrated navigation data.

[0104] The step 5 divides the navigation system performance into excellent, good, medium and poor by setting thresholds P1, P2 and P3, sends a warning signal to step 6 when the navigation system performance is poor, analyzes the contribution of each sub-item, and corrects or compensates for the subsystem with the worst performance.

[0105] Step 6: Integrated navigation: Use the directional data corrected in step 2, combine it with the map information in step 3, fuse the multi-source data collected in step 4, and optimize it according to the analysis results of step 5 to finally achieve integrated navigation.

[0106] The step 6 includes a data fusion preprocessing step, a multi-source data fusion step, a navigation solution and output step, an adaptive optimization mechanism, and a real-time navigation service step.

[0107] The data fusion preprocessing link realizes data collection by constructing a data receiving buffer with a sampling frequency of 100 Hz at the hardware level, and processes the magnetic compass data with a second-order Butterworth low-pass filter with a cutoff frequency of 5 Hz. In terms of GNSS data processing, the system performs a PDOP value check and sets the threshold to 6, cooperates with the data synchronization mechanism based on the UTC time reference to ensure the timing consistency of the data, and identifies and eliminates abnormal data through a preset integrity check algorithm.

[0108] The data fusion preprocessing link achieves real-time and integrity of data acquisition through high-frequency data sampling and buffering mechanism; the application of second-order Butterworth filter significantly reduces the noise component in magnetic compass data; PDOP value threshold control ensures that GNSS positioning accuracy meets navigation requirements; UTC-based time synchronization mechanism realizes precise alignment of multi-source data, and the deployment of integrity check algorithm effectively improves data quality, laying a solid foundation for subsequent fusion processing.

[0109] The multi-source data fusion link adopts a multi-level composite filtering strategy. The core implementation includes an extended Kalman filter, which processes the state vector including position, velocity and attitude angle through a 15th-order error state model, and integrates GNSS position, velocity and magnetic compass heading data as observation vectors. In terms of heading data processing, a complementary filter with a cutoff frequency of 0.1 Hz is used to fuse the low-frequency data of the magnetic compass and the high-frequency integral value of the gyroscope, and combined with a temperature compensation mechanism. Map matching is based on the principle of minimum distance from point to curve, and position correction is performed within a 50-meter search radius in combination with road section connectivity constraints and driving direction information.

[0110] The application of the extended Kalman filter in the multi-source data fusion link realizes high-precision estimation of position, speed and attitude, significantly improving the reliability and robustness of the navigation system; the use of complementary filters ensures the smoothness and reliability of heading data; and the map matching based on the minimum distance principle ensures the accurate correspondence between the vehicle position and the actual road, greatly improving the navigation accuracy.

[0111] The navigation solution and output link adopts a dual GPS antenna solution to achieve high-precision positioning, and combines odometer data to realize the Dead Reckoning function. The motion trajectory is smoothed by a particle filter algorithm. In terms of path planning, the optimal path is calculated based on the improved A* algorithm, and the planning strategy can be dynamically adjusted according to real-time traffic information to achieve multi-level path planning.

[0112] The navigation solution and output link uses dual GPS antennas in conjunction with Dead Reckoning technology to significantly improve the accuracy and reliability of positioning, ensuring continuous and stable positioning output; the application of the particle filter algorithm makes the vehicle motion trajectory smoother and more natural; the improved A* algorithm can quickly plan the optimal path and dynamically adjust it according to road conditions, providing a better navigation experience.

[0113] In an open environment, the adaptive optimization mechanism has a GNSS weight of 0.7, an inertial navigation weight of 0.2, and a magnetic compass weight of 0.1; in an urban canyon environment, the GNSS weight is adjusted to 0.3, the inertial navigation weight is 0.5, and the magnetic compass weight is 0.2; in a tunnel environment, it mainly relies on inertial navigation and magnetic compass, with weights of 0.7 and 0.3 respectively, and establishes a sensor error compensation mechanism, including a gyroscope zero bias model, accelerometer calibration compensation, and magnetic compass soft iron compensation, and maintains accuracy through regular online calibration.

[0114] The adaptive optimization mechanism can maintain stable navigation performance in different environments such as open areas, urban canyons and tunnels through dynamic weight adjustment; the perfect error compensation mechanism effectively reduces the error impact of various sensors, and regular online calibration ensures the long-term stability of system performance.

[0115] The real-time navigation service updates location information at a frequency of 10 Hz, provides early warning prompts when approaching critical intersections, and regularly updates arrival time predictions based on real-time traffic conditions. The navigation interface supports 2D / 3D display mode switching, displays traffic information through color coding, provides lane-level navigation guidance, and automatically adjusts the display ratio at critical intersections.

[0116] The real-time navigation service ensures the real-time nature of navigation information through high-frequency location updates and timely warning prompts; the diverse map display modes and intuitive road condition signs provide a clear visual experience; the lane-level navigation and magnified display of key intersections further enhance the accuracy and practicality of navigation, providing users with a comprehensive and convenient navigation service.

[0117] refer to Figure 2 , a map navigation system based on integrated navigation, comprising:

[0118] Compass data acquisition module: collects geomagnetic field data in real time through magnetic sensors, then collects acceleration sensor data for attitude compensation, records sampling timestamp and frequency information, and finally performs preliminary digital filtering and noise reduction processing;

[0119] Magnetic declination correction module: reads the magnetic declination database from the ROM, then queries the local magnetic declination value based on the current position, compensates for hard iron and soft iron interference, then performs tilt compensation processing, and finally outputs the corrected geographic direction data;

[0120] Map integration module: first load the preset electronic map data, then establish the POI marking system, build the road network topology, realize map projection and coordinate conversion, and finally establish the map cache mechanism;

[0121] Navigation system data acquisition module: used to collect inertial navigation basic data, GNSS raw data and integrated navigation data;

[0122] Navigation system data analysis module: including inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation;

[0123] Integrated navigation module: including data fusion preprocessing, multi-source data fusion, navigation solution and output, adaptive optimization mechanism and real-time navigation service.

[0124] The compass data acquisition can accurately obtain the current attitude of the device by collecting geomagnetic field data and acceleration sensor data in real time for attitude compensation, and provide accurate basic data for subsequent magnetic declination correction and integrated navigation. The moving average filtering and abnormal data correction in the data preprocessing link effectively remove random noise and ensure the stability and reliability of the data. The attitude compensation processing eliminates the influence of gravity on the magnetic field measurement and improves the accuracy of the magnetic field measurement. The magnetic declination correction can correct the magnetic declination of the current position in real time through the magnetic declination database stored in the ROM, output the real geographic direction data, and provide accurate direction reference for integrated navigation. The error compensation processing includes hard iron effect compensation, soft iron effect correction and tilt compensation, which further improves the accuracy of magnetic declination correction. The WMM model is used to construct the magnetic declination database, and the basic data is updated every five years to ensure the timeliness and accuracy of the data. The map integration provides a clear spatial reference framework and geographic information support for navigation by presetting the electronic map and establishing the POI marking system. The construction of the road network topology relationship and the realization of map projection and coordinate conversion ensure the accuracy and availability of the map data. The use of the map cache mechanism It improves the loading speed and response performance of map data and reduces the waiting time for users. The navigation system data acquisition provides a rich data source for the subsequent navigation system data analysis by collecting inertial navigation basic data, GNSS raw data and integrated navigation data. The collection methods and standards of various data are clear to ensure the accuracy and reliability of the data. The navigation system data analysis collects inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation, comprehensively evaluates the accuracy and reliability of the navigation system, and divides the navigation system performance into excellent, good, medium and poor by setting thresholds and weight coefficients, providing optimization direction for subsequent integrated navigation. When the navigation system performance is poor, an early warning signal is issued, and the contribution of each sub-item is analyzed, which helps to timely discover and solve problems. The integrated navigation data fusion preprocessing link ensures the real-time, integrity and quality of multi-source data, providing a reliable foundation for subsequent data fusion. The multi-source data fusion link adopts a multi-level composite filtering strategy to achieve high-precision estimation of position, speed and attitude, and improves the reliability and robustness of the navigation system. The navigation solution and output link adopts dual GPS antennas and Dead Reckoning technology improves the accuracy and reliability of positioning. At the same time, the improved A* algorithm can quickly plan the optimal path and dynamically adjust it according to road conditions. The adaptive optimization mechanism can dynamically adjust the sensor weights according to different environments, reducing the error impact of various sensors and ensuring the long-term stability of system performance. The real-time navigation service provides high-frequency location updates, timely warning prompts, diverse map display modes and intuitive road condition signs, providing users with comprehensive and convenient navigation services.

[0125] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0126] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A map navigation method based on integrated navigation, characterized in that: include: Step 1: Compass data acquisition: Obtain raw geomagnetic data through the magnetic sensor to provide input for step 2 and provide direction reference for step 6; The step 1 collects geomagnetic field data in real time through a magnetic sensor, then collects acceleration sensor data for attitude compensation, and records sampling timestamp and frequency information, and finally performs preliminary digital filtering and noise reduction processing; Step 2: Magnetic declination correction: used to receive the raw data from step 1, perform correction using the magnetic declination data stored in the ROM, and output the true geographic direction data for the integrated navigation in step 6; The step 2 reads the magnetic declination database from the ROM, then queries the local magnetic declination value based on the current position, compensates for hard iron and soft iron interference, then performs tilt compensation processing, and finally outputs the corrected geographic direction data; The step 2 is based on the WMM model and constructs a magnetic declination database in the ROM. The database uses a longitude and latitude grid with a 0.5 degree interval to store the magnetic declination value and the annual change rate. Each data point contains four key parameters: longitude, latitude, magnetic declination and annual change rate. The database updates the basic data every five years and uses the annual change rate to perform daily dynamic corrections to ensure data timeliness. For locations outside the grid points, a bilinear interpolation algorithm is used to calculate the accurate magnetic declination value by considering the weighted influence of the four adjacent grid points. The real-time correction process first obtains the geographic location coordinates through GPS. When the GPS signal is unavailable, network positioning is used as an alternative. The obtained location data is smoothed by Kalman filtering to eliminate jumps and noise. Then, the magnetic declination data of the surrounding grid points is queried based on the current location. The precise magnetic declination value of the location is obtained through interpolation. The magnetic declination value is combined with the magnetic sensor data read in real time to obtain the direction angle based on the true north. The output range is 0 to 359.99 degrees; the error compensation process includes three steps. The first is hard iron effect compensation. By recording the data during the 360-degree rotation of the sensor, the eccentricity of the magnetic field intensity ellipse is calculated, and a compensation matrix is ​​established to eliminate fixed offsets. The second is soft iron effect correction. By measuring the magnetic field distortion in different directions, a 3×3 correction matrix is ​​established to correct the sensor data in real time. Finally, tilt compensation is performed. The three-axis data provided by the acceleration sensor is used to calculate the inclination and roll angle of the device, and real-time attitude compensation is performed through the rotation matrix. Step 3: Map integration: As a spatial reference framework for navigation, preset electronic maps and create POI markers to provide geographic information support for step 6, and also provide geographic reference for data collection in step 4; The step 3 first loads the preset electronic map data, then establishes a POI marking system, constructs a road network topology, implements map projection and coordinate conversion, and finally establishes a map cache mechanism; Step 4: Navigation system data collection: Expand the data scope of step 1 for analysis in step 5 and integrated navigation in step 6; The step 4 is used to collect inertial navigation basic data, GNSS raw data and integrated navigation data for analysis in step 5; Step 5: Navigation system data analysis: used to analyze and process the multi-source data collected in step 4, evaluate the system accuracy and reliability, and use the analysis results to optimize the navigation effect of step 6; The step 5 includes inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation, which are used to optimize the navigation effect of step 6; Step 6: Integrated navigation: Use the directional data corrected in step 2, combine it with the map information in step 3, fuse the multi-source data collected in step 4, and optimize it according to the analysis results of step 5 to finally achieve integrated navigation; The step 6 includes data fusion preprocessing, multi-source data fusion, navigation solution and output, adaptive optimization mechanism and real-time navigation service. The data fusion preprocessing link realizes data collection by constructing a data receiving buffer with a sampling frequency of 100Hz at the hardware level, and processes the magnetic compass data with a second-order Butterworth low-pass filter with a cutoff frequency of 5Hz. In terms of GNSS data processing, the system performs PDOP value inspection and sets the threshold to 6, cooperates with the data synchronization mechanism based on the UTC time reference to ensure the time consistency of the data, and identifies and eliminates abnormal data through a preset integrity check algorithm; The multi-source data fusion link adopts a multi-level composite filtering strategy, the core implementation of which includes an extended Kalman filter, which processes state vectors including position, velocity and attitude angle through a 15th-order error state model, and integrates GNSS position, velocity and magnetic compass heading data as observation vectors. In terms of heading data processing, a complementary filter with a cutoff frequency of 0.1 Hz is used to fuse the low-frequency data of the magnetic compass and the high-frequency integral value of the gyroscope, and a temperature compensation mechanism is combined. Map matching is based on the principle of minimum distance from point to curve, and position correction is performed within a 50-meter search radius in combination with road section connectivity constraints and driving direction information; The navigation solution and output link adopts a dual GPS antenna solution to achieve high-precision positioning, and combines odometer data to realize the Dead Reckoning function, and uses a particle filter algorithm to smooth the motion trajectory. In terms of path planning, the optimal path is calculated based on the improved A* algorithm, and the planning strategy can be dynamically adjusted according to real-time traffic information to achieve multi-level path planning; In an open environment, the adaptive optimization mechanism uses a GNSS weight of 0.7, an inertial navigation weight of 0.2, and a magnetic compass weight of 0.1; in an urban canyon environment, the GNSS weight is adjusted to 0.3, the inertial navigation weight is 0.5, and the magnetic compass weight is 0.2; in a tunnel environment, the inertial navigation and magnetic compass are mainly relied on, with weights of 0.7 and 0.3 respectively, and a sensor error compensation mechanism is established, including a gyroscope zero bias model, accelerometer calibration compensation, and magnetic compass soft iron compensation, and accuracy is maintained through regular online calibration; The real-time navigation service updates location information at a frequency of 10 Hz, provides early warning prompts when approaching critical intersections, and regularly updates arrival time predictions based on real-time traffic conditions. The navigation interface supports 2D / 3D display mode switching, displays traffic information through color coding, provides lane-level navigation guidance, and automatically adjusts the display ratio at critical intersections.

2. A map navigation method based on integrated navigation according to claim 1, characterized in that: The step 1 first accesses the internal register of the sensor through the I2C or SPI communication interface, the sampling rate of the magnetic sensor includes 50Hz and 100Hz, and the range of ±8 Gauss and the 16-bit ADC resolution are configured at the same time. The acceleration sensor is configured to the ±2g range and the low-pass filter is enabled to ensure synchronization with the magnetic sensor sampling. Finally, a 2048-byte FIFO circular buffer is established for data temporary storage, and the corresponding overflow processing mechanism and interrupt trigger condition are set; the original ADC values ​​of the magnetic field of the three axes of X, Y, and Z are read from the sensor register and converted into μT units, the three-axis acceleration data are synchronously collected and converted into m / s² units, and the sampling sequence number and microsecond timestamp are recorded each time the data is read; In the data preprocessing stage, a sliding window of 8-16 points is used to implement moving average filtering, which effectively removes random noise while maintaining the fast response characteristics of the signal. By setting a detection threshold of ±3 times the standard deviation, mutation points and outliers are identified, and the detected abnormal data is corrected using the interpolation method. The sensor temperature value is read, and the compensation coefficient is obtained by looking up the table according to the temperature-performance curve, and the temperature linear compensation calculation is performed; the attitude compensation processing first uses the acceleration data to calculate the pitch angle and roll angle of the device through the inverse tangent function, establishes a quaternion describing the current attitude, and then establishes the local coordinate system of the device. The sensor data is converted from the device coordinate system to the geographic coordinate system through the rotation matrix. Finally, the projection of the magnetic field component on the horizontal plane is calculated to eliminate the influence of gravity on the magnetic field measurement, and update the compensated magnetic field vector; During the data output formatting process, the processed magnetic field data is converted into polar coordinates, the magnetic field azimuth is calculated and normalized, the angular resolution is quantified according to actual needs, and then the data is encapsulated into a standard format, frame synchronization words and status information are added, and a checksum is generated. Finally, the information is output to the data queue through a predefined data structure, and the corresponding update flag is set for use by the upper-level application.

3. The map navigation method based on integrated navigation according to claim 1, characterized in that: The step 3 first parses the vector data in Shape and GeoJSON format through the GDAL / OGR library, stores the parsed geographic elements in the spatial data table, and uses a tree structure to manage different layers during data storage. Each layer has a unique identifier, and a dependency relationship is established between layers to achieve on-demand data loading and an incremental update mechanism. The POI tagging system includes data storage and retrieval. The PostGIS spatial database is used for data storage, and a POI classification system and attribute fields are designed, including name, address, and contact information, and a spatial index is added. The retrieval function needs to integrate the Elasticsearch full-text retrieval engine to achieve fuzzy matching and pinyin retrieval functions, and implement range queries based on R-trees to support multi-condition combination screening. The road network topology construction includes the design of data structures and index optimization, using adjacency lists to store the connection relationship of the road network, establishing a mapping relationship between road sections and nodes, and recording turn restriction information and roads, etc. Level and traffic condition attributes. In terms of index optimization, a quadtree spatial index structure is implemented, and a road section fast lookup table is established; coordinate conversion includes two levels: geographic coordinate conversion and screen mapping. In terms of geographic coordinate conversion, the PROJ library is used to implement conversion between different coordinate systems, support common geographic coordinate systems, implement Mercator projection, and handle cross-zone conversion issues. In terms of screen mapping, it is necessary to accurately calculate the scale, handle map rotation transformation, optimize pixel coordinate accuracy, and implement dynamic resolution adaptation; at the memory level, the map cache mechanism uses the LRU algorithm to manage tile data. When the memory cache reaches the preset upper limit, the least accessed data is automatically cleared. The system predicts the tiles that may be needed based on the user's browsing behavior, loads data in advance, and reduces waiting time. At the disk level, persistent tile storage is established, local caches are maintained for frequently visited areas, and repeated network requests are avoided. At the same time, an incremental update mechanism is implemented. When the server data changes, only the changed tiles are updated.

4. The map navigation method based on integrated navigation according to claim 1, characterized in that: The inertial navigation basic data include gyroscope bias drift rate, accelerometer scale factor stability, gyroscope random walk error and accelerometer bias instability, which are marked as GBD, ASF, ARW and ASI respectively. The GNSS raw data include carrier phase measurement noise, multipath delay spread parameter, ionospheric delay gradient and satellite geometric precision factor, which are marked as CPM, MDS, IDG and PDOP respectively. The integrated navigation data include attitude angle error accumulation rate, velocity drift coefficient, position error growth rate and heading angle deviation standard deviation, which are marked as ARE, VDC, PEG and HAD respectively.

5. The map navigation method based on integrated navigation according to claim 1, characterized in that: The inertial navigation basic data analysis specifically includes: Bias Drift Assessment: ; Scaling factor modeling: ; Random error analysis: ; Bias instability: ; δθ(t) represents the gyroscope output angle error, t represents the time variable (h), ε represents the random white noise term, S(t) represents the actual output ratio of the accelerometer, α(t) represents the time-varying coefficient, σ²(t) represents the gyroscope angle random walk variance, B(t) represents the accelerometer zero bias value, and τ represents the zero bias change characteristic time constant; The GNSS data analysis specifically includes: Phase measurement: ; Latency estimation: ; Accuracy assessment: ; R(t) represents the total error of carrier phase measurement, D(t) represents the total delay error, φ represents the satellite elevation angle, σp represents the position accuracy estimate, and σUERE represents the user equivalent ranging error; The integrated navigation performance analysis specifically includes: Attitude error: ; Speed ​​error: ; Position error: ; Heading accuracy: ; θe(t) represents the cumulative attitude error, θ0 represents the initial attitude error, v(t) represents the velocity error, p(t) represents the position error, σH represents the standard deviation of the heading angle, and k represents the time correlation coefficient.

6. A map navigation system based on integrated navigation, used for using the map navigation method based on integrated navigation as claimed in any one of claims 1 to 5, characterized in that: include: Compass data acquisition module: collects geomagnetic field data in real time through magnetic sensors, then collects acceleration sensor data for attitude compensation, records sampling timestamp and frequency information, and finally performs preliminary digital filtering and noise reduction processing; Magnetic declination correction module: reads the magnetic declination database from the ROM, then queries the local magnetic declination value based on the current position, compensates for hard iron and soft iron interference, then performs tilt compensation processing, and finally outputs the corrected geographic direction data; Map integration module: first load the preset electronic map data, then establish the POI marking system, build the road network topology, realize map projection and coordinate conversion, and finally establish the map cache mechanism; Navigation system data acquisition module: used to collect inertial navigation basic data, GNSS raw data and integrated navigation data; Navigation system data analysis module: including inertial navigation basic data analysis, GNSS data analysis, integrated navigation performance analysis and system overall performance evaluation; Integrated navigation module: including data fusion preprocessing, multi-source data fusion, navigation solution and output, adaptive optimization mechanism and real-time navigation service.

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