Magnetic graph drawing system and drawing method based on multi-data fusion

Abstract

The invention provides a magnetic map drawing system and method based on multi-data fusion, and the system comprises a hardware collection layer, a data processing layer, a core algorithm layer and an output layer. The hardware collection layer is used for obtaining magnetic field data and optical positioning base station data in a target magnetic field scene, and transmitting the obtained data to the data processing layer; the data processing layer is used for receiving the magnetic field data and the optical positioning base station data of the hardware acquisition layer and normalizing the acquired data; the core algorithm layer is used for receiving the magnetic field data and the optical positioning base station data normalized by the data processing layer, and generating a magnetic field map and evaluating the quality of the magnetic field map based on the magnetic field data and the optical positioning base station data; and the output layer is used for receiving the magnetic field map output by the core algorithm layer, performing visualization processing, and performing magnetic field navigation positioning of the target area according to the magnetic field data. The invention also provides a drawing method realized by the system. According to the invention, the precision of the drawn magnetic map can be improved.

Description

Technical Field

[0001] This invention relates to the technical field of magnetic field data acquisition, specifically to a magnetic map drawing system based on multi-data fusion and a drawing method implemented by the system. Background Technology

[0002] With the development of indoor navigation, autonomous driving, robot positioning and other fields, traditional GPS satellite navigation faces problems such as signal blockage in complex environments such as indoors, underground and urban canyons, as well as the fact that civilian GPS positioning is easily interfered with and it is difficult to guarantee positioning needs. Therefore, the research on magnetic field navigation systems has received widespread attention.

[0003] Chinese invention patent application CN109633763A discloses a precision geomagnetic mapping system based on a magnetometer and GPS, including a magnetometer, a data acquisition unit, a GPS positioning module, a coordinate unit, a controller, and a data center. The magnetometer detects geomagnetic field information. The data acquisition unit collects the geomagnetic field information detected by the magnetometer and transmits it to the controller. The GPS positioning module synchronously acquires measurement location information. The coordinate unit converts the location information measured by the GPS positioning module into location coordinates and sends the location coordinates to the controller. The controller integrates the geomagnetic field information and location coordinates and transmits the integrated geomagnetic data to the data center. The data center receives the integrated geomagnetic data and uses it as a reference to draw a geomagnetic map, thus completing the mapping.

[0004] However, existing methods for drawing magnetic maps do not take into account the time deviation of different data sources and the magnetic field measurement deviation caused by motion. The data processing is not comprehensive enough, resulting in inaccurate magnetic maps. Summary of the Invention

[0005] The first objective of this invention is to provide a magnetic map drawing system based on multi-data fusion that can improve the accuracy of drawing magnetic maps.

[0006] A second objective of this invention is to provide a drawing method for the above-mentioned magnetograph drawing system based on multi-data fusion.

[0007] To achieve the aforementioned first objective, the magnetic map mapping system based on multi-data fusion provided by this invention includes a hardware acquisition layer, a data processing layer, a core algorithm layer, and an output layer: The hardware acquisition layer is used to acquire magnetic field data and optical positioning base station data in the target magnetic field scene, and sends the acquired data to the data processing layer; The data processing layer is used to receive the magnetic field data and optical positioning base station data from the hardware acquisition layer, and perform normalization processing on the acquired data; The core algorithm layer is used to receive the normalized magnetic field data and optical positioning base station data from the data processing layer, generate and evaluate the quality of the magnetic field map based on the magnetic field data and optical positioning base station data, and transmit the magnetic field map generated from the magnetic field data that has passed the quality evaluation to the output layer; Resampling determination and subsequent path planning are performed based on the magnetic field data that has not passed the quality evaluation, and acquisition instructions are sent to the hardware acquisition layer; The output layer is used to receive the magnetic field map output by the core algorithm layer and perform visualization processing, and perform magnetic field navigation and positioning of the target area based on the magnetic field data.

[0008] As can be seen from the above scheme, this invention maintains the phase consistency of non-uniformly sampled data by constructing a multi-source data spatiotemporal alignment module, and solves the problem of magnetic field measurement distortion caused by carrier motion by fusing inertial data and optical positioning information to establish a six-degree-of-freedom motion compensation model. Furthermore, this invention improves the accuracy of magnetic field map creation by introducing Maxwell's equations as hard constraints in the magnetic map generation to achieve joint constraints of multiple physics fields.

[0009] A preferred approach is that the hardware acquisition layer is also used to receive resampling instructions from the core algorithm layer to perform supplementary sampling on the magnetograph drawing system.

[0010] A further approach involves a hardware acquisition layer comprising: a magnetic field sensor for generating magnetic field data; and an optical positioning base station for generating optical positioning data, which includes accelerometer data, positioning information, and attitude information.

[0011] A further proposed solution is that the data processing layer includes: a multi-source data spatiotemporal alignment module, used to align the timestamp information of the optical positioning base station and the magnetic field sensor; a multi-coordinate dynamic transformation module, used to transform the magnetic field data from the carrier coordinate system to the global coordinate system, and to transform the attitude information obtained by the optical positioning base station to the global coordinate system; and an intelligent data filtering module, used to remove abnormal magnetic field data, dynamically optimize the quality of the input data, and transmit the processed data to the core algorithm layer.

[0012] A further approach is to integrate the converted data using the multi-coordinate dynamic transformation module, establish a motion compensation model, and compensate for the impact of carrier motion on magnetic field measurement.

[0013] A further solution is that the multi-source data spatiotemporal alignment module includes: a dynamic frequency adaptive unit, used to receive magnetic field data and optical positioning data from the hardware acquisition layer, perform time window alignment and bidirectional linear interpolation calculation, and complete the alignment operation of magnetic field data and optical positioning data in the time dimension; and a timestamp anomaly compensation unit, used to correct the time deviation between magnetic field data and optical positioning data.

[0014] A further solution is that the multi-coordinate dynamic transformation module includes: an attitude decoupling transformation unit, used to transform the carrier coordinate system data measured by the magnetic field sensor to the global coordinate system, and to transform the data of the optical positioning base station to the global coordinate system; a motion compensation unit, used to compensate for the error caused by the measurement carrier to the magnetic field measurement when it is moving; and a multi-dimensional anomaly detection unit, used to realize the joint detection of spatial dimension detection, statistical dimension detection and motion dimension detection, and realize intelligent anomaly data filtering.

[0015] A further approach involves a core algorithm layer comprising: a magnetic map generator, which receives magnetic field data and optical positioning base station data output from the intelligent data filtering module of the data processing layer and generates a magnetic field map; a quality evaluator, which evaluates the quality of the generated magnetic field map and transmits qualified magnetic field maps to the output layer; and a resampling unit, which receives the resampling signal output from the quality evaluator, performs sampling area calculation and path planning, and transmits the resampling signal to the hardware acquisition layer so that the hardware acquisition layer can resample the data.

[0016] A further approach is to include an output layer comprising: a vector map display for receiving and visualizing the magnetic field map from the core algorithm layer; and a navigation and positioning interface for acquiring magnetic field data of the target location in the magnetic field navigation algorithm.

[0017] To achieve the second objective mentioned above, the magnetic map drawing method based on multi-data fusion provided by the present invention includes: acquiring magnetic field data and optical positioning base station data in the target magnetic field scene by a hardware acquisition layer; receiving the magnetic field data and optical positioning base station data from the hardware acquisition layer by a data processing layer, and performing normalization processing on the acquired data; receiving the normalized magnetic field data and optical positioning base station data from the data processing layer by a core algorithm layer, generating and quality-evaluating a magnetic field map based on the magnetic field data and optical positioning base station data, and transmitting the magnetic field map generated from the magnetic field data that has passed the quality evaluation to the output layer; performing resampling judgment and subsequent path planning based on the magnetic field data that has not passed the quality evaluation; and receiving the magnetic field map output by the core algorithm layer and performing visualization processing by the output layer, and performing magnetic field navigation and positioning of the target area based on the magnetic field data. Attached Figure Description

[0018] Figure 1This is a structural block diagram of an embodiment of the magnetic map drawing system based on multi-data fusion of the present invention.

[0019] Figure 2 This is a block diagram illustrating the software and hardware integration of an embodiment of the magnetic map drawing system based on multi-data fusion of the present invention.

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0021] The magnetic map drawing system based on multi-data fusion of the present invention realizes the drawing of magnetic maps by fusing data obtained from multiple data sources, and the magnetic map drawing method based on multi-data fusion of the present invention realizes the drawing of magnetic maps based on the above-mentioned system.

[0022] Example of a magnetograph mapping system based on multi-data fusion: See Figure 1 The magnetic map mapping system based on multi-data fusion in this embodiment includes a hardware acquisition layer 10, a data processing layer 20, a core algorithm layer 30, and an output layer 40. The hardware acquisition layer 10 includes a magnetic field sensor 11 and an optical positioning base station 12. The magnetic field sensor 11 collects magnetic field data, including the X-axis value MagneticField_X, the Y-axis value MagneticField_Y, and the Z-axis value MagneticField_Z. The optical positioning base station 12 collects optical positioning data, including accelerometer data, positioning information, and attitude information. The accelerometer data includes the X-axis value Accel_X, the Y-axis value Accel_Y, and the Z-axis value Accel_Z. The positioning information includes the X-axis information True_X, the Y-axis information True_Y, and the Z-axis information True_Z of the optical positioning base station 12. The attitude information includes the attitude quaternions of the optical positioning base station 12, namely Orientation_X, Orientation_Y, Orientation_Z, and Orientation_W.

[0023] The data processing layer 20 includes a multi-source data spatiotemporal alignment module 21, a multi-coordinate dynamic transformation module 22, and an intelligent data filtering module 23. (See also...) Figure 2 The multi-source data spatiotemporal alignment module 21 includes a dynamic frequency adaptive unit 212 and a timestamp anomaly compensation unit 211. The dynamic frequency adaptive unit 212 includes two algorithm modules: time window alignment and bidirectional linear interpolation. The timestamp anomaly compensation unit 211 includes two modules: time deviation model and least squares method. The multi-coordinate dynamic transformation module 22 includes an attitude decoupling transformation unit 221 and a motion compensation unit 222. The intelligent data filtering module 23 includes an environmental interference suppression unit 231 and a multi-dimensional anomaly detection unit 232.

[0024] The core algorithm layer 30 includes a magnetic map generator 31, a quality evaluator 32, and a resampling unit 33. The magnetic map generator 31 includes a multi-resolution fusion unit 311 and a vector field kriging interpolation unit 312. The quality evaluator 32 includes a residual distribution heatmap 322 and a resampling decision mechanism 321. The resampling unit 33 includes a sampling region generation unit 331 and a path planning unit 332.

[0025] The output layer 40 includes a vector magnetograph display 41 and a navigation and positioning interface 42. The vector magnetograph display 41 includes a discrete sampling unit 412 and a visualization unit 411. The navigation and positioning interface 42 is provided with a magnetic field data conversion unit.

[0026] This embodiment achieves magnetic field data acquisition and magnetic map drawing through an ordered hardware acquisition layer 10, data processing layer 20, core algorithm layer 30, and output layer 40. The hardware acquisition layer 10 transmits the acquired data to the data processing layer 20, which performs relevant data processing on the data and transmits the processed data to the core algorithm layer 30. The core algorithm layer 30 draws the magnetic field map based on the data sent by the data processing layer 20 and transmits it to the output layer 40. The core algorithm layer 30 transmits the magnetic field map information requiring resampling to the hardware acquisition layer 10 for data resampling. The output layer 40 provides a visual output of the high-precision magnetic field map.

[0027] Specifically, the hardware acquisition layer 10 includes a magnetic field sensor 11 and an optical positioning base station 12. The magnetic field sensor 11 is used to collect magnetic field data and transmit it to the data processing layer 20. The optical positioning base station 12 transmits magnetic field data 15, accelerometer data 16, positioning information 17 and attitude information 18 to the data processing layer 20.

[0028] The data processing layer 20 includes a multi-source data spatiotemporal alignment module 21, a multi-coordinate dynamic transformation module 22, and an intelligent data filtering module 23. The multi-source data spatiotemporal alignment module 21 aligns the magnetic field data 15 and optical positioning data from the hardware acquisition layer 10 in both time and space, and then transmits the aligned data to the multi-coordinate dynamic transformation module 22. The multi-source data spatiotemporal alignment module 21 includes a dynamic frequency adaptive unit 211 and a timestamp anomaly compensation unit 211. The dynamic frequency adaptive unit 211 aligns the magnetic field data 15 and optical positioning data from the hardware acquisition layer 10 within a time window, and then performs bidirectional linear interpolation to achieve adaptive dynamic alignment. The timestamp anomaly compensation unit 211 corrects the time deviation model and performs least-squares fitting of the coefficients of the time-window aligned magnetic field data and optical positioning data to compensate for abnormal data. The multi-coordinate dynamic transformation module 22 includes an attitude decoupling transformation unit 221 and a motion compensation unit 222. The attitude decoupling transformation unit 221 is used to decouple the spatiotemporally aligned magnetic field data 15 and optical positioning data, thereby unifying the magnetic field data 15 and optical positioning data in the coordinate system. The motion compensation unit 222 is used to compensate the magnetic field data and optical positioning data after coordinate system unification, solving the problem of magnetic field measurement distortion caused by carrier motion. The intelligent data filtering module 23 includes a multi-dimensional anomaly detection unit 232 and an environmental interference suppression unit 231. The multi-dimensional anomaly detection unit 232 is used to perform spatial dimension detection, statistical dimension detection, and motion dimension detection on the motion-compensated magnetic field data and optical positioning data, thereby achieving intelligent anomaly data filtering of the target data in three dimensions. The environmental interference suppression unit 231 is used to eliminate the influence of periodic electromagnetic interference in the environment on magnetic field measurement, achieving accurate noise reduction through a dual mechanism of frequency domain analysis and adaptive filtering.

[0029] Furthermore, the dynamic frequency adaptive unit 211 receives magnetic field data 15 and optical positioning data from the hardware acquisition layer 10, and performs time window alignment and bidirectional linear interpolation algorithms to complete the alignment operation of the magnetic field data and optical positioning data in the time dimension. In this embodiment, the time window alignment operation adopts a sliding window dynamic resampling strategy, defining... Its formula is ,in The sampling frequency of the magnetic field sensor 11 The sampling frequency for optical positioning base station 12. Bidirectional linear interpolation is used to construct synchronization timing, and its expression formula is: ,in Optimization was achieved through Lomb-Scargle periodogram analysis. The synchronized magnetic field vector value. For the target synchronized time series, For the original magnetic field sensor 11 at time 11 The measured value, This is the original sampling time. For the interpolation kernel function bandwidth, It is a nearest neighbor index.

[0030] The timestamp anomaly compensation unit 211 is used to correct the time deviation between magnetic field data and optical positioning data. It has two modules: a time deviation model and a least squares method. The formula for the time deviation model is as follows: ,in This represents the instantaneous deviation between the magnetic field data time and the optical positioning time. This represents the initial time deviation amplitude. This is the deviation attenuation coefficient. γ represents the continuous running time and the steady-state time deviation. The least squares method is used to fit the data in the time deviation model, solve for the compensation coefficient, and complete the timestamp anomaly compensation.

[0031] The attitude decoupling transformation unit 221 is used to transform the carrier coordinate system data measured by the magnetic field sensor 11 to the global coordinate system, and to transform the data from the optical positioning base station 12 to the global coordinate system, completing coordinate system unification and attitude decoupling. This eliminates the influence of carrier rotation on magnetic field measurement, making the magnetic field data reflect the distribution of the environmental magnetic field and independent of the sensor's own orientation. Specifically, decoupling is achieved using the quaternion chain differential method, expressed as follows: Where q is the unit quaternion provided by the optical positioning base station 12. For the conjugate of quaternions, The magnetic field vector in the global coordinate system. This refers to the magnetic field vector in the carrier coordinate system, i.e., the original coordinate system of the magnetic field data. For quaternion multiplication, it represents the composition of rotation operations.

[0032] The motion compensation unit 222 is used to compensate for errors in magnetic field measurement caused by the movement of the measuring carrier. Its core function lies in establishing the acceleration compensation term, expressed by the following formula: ,in The measured magnetic field value is after motion compensation. The original magnetic field sensor 11 measured the value. denoted as vacuum permeability, a as carrier acceleration, and r as sensor mounting eccentricity.

[0033] The multi-dimensional anomaly detection unit 232 is used for joint detection in three dimensions to achieve intelligent anomaly data filtering. Specifically, the multi-dimensional anomaly detection unit 232 is used to perform detection in three dimensions: spatial dimension detection, statistical dimension detection, and motion dimension detection. Spatial dimension detection is magnetic anomaly threshold detection, and a magnetic field gradient threshold is set. It identifies regions of drastic local magnetic field changes and effectively filters abrupt data caused by temporary interference from ferromagnetic objects. The statistical dimension detection is performed using a dynamic statistical filter, expressed as follows: Q1 is the first quartile, which is the value at the 25th percentile of the data, and Q3 is the third quartile, which is the value at the 75th percentile of the data. The criteria can capture global anomalies outside the Gaussian distribution (such as sensor failure and strong electromagnetic interference), the IQR box method can suppress local outliers in non-Gaussian distributions (such as instantaneous motion interference and impulse noise), and the dual statistical verification mechanism ( The criteria, combined with the IQR box method, can ensure the reasonableness of data distribution and adaptively update the mean. ) and standard deviation ( To cope with the time-varying characteristics of the environmental magnetic field, and simultaneously detect global anomalies. Criteria) and local anomalies (IQR method). The motion detection dimension is the motion state discriminator, which sets an angular velocity threshold: It can identify the state of violent rotation of the carrier, and once the state of violent rotation of the carrier is determined, it triggers data freeze to realize the anomaly detection in the motion dimension.

[0034] The environmental interference suppression unit 231 is used to eliminate the influence of periodic electromagnetic interference in the environment on magnetic field measurements, achieving precise noise reduction through a dual mechanism of frequency domain analysis and adaptive filtering. Frequency domain analysis involves constructing a frequency domain feature matrix, expressed by the following formula: , Where B(n) is the original magnetic field strength value collected by the sensor at the nth point, and the adaptive filtering is a power frequency notch filter bank with adaptive switching of 50 / 60Hz, which eliminates periodic electromagnetic interference in the environment by dynamically configuring the filter parameters.

[0035] The core algorithm layer 30 includes a magnetic map generator 31, a quality evaluator 32, and a resampling unit 33. The magnetic map generator 31 includes a vector field kriging interpolation unit 312 and a multi-resolution fusion unit 311. The vector field kriging interpolation unit 312 is used to reconstruct an accurate magnetic field map from sparsely sampled magnetic field data and optical positioning data from the data processing layer 20. The multi-resolution fusion unit 311 is used to perform layered modeling of the accurately reconstructed magnetic field map from the vector field kriging interpolation unit 312, achieving a unified global information and local details of the magnetic field map. The quality evaluator 32 includes a residual distribution heatmap 322 and a resampling decision mechanism 321. The residual distribution heatmap 322 is used to calculate the error between the vector-fitted magnetic map from the magnetic map generator and the discrete magnetic field map from the intelligent data filtering module 23. The resampling decision mechanism 321 is used to transmit magnetic field maps with errors less than the error threshold in the residual heatmap to the output layer 40, and magnetic field maps with errors greater than the error threshold to the resampling unit 33. The resampler 33 includes a sampling area generation unit 331 and a path planning unit 332. The sampling area generation unit 331 is used to generate a target sampling area based on a local area of ​​the magnetic field map with large errors. The path planning unit 332 is used to generate a sampling path of the magnetic field based on the target sampling area and transmit it to the hardware acquisition layer 10 to complete the resampling and supplementary sampling operations.

[0036] Specifically, the vector field kriging interpolation unit 312 is used to achieve accurate magnetic field map reconstruction and prediction based on sparse sampling point data. Its core idea lies in establishing an anisotropic covariance function. ,in Spatial position interval is The covariance between the two magnetic field vectors at two points This represents the prior variance of the magnetic field strength (reflecting the spatial fluctuations of the magnetic field). It is a three-dimensional spatial interval vector (the coordinate difference between two points). , , These are the anisotropic correlation length (reflecting the rate of decay of spatial correlation of the magnetic field in the x / y / z directions), and parameters. Automatic optimization is achieved through variational methods.

[0037] The multi-resolution fusion unit 311 is used to perform layered modeling of the accurately reconstructed magnetic field map from the vector field Kriging interpolation unit 312, achieving a unification of global information and local details in the magnetic field map. Layered modeling includes two methods: a coarse-grid layer (1m*1m) and a fine-grid layer (0.2m*0.2m). The coarse-grid layer uses RBF (radial basis function) global fitting, while the fine-grid layer uses an improved Kriging local optimization. After layered modeling, a regularization constraint term is introduced to ensure that the reconstruction result simultaneously conforms to mathematical optimality and physical reality. The constraint formula is as follows: ,in Ensure the smoothness of the solution. The constrained curl field conforms to Maxwell's equations. Forced to satisfy the non-dispersion property of the magnetic field.

[0038] The residual distribution heatmap 322 is used for visual analysis of the error between the reconstructed magnetic field map data and the actual sampled magnetic field map data. The error analysis formula is as follows: ,in For the reconstructed magnetic field map data of coordinate point (x, y), For the actual sampled magnetic field map data of the coordinate point (x, y), This represents the error at the coordinate point (x, y).

[0039] The resampling determination mechanism 321 is used to determine whether the magnetic field data needs to be resampled based on the error between the reconstructed magnetic field map data and the actual sampled magnetic field map data in the residual distribution heatmap 322. Preferably, the determination criterion is: when the local error is less than 0.3 μT, the magnetic field map data is transmitted to the output layer 40; when the local error is greater than 0.3 μT, a resampling signal is transmitted to the resampler 33.

[0040] The sampling area generation unit 331 of the resampler is used to generate a local resampled area of ​​the magnetograph measurement area after receiving the resampled signal. The path planning unit 332 is used to receive the target sampling area and complete the corresponding path planning, and transmit the path planning signal to the hardware acquisition layer 10 to complete the data resampling.

[0041] The output layer 40 includes a vector map display 41 and a navigation and positioning interface 42. The vector map display 41 includes a discrete sampling unit 412 and a visualization unit 411. The discrete sampling unit 412 discretizes the vector magnetic field map from the quality evaluator 32 according to different resolution requirements. The visualization unit 411 visualizes the magnetic field map. The navigation and positioning interface 42 is used to input target location information to obtain magnetic field data for the target location, providing accurate and continuous magnetic field information for the magnetic field navigation algorithm. The magnetic field data conversion unit is also used to input target location information to obtain magnetic field data for the target location, providing accurate and continuous magnetic field information for the magnetic field navigation algorithm.

[0042] Example of a magnetograph plotting method based on multi-data fusion: The following describes the method for magnetic map drawing using the multi-data fusion-based magnetic map drawing system described above. First, the multi-source data spatiotemporal alignment module 21 defines the time window and then performs bidirectional linear interpolation to achieve frequency adaptation. Next, time deviation correction and least-squares fitting calculations are performed on the magnetic field data and optical positioning data to compensate for outliers in the timestamps, completing the spatiotemporal alignment of the multi-source data. Then, the magnetic field data and optical positioning data are transmitted to the multi-coordinate dynamic transformation module 22, which performs attitude decoupling transformation and motion compensation calculations on the magnetic field data and optical positioning data to unify their coordinate systems. Finally, the data is transmitted to the intelligent data filtering module 23, which performs multi-dimensional anomaly detection on the magnetic field data and optical positioning data in spatial, statistical, and motion dimensions. The environmental interference suppression unit 231 then eliminates the influence of periodic electromagnetic interference on the magnetic field measurement. Finally, the data is transmitted to the core algorithm layer 30 to generate the magnetic field map.

[0043] After receiving magnetic field data and optical positioning data from the data processing layer 20, the magnetic map generator 31 and quality evaluator 32 in the core algorithm layer 30 first perform accurate magnetic field map reconstruction and prediction using the vector field kriging interpolation unit 312 of the magnetic map generator 31. Then, the reconstructed vector magnetic field map is modeled in layers of coarse and fine grids, and regularization constraints are introduced to achieve multi-resolution fusion of the vector magnetic field map, making the reconstructed vector magnetic field map information uniform and conforming to mathematical and physical laws. Then, the vector magnetic map generated by the magnetic map generator 31 is compared with the magnetic field data from the data processing layer 20 for quality evaluation, and a residual distribution heatmap 322 is generated. Then, based on the error threshold in the residual distribution heatmap 322, resampling is determined. Magnetic field maps with errors less than the error threshold are transmitted to the output layer 40, and local areas of magnetic field maps with errors greater than the error threshold are transmitted to the resampler 33. The magnetic field maps transmitted to the resampler 33 are used for sampling area generation and path planning, and the planned target path is transmitted to the hardware acquisition layer 10 to complete the resampling.

[0044] Finally, the output layer 40 receives the vector magnetic map from the core algorithm layer 30. Specifically, the vector magnetic map display 41 receives the vector magnetic map from the core algorithm layer 30, performs discretization sampling according to different resolution requirements, and then completes the visualization of the magnetic map. Then, the navigation and positioning interface 42 obtains the magnetic field data of the target location from the input target location information, providing accurate and continuous magnetic field information for the magnetic field navigation algorithm.

[0045] Therefore, this invention maintains phase consistency of non-uniformly sampled data through a multi-source data spatiotemporal alignment module, and solves the problem of magnetic field measurement distortion caused by carrier motion by fusing inertial data and optical positioning information to establish a six-degree-of-freedom motion compensation model. Furthermore, this invention introduces Maxwell's equations as hard constraints in magnetic map generation to achieve joint constraints of multiple physics fields, improving the accuracy of magnetic field map creation. Thus, this invention can generate images of both static and dynamic scenes in a laboratory environment. In static scenes, the positioning accuracy is better than 0.006m (3σ), while in dynamic scenes, the real-time positioning error is less than 0.01m. In addition, this invention also achieves a breakthrough in magnetic field reconstruction resolution, controlling the residual within 0.2μT at a 0.1m grid. Therefore, applying the method of this invention can improve the accuracy of the generated magnetic maps, providing accurate and continuous magnetic field information for magnetic field navigation algorithms.

[0046] Finally, it should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A magnetic map drawing system based on multi-data fusion, characterized in that, It includes a hardware acquisition layer, a data processing layer, a core algorithm layer, and an output layer: The hardware acquisition layer is used to acquire magnetic field data and optical positioning base station data in the target magnetic field scene, and send the acquired data to the data processing layer; The data processing layer is used to receive the magnetic field data and optical positioning base station data from the hardware acquisition layer, and to perform normalization processing on the acquired data. The core algorithm layer is used to receive the magnetic field data and the optical positioning base station data after normalization processing by the data processing layer, generate and evaluate the quality of the magnetic field map based on the magnetic field data and the optical positioning base station data, and transmit the magnetic field map generated by the magnetic field data that has passed the quality evaluation to the output layer. Based on the magnetic field data that failed the quality assessment, resampling determination and subsequent path planning are performed, and the acquisition command is sent to the hardware acquisition layer; The output layer is used to receive the magnetic field map output by the core algorithm layer, perform visualization processing, and perform magnetic field navigation and positioning of the target area based on the magnetic field data.

2. The magnetic map drawing system based on multi-data fusion according to claim 1, characterized in that: The hardware acquisition layer is also used to receive resampling instructions from the core algorithm layer and to perform supplementary sampling on the magnetograph drawing system.

3. The magnetograph mapping system based on multi-data fusion according to claim 1 or 2, characterized in that, The hardware acquisition layer includes: A magnetic field sensor is used to generate the magnetic field data; An optical positioning base station is used to generate optical positioning data, which includes accelerometer data, positioning information, and attitude information.

4. The magnetic map drawing system based on multi-data fusion according to claim 3, characterized in that, The data processing layer includes: A multi-source data spatiotemporal alignment module is used to align the timestamp information of the optical positioning base station and the magnetic field sensor; A multi-coordinate dynamic transformation module is used to transform the magnetic field data from the carrier coordinate system to the global coordinate system, and to transform the attitude information obtained by the optical positioning base station to the global coordinate system; The intelligent data filtering module is used to remove abnormal magnetic field data and dynamically optimize the quality of input data, and transmit the processed data to the core algorithm layer.

5. The magnetic map drawing system based on multi-data fusion according to claim 4, characterized in that: The multi-coordinate dynamic conversion module also fuses the converted data to establish a motion compensation model and compensate for the influence of carrier motion on magnetic field measurement.

6. The magnetic map drawing system based on multi-data fusion according to claim 4, characterized in that, The multi-source data spatiotemporal alignment module includes: The dynamic frequency adaptive unit is used to receive magnetic field data and optical positioning data from the hardware acquisition layer, perform time window alignment and bidirectional linear interpolation calculation, and complete the alignment operation of magnetic field data and optical positioning data in the time dimension. The timestamp anomaly compensation unit is used to correct the time deviation between magnetic field data and optical positioning data.

7. The magnetic map drawing system based on multi-data fusion according to claim 4, characterized in that, The multi-coordinate dynamic transformation module includes: The attitude decoupling conversion unit is used to convert the carrier coordinate system data measured by the magnetic field sensor to the global coordinate system, and to convert the data of the optical positioning base station to the global coordinate system; The motion compensation unit is used to compensate for errors in magnetic field measurement caused by the movement of the measuring carrier; The multi-dimensional anomaly detection unit is used to achieve joint detection of spatial dimension detection, statistical dimension detection and motion dimension detection, so as to realize intelligent anomaly data filtering.

8. The magnetograph mapping system based on multi-data fusion according to claim 4, characterized in that, The core algorithm layer includes: A magnetic map generator is used to receive magnetic field data and optical positioning base station data output by the intelligent data filtering module of the data processing layer, and generate a magnetic field map. A quality evaluator is used to evaluate the quality of the generated magnetic field map and transmit the qualified magnetic field map to the output layer. The resampler is used to receive the resampling signal output by the quality evaluator, perform sampling area calculation and path planning, and transmit the resampling signal to the hardware acquisition layer so that the hardware acquisition layer can perform data resampling.

9. The magnetic map drawing system based on multi-data fusion according to claim 1 or 2, characterized in that, The output layer includes: A vector map display is used to receive the magnetic field map of the core algorithm layer and perform visualization processing. The navigation and positioning interface is used to obtain the magnetic field data of the target location in the magnetic field navigation algorithm.

10. A method for drawing magnetographs based on multi-data fusion, characterized in that, include: The hardware acquisition layer acquires magnetic field data and optical positioning base station data from the target magnetic field scene. The data processing layer receives the magnetic field data and optical positioning base station data from the hardware acquisition layer and performs normalization processing on the acquired data. The core algorithm layer receives the magnetic field data and the optical positioning base station data after they have been normalized by the data processing layer. Based on the magnetic field data and the optical positioning base station data, it generates and evaluates the quality of the magnetic field map. The magnetic field map generated from the magnetic field data that has passed the quality evaluation is then transmitted to the output layer. Resampling determination and subsequent path planning are based on magnetic field data that failed the quality assessment; The output layer is used to receive the magnetic field map output by the core algorithm layer and perform visualization processing, and to perform magnetic field navigation and positioning of the target area based on the magnetic field data.

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