Method for constructing dynamic geomagnetic positioning reference library, geomagnetic positioning method, device, storage medium and product

Abstract

The present application discloses a method for constructing a dynamic geomagnetic positioning reference library, a geomagnetic positioning method, a device, a storage medium, and a product, which relate to the technical field of geomagnetic matching positioning. The method for constructing the dynamic geomagnetic positioning reference library includes: obtaining a measured line magnetic field value signal, which is obtained by a MEMS magnetic measurement device; performing blind source separation and noise reduction processing on the measured line magnetic field value signal, and then performing matching feature point processing to obtain a roadway geomagnetic matching feature data set; respectively performing proportional extension according to the placement height and moving speed of the measurement device to obtain a first dynamic geomagnetic feature point set and a second dynamic geomagnetic feature point set; combining the roadway geomagnetic matching feature data set, the first dynamic geomagnetic feature point set, and the second dynamic geomagnetic feature point set into a dynamic geomagnetic positioning reference library. The present application uses a MEMS magnetic measurement device to obtain a measured line magnetic field value signal, and can achieve accurate dynamic geomagnetic positioning in a small indoor area at low cost.

Description

Technical Field

[0001] This application relates to the technical field of geomagnetic matching positioning, and particularly to a method for constructing a dynamic geomagnetic positioning reference library, a geomagnetic positioning method, a device, a storage medium, and a product. Background Art

[0002] With the development and transformation of positioning technology towards ubiquity, continuity, precision, and intelligence, indoor autonomous positioning technology based on MEMS (Micro ElectroMechanical Systems) has become a hot research direction. Low-cost MEMS IMU (Inertial Measurement Unit) sensors are similar to mobile phone integrated sensors (including magnetometers, accelerometers, and gyroscopes), and are convenient and fast for data acquisition. They are important data acquisition devices for future PDR positioning, geomagnetic matching positioning, inertial integrated positioning, and other methods. However, how to achieve rapid magnetic data acquisition and database construction, accurate extraction of magnetic sequence features, and matching positioning for such devices has always been a key point that needs to be broken through.

[0003] The geomagnetic reference library is the basis for realizing geomagnetic positioning. During the positioning process, a magnetometer is used to measure geomagnetic data in real time, and it is compared with a pre-constructed geomagnetic reference map to determine the user's location information. Currently, the construction methods of the geomagnetic reference library are divided into two categories: static single-point acquisition and dynamic sequence acquisition.

[0004] The static single-point acquisition method is relatively mature. For a small indoor area, at regular grid points with a certain width (1 meter, 2 meters) in the area division, a static magnetic measurement device is used to repeatedly measure all grid points. After determining the geomagnetic values of the grid points, a certain mathematical method, such as linear interpolation, polynomial interpolation, Kriging interpolation, etc., is selected to interpolate the magnetic data of the grid points, and then a geomagnetic database for regional geomagnetic matching positioning is generated. This construction method is time-consuming and laborious, not conducive to the update of the geomagnetic fingerprint library, and has low practicability. At the same time, when a real pedestrian performs geomagnetic matching positioning and obtains the geomagnetic value of the passing path under dynamic conditions, the observed value will include various uncertain noises such as walking steps and posture changes, resulting in a low matching probability.

[0005] The construction of a dynamic geomagnetic reference library is an act of quickly acquiring geomagnetic data during movement when a person carries geomagnetic measurement devices such as a handheld magnetic field tester and a magnetic field sensor under dynamic positioning conditions. Currently, there is no well-defined method for constructing a dynamic geomagnetic database. Zhang Yongwang et al. used an unmanned aerial vehicle (UAV) for low-altitude magnetic survey, equipped with a Canadian GTK-R15 rubidium optical pumping magnetometer, and conducted dynamic magnetic measurements at a speed of 22.4 m / s, enabling the rapid construction of a geomagnetic fingerprint library. This method is applicable to exploration blind areas with dangerous indoor terrains. For small indoor areas, it greatly increases the operation difficulty of the UAV, and the accuracy of the indoor IMU device is relatively low. The magnetic measurement values are extremely susceptible to changes in the flight angle of the UAV, and its applicability is extremely weak. Kuang Jian et al. proposed using two IMU devices for data collection, which were respectively fixed on the feet and back. Through control point correction and reverse smoothing algorithms, the collected data was corrected and denoised. However, it has high requirements for equipment, requiring the integration of a GNSS board and a high-performance IMU combination. In addition, a thoracolumbar fixation bracket needs to be added to fix the equipment posture, making data collection relatively inconvenient. Summary of the Invention

[0006] The purpose of this application is to provide a method for constructing a dynamic geomagnetic positioning reference library, a geomagnetic positioning method, a device, a storage medium, and a product, which can achieve accurate dynamic geomagnetic positioning in small indoor areas at low cost.

[0007] To achieve the above purpose, this application provides the following solutions:

[0008] In the first aspect, this application provides a method for constructing a dynamic geomagnetic positioning reference library, and the method for constructing a dynamic geomagnetic positioning reference library includes:

[0009] Obtain a magnetic field value signal of a survey line; the magnetic field value signal of the survey line is obtained by a MEMS-based magnetic measurement device; the magnetic field value signal of the survey line includes: the placement height of the measurement device during measurement, the moving speed of the measurement device, and magnetic field data; the magnetic field data includes: the spatial point coordinate position and the magnetic field value at the corresponding position;

[0010] Perform blind source separation and noise reduction processing on the magnetic field value signal of the survey line to obtain noise-reduced magnetic data;

[0011] Perform matching feature point processing on the noise-reduced magnetic data to obtain a roadway geomagnetic matching feature data set;

[0012] According to the placement height of the measurement device during measurement, extend the roadway geomagnetic matching feature data set proportionally up and down to obtain a set of geomagnetic matching survey line feature points with height changes, and obtain a first dynamic geomagnetic feature point set;

[0013] According to the moving speed of the measurement device, the roadway geomagnetic matching feature data set is stretched and extended proportionally to obtain a set of geomagnetic matching survey line feature points with speed variation, thereby obtaining a second set of dynamic geomagnetic feature points;

[0014] Combine the roadway geomagnetic matching feature data set, the first set of dynamic geomagnetic feature points, and the second set of dynamic geomagnetic feature points into a dynamic geomagnetic positioning reference library; the dynamic geomagnetic positioning reference library is used for dynamic geomagnetic positioning in a small indoor area.

[0015] Optionally, performing matching feature point processing on the noise-reduced magnetic data to obtain a roadway geomagnetic matching feature data set, specifically including:

[0016] Using the moving window extreme value method to extract the feature points of the magnetic data of each survey line in the noise-reduced magnetic data, and assigning an attribute weight to each obtained feature point to obtain a first set of feature points to be matched;

[0017] Using the normal vector angle mean method to extract the feature points of the magnetic data of each survey line in the noise-reduced magnetic data, and assigning an attribute weight to each obtained feature point to obtain a second set of feature points to be matched;

[0018] Merge the first set of feature points to be matched and the second set of feature points to be matched to obtain a roadway geomagnetic matching feature data set.

[0019] Optionally, the attribute weight of each feature point in the first set of feature points to be matched is assigned a value of 2;

[0020] The attribute weight of each feature point in the second set of feature points to be matched is assigned a value of -1.

[0021] Optionally, after merging the first set of feature points to be matched and the second set of feature points to be matched to obtain a roadway geomagnetic matching feature data set, it further includes:

[0022] When the number of feature points in the roadway geomagnetic matching feature data set is less than the first preset threshold, perform linear interpolation on the corresponding sequence of the roadway geomagnetic matching feature data set;

[0023] When the number of feature points in the roadway geomagnetic matching feature data set is greater than the second preset threshold, perform resampling on the roadway geomagnetic matching feature data set.

[0024] Optionally, the obtaining of the survey line magnetic field value signal; specifically includes:

[0025] Obtain the acceleration data, magnetic field value data, placement height data of the measuring device, and moving speed data of the measuring device for all preset measuring lines during underground roadway measurement; each of the underground roadways is provided with preset measuring lines at equal intervals along the roadway extension direction; the MEMS magnetic measuring device includes: a fluxgate meter, an accelerometer, and a noise-reducing inductor;

[0026] Calculate the coordinate position of each spatial point and the magnetic field value at the corresponding position according to the acceleration data and the magnetic field value data.

[0027] In a second aspect, the present application provides a geomagnetic positioning method, and the geomagnetic positioning method includes:

[0028] Obtain the magnetic field value signal of the measuring line in the area to be positioned;

[0029] Perform blind source separation and noise reduction processing on the magnetic field value signal of the measuring line in the area to be positioned to obtain the noise-reduced magnetic data to be positioned;

[0030] Perform matching feature point processing on the noise-reduced magnetic data to be positioned to obtain the geomagnetic matching feature data of the roadway to be positioned;

[0031] Use the magnetic feature point weight adjustment matching algorithm to calculate the optimal fitting sequence between the dynamic geomagnetic positioning reference library and the geomagnetic matching feature data of the roadway to be positioned, and obtain the coordinate position of the area to be positioned; the dynamic geomagnetic positioning reference library is constructed by the dynamic geomagnetic positioning reference library construction method described in any one of the above.

[0032] Optionally, the calculation formula for the optimal fitting sequence is:

[0033] ;

[0034] wherein, represents the optimal fitting sequence; Q represents a magnetic sequence in the dynamic geomagnetic positioning reference library; C represents a magnetic sequence in the geomagnetic matching feature data of the roadway to be positioned; , represents the similarity of each point between the sequences and ; represents the minimum cumulative distance between the current cell distance and adjacent elements; is the corresponding feature point weight; ; in the formula, is a constant, is the attribute weight of the feature point , is the attribute weight of the feature point .

[0035] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for constructing a dynamic geomagnetic positioning reference library or the geomagnetic positioning method described in any one of the above.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for constructing a dynamic geomagnetic positioning reference library or the geomagnetic positioning method described in any one of the above.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method for constructing a dynamic geomagnetic positioning reference library or the geomagnetic positioning method described in any one of the above.

[0038] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0039] The present application provides a method for constructing a dynamic geomagnetic positioning reference library, a geomagnetic positioning method, a device, a storage medium, and a product. The method for constructing a dynamic geomagnetic positioning reference library includes: obtaining a magnetic field value signal of a survey line; the magnetic field value signal of the survey line is obtained by a MEMS magnetic measurement device; the magnetic field value signal of the survey line includes: the placement height of the measurement device during measurement, the moving speed of the measurement device, and magnetic field data; the magnetic field data includes: the spatial point coordinate position and the magnetic field value at the corresponding position; performing blind source separation and noise reduction processing on the magnetic field value signal of the survey line to obtain noise-reduced magnetic data; performing matching feature point processing on the noise-reduced magnetic data to obtain a roadway geomagnetic matching feature data set; according to the placement height of the measurement device during measurement, extending the roadway geomagnetic matching feature data set up and down proportionally to obtain a set of geomagnetic matching survey line feature points with height changes, thereby obtaining a first set of dynamic geomagnetic feature points; according to the moving speed of the measurement device, stretching and extending the roadway geomagnetic matching feature data set proportionally to obtain a set of geomagnetic matching survey line feature points with speed changes, thereby obtaining a second set of dynamic geomagnetic feature points; combining the roadway geomagnetic matching feature data set, the first set of dynamic geomagnetic feature points, and the second set of dynamic geomagnetic feature points into a dynamic geomagnetic positioning reference library; the dynamic geomagnetic positioning reference library is used for dynamic geomagnetic positioning in a small indoor area. The present application uses a MEMS magnetic measurement device to obtain the magnetic field value signal of the survey line. The MEMS magnetic measurement device has a low cost and is easy to carry. Coupled with the complete dynamic geomagnetic data processing flow provided by the present application, accurate dynamic geomagnetic positioning in a small indoor area can be achieved on the premise of low cost. Description of the Drawings

[0040] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is an application environment diagram of a method for constructing a dynamic geomagnetic positioning reference library or a geomagnetic positioning method in an embodiment of the present application;

[0042] Figure 2 It is a flowchart of a method for constructing a dynamic geomagnetic positioning reference library provided in an embodiment of the present application;

[0043] Figure 3 It is a structural diagram of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0045] The present application uses a low-cost MEMS magnetic measurement device, places it at the waist in a relatively stable posture, and can collect data in the state of normal walking according to the normal walking step length or step frequency. The collection of a geomagnetic fingerprint library with a length of 100m can be completed in only 90s, realizing the rapid construction of the geomagnetic fingerprint library. At the same time, by processing the collected magnetic data, a complete set of new processing processes under dynamic conditions are established, including data collection, noise reduction processing, feature point extraction, and the mathematical model of the matching model, completing the invention of the key mathematical models for the establishment of the dynamic geomagnetic database and matching positioning application. The present application can complete dynamic magnetic measurement and dynamic geomagnetic matching calculation in pedestrian active positioning, provide a method for the embedded development of pedestrian active positioning devices, and provide a new method for improving the accuracy of existing geomagnetic matching positioning and the combination of geomagnetism and PDR inertial positioning.

[0046] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will further describe the present application in detail with reference to the drawings and specific implementation manners.

[0047] The method for constructing a dynamic geomagnetic positioning reference library and the geomagnetic positioning method provided in the embodiments of the present application can both be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, placed on the cloud or other servers. Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0048] In an exemplary embodiment, as Figure 2 shown, a method for constructing a dynamic geomagnetic positioning reference library is provided. This method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server in as an example for illustration, it includes the following steps S1 to S6. Among them:

[0049] S1. Obtain the magnetic field value signal of the survey line; the magnetic field value signal of the survey line is obtained by a MEMS magnetic measurement device; the magnetic field value signal of the survey line includes: the placement height of the measurement device during measurement, the moving speed of the measurement device, and the magnetic field data; the magnetic field data includes: the spatial point coordinate position and the magnetic field value at the corresponding position.

[0050] In this embodiment, the acceleration data, magnetic field value data, measurement device placement height data, and measurement device moving speed data of all preset survey lines during underground roadway measurement are obtained; each of the underground roadways is provided with preset survey lines at equal intervals along the roadway extension direction; the MEMS magnetic measurement device includes: a fluxgate meter, an accelerometer, and a noise reduction inductor.

[0051] Except for magnetic field interference-prone roadways such as main haulage roadways, large-scale electromechanical equipment roadways, and mining face roadways, according to the design positioning accuracy requirements of underground roadways, each underground roadway is provided with 1 or 3 survey lines at equal intervals along the roadway extension direction. For narrow underground roadways with a width of less than 2 meters, the survey line is selected along the center line of the underground roadway; for wide roadways greater than 2 meters, 1 survey line is arranged along the center line, and 2 survey lines are symmetrically arranged at equal intervals, with a total of 3 survey lines arranged.

[0052] The acquisition device is a geomagnetic MEMS (Micro Electro Mechanical Systems) - type combined positioning device. In this embodiment, it is required that the positioning device is internally equipped with sensors such as 2 fluxgate meters, an accelerometer, and a noise - reducing inductor, which can measure the magnetic field value data, acceleration, and angular velocity of the pedestrian's passing path in real - time.

[0053] The personnel for magnetic field measurement carry an MEMS - type magnetic measurement device, which is fixed at the waist height, about 1.35 meters. At the same time, no other items that are likely to cause interference, such as safety helmets, miner's lamps, mobile phones, etc., should be carried near the waist.

[0054] During magnetic field measurement, the set sampling frequency should not be lower than 5HZ, and the set uniform walking speed should be about 1.5m / s. During measurement, just walk along the pre - designed survey line at an average speed. Every time about 30 meters are advanced, it is necessary to stop walking and stand for about dozens of seconds (it is recommended to be 30 seconds), and then continue to measure about 30 meters, and then stop walking and stand for about dozens of seconds again, until the acceleration and magnetic field value data of all survey lines in the underground roadway are measured.

[0055] According to the acceleration change value and magnetic field value data when the measurement personnel are walking, the approximate distance coordinates, magnetic field values of each spatial point on the survey line can be deduced, generating the original acquired magnetic measurement data A - data, the serial number, roadway coordinates, and magnetic field values of each recording point.

[0056] Design the serial number of the recording point according to the sampling frequency, obtain the accelerometer value and magnetometer value of the corresponding sampling point, so as to deduce the forward distance of the pedestrian and deduce the forward sequence coordinates of the pedestrian. Among them, the sampling frequency is set on the instrument during data acquisition; the forward distance is the product of the traveling speed and time.

[0057] S2. Perform blind source separation and noise reduction processing on the magnetic field value signal of the survey line to obtain noise - reduced magnetic data.

[0058] Since the magnetic field value collected by the personnel for magnetic field measurement carrying an MEMS - type magnetic measurement device at each point includes: the magnetic field value corresponding to the spatial point of the passing path, the magnetic perturbation caused by the pedestrian's stepping, the magnetic perturbation caused by the equipment vibration, and the magnetic perturbation of the surrounding environment. Therefore, perform MNMF (Magnetic Nonnegative Matrix Factorization) blind source separation and noise reduction processing on the collected magnetic field value data of the roadway to generate noise - reduced magnetic data, that is, the magnetic data B - data of the underground roadway after noise reduction.

[0059] The blind source separation calculation of MNMF (Magnetic Nonnegative Matrix Factorization) is to perform blind source separation and noise reduction on the dynamic observed magnetic sequences (i.e., the magnetic field value data in the above text). Assuming that during the actual observation process, N dynamic observed magnetic sequence data (hereinafter referred to as samples) in the same state are measured, the linear mixing model matrix form is defined as:

[0060] ;

[0061] In the formula, represents N dynamic observed magnetic sequence signal vectors, where is the time length; represents the basis vectors of the total magnetic field at the space points of the passing path, the magnetic perturbation of the human gait, the magnetic perturbation of the MEMS attitude, and the magnetic perturbation generated by environmental interference. R represents the set of real numbers, represents the number of individual signals, specifically expressed as:

[0062] ;

[0063] represents the abundance matrix of each individual signal in the mixed data. represents random noise, model error, or uncertainty factor. It is necessary to satisfy the non-negativity constraint condition. Each component vector of the mixed abundance matrix A and each element of the abundance matrix A need to satisfy the non-negativity and the constraint condition that the sum of each component vector is 1:

[0064]

[0065] The unmixing process assumes that the component signal basis vector A and the mixing ratio S are unknown. Starting from two matrices, through processing and iterative calculation methods, continuous alternating iterations are performed to minimize the objective function, so as to achieve the optimal decomposition result. The blind source separation steps are as follows:

[0066] step1: Perform maximum-minimum normalization processing on the dynamic observed magnetic sequences.

[0067] step2: Initially separate the primitive signal S and the component matrix A according to the following formula; the objective function is to minimize the Euclidean distance:

[0068] ;

[0069] where, represents the values of A and S when the objective function reaches the minimum.

[0070] Step3: Use the component matrix A solved in the previous step to re-iterate and calculate the next weight matrix ;

[0071] ;

[0072] Among them, and in, K represents the number of iterations, i, j represent the numbers in the i-th row and the j-th column; eps is an infinitesimal number to prevent overflow when taking the reciprocal of this number close to 0 in the next calculation.

[0073] Step4: Use the following formula to iteratively calculate the separated basis element signal S and the component matrix A again;

[0074] ;

[0075] Among them, represents the values of S and W when the function reaches the minimum.

[0076] Step5: Repeat steps 2 to 4 until the difference between two adjacent calculations is 0.001, and terminate the iterative calculation;

[0077] Step6: Detect the separation accuracy of the demixed basis element signal S according to indicators such as the global matrix, signal-to-noise ratio, and mean square error.

[0078] S3. Perform matching feature point processing on the noise-reduced magnetic data to obtain a roadway geomagnetic matching feature data set.

[0079] Among them, S31. Use the moving window extreme value method to extract the feature points of each survey line magnetic data in the noise-reduced magnetic data, and assign attribute weights to each obtained feature point to obtain the first set of feature points to be matched.

[0080] Process the magnetic data B-data of the underground roadway after noise reduction using the moving window extreme value method, extract the feature points of each survey line magnetic data, generate the first set of feature points to be matched BC1-data, and the corresponding feature point attribute weight value is P1.

[0081] The method for extracting feature points by the moving window extreme value method is as follows: Select a certain survey line length (reference 2 meters) range as the moving window, and use the following discriminant formula to traverse and discriminate all data in the window magnetic sequence data. When there is a certain point: If it is continuously greater than or less than its left and right values, that point is considered a feature point. Among them, is the number of neighborhoods, which can be adjusted according to the number and quality of feature points. Its mathematical model is specifically as follows:

[0082] Assume that the magnetic sequence consists of n points and can be written as where represents the th value in the sequence.

[0083] ;

[0084] Or:

[0085] ;

[0086] Then is the extreme point of this moving window. For the same sequence, the number of feature points will vary slightly depending on the selected window size or the number of neighborhoods is different.

[0087] The extreme points of the moving window can extract the maximum points, minimum points, and main inflection point feature points on the survey line. The extreme points of all moving windows on the survey line are collected to generate the first feature point dataset BC1-data to be matched, and the attribute weights of each feature point are assigned, with the value P1 = 2. Since the feature points detected by the moving window and the feature points detected by the average value of the included angle of the normal vectors behind have different effects in geomagnetic matching, different weights are given to distinguish the contribution rates.

[0088] S32. Use the method of the average value of the included angle of the normal vectors to extract the feature points of the magnetic data of each survey line in the denoised magnetic data, and assign the attribute weights of each obtained feature point to obtain the second set of feature points to be matched.

[0089] Process the magnetic data B-data of the underground roadway after denoising by the method of the average value of the included angle of the normal vectors, extract the feature points of the magnetic data of each survey line, generate the second set of feature points BC2-data to be matched, and the corresponding attribute weight value of the feature point is P2.

[0090] (1) Suppose the magnetic sequence consists of n points and can be written as , where represents the th value in the sequence. For a point in the sequence, based on its k neighborhood points, establish the covariance matrix Cov of point , and its form is:

[0091] ;

[0092] ;

[0093] In the formula, is the neighborhood point of point ; is the centroid of the K neighborhood points; is the mth eigenvalue of the covariance matrix ; is the eigenvector corresponding to the eigenvalue ; Suppose the obtained eigenvalues are , then the normal vector of the sampling point That is corresponding eigenvector .

[0094] (2) Thus, according to the sampling points and the average value of the normal vector angles between the neighborhood points it is possible to determine whether the point is a feature point, as shown in the following formula. Set an appropriate threshold . When of is greater than the threshold, then the point is recognized as a feature point extracted by the normal vector method. Usually, any value between can be set

[0095] ;

[0096] In the formula is the calculated average value of the normal vector angles, which is an empirically set threshold , is the normal vector corresponding to the sampling point in the sequence is the normal vector corresponding to the sampling point in the sequence

[0097] (3) The method of the average value of the normal vector angles can extract the detailed feature points in the region with a large curvature, and can well display the local contour features. The set of all feature points obtained by the method of the average value of the normal vector angles on the survey line is collected to generate the second feature point data set BC2-data to be matched, and the attribute weight of each feature point inside is assigned, and the value is P2 = -1

[0098] S33. Merge the set of the first feature points to be matched and the set of the second feature points to be matched to obtain the roadway geomagnetic matching feature data set

[0099] For the above-generated set of the first feature points to be matched BC1-data and the corresponding attribute weight value P1, and the second feature point data set BC2-data and the attribute weight value P2, perform feature merging, resampling of the feature points, and adjustment of the feature point density. Generate the set of feature points BC-data of the roadway geomagnetic matching survey line, which includes the serial number, roadway coordinates, magnetic field value, and attribute weight value of the feature points extracted from the survey line

[0100] Feature point merging is to find the union of the two sets of BC1-data and BC2-data. If the same point appears in both BC1-data and BC2-data, the corresponding attribute weight takes P1 = 2. Resampling: When the sequence feature points are too sparse according to a certain discrimination threshold, linear interpolation of the corresponding sequence is performed; if the sequence feature points are too dense according to a certain discrimination threshold, quadratic resampling at equal intervals is performed. These processing methods are all automatically completed by combining a mathematical model with a program.

[0101] S4. According to the placement height of the measurement device during measurement, extend the roadway geomagnetic matching feature data set up and down proportionally to obtain a set of geomagnetic matching survey line feature points with height changes, thereby obtaining a first dynamic geomagnetic feature point set.

[0102] S5. According to the moving speed of the measurement device, extend the roadway geomagnetic matching feature data set by stretching proportionally to obtain a set of geomagnetic matching survey line feature points with speed changes, thereby obtaining a second dynamic geomagnetic feature point set.

[0103] S6. Combine the roadway geomagnetic matching feature data set, the first dynamic geomagnetic feature point set, and the second dynamic geomagnetic feature point set into a dynamic geomagnetic positioning reference library; the dynamic geomagnetic positioning reference library is used for dynamic geomagnetic positioning in a small indoor area.

[0104] In this embodiment, an interpolation parameter variable is set for the roadway geomagnetic matching feature data set to generate an underground personnel geomagnetic matching reference database.

[0105] For the above-generated roadway geomagnetic matching feature data set BC-data, mark the basic parameters when this data is obtained, including the height of the person during measurement (or the placement height of the device), the sampling frequency, the walking speed of the person, whether the data has been noise-reduced, the feature point extraction method, the number of survey lines, etc. When storing the data set, there is a header file that describes parameters such as the name of the acquisition device, the speed and method during acquisition, the feature point extraction method, and the number.

[0106] For the roadway geomagnetic matching feature data set BC-data, extend it up and down proportionally according to the height change to obtain a corresponding first dynamic geomagnetic feature point set BCH-data with height changes. Extending upward and downward is proportional difference.

[0107] Similarly, for the roadway geomagnetic matching feature data set, extend it by stretching according to the change in the walking speed of the person (such as uniform speed, fast walking, etc.) to obtain a corresponding second dynamic geomagnetic feature point set BCP-data with speed changes.

[0108] The dataset file is input into the internal storage chip of the geomagnetic MEMS-based combined positioning device according to a certain positioning method data structure as the basic database for real-time geomagnetic matching positioning or geomagnetic-based combined positioning of the positioning device.

[0109] Based on the same inventive concept, an embodiment of the present application further provides a geomagnetic positioning method, including:

[0110] A1. Obtain the magnetic field value signal of the survey line in the area to be positioned. The underground personnel carry an MEMS-based positioning device with a sampling frequency set to not less than 5HZ. After walking a certain distance normally, obtain the magnetic sequence L of the passing path (i.e., the magnetic field value signal of the survey line in the area to be positioned). According to the sampling frequency, the magnetic sequence L contains multiple point magnetic field values.

[0111] A2. Perform blind source separation and noise reduction processing on the magnetic field value signal of the survey line in the area to be positioned to obtain the denoised magnetic data to be positioned.

[0112] Perform MNMF (Magnetic Nonnegative Matrix Factorization) blind source separation and noise reduction processing on the obtained roadway magnetic field value to generate the magnetic data L-data of the personnel passing path after noise reduction.

[0113] A3. Perform matching feature point processing on the denoised magnetic data to be positioned to obtain the geomagnetic matching feature data of the roadway to be positioned.

[0114] Process the magnetic data L-data of the underground roadway after noise reduction using the moving window extreme value method to extract the feature points of the magnetic data sequence and generate the first set of feature points LC1-data to be matched, with the corresponding feature point attribute weight being P1. Process the magnetic data L-data of the underground roadway after noise reduction using the mean value method of the normal vector angle to extract the feature points of the magnetic data sequence and generate the first set of feature points LC2-data to be matched, with the corresponding feature point attribute weight being P2. And merge the two sets of feature points to generate the sequence LB-data of the personnel passing to be matched.

[0115] A4. Use the magnetic feature point weight adjustment matching algorithm to calculate the optimal fitting sequence between the dynamic geomagnetic positioning reference library and the geomagnetic matching feature data of the roadway to be positioned, and obtain the coordinate position of the area to be positioned; the dynamic geomagnetic positioning reference library is constructed by the dynamic geomagnetic positioning reference library construction method described above. Among them, the coordinate position of the area to be positioned is obtained through the optimal fitting sequence because the data structure in the dynamic geomagnetic reference library contains the magnetic field values and coordinate values of each point. When the measured geomagnetic sequence to be matched during positioning is matched with the geomagnetic sequence in the reference library, the position coordinate information of the points in the sequence to be matched can be obtained.

[0116] According to a certain search rule, the built-in geomagnetic matching reference databases BC-data, BCH-data, and BCP-data of the device are called to generate a set of magnetic sequences QS to be matched; the magnetic sequence LB-data of the characteristic points of the personnel passage path is used for matching calculation, and the following magnetic characteristic point weight adjustment matching MP-DTW calculation is used to solve the optimal fitting sequence between the set QS and the magnetic sequence LB-data, so as to deduce the approximate estimated coordinates of the personnel. This algorithm can be written into the MEMS-class positioning device carried by underground personnel. Among them, there are many search rules, such as the equal-value range search method, the ant colony search method, the traversal search method, and so on.

[0117] Let any magnetic sequence in the set QS of magnetic sequences to be matched be , and the magnetic sequence LB-data of the characteristic points of the personnel passage path be . Their lengths are not necessarily equal, which are and respectively.

[0118] Among them, ; . First, construct a distance matrix grid. The matrix element represents that and are aligned, and its value is and the distance between two coordinate points. One vector is used as a row and one vector is used as a column, and the elements inside correspond one by one to generate a grid matrix, similar to DTW.

[0119] Then, the optimal fitting sequence between the set QS and the magnetic sequence LB-data is solved to satisfy the following formula:

[0120] ;

[0121] Among them, represents the optimal fitting sequence; Q represents a magnetic sequence in the dynamic geomagnetic positioning reference library; C represents a magnetic sequence in the geomagnetic matching characteristic data of the roadway to be located; , this distance reflects the and similarity of each point between the sequences. The smaller the difference, the higher the similarity; represents the minimum cumulative distance between the current cell distance and adjacent elements. DTW (Dynamic Time Warping) can calculate the similarity of two time series, especially suitable for time series with different lengths and different rhythms, and q i , c j represent the corresponding points in two different sequences, qi-1 , c j-1 is a point in the Q and C magnetic sequences and is also q i and c j the previous neighborhood point of, for details, refer to step S32 in the above text; is the weight of the corresponding feature point; ; in the formula, is a constant, is the attribute weight of the feature point , is the attribute weight of the feature point . Both are the first type of feature points or the second type of feature points, indicating the same type. The smaller the position difference, the higher the possibility of similarity between the two points, and the smaller the weight value. Otherwise, a larger weight is applied to increase the matching difficulty. is the weight adjustment constant, that is, the convergence adjustment constant. = 0, all points have the same weight, not affected by the phase difference and type. As it continues to increase, the weight function curve will change.

[0122] The acquisition device has low cost, the acquisition process is simple to operate, convenient and fast. The entire data database construction and matching are completed under dynamic conditions. The sequence feature point mode is used, and the embedded development of the pedestrian active positioning device can be realized.

[0123] In this embodiment, a low-cost MEMS magnetic measurement device is adopted. It is placed at the waist in a relatively stable posture, and data can be collected in the state of normal walking at the normal walking step length or step frequency. The acquisition of a 100m-long geomagnetic fingerprint database can be completed in only 90s, realizing the rapid construction of the geomagnetic fingerprint database. At the same time, the collected magnetic data is processed to establish a complete set of new processing procedures under dynamic conditions, including data acquisition, noise reduction processing, feature point extraction and the mathematical model of the matching model, completing the invention of the key mathematical models for the establishment and matching positioning application of the dynamic geomagnetic database. This invention can complete dynamic magnetic measurement and dynamic geomagnetic matching calculation in pedestrian active positioning, and will provide a method for the embedded development of pedestrian active positioning devices, and provide a new method for improving the accuracy of existing geomagnetic matching positioning and the combination of geomagnetism and PDR inertial positioning.

[0124] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a method for constructing a dynamic geomagnetic positioning reference library or a geomagnetic positioning method.

[0125] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0126] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the steps in the above method embodiments are realized.

[0127] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are realized.

[0128] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are realized.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0131] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0132] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0133] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for constructing a dynamic geomagnetic positioning reference library, characterized in that: The dynamic geomagnetic positioning reference library construction method comprises: Acquire a measurement line magnetic field value signal; the measurement line magnetic field value signal is acquired by a MEMS type magnetic measurement device; the measurement line magnetic field value signal includes: the placement height of the measurement device during measurement, the moving speed of the measurement device and magnetic field data; the magnetic field data includes: the coordinate position of the spatial point and the magnetic field value of the corresponding position; Performing blind source unmixing and noise reduction processing on the magnetic field value signal of the survey line to obtain noise-reduced magnetic data; Performing matching feature point processing on the de-noised magnetic data to obtain a roadway geomagnetic matching feature data set; According to the placement height of the measuring device during the measurement, the tunnel geomagnetic matching feature data set is extended up and down proportionally to obtain a geomagnetic matching survey line feature point set with a height change, so as to obtain a first dynamic geomagnetic feature point set; According to the moving speed of the measuring device, the tunnel geomagnetic matching feature data set is stretched and extended proportionally to obtain a geomagnetic matching survey line feature point set with a changing speed, so as to obtain a second dynamic geomagnetic feature point set; The tunnel geomagnetic matching feature data set, the first dynamic geomagnetic feature point set and the second dynamic geomagnetic feature point set are combined into a dynamic geomagnetic positioning reference library; the dynamic geomagnetic positioning reference library is used for dynamic geomagnetic positioning of a small indoor area; The de-noised magnetic data is processed with matching feature points to obtain a roadway geomagnetic matching feature data set, specifically including: The moving window extreme value method is used to extract the characteristic points of the magnetic data of each survey line in the de-noised magnetic data, and the attribute weight of each characteristic point is assigned to obtain a first characteristic point set to be matched; The feature points of the magnetic data of each survey line in the de-noised magnetic data are extracted by using the normal vector angle mean method, and the attribute weight of each obtained feature point is assigned to obtain a second feature point set to be matched; The first feature point set to be matched is combined with the second feature point set to be matched to obtain a tunnel geomagnetic matching feature data set.

2. The method for constructing a dynamic geomagnetic positioning reference library according to claim 1, characterized in that: The attribute weight of each feature point in the first feature point set to be matched is assigned a value of 2; The attribute weight of each feature point in the second feature point set to be matched is assigned a value of -1.

3. The method for constructing a dynamic geomagnetic positioning reference library according to claim 1, characterized in that: After merging the first feature point set to be matched with the second feature point set to be matched to obtain a tunnel geomagnetic matching feature data set, the method further includes: When the number of feature points in the lane geomagnetic matching feature data set is less than a first preset threshold, performing corresponding sequence linear interpolation on the lane geomagnetic matching feature data set; When the number of feature points in the lane geomagnetic matching feature data set is greater than a second preset threshold, the lane geomagnetic matching feature data set is resampled.

4. The method for constructing a dynamic geomagnetic positioning reference library according to claim 1, characterized in that: The obtaining of the measurement line magnetic field value signal specifically includes: Acquire acceleration data, magnetic field value data, measurement device placement height data and measurement device movement speed data of all preset measurement lines when measuring underground tunnels; each of the underground tunnels has preset measurement lines arranged at equal intervals along the tunnel extension direction; the MEMS magnetic measurement device includes: a fluxgate meter, an accelerometer and a noise reduction inductor; The coordinate position of each spatial point and the magnetic field value of the corresponding position are calculated according to the acceleration data and the magnetic field value data.

5. A geomagnetic positioning method, characterized in that: The geomagnetic positioning method comprises: Obtaining the magnetic field value signal of the measurement line in the area to be located; Performing blind source unmixing and noise reduction processing on the magnetic field value signal of the survey line in the area to be located to obtain noise-reduced magnetic data to be located; Performing matching feature point processing on the noise-reduced magnetic data to be located to obtain geomagnetic matching feature data of the lane to be located; The magnetic feature point weight adjustment matching algorithm is used to calculate the optimal fitting sequence between the dynamic geomagnetic positioning reference library and the geomagnetic matching feature data of the tunnel to be located, so as to obtain the coordinate position of the area to be located; the dynamic geomagnetic positioning reference library is constructed by the dynamic geomagnetic positioning reference library construction method described in any one of claims 1 to 4.

6. The geomagnetic positioning method according to claim 5, characterized in that: The calculation formula of the best fitting sequence is: ； in, represents the best fitting sequence; Q Represents a magnetic sequence in the dynamic geomagnetic positioning reference library; C Represents a magnetic sequence in the geomagnetic matching feature data of the tunnel to be located; , which indicates the sequence and The similarity of each point between Indicates the minimum cumulative distance between the current cell and adjacent elements; is the weight of the corresponding feature point; ; In the formula, is a constant. It is a feature point The property rights, It is a feature point property rights.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a dynamic geomagnetic positioning reference library as described in any one of claims 1 to 4 or the geomagnetic positioning method as described in any one of claims 5 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a dynamic geomagnetic positioning reference library according to any one of claims 1 to 4 or the geomagnetic positioning method according to any one of claims 5 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for constructing a dynamic geomagnetic positioning reference library according to any one of claims 1 to 4 or the geomagnetic positioning method according to any one of claims 5 to 6 is implemented.

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