Dynamic geomagnetic positioning reference library construction method, geomagnetic positioning method, equipment, storage medium and product

Abstract

The invention discloses a dynamic geomagnetic positioning reference library construction method, a geomagnetic positioning method, equipment, a storage medium and a product, and relates to the technical field of geomagnetic matching positioning, and the dynamic geomagnetic positioning reference library construction method comprises the steps: obtaining a measuring line magnetic field value signal; a measuring line magnetic field value signal is obtained by the MEMS type magnetic measuring device; blind source unmixing and noise reduction processing is carried out on the measuring line magnetic field value signals, then matching feature point processing is carried out, and a roadway geomagnetic matching feature data set is obtained; according to the placement height and the moving speed of the measuring device, carrying out proportional continuation to obtain a first dynamic geomagnetic feature point set and a second dynamic geomagnetic feature point set; and 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. According to the invention, the MEMS type magnetic measurement device is used for obtaining the measuring line magnetic field value signal, and accurate dynamic geomagnetic positioning of a small indoor area can be realized on the premise of low cost.

Description

Technical Field

[0001] The present application relates to the field of geomagnetic matching and positioning technology, and in particular to a method for constructing a dynamic geomagnetic positioning reference library, a geomagnetic positioning method, equipment, storage medium and product. Background Art

[0002] With the development and transformation of positioning technology to be ubiquitous, continuous, precise and intelligent, 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 integrated sensors in mobile phones (including magnetometers, accelerometers, and angular velocity meters). They are convenient and fast to collect data and are important data collection devices for future PDR positioning, geomagnetic matching positioning, inertial combination positioning and other methods. However, how to achieve rapid collection and database construction of magnetic data for such equipment, accurate extraction of magnetic sequence features, and matching positioning has always been a key point that needs to be broken through.

[0003] The geomagnetic reference library is the basis for geomagnetic positioning. During the positioning process, the geomagnetic data is measured in real time using a magnetometer and compared with the pre-built geomagnetic reference map to determine the user's location information. Currently, there are two ways to build a geomagnetic reference library: static single-point acquisition and dynamic sequence acquisition.

[0004] The static single-point acquisition method is relatively mature. For small indoor areas, the area is generally divided into regular grid points of a certain width (1 meter, 2 meters). Static magnetic measurement equipment is used to repeatedly measure all grid points. After determining the geomagnetic values ​​of the grid points, a mathematical method such as linear interpolation, polynomial interpolation, Kriging interpolation and other interpolation methods are selected to interpolate the grid point magnetic data to generate a geomagnetic database for regional geomagnetic matching and positioning. This method is time-consuming and labor-intensive to construct, which is not conducive to the update of the geomagnetic fingerprint library and has low practicality. At the same time, when real pedestrians are performing geomagnetic matching and positioning, dynamic conditions are measured when obtaining the geomagnetic values ​​of the passage path. The observed values ​​will contain a variety of uncertain noises such as walking steps and posture changes, resulting in a low matching probability.

[0005] The construction of dynamic geomagnetic reference library is the behavior of people carrying geomagnetic measuring devices such as handheld magnetic field testers and magnetic field sensors to quickly obtain geomagnetic data while moving under dynamic positioning. At present, there is no clear method for building dynamic geomagnetic database. Zhang Yongwang et al. used drones for low-altitude aerial magnetic surveys, equipped with Canadian GTK-R15 rubidium optical pump magnetometers, and carried out dynamic magnetic measurements at a speed of 22.4m / s, which can realize the rapid construction of geomagnetic fingerprint libraries. This method is suitable for exploration blind areas with rugged indoor terrain. For small indoor areas, the difficulty of drone operation is greatly increased, and the accuracy of indoor IMU devices is relatively low. The magnetic measurement values ​​are easily affected by the change of the flight angle of the drone, and the applicability is extremely weak. In their research, Kuang Jian et al. proposed to use two IMU devices for data collection, fixed on the feet and back respectively, and corrected and denoised the collected data through control point correction and reverse smoothing algorithm, but it has high requirements for equipment, requiring integrated GNSS board and high-performance IMU combination. In addition, a thoracic and lumbar fixation bracket is required to fix the equipment posture, which makes data collection 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, equipment, storage medium and product, which can realize accurate dynamic geomagnetic positioning of small indoor areas at a low cost.

[0007] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for constructing a dynamic geomagnetic positioning reference library, the method comprising: 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 upward and downward in proportion 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 small indoor areas.

[0008] Optionally, matching feature point processing is performed on the de-noised magnetic data 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.

[0009] Optionally, 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.

[0010] Optionally, after merging the first feature point set to be matched with the second feature point set to be matched to obtain a roadway 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.

[0011] Optionally, 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.

[0012] In a second aspect, the present application provides a geomagnetic positioning method, the geomagnetic positioning method comprising: 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 optimal fitting sequence between the dynamic geomagnetic positioning reference library and the geomagnetic matching feature data of the tunnel to be located is calculated by using the magnetic feature point weight adjustment matching algorithm 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 of the above items.

[0013] Optionally, 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.

[0014] In a third aspect, the present application provides 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 any of the above-described methods for constructing a dynamic geomagnetic positioning reference library or geomagnetic positioning methods.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for constructing a dynamic geomagnetic positioning reference library or geomagnetic positioning methods.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for constructing a dynamic geomagnetic positioning reference library or geomagnetic positioning methods.

[0017] According to the specific embodiments provided in this application, this application discloses the following technical effects: 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 survey line magnetic field value signal; the survey line magnetic field value signal is obtained by a MEMS magnetic measuring device; the survey line magnetic field value signal includes: the placement height of the measuring device during measurement, the moving speed of the measuring device and the magnetic field data; the magnetic field data includes: the coordinate position of a spatial point and the magnetic field value of the corresponding position; blind source unmixing and denoising processing is performed on the survey line magnetic field value signal to obtain denoised magnetic data; matching feature point processing is performed on the denoised magnetic data to obtain a tunnel 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 proportionally extended up and down to obtain a set of geomagnetic matching survey line feature points with height changes, and a first dynamic geomagnetic feature point set is obtained; According to the moving speed of the measuring device, the tunnel geomagnetic matching feature data set is proportionally stretched and extended to obtain a set of geomagnetic matching survey line feature points with speed changes, and a second dynamic geomagnetic feature point set is obtained; 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 small indoor areas. The present application utilizes a MEMS-type magnetic measuring device to obtain a survey line magnetic field value signal. The MEMS-type magnetic measuring device is low in cost and easy to carry. With the complete dynamic geomagnetic data processing process provided by the present application, accurate dynamic geomagnetic positioning of small indoor areas can be achieved at a low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is an application environment diagram of a method for constructing a dynamic geomagnetic positioning reference library or a geomagnetic positioning method in one embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a method for constructing a dynamic geomagnetic positioning reference library provided in one embodiment of the present application; Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] This application uses a low-cost MEMS magnetic measurement device, which is placed at the waist in a relatively stable posture, and data can be collected in a normal walking state according to the normal walking step length or step frequency. The acquisition of a geomagnetic fingerprint library of 100m in length can be completed in just 90s, realizing the rapid construction of the geomagnetic fingerprint library. At the same time, for the processing of collected magnetic data, a complete and new processing flow under dynamic conditions is established, including mathematical models for data acquisition, noise reduction processing, feature point extraction and matching models, completing the invention of key mathematical models for dynamic geomagnetic database establishment and matching positioning applications. This application can complete dynamic magnetic measurement and dynamic geomagnetic matching calculation in active pedestrian positioning, and will provide a method for the embedded development of active pedestrian positioning equipment, and provide a new method for improving the accuracy of existing geomagnetic matching positioning and geomagnetic and PDR inertial combination positioning.

[0022] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0023] The dynamic geomagnetic positioning reference library construction method and geomagnetic positioning method provided in the embodiments of the present application can be applied to Figure 1 In 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, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. Among them, the terminal 102 can be but is not limited to various desktop computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers, and it can also be a cloud server.

[0024] In an exemplary embodiment, Figure 2 As shown, a method for constructing a dynamic geomagnetic positioning reference library is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1The server in the example is used to illustrate, including the following steps S1 to S6. Among them: S1. Obtain a measurement line magnetic field value signal; the measurement line magnetic field value signal is obtained 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 the magnetic field data; the magnetic field data includes: the coordinate position of the spatial point and the magnetic field value of the corresponding position.

[0025] This embodiment obtains 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.

[0026] Except for the transportation lanes, large electromechanical equipment lanes, mining face lanes and other lanes that are easily disturbed by the magnetic field, according to the positioning accuracy requirements of the underground lane design, each underground lane is equipped with 1 or 3 measuring lines at equal intervals along the extension direction of the lane. For narrow underground lanes with a width of less than 2 meters, the center line of the underground lane is selected for the measuring line; for spacious lanes with a width greater than 2 meters, 1 line is laid out along the center line, and 2 measuring lines are laid out symmetrically at equal intervals, for a total of 3 measuring lines.

[0027] The acquisition device is a geomagnetic MEMS (Micro Electro Mechanical Systems) type combined positioning device. This embodiment requires that the positioning device has two built-in fluxgate meters, accelerometers, noise reduction inductors and other sensors, which can measure the magnetic field value data, acceleration and angular velocity of the pedestrian's passage path in real time.

[0028] Magnetic field measurement personnel carry a MEMS magnetic measurement device, which is fixed at waist height, about 1.35 meters. At the same time, other easily interfered items such as safety helmets, mining lamps, mobile phones, etc. are not allowed to be carried near the waist.

[0029] When measuring the magnetic field, the sampling frequency should be set to no less than 5HZ, and the uniform walking speed should be set to about 1.5m / s. When measuring, you only need to walk along the pre-designed measuring line at an average speed. Every time you move forward about 30 meters, you need to stop walking and stand for about tens of seconds (30 seconds is recommended), then continue measuring for about 30 meters, and then stop walking and stand for about tens of seconds, and do this until the acceleration and magnetic field value data of all measuring lines in the underground tunnel are measured.

[0030] Based on the acceleration change value and magnetic field value data when the surveyor walks, the approximate distance coordinates and magnetic field value of each spatial point on the survey line can be calculated to generate the original collected magnetic measurement data A-data, the serial number, lane coordinates, and magnetic field value of each recording point.

[0031] The serial number of the recording point is designed according to the sampling frequency, and the acceleration count value and magnetic count value of the corresponding sampling point are obtained to calculate the pedestrian's forward distance and the pedestrian's forward sequence coordinates. Among them, the sampling frequency is set on the instrument during data collection; the travel speed × time is the forward distance.

[0032] S2. Perform blind source unmixing and noise reduction processing on the magnetic field value signal of the survey line to obtain noise-reduced magnetic data.

[0033] Since the magnetic field measurement personnel carry MEMS magnetic measurement devices to collect the magnetic field value of each point, including: the corresponding magnetic field value of the spatial point of the passage path, the magnetic disturbance of pedestrian steps, the magnetic disturbance of equipment vibration and the magnetic disturbance of the surrounding environment. Therefore, the collected roadway magnetic field value data is subjected to MNMF (Magnetic Nonnegative Matrix Factorization) blind source unmixing and noise reduction processing to generate noise-reduced magnetic data, that is, the noise-reduced magnetic data B-data of the underground roadway.

[0034] MNMF (Magnetic Nonnegative Matrix Factorization) blind source unmixing is to perform blind source unmixing and noise reduction on the dynamic observation magnetic sequence (i.e. the magnetic field value data mentioned above). Assuming that in the actual observation process, N dynamic observation magnetic sequence data (hereinafter referred to as samples) under the same state are measured, the linear mixed model matrix form is defined as: ; In the formula, represents N dynamic observation magnetic sequence signal vectors, where is the length of time; represents the basis vector of the total magnetic field of the spatial point of the passage path, the magnetic disturbance caused by the gait of personnel, the magnetic disturbance caused by MEMS posture and the magnetic disturbance caused by environmental interference. R represents the real number set. Indicates the number of separate signals, specifically expressed as: ; A matrix representing the abundance of each individual signal in the mixed data. Represents random noise, model error or uncertainty factor. It needs to satisfy the non-negativity constraint, and each component vector of the mixed abundance matrix A and each element of the abundance matrix A need to satisfy the non-negativity constraint and the sum of each component vector is 1:

[0035] 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, iterative iteration is continuously performed to minimize the objective function, thereby achieving the optimal decomposition result. The steps of blind source separation are as follows: Step 1: Dynamically observe the magnetic sequence and perform normalization of the maximum and minimum values.

[0036] Step 2: Separate the primitive signal S and the component matrix A for the first time according to the following formula; the objective function is to minimize the Euclidean distance: ; in, Indicates the values ​​of A and S when the objective function reaches its minimum value.

[0037] Step 3: Use the component matrix A solved in the previous step to iterate and calculate the next weight matrix ; ; in, and In the equation, K represents the number of iterations, i and j represent the number of the i-th row and j-th column; eps is an infinitesimal number to prevent the number from approaching 0 and causing overflow when the reciprocal is calculated next time.

[0038] Step 4: Use the following formula to iteratively calculate the separation primitive signal S and component matrix A again; ; in, Represents the values ​​of S and W when the function reaches its minimum.

[0039] Step 5: Repeat steps 2 to 4 until the difference between two consecutive calculations is within 0.001, and terminate the iterative calculation; Step 6: According to the global matrix, signal-to-noise ratio and mean square error and other indicators, the separation accuracy of the unmixed primitive signal S is detected.

[0040] S3. Perform matching feature point processing on the de-noised magnetic data to obtain a tunnel geomagnetic matching feature data set.

[0041] Among them, S31, using the moving window extremum method to extract the feature points of the magnetic data of each survey line in the de-noised magnetic data, and assigning the attribute weight of each obtained feature point to obtain a first feature point set to be matched.

[0042] The moving window extreme value method is used to process the denoised magnetic data B-data of the underground tunnel, extract the feature points of the magnetic data of each survey line, and generate the first feature point set BC1-data to be matched, and the corresponding feature point attribute weight is P1.

[0043] The moving window extreme value method is as follows: select a certain measurement line length (reference 2 meters) as a moving window, and use the following discriminant formula to traverse and discriminate all the data in the window. When a point exists: if it is continuously greater than or less than its left and right sides value, that is, the 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 as follows: Assume that the magnetic sequence consists of n points, which can be written as ,in Indicates the first value.

[0044] ; or: ; So This is the extreme point of the moving window. The same sequence may vary depending on the selected window size or number of neighbors. The number of feature points will be slightly different.

[0045] The extreme points of the moving window can extract the maximum value, minimum value and main turning point feature points on the survey line. All the extreme points of the moving window on the survey line are extracted and collected to generate the first feature point data set BC1-data to be matched. The attribute weight of each feature point is assigned, and the value is P1=2. Because the feature points detected by the moving window and the feature points detected by the mean angle of the normal vector are different in geomagnetic matching, different weights are given to distinguish the contribution rate.

[0046] S32, using the normal vector angle mean method to extract feature points of the magnetic data of each survey line in the de-noised magnetic data, and assigning an attribute weight to each obtained feature point to obtain a second feature point set to be matched.

[0047] The normal vector angle mean method is used to process the denoised magnetic data B-data of the underground tunnel, extract the feature points of the magnetic data of each survey line, and generate the second feature point set BC2-data to be matched, and the corresponding feature point attribute weight is P2.

[0048] (1) Assume that the magnetic sequence consists of n points, which can be written as ,in Indicates the first For a point in the sequence , establish a point based on its k neighboring points The covariance matrix Cov of is in the form: ; ; In the formula, For point Field points; is the center of gravity of the K field point; is the covariance matrix The mth eigenvalue of ; is the eigenvalue The corresponding eigenvector; let the eigenvalue be , then the sampling point The normal vector That is The corresponding eigenvector .

[0049] (2) Therefore, according to the sampling point The mean angle between the normal vector and the neighboring points Can judge Whether it is a feature point, as shown in the following formula. Set the appropriate threshold ,when of When it is greater than the threshold, the point is recognized Extract feature points using the normal vector method. Usually, you can set Any value in between.

[0050] ; In the formula, is the calculated mean of the normal vector angle, which is an artificially set empirical threshold , is the sampling point in the sequence The corresponding normal vector is is the sampling point in the sequence The corresponding normal vector.

[0051] (3) The normal vector angle mean method can extract detailed feature points in areas with large curvatures and can well display local contour features. All normal vector angle mean method feature points on the survey line are collected to generate the second feature point data set BC2-data to be matched, in which the attribute weight of each feature point is assigned, and the value is P2=-1.

[0052] S33: Merge 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.

[0053] The first feature point set BC1-data to be matched and the corresponding attribute weight P1, and the second feature point data set BC2-data and the attribute weight P2 are merged, feature points are resampled, and the density of feature points is adjusted. The feature point set BC-data of the roadway geomagnetic matching survey line is generated, which includes the serial number, roadway coordinates, magnetic field value and attribute weight of the feature points extracted from the survey line.

[0054] 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 is P1=2. Resampling: When the feature points of the sequence are too sparse according to a certain discrimination threshold, the corresponding sequence is linearly interpolated; if the feature points of the sequence are too dense according to a certain discrimination threshold, the points are resampled twice at equal intervals. These processing methods are all completed automatically using mathematical models combined with programs.

[0055] S4. According to the placement height of the measuring device during the measurement, the tunnel geomagnetic matching feature data set is extended upward and downward in proportion to obtain a geomagnetic matching survey line feature point set with a varying height, so as to obtain a first dynamic geomagnetic feature point set.

[0056] S5. According to the moving speed of the measuring device, the tunnel geomagnetic matching feature data set is stretched and extended in proportion 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.

[0057] S6. Combining the tunnel 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 of small indoor areas.

[0058] In this embodiment, the tunnel geomagnetic matching feature data set is used to set interpolation parameter variables to generate an underground personnel geomagnetic matching benchmark database.

[0059] For the above-generated tunnel geomagnetic matching feature dataset BC-data, the basic parameters of the data acquisition are marked, including the height of the person (or the height of the device), sampling frequency, walking speed of the person, whether the data has been denoised, feature point extraction method, number of measurement lines, etc. When storing the dataset, there is a header file, which describes the name of the acquisition device, the speed and method of acquisition, the feature point extraction method, number and other parameters.

[0060] The BC-data dataset of the roadway geomagnetic matching feature data set is extended up and down according to the proportional parameter to obtain the first dynamic geomagnetic feature point set BCH-data with corresponding height changes. Upward and downward extension is proportional difference.

[0061] Similarly, the tunnel geomagnetic matching feature data set is stretched and extended according to the parameters according to the changes in the walking speed of the personnel (constant speed, fast walking, etc.) to obtain the second dynamic geomagnetic feature point set BCP-data with corresponding speed changes.

[0062] The data set file is input into the file in the internal storage chip of the geomagnetic MEMS type combined positioning device according to a certain positioning method data structure, and serves as the basic database for the real-time geomagnetic matching positioning or geomagnetic type combined positioning of the positioning device.

[0063] Based on the same inventive concept, the embodiment of the present application also provides a geomagnetic positioning method, including: A1. Obtain the magnetic field value signal of the survey line in the area to be located. The underground personnel carry a MEMS positioning device with them, and the sampling frequency should be set to no less than 5HZ. After walking a certain distance normally, the magnetic sequence L of the passage path (i.e. the magnetic field value signal of the survey line in the area to be located) is obtained. The magnetic sequence L contains multiple point magnetic field values ​​according to the sampling frequency.

[0064] A2. Perform blind source unmixing and noise reduction processing on the magnetic field value signals of the survey lines in the area to be located to obtain noise-reduced magnetic data to be located.

[0065] The acquired lane magnetic field values ​​are subjected to MNMF (Magnetic Nonnegative Matrix Factorization) blind source unmixing and denoising processing to generate the denoised magnetic data L-data of the personnel passage path.

[0066] A3. Perform 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.

[0067] The moving window extreme value method is used to process the noise-reduced magnetic data L-data of the underground tunnel, extract the feature points of the sequence magnetic data, and generate the first feature point set LC1-data to be matched, and the corresponding feature point attribute weight is P1. The normal vector angle mean method is used to process the noise-reduced magnetic data L-data of the underground tunnel, extract the feature points of the sequence magnetic data, and generate the first feature point set LC2-data to be matched, and the corresponding feature point attribute weight is P2. The two feature point sets are merged to generate the personnel passage sequence to be matched LB-data.

[0068] 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 laneway 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 value and coordinate value of each point. When positioning, the geomagnetic sequence to be matched dynamically measured is matched with the geomagnetic sequence in the reference library, and the position coordinate information of the point in the sequence to be matched can be obtained.

[0069] According to a certain search rule, the device's built-in geomagnetic matching reference database BC-data, BCH-data, and BCP-data are called to generate a set of magnetic sequences to be matched QS; matching calculations are performed with the magnetic sequence LB-data of the characteristic points of the personnel's passage path, and the following magnetic feature 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, thereby calculating the approximate estimated coordinates of the personnel. This algorithm can be written into the MEMS positioning device carried by underground personnel. Among them, there are many search rules, such as equal value domain search method, ant colony search method, traversal search method, etc.

[0070] Suppose any magnetic sequence in the set of magnetic sequences to be matched QS is , the magnetic sequence LB-data of the characteristic points of the personnel passage path is , the lengths of the two are not necessarily equal, they are and .

[0071] in, ; First build The distance matrix grid, the matrix elements express and Alignment, its value is and The distance between two coordinate points, one vector as row, one vector as column, the elements in it correspond one to one, and a grid matrix is ​​generated, which is similar to DTW.

[0072] Then, the optimal fitting sequence between the set QS and the magnetic sequence LB-data is solved to satisfy the following formula: ; 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 reflects the sequence and The similarity of each point between them, the smaller the difference, the higher the similarity; Indicates the minimum cumulative distance between the current cell distance and the adjacent elements. DTW (Dynamic Time Warping) can calculate the similarity of two time series, especially for time series of different lengths and rhythms. i ,c j Represents the corresponding points in two different sequences, q i-1 ,c j-1 is a point in the Q and C magnetic series, and also q i and c j The previous neighborhood point of , for details, please refer to step S32 above; 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 If both are first-class feature points or second-class feature points, it means they are of the same type. The smaller the position difference, the higher the possibility of similarity between the two points, and the smaller the weight. Otherwise, a larger weight is applied, which increases the difficulty of matching. is the weight adjustment constant, that is, the convergence adjustment constant. =0All points have the same weight and are not affected by phase difference and type. As it continues to increase, the weight function curve will change.

[0073] The acquisition equipment is low-cost, and the acquisition process is simple, convenient and fast. The entire data database construction and matching are completed under dynamic conditions, using the sequence feature point mode, which can realize the embedded development of pedestrian active positioning equipment.

[0074] This embodiment uses a low-cost MEMS-type magnetic measurement device, which is placed at the waist in a relatively stable posture, and data can be collected in a normal walking state according to the normal walking step length or step frequency. The acquisition of a geomagnetic fingerprint library of 100m length can be completed in just 90s, realizing the rapid construction of the geomagnetic fingerprint library. At the same time, for the processing of collected magnetic data, a complete and new processing flow under dynamic conditions is established, including mathematical models for data acquisition, noise reduction processing, feature point extraction and matching models, completing the invention of key mathematical models for dynamic geomagnetic database establishment and matching positioning applications. The invention can complete dynamic magnetic measurement and dynamic geomagnetic matching calculation in active pedestrian positioning, and will provide a method for embedded development of pedestrian active positioning equipment, and provide a new method for improving the accuracy of existing geomagnetic matching positioning and geomagnetic and PDR inertial combination positioning.

[0075] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. 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. 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 an external device. 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, a dynamic geomagnetic positioning reference library construction method or a geomagnetic positioning method is implemented.

[0076] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure 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 a different arrangement of components.

[0077] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0078] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0079] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0080] 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 used for analysis, stored data, displayed data, etc.) involved in this 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 must comply with relevant regulations.

[0081] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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 may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0082] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0083] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0084] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this 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 small indoor areas.

2. The method for constructing a dynamic geomagnetic positioning reference library according to claim 1, characterized in that: 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.

3. The method for constructing a dynamic geomagnetic positioning reference library according to claim 2, 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.

4. The method for constructing a dynamic geomagnetic positioning reference library according to claim 2, 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 roadway 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.

5. 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 comprises: 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.

6. 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 5.

7. The geomagnetic positioning method according to claim 6, 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 represents 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.

8. 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 5 or the geomagnetic positioning method as described in any one of claims 6 to 7.

9. 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 5 or the geomagnetic positioning method according to any one of claims 6 to 7 is implemented.

10. 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 5 or the geomagnetic positioning method according to any one of claims 6 to 7 is implemented.

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