Magnetic source positioning device and method

Abstract

The invention discloses a magnetic source positioning device and method. The device comprises a cross-shaped measuring support; a plurality of magnetic sensing devices which are connected with the cross-shaped measurement support and are used for obtaining magnetic field measurement data; and the data analysis equipment is connected with the magnetic sensing equipment and is used for carrying out magnetic source positioning according to the magnetic field measurement data. The method comprises the steps of obtaining magnetic field measurement data; fourier transform is carried out on the magnetic field measurement data to obtain transformed data; performing wavelet transformation on the magnetic field measurement data to obtain wavelet transformation data; calculating wavelet entropy according to the discrete random variable valuing probability and wavelet energy; constructing a risk function according to the wavelet coefficient; performing wavelet denoising on the transformation data according to the wavelet transformation data, the wavelet entropy and the risk function to obtain wavelet denoising data; and performing magnetic source positioning according to the magnetic gradient tensor and the wavelet denoising data. According to the invention, magnetic source positioning is realized, and efficiency and accuracy are improved. The method can be applied to the technical field of magnetic detection.

Description

Technical Field

[0001] The present invention relates to the field of magnetic detection technology, and in particular to a magnetic source positioning device and method. Background Art

[0002] Magnetic detection is a method of measuring magnetic field signals by mounting a magnetometer on a moving platform. In magnetic detection applications, magnetic sensors obtain information related to the magnetic field in space. The amount of magnetic field data collected by magnetic sensors is generally very large, and the data is the magnetic field vector value at different longitudes and latitudes. The traditional magnetic source positioning method uses a single measurement point to obtain magnetic field data multiple times for positioning, which is inefficient, noisy, and has low accuracy.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The embodiments of the present invention provide a magnetic source positioning device and method, which effectively improve the efficiency and accuracy.

[0005] On the one hand, an embodiment of the present invention provides a magnetic source positioning device, comprising:

[0006] Cross measuring bracket;

[0007] A plurality of magnetic sensing devices, the magnetic sensing devices are connected to the cross measurement bracket and are used to obtain magnetic field measurement data;

[0008] A data analysis device is connected to the magnetic sensing device and is used to locate the magnetic source based on the magnetic field measurement data.

[0009] In some embodiments, the cross measurement bracket is made of anti-magnetic aluminum.

[0010] In some embodiments, the number of the magnetic sensing devices is four.

[0011] In some embodiments, the magnetic sensing device is connected to an end of the cross measurement bracket.

[0012] In some embodiments, the magnetic sensing device comprises a three-axis magnetic sensor.

[0013] The beneficial effects of the present invention are as follows:

[0014] A magnetic source positioning device provided by an embodiment of the present invention includes a cross measurement bracket, a plurality of magnetic sensing devices and a data analysis device. The magnetic sensing device is connected to the cross measurement bracket, and magnetic field measurement data is obtained through the magnetic sensing device. The data analysis device is connected to the magnetic sensing device, and magnetic source positioning is performed through the data analysis device according to the magnetic field measurement data, so that magnetic source positioning can be achieved through the magnetic sensing device and the data analysis device, thereby improving efficiency and accuracy.

[0015] On the other hand, an embodiment of the present invention provides a magnetic source positioning method applied to the device, comprising the following steps:

[0016] Obtaining magnetic field measurement data;

[0017] Performing Fourier transform on the magnetic field measurement data to obtain transformed data, wherein the Fourier transform includes frequency domain analysis, time domain analysis or time-frequency domain analysis;

[0018] Performing wavelet transformation on the magnetic field measurement data to obtain wavelet transformation data;

[0019] Calculate the wavelet entropy based on the probability of discrete random variables and wavelet energy;

[0020] According to the wavelet coefficients, the risk function is constructed;

[0021] According to the wavelet transform data, the wavelet entropy and the risk function, performing wavelet denoising on the transform data to obtain wavelet denoised data;

[0022] Magnetic source localization is performed based on the magnetic gradient tensor and the wavelet denoised data.

[0023] In some embodiments, performing a wavelet transform on the magnetic field measurement data to obtain wavelet transform data includes:

[0024] According to the total number of vanishing moments, the mother wavelet constraint is constructed;

[0025] According to the basic wavelet function, construct the basic wavelet function constraint;

[0026] The wavelet transform data is calculated according to the magnetic field measurement data, the mother wavelet constraint and the basic wavelet function constraint.

[0027] In some embodiments, the step of calculating wavelet entropy based on the probability of discrete random variables taking values ​​and wavelet energy includes:

[0028] Calculating initial entropy according to the probability of the discrete random variable taking a value;

[0029] Process the wavelet transform coefficient matrix to obtain a probability distribution sequence;

[0030] The wavelet entropy is calculated according to the wavelet energy, the initial entropy and the probability distribution sequence.

[0031] In some embodiments, constructing a risk function according to wavelet coefficients includes:

[0032] constructing an observation signal according to the wavelet coefficients and a preset threshold;

[0033] Calculating a noise signal according to the observation signal and the useful signal;

[0034] Calculate the threshold coefficient according to the median of the absolute values ​​of the wavelet coefficients;

[0035] Calculating a universal threshold value according to the threshold coefficient;

[0036] The risk function is constructed according to the noise signal and the universal threshold.

[0037] In some embodiments, the performing of magnetic source positioning according to the magnetic gradient tensor and the wavelet denoising data comprises:

[0038] constructing an initial positioning equation according to the magnetic gradient tensor and the wavelet denoising data;

[0039] Converting the initial positioning equation to obtain a target positioning equation;

[0040] Calculating the distance from the magnetic sensing device to the target magnetic source according to the third-order magnetic field tensor, the target positioning equation and the magnetic gradient tensor;

[0041] The magnetic source is located according to the distance from the magnetic sensing device to the target magnetic source.

[0042] The beneficial effects of the present invention are as follows:

[0043] The embodiment of the present invention first acquires magnetic field measurement data, performs Fourier transform on the magnetic field measurement data to obtain transformed data, then performs wavelet transform on the magnetic field measurement data to obtain wavelet transformed data, calculates wavelet entropy according to discrete random variable value probability and wavelet energy, and constructs a risk function according to wavelet coefficients, then performs wavelet denoising on the transformed data according to the wavelet transform data, wavelet entropy and risk function to obtain wavelet denoised data, and finally performs magnetic source positioning according to the magnetic gradient tensor and the wavelet denoised data, so that magnetic source positioning can be achieved through Fourier transform and wavelet denoising, thereby improving efficiency and accuracy.

[0044] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of 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 creative work.

[0046] Figure 1 This is a schematic structural diagram of a magnetic source positioning device according to an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of a magnetic field measurement array according to an embodiment of the present invention;

[0048] Figure 3 This is a structural schematic diagram of magnetic field data collection and analysis according to an embodiment of the present invention;

[0049] Figure 4 A flow chart of a magnetic source positioning method according to an embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of a magnetic source positioning principle according to an embodiment of the present invention;

[0051] Figure 6 The figure is a schematic diagram of the overall process of magnetic field measurement data processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0053] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0054] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0056] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0057] Magnetic source positioning: refers to determining the location of the magnetic source through specific technologies and methods.

[0058] In related technologies, magnetic phenomena are widely used in the fields of communication, navigation, forecasting, magnetic detection, etc. Magnetic detection is a method of measuring magnetic field signals by mounting a magnetometer on a moving platform. It has the advantages of high efficiency, all-weather, large operating area, and flexibility. Magnetic detection technology has developed due to the need to detect submarines. It has many applications in geological navigation, earthquake prediction, geological survey and other geological related detection and forecasting. In addition, magnetic detection also has high application value in rescue and safety, such as rescue search, unexploded bomb detection and other fields. In magnetic detection applications, magnetic sensors obtain information related to the magnetic field in space. The amount of magnetic field data collected by magnetic sensors is generally large, and the data is the magnetic field vector value at different longitudes and latitudes. Due to the rich amount of data and the presence of certain noise in the data collection process, it is of great significance to quickly organize and analyze a large amount of magnetic field data and remove the noise in the data for engineering applications. In addition, with the rapid development of computer technology, data analysis and processing through computer software will be simpler and faster. Traditional target magnetic source positioning treats magnetic objects as equivalent to dipole models. In the target positioning model under the magnetic gradient tensor, magnetic target positioning is achieved with one measurement point, which is inefficient, noisy and inaccurate. In view of the problem that the traditional Euler method is easily disturbed by geomagnetic interference, the tensor measurement system is improved, and the improved calculation method of the magnetic gradient tensor with a regular hexahedron structure is adopted. Alternatively, the relationship between the eigenvalue and position of the target magnetic gradient tensor is used to locate the target magnetic source using the positioning method of the eigenvalue and eigenvector of the magnetic gradient tensor of the target magnetic field. The existing device for measuring the three-component data of the magnetic field in three-dimensional space cannot obtain the three-component data at the same time, and the error of multiple acquisitions is large. The function of the upper computer for magnetic field data analysis is single, and it lacks the ability to compensate for magnetic field errors and analyze data. The existing magnetic field measurement and analysis device using the Hall effect of the Hall probe on the Gaussian meter measures the magnetic field on the magnet surface according to the fixed-point method according to different needs, collects the magnetic field strength at different points, and draws a curve graph of the magnetic field through the corresponding software, but the ability to analyze magnetic field data is poor.

[0059] In view of this, this embodiment solves the problems of confusion in the magnetic field measurement process, single data processing method, lack of magnetic field compensation, time domain and frequency domain analysis, etc. through magnetic field compensation and spectrum analysis, and improves the use efficiency and data processing and analysis capabilities based on actual application scenarios. This embodiment can compensate the magnetic field measurement data in real time, display the original magnetic field data, compensated magnetic field data, and spectrum analysis through the upper computer (data analysis equipment) software interface, and has built-in measurement error compensation, which can realize the positioning of the target magnetic source.

[0060] The following is a detailed explanation of the embodiments of the present application in conjunction with the accompanying drawings:

[0061] like Figure 1 As shown, an embodiment of the present invention provides a magnetic source positioning device, comprising:

[0062] Cross measuring bracket 101;

[0063] A plurality of magnetic sensor devices 102, the magnetic sensor devices are connected to a cross measurement bracket and are used to obtain magnetic field measurement data;

[0064] The data analysis device 103 is connected to the magnetic sensing device and is used to locate the magnetic source according to the magnetic field measurement data.

[0065] In some embodiments, a magnetic source positioning device provided in this embodiment includes a cross measurement bracket 101, a plurality of magnetic sensing devices 102 and a data analysis device 103. Among them, the magnetic sensing device is connected to the cross measurement bracket, and the data analysis device is connected to the magnetic sensing device. The magnetic field measurement data can be obtained through the magnetic sensing device. The magnetic source can be positioned by the data analysis device based on the magnetic field measurement data. In the measurement bracket, compared with the magnetic field measurement arrays of equilateral triangle, right tetrahedron, plane cross and square structures, the cross structure has a smaller measurement error than other structures. The cross array structure is simple, easy to design and install, and a plane cross structure array (cross measurement bracket) can be selected to obtain the spatial magnetic field. In some embodiments, the material of the cross measurement bracket is antimagnetic aluminum. Exemplarily, the cross measurement bracket can be formed by cross-crossing installation of antimagnetic aluminum. In some embodiments, the number of magnetic sensing devices is 4. The magnetic sensing device is connected to the end of the cross measurement bracket. The magnetic sensing device may include a three-axis magnetic sensor. Exemplarily, the magnetic field measurement array is such as Figure 2 As shown, four three-axis magnetic sensors can be mounted on a cross measurement bracket to form a magnetic field measurement array.

[0066] In some embodiments, in the magnetic sensing device, a GC-CC-304 high-precision magnetic sensor can be selected. This is a three-axis magnetic sensor based on the giant magneto-impedance effect and with axial acquisition characteristics. A dedicated signal processing circuit achieves high-sensitivity detection. In an environment where the ambient magnetic field is relatively stable, the detection noise level can reach 20pT. Through software correction, a higher inter-axis orthogonality can be achieved. The magnetic sensor can be widely used in magnetic anomaly detection, high-precision magnetic measurement, scientific research, and other fields that have high requirements for magnetic field detection sensitivity. Compared with other types of magnetic sensors, the GC-CC-304 high-precision magnetic sensor has the advantages of high precision, high response speed, high reliability, miniaturization, low power consumption, and strong anti-interference ability. Its main performance parameters include: (1) Range: +60000nT (+0.6Gs); (2) Noise: ≤20pT / @1Hz; (3) Signal output mode: digital output; (5) Sampling rate: 100Sa / s; (6) Temperature drift: 10ppm / ℃ (FS); (7) Time drift: ≤1nT / 24h; (8) On-line repeatability: +0.5nT; (9) Orthogonality: 90°+0.59; (10) Power supply voltage: DC9V ±5%; (11) Power consumption: ≤800mW; (12) Dimensions: 190×32×32mm. The magnetic sensor consists of a sensing element and a signal processing integrated circuit. The data transmission is transmitted to the host computer software (data analysis equipment) by wired communication, which can reduce the development difficulty and ensure the stability of the system. In the data analysis equipment, the host computer can be used for analysis. The magnetic field data acquisition and analysis structure is as follows: Figure 3 As shown, after the magnetic field measurement array acquires the magnetic field measurement data, it is transmitted to the host computer. The functions implemented by the host computer include: magnetic field data storage, original magnetic field display, compensated magnetic field display, spectrum analysis or magnetic source positioning. In order to perform subsequent processing and analysis on the uploaded magnetic field measurement data, the magnetic field data analysis host computer software can be written on the computer side. The host computer is responsible for receiving the magnetic field measurement data and saving the data. At the same time, it integrates the magnetic field compensation algorithm, displays the original magnetic field, compensated magnetic field and abnormal magnetic field spectrum through the interface, and realizes the positioning of the target magnetic source through the spatially rich magnetic field information.

[0067] The beneficial effects of implementing the embodiments of the present invention include: a magnetic source positioning device provided by the embodiments of the present invention includes a cross measurement bracket, a plurality of magnetic sensing devices and a data analysis device. The magnetic sensing device is connected to the cross measurement bracket, and magnetic field measurement data is obtained through the magnetic sensing device. The data analysis device is connected to the magnetic sensing device, and magnetic source positioning is performed through the data analysis device according to the magnetic field measurement data, so that magnetic source positioning can be achieved through the magnetic sensing device and the data analysis device, thereby improving efficiency and accuracy.

[0068] A magnetic source positioning method provided in an embodiment of the present application relates to the field of magnetic detection technology. A magnetic source positioning method provided in an embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a magnetic source positioning method, etc., but is not limited to the above forms.

[0069] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0070] Figure 4 is an optional flow chart of a magnetic source positioning method provided in an embodiment of the present application. Figure 4 The method may include but is not limited to steps S201 to S207.

[0071] Step S201, obtaining magnetic field measurement data;

[0072] Step S202, performing Fourier transform on the magnetic field measurement data to obtain transformed data, where the Fourier transform includes frequency domain analysis, time domain analysis or time-frequency domain analysis;

[0073] Step S203, performing wavelet transform on the magnetic field measurement data to obtain wavelet transform data;

[0074] Step S204, calculating the wavelet entropy according to the probability of discrete random variables and the wavelet energy;

[0075] Step S205: construct a risk function according to the wavelet coefficients;

[0076] Step S206, performing wavelet denoising on the transformed data according to the wavelet transformed data, wavelet entropy and risk function to obtain wavelet denoised data;

[0077] Step S207: locating the magnetic source according to the magnetic gradient tensor and the wavelet denoising data.

[0078] Steps S201 to S207 shown in the embodiment of the present application realize magnetic source positioning and improve efficiency and accuracy.

[0079] In step S201 of some embodiments, magnetic field measurement data may be obtained by a magnetic sensor device or by other methods, without limitation.

[0080] In some embodiments, in step S202, the magnetic field measurement data can be Fourier transformed to obtain transformed data. Among them, the Fourier transform can include frequency domain analysis, time domain analysis or time-frequency domain analysis. The biggest advantage of Fourier transform is that the time dimension and the frequency dimension can be transformed into each other. Time domain analysis is to represent the relationship between dynamic signals with the time axis as the coordinate; frequency domain analysis is to transform the signal into a frequency axis as the coordinate. The representation of the time domain is more vivid and intuitive, while the frequency domain analysis is more concise. The frequency domain analysis uses Fourier transform. According to Fourier's theorem, any continuously measured time series or signal can be expressed as an infinite superposition of sinusoidal signals of different frequencies. The sensor output signal can be regarded as a signal mixed by different sinusoidal signals. Through Fourier transform, this mixed signal can be decomposed into sinusoidal waves with different frequencies, thereby obtaining information in the frequency domain. Frequency domain analysis can not only be used to analyze task state data, but also often used to analyze resting state data. However, frequency domain analysis cannot reflect the change of frequency over time. Therefore, single-dimensional time domain analysis or frequency domain analysis cannot fully reflect the signal characteristics. At this time, time-frequency analysis is needed. In short, the frequency domain analysis of magnetic field signals requires Fourier transform of the signals of each subject, each channel, and each segment to obtain the energy at each frequency point and then perform statistical analysis. The statistical methods can be divided into: (1) Statistics along frequency points: select a certain spatial area and perform statistical tests along the frequency points. (2) Statistics along channels: select a certain frequency band and perform statistical tests along the channels. (3) Extract the energy values ​​of specific channels and specific frequency bands for statistical tests.

[0081] Time domain analysis can only reflect the change of amplitude over time, and frequency domain analysis can only view the distribution of frequency band energy, but cannot reflect the change of energy in a specific frequency band over time. At this time, time-frequency analysis is necessary. Time-frequency analysis contains both time domain and frequency domain information. The method is to perform windowing on the EEG data and assume that the data is steady in the time window, so as to perform Fourier transform and extract the frequency domain information in the time window. Slide the window forward along the time axis and perform the same processing on the data in each time window, so that the information of the frequency that changes over time can be obtained, and the result is the time-frequency diagram. The horizontal axis represents time, the vertical axis represents frequency, and the point corresponding to each time frequency represents the energy value. The size of the window will affect the time accuracy and frequency accuracy. The larger the window, the lower the time accuracy and the higher the frequency accuracy, which is suitable for analyzing low-frequency slow waves. The smaller the window, the higher the time accuracy and the lower the frequency accuracy, which is suitable for analyzing high-frequency fast waves. The specific setting should be based on research needs. The time window can be fixed or adaptive. For example, the time window of short-time Fourier transform is fixed in size, while the time window of wavelet transform can be expanded and contracted with the frequency change, making it more flexible to use. After frequency domain analysis, the frequency curve of the original data can be drawn to analyze the frequency changes at different times.

[0082] Spectral analysis is a technique for breaking down complex noise signals into simpler signals. Many physical signals can be represented as the sum of many simple signals of different frequencies. Spectral analysis is the process of finding out what a signal has at different frequencies (which may be amplitude, power, intensity or phase, etc.). If a signal changes over time and can be represented by amplitude, it has a corresponding spectrum. This includes visible light (color), music, radio waves, vibrations, etc. When these physical phenomena are represented by a spectrum, they can provide some information about the cause of the signal. Spectral analysis can be performed on the entire signal. However, sometimes the signal is divided into several segments, and then the spectrum analysis is performed on the signals of each segment. Periodic functions are most suitable for spectrum analysis of signals that only consider one period. There are many mathematical tools needed for analyzing non-periodic functions in Fourier analysis.

[0083] The Fourier transform of a function includes all the information in the original signal, but the representation is different. Therefore, the inverse Fourier transform can be used to reconstruct the original signal. To completely reconstruct the original signal, the amplitude and phase at each frequency are required. This information can be represented by a two-dimensional vector, a complex number, or the size and angle in polar coordinates. In signal processing, the square of the amplitude, that is, the power, is often considered, and the result is the power spectrum density. In fact, most instruments and software use fast Fourier transform to generate spectrum signals. Fast Fourier transform is a mathematical tool that calculates the discrete Fourier transform for a sampled signal and can approximate the result of Fourier transform. The Fourier transform of a random signal (or noise) is also random. Some averaging methods are needed to obtain its frequency distribution. Generally speaking, the data is divided into certain time segments, and each segment of the data is Fourier transformed. Then the transformed amplitude or amplitude square (amplitude square is more commonly used) is averaged to obtain the average value of the Fourier transform. When processing time domain data, the above method is often used in conjunction with discrete Fourier transform. This processing method is called Welch method. If the obtained spectrum is flat, the signal will be regarded as "white noise", but many signals look like noise in the time domain, but some frequency domain information can be obtained through such processing. The collected magnetic field measurement data can be subjected to spectrum analysis, and the spectrum analysis can draw the spectrum of the magnetic anomaly field, which can well find the time and intensity changes of the magnetic anomaly field, which is conducive to further analysis of the magnetic field in the area.

[0084] More often, magnetic anomalies are mainly additional magnetic fields generated by rocks with different magnetic properties in the crust being magnetized by the geomagnetic field. Geomagnetic anomalies are generally divided into continental anomalies, regional anomalies and local anomalies according to their area size. The characteristics of magnetic anomalies are mainly determined by the shape, strike, burial depth, inclination and extension, magnetization intensity, and especially the magnetization direction of the magnetic body. The above factors are not isolated but interrelated in their influence on the characteristics of magnetic anomalies. The geomagnetic field is composed of multiple magnetic fields generated by magnetic rocks inside the earth and currents distributed inside and outside the earth, and is divided into endogenous fields and exogenous fields. The endogenous field includes the main magnetic field originating from the core of the earth (core field) and the lithospheric magnetic field originating from the crust (crust field). The exogenous field is mainly caused by the current system distributed in the magnetosphere, ionosphere and interstellar space, and is the direct manifestation of the space electromagnetic environment on the earth. In addition, the exogenous field will also generate induced currents and induced magnetic fields inside the earth. From a global average, the core field accounts for more than 95% of the total magnetic field, the crustal magnetic field accounts for about 4%, and the exogenous variable magnetic field and its induced magnetic field account for only 1% of the total magnetic field. Although the main magnetic field does not change as quickly and drastically as the external field, it still changes over a long period of time. The most notable manifestations are changes in the dipole moment and movement of the magnetic poles.

[0085] In some embodiments, in step S203, performing wavelet transform on the magnetic field measurement data to obtain wavelet transform data may include, but is not limited to, the following steps:

[0086] Construct a mother wavelet constraint according to the total number of vanishing moments;

[0087] Construct a basic wavelet function constraint according to the basic wavelet function;

[0088] Calculate the wavelet transform data according to the magnetic field measurement data, the mother wavelet constraint, and the basic wavelet function constraint.

[0089] In some embodiments, wavelet transform theory provides a new idea for the problem of data noise cancellation. The main idea is that according to the linearity of wavelet transform, if the noise energy is significantly less than the signal energy, the wavelet coefficients corresponding to the noise will also be significantly less than the wavelet coefficients corresponding to the signal; therefore, denoising can be achieved by removing the wavelet coefficients less than a certain threshold. First, a mother wavelet constraint can be constructed according to the total number of vanishing moments, where the expression of the mother wavelet constraint is: In the formula, t is time, m is the total number of vanishing moments, and Ψ(t) is the mother wavelet function. It can be understood that in most cases, it is required that Ψ is continuous and has a large integer M with one moment being 0, that is, for all integers m < M, it satisfies That is, the mother wavelet has M vanishing moments and M is not equal to 0, which means that the mother wavelet must not be a constant and has a mean value of 0. Then, a basic wavelet function constraint is constructed according to the basic wavelet function, where the basic wavelet function constraint includes: In the formula, Ψ(t) is the basic wavelet function, also known as the mother wavelet function. It can be understood that a characteristic of wavelet transform is that there is no fixed kernel function, but not any function can be used as the basic wavelet function Ψ(t) of wavelet transform, and the wavelet adopted must satisfy the basic wavelet function constraint. Finally, the wavelet transform data is calculated according to the magnetic field measurement data, the mother wavelet constraint, and the basic wavelet function constraint. Let x(t) be a square-integrable function, that is, x(t) ∈ L 2 (R), then the wavelet transform of x(t) is: In the formula, WT x (a, b) is the wavelet transform data, a is the scale factor, a > 0, b reflects the displacement and can be positive or negative, Ψ(t) is the basic wavelet function or the mother wavelet function, is the displacement and scale stretching of the basic wavelet, and the superscript * represents taking the conjugate. The purpose of adding the factor a,b before Ψ is to make Ψ under different a values a,b(t) energy conservation. It can be understood that the magnetic field measurement data can be converted into a square integrable function, and then the wavelet transform data is calculated, and the mother wavelet constraint and the basic wavelet function constraint need to be satisfied.

[0090] In some embodiments, in step S204, the wavelet entropy is calculated according to the probability of discrete random variables taking values ​​and the wavelet energy, which may include but is not limited to the following steps:

[0091] Calculate the initial entropy based on the probability of discrete random variables taking values;

[0092] Process the wavelet transform coefficient matrix to obtain a probability distribution sequence;

[0093] The wavelet entropy is calculated based on the wavelet energy, initial entropy and probability distribution sequence.

[0094] In some embodiments, in information theory, entropy is used to describe the average amount of information provided by each symbol and the average uncertainty of the information source. The initial entropy can be calculated based on the probability of the discrete random variable taking a value. Suppose the discrete random variable x takes a value of i The probability of p(a i ),i=1,2,…q, and p(a i )>0, Then the calculation formula of initial entropy is: Where H(x) is the initial entropy, p(a i ) is the probability of a discrete random variable taking a value. It can be understood that the size of the entropy value actually reflects the uniformity of its probability distribution, that is, the closer the probability distribution of the signal is to a disordered distribution, the larger its entropy value is; conversely, the smaller its entropy value is. Then the wavelet transform coefficient matrix is ​​processed to obtain a probability distribution sequence. It can be understood that the coefficient matrix of the wavelet transform can be processed into a probability distribution sequence, and the entropy value calculated from it reflects the sparsity of the coefficient matrix, that is, the degree of order of the signal probability distribution. This entropy is called wavelet entropy. Finally, the wavelet entropy is calculated based on the wavelet energy, initial entropy and probability distribution sequence. p(a i ) is replaced by the relative wavelet energy p that reflects the energy intensity ratio of a certain section of the signal j , transform the initial entropy equation, and the calculation formula of wavelet entropy is: S wT =-∑ j p j ln(p j ), where S WT is the wavelet entropy, p j is the wavelet energy.

[0095] In some embodiments, in step S205, constructing a risk function according to the wavelet coefficients may include but is not limited to the following steps:

[0096] Construct an observation signal based on the wavelet coefficients and the preset threshold;

[0097] Calculate the noise signal based on the observed signal and the useful signal;

[0098] Calculate the threshold coefficient according to the median of the absolute values ​​of the wavelet coefficients;

[0099] According to the threshold coefficient, a universal threshold is calculated;

[0100] Based on the noise signal and the universal threshold, a risk function is constructed.

[0101] In some embodiments, after the noise signal is transformed by orthogonal wavelet, the correlation of the useful signal can be removed to the greatest extent, and the energy can be concentrated on a few sparse wavelet coefficients with relatively large amplitudes. The wavelet coefficients corresponding to the noise are still uncorrelated with each other, and will be distributed on all time axes at all scales, and the amplitude is not very large. If the few wavelet coefficients that concentrate most of the energy of the useful signal are retained at each scale of the wavelet transform, and the other points are set to zero, or minimized to the greatest extent, and then the processed wavelet coefficients are used for inverse wavelet transform, the noise can be suppressed. Therefore, the wavelet threshold plays a decisive role in the denoising process and will directly affect the effect of wavelet denoising. The observation signal can be constructed first based on the wavelet coefficients and the preset threshold. Exemplarily, let the observation signal x(n) = s(n) + u(n), where s(n) is the useful signal and u(n) is the noise. After applying the preset threshold λ to the wavelet coefficients, the reconstructed observation signal x is obtained. λ Then, based on the observed signal and the useful signal, the noise signal, u λ is x λ The residual noise in λ =x λ -s. Then, the threshold coefficient is calculated according to the median of the absolute value of the wavelet coefficient, where the calculation formula of the threshold coefficient is: In the formula, σ is the threshold coefficient, and MAD is the median of the absolute value of the wavelet coefficients at a certain scale. Then, according to the threshold coefficient, the universal threshold is calculated, where the calculation formula of the universal threshold is: In the formula, λ univ is the universal threshold, and N is the data length. Finally, based on the noise signal and the universal threshold, a risk function is constructed, which is λ The hazard function generated by approximation s is defined as: In the formula, R(λ) is the risk function, MSE(λ) is the mean square error, and u λis a noise signal. It is understandable that since the minimum mean square error is difficult to estimate in practice, a universal threshold can be used for calculation. In the process of determining the threshold, the high-frequency information of each decomposition scale is regarded as a separate signal source, and each layer of high-frequency wavelet coefficients is divided into n equal small intervals. The wavelet entropy of each small interval is calculated, and the median of the small interval with the largest entropy value is selected as the variance of the noise at this scale. Then, the threshold at each scale is calculated according to the above formula to realize the adaptive selection of thresholds based on wavelet entropy.

[0102] In some embodiments, in step S206, wavelet denoising may be performed on the transformed data according to the wavelet transformed data, wavelet entropy and risk function to obtain wavelet denoised data.

[0103] In some embodiments, in step S207, magnetic source positioning is performed according to the magnetic gradient tensor and the wavelet denoising data, which may include but is not limited to the following steps:

[0104] Based on the magnetic gradient tensor and wavelet denoised data, the initial positioning equation is constructed;

[0105] Transform the initial positioning equation to obtain the target positioning equation;

[0106] Calculate the distance from the magnetic sensing device to the target magnetic source based on the third-order magnetic field tensor, the target positioning equation and the magnetic gradient tensor;

[0107] The magnetic source is located according to the distance from the magnetic sensing device to the target magnetic source.

[0108] In some embodiments, an initial positioning equation can be constructed based on the magnetic gradient tensor and wavelet denoising data. The magnetic source of the target is located using the magnetic gradient tensor, and the first derivative of the single-point magnetic gradient tensor positioning formula is obtained to obtain a new magnetic gradient tensor target positioning algorithm (i.e., the initial positioning equation): In the formula, G is the magnetic gradient tensor, r is the distance from the magnetic sensing device to the target magnetic source, and B is the wavelet denoising data. Then the initial positioning equation is transformed to obtain the target positioning equation, where the expression of the target positioning equation is: Then, according to the third-order magnetic field tensor, the target positioning equation and the magnetic gradient tensor, the distance from the magnetic sensing device to the target magnetic source is calculated. The target positioning equation can be converted to obtain the calculation formula for the distance from the magnetic sensing device to the target magnetic source: Where r is the distance from the magnetic sensing device to the target magnetic source, T is the third-order magnetic field tensor, Finally, the magnetic source is located according to the distance from the magnetic sensor device to the target magnetic source. A planar cross array can be mounted on the positioning platform to obtain the magnetic gradient tensor at the measurement point. After the third-order magnetic field tensor T and the magnetic gradient tensor G at a certain point are known, the target magnetic source can be located. The principle diagram of magnetic source positioning is shown in the figure below. Figure 5 As shown in the figure, the platform moves and the magnetic target is stationary. The magnetic gradient tensor measurement system is fixed on the moving platform. The y-axis coincides with the axis of the platform, the x-axis points to the left of the platform's movement direction, and the z-axis is vertically downward. Assume that the magnetic target is located at the origin of the spatial rectangular coordinate system, with the geographic north as the x-axis, the east as the y-axis, and the vertical downward as the z-axis. The magnetic source is located using the distance r from the magnetic sensing device to the target magnetic source.

[0109] In some embodiments, the overall process of processing magnetic field measurement data is as follows: Figure 6 As shown, the magnetic field measurement data can be first used as data input, then Fourier transform is performed, and then wavelet denoising is performed, and finally data output is obtained to realize data processing.

[0110] In some embodiments, the device of this embodiment uses a magnetic field measurement array to obtain data, and uses a data analysis host computer (data analysis equipment) to process the data, providing assistance for resource exploration and indoor and outdoor magnetic positioning, which has great value in engineering applications. This embodiment uses wavelet transform magnetic field compensation and third-order tensor positioning methods to effectively improve the accuracy of magnetic source positioning;

[0111] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of the present invention first acquire magnetic field measurement data, perform Fourier transform on the magnetic field measurement data to obtain transformed data, then perform wavelet transform on the magnetic field measurement data to obtain wavelet transformed data, calculate wavelet entropy according to the probability of discrete random variable values ​​and wavelet energy, and construct a risk function according to the wavelet coefficients, then perform wavelet denoising on the transformed data according to the wavelet transform data, wavelet entropy and risk function to obtain wavelet denoised data, and finally perform magnetic source positioning according to the magnetic gradient tensor and the wavelet denoised data, so that magnetic source positioning can be achieved through Fourier transform and wavelet denoising, thereby improving efficiency and accuracy.

[0112] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A magnetic source positioning device, characterized in that: include: Cross measuring bracket; A plurality of magnetic sensing devices, the magnetic sensing devices are connected to the cross measurement bracket and are used to obtain magnetic field measurement data; A data analysis device is connected to the magnetic sensing device and is used to locate the magnetic source based on the magnetic field measurement data.

2. The device according to claim 1, characterized in that The material of the cross measuring bracket is anti-magnetic aluminum material.

3. The device according to claim 1, characterized in that The number of the magnetic sensing devices is 4.

4. The device according to claim 1, characterized in that The magnetic sensor device is connected to the end of the cross measurement bracket.

5. The device according to claim 1, characterized in that The magnetic sensing device includes a three-axis magnetic sensor.

6. A magnetic source positioning method applied to the device according to any one of claims 1 to 5, characterized in that: The following steps are involved: Obtaining magnetic field measurement data; Performing Fourier transform on the magnetic field measurement data to obtain transformed data, wherein the Fourier transform includes frequency domain analysis, time domain analysis or time-frequency domain analysis; Performing wavelet transformation on the magnetic field measurement data to obtain wavelet transformation data; Calculate the wavelet entropy based on the probability of discrete random variables and wavelet energy; According to the wavelet coefficients, construct the risk function; According to the wavelet transform data, the wavelet entropy and the risk function, performing wavelet denoising on the transform data to obtain wavelet denoised data; Magnetic source localization is performed based on the magnetic gradient tensor and the wavelet denoised data.

7. The method according to claim 6, characterized in that The step of performing wavelet transform on the magnetic field measurement data to obtain wavelet transform data includes: According to the total number of vanishing moments, the mother wavelet constraint is constructed; According to the basic wavelet function, construct the basic wavelet function constraint; The wavelet transform data is calculated according to the magnetic field measurement data, the mother wavelet constraint and the basic wavelet function constraint.

8. The method according to claim 6, characterized in that The step of calculating the wavelet entropy according to the probability of discrete random variables taking values ​​and the wavelet energy includes: Calculating initial entropy according to the probability of the discrete random variable taking a value; Process the wavelet transform coefficient matrix to obtain a probability distribution sequence; The wavelet entropy is calculated according to the wavelet energy, the initial entropy and the probability distribution sequence.

9. The method according to claim 6, characterized in that The risk function is constructed according to the wavelet coefficients, including: constructing an observation signal according to the wavelet coefficients and a preset threshold; Calculating a noise signal according to the observation signal and the useful signal; Calculate the threshold coefficient according to the median of the absolute values ​​of the wavelet coefficients; Calculating a universal threshold value according to the threshold coefficient; The risk function is constructed according to the noise signal and the universal threshold.

10. The method according to claim 6, characterized in that The method of performing magnetic source positioning according to the magnetic gradient tensor and the wavelet denoising data comprises: constructing an initial positioning equation according to the magnetic gradient tensor and the wavelet denoising data; Converting the initial positioning equation to obtain a target positioning equation; Calculating the distance from the magnetic sensing device to the target magnetic source according to the third-order magnetic field tensor, the target positioning equation and the magnetic gradient tensor; The magnetic source is located according to the distance from the magnetic sensing device to the target magnetic source.

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