Omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality

Abstract

The invention relates to the technical field of augmented reality, and discloses an omni-directional magnetic field real-time monitoring and visualization system based on augmented reality, and the system comprises a data collection and transmission module which collects a magnetic field signal outputted by a magnetic field measuring instrument, and transmits the magnetic field signal to a data filtering module; the data filtering module adopts a denoising method based on CEEMDAN threshold filtering to remove noise; the AR environment modeling module adopts an SLAM algorithm to calculate the pose of AR equipment in the environment, and constructs a map; the coordinate conversion module integrates the equipment pose output by the SLAM and converts the filtered magnetic field data into a world coordinate system of the AR equipment; the magnetic field direction calculation module calculates the magnetic field direction by using the converted world coordinate system data; and the visualization module adopts an API of Unity to receive the magnetic field data, and renders a magnetic field direction indication arrow in an AR scene. According to the invention, based on noise suppression of CEEMDAN threshold filtering, coordinate conversion of SLAM pose driving and a lightweight three-dimensional rendering technology, accurate visualization of an omnidirectional magnetic field is realized, and industrial-grade real-time monitoring requirements are met.

Description

Technical Field

[0001] The present invention relates to the field of augmented reality technology, and particularly to an omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality. Background Art

[0002] Magnetic field measurement devices are instruments used to detect the magnetic field intensity, direction, and distribution in space, and are widely used in fields such as scientific research, industry, geological exploration, and electronic equipment debugging. Their core function is to convert the physical signals of the magnetic field into quantifiable electrical signals or digital signals for analysis, display, or control. The real-time monitoring and visualization of the magnetic field are crucial for equipment fault diagnosis and the analysis of the spatial magnetic field distribution. Traditional magnetic field measurement devices rely on physical probes to collect magnetic field data and present it in the form of numerical values, two-dimensional curves, etc. through a screen or an external terminal, and have the following defects:

[0003] 1. Low data understanding efficiency: Users need to manually associate the spatial position of the probe with the magnetic field data, and it is difficult to intuitively perceive the dynamic changes of the magnetic field in three-dimensional space. Especially in complex environments (such as multi-magnetic source coupling scenarios), misjudgment is likely to occur due to spatial cognitive deviation.

[0004] 2. Insufficient environmental adaptability: Existing solutions are mostly based on single-point measurement or static modeling, and cannot track the coupling relationship between the probe pose and the magnetic field distribution in real time.

[0005] 3. Problem of multi-source noise interference: Electromagnetic noise in industrial environments (such as inverter harmonics and eddy currents in metal structures) can easily cause distortion of magnetic field signals. The denoising effect of traditional filtering algorithms (such as Kalman filtering and wavelet filtering) is limited in non-stationary noise scenarios, affecting the subsequent direction calculation accuracy.

[0006] 4. Defect in the accuracy of virtual-real fusion: Early AR magnetic field visualization solutions directly used IMU magnetometers to calculate the magnetic field direction, without calibrating the installation deviation of the sensor and environmental magnetic interference, resulting in misalignment between the virtual vector and the real magnetic field direction; and relying on fixed coordinate system mapping, without combining the real-time pose of the AR device (such as the SLAM positioning result), there is a spatial anchoring error in dynamic moving scenarios.

[0007] In view of the above problems, the existing technology urgently needs an augmented reality system that can fuse high-precision magnetic field data, dynamic environment modeling, and device pose in real time to solve the problems of intuitiveness, accuracy, and real-time of magnetic field monitoring in complex scenarios. Summary of the Invention

[0008] The present invention provides an omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality, which realizes the precise visualization of the omnidirectional magnetic field and meets the industrial-level real-time monitoring requirements based on noise suppression by CEEMDAN threshold filtering, coordinate transformation driven by SLAM pose, and lightweight three-dimensional rendering technology.

[0009] The present invention provides an omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality, including a data acquisition and transmission module, a data filtering module, an AR environment modeling module, a coordinate transformation module, a magnetic field direction calculation module, and a visualization module, which are connected in sequence;

[0010] The data acquisition and transmission module is used to collect the magnetic field signals output by the magnetic field measuring instrument and transmit the magnetic field data to the data filtering module;

[0011] The data filtering module is used to remove the noise in the magnetic field signals by using a denoising method based on CEEMDAN threshold filtering to obtain the denoised magnetic field data;

[0012] The AR environment modeling module is used to adopt the SLAM algorithm to calculate the pose of the AR device in the environment in real time through the camera and IMU, and construct a map to provide a coordinate reference for the spatial anchoring of virtual objects;

[0013] The coordinate transformation module is used to convert the filtered magnetic field data into the world coordinate system of the AR device by integrating the device pose output by SLAM;

[0014] The magnetic field direction calculation module is used to calculate the magnetic field direction by using the data in the converted world coordinate system for AR visualization;

[0015] The visualization module uses the API of Unity to receive the magnetic field data and renders magnetic field direction indication arrows in the AR scene.

[0016] Further, in the data acquisition and transmission module, the RS-232 interface of the magnetic field measuring instrument is connected to the Arduino Uno through the MAX3232 level conversion module, the serial port data is parsed in the Arduino, and then the HC-05 Bluetooth module is used to transmit it to the data filtering module in JSON format through Bluetooth.

[0017] Further, the data filtering module uses a denoising method based on CEEMDAN threshold filtering to remove the noise in the magnetic field signals, specifically including:

[0018] S1. Perform complete decomposition on the magnetic field signals by the CEEMDAN method, and determine the boundary between the decomposed noise components and effective components by using the autocorrelation coefficient and Fourier transform;

[0019] S2. Extract the useful signals in the signal containing noise components by using the wavelet soft threshold method, and finally perform signal reconstruction to obtain the denoised magnetic field signals.

[0020] Further, the specific steps of S1 include:

[0021] Define an operator E j (·), which represents the jth IMF generated by EMD, ω i (n) is white noise of N(0,1), ε i is the amplitude coefficient of white noise, and x(n) is the original magnetic field signal;

[0022] S101. First, add white noise ε0ω i (n) to the original magnetic field signal x(n) to obtain signal X i (n) is:

[0023] X i (n) = x i (n) + ε0ω i (n)

[0024] where i is the number of times of adding Gaussian white noise, i = 1, 2,..., N; first, obtain the first-order IMF component IMF1. For X i (n) with white noise added for the ith time, perform EMD decomposition to obtain IMF i1 , repeat the calculation N times, and the first-order IMF can be obtained by integrating and averaging it as:

[0025]

[0026] Obtain the first-order residual from the original magnetic field signal x(n) and the first-order IMF as:

[0027] r1(n) = x(n) - IMF1

[0028] S102. Add white noise ε1ω i (n) to the first-order residual r1(n) to obtain the signal r1(n) + ε1ω i (n), and perform EMD decomposition in the same way to obtain IMF i2 , repeat the calculation N times, and the second-order IMF can be obtained by integrating and averaging it as:

[0029]

[0030] Obtain the second-order residual from the first-order residual r1(n) and the second-order IMF as:

[0031] r2(n) = r1(n) - IMF2

[0032] S103. Add white noise ε k-1 (n) to the residual r k-1 ω i (n) to obtain the signal r k-1 (n) + ε k-1 ω i(n), perform EMD decomposition to obtain IMFs ik , calculate N times repeatedly, then the k-th order IMF is:

[0033]

[0034] The k-th order residue obtained by decomposition is:

[0035] rk(n) = rk -1 (n) - IMF k

[0036] S104. Return to step S103 to continue the next decomposition until the residue cannot be decomposed. The final residue is R(n);

[0037] S105. Obtain the final decomposition result. The original magnetic field signal x(n) is expressed as:

[0038]

[0039] S106. Set the original magnetic field signal x(n) as x(t). Its autocorrelation function expression is:

[0040] R x (t1, t2) = E[x(t1), x(t2)]

[0041] Calculate the autocorrelation function values of the noise and the signal modal components using the normalized autocorrelation function. The expression of the normalization function is:

[0042]

[0043] Perform CEEMDAN decomposition on the geomagnetic signal containing random noise to obtain IMF components. According to the above characteristics, calculate the autocorrelation function of each IMF, and then judge the boundary between the noise-containing IMF components and the effective signal IMFs. Perform Fourier analysis on each IMF component to analyze the signal in the frequency domain, and further determine the demarcation point based on the low-frequency characteristics of the geomagnetic signal.

[0044] Furthermore, the specific steps of step S2 include:

[0045] Use threshold filtering for the first (k - 1) IMF noise components. The threshold processing form adopts the soft threshold method, and the expression is:

[0046]

[0047] where imf′ i is the denoised i-th order IMF component, and T i is the threshold of the i-th order IMF component. The method for determining the threshold adopts the threshold calculation model in the EMD method:

[0048]

[0049] Among them, σ is a unique constant, and E i is the energy corresponding to the i-th order IMF, and its value is estimated by the following formula:

[0050]

[0051] After performing wavelet soft threshold processing on the first k order IMFs, the first k order filtered IMF components are IMF′, and the reconstructed signal can be obtained from the IMF components after the k-th order as:

[0052]

[0053] Among them, x′ is the reconstructed magnetic field signal.

[0054] Furthermore, the coordinate conversion module converts the filtered magnetic field data into the world coordinate system of the AR device by aggregating the device poses output by SLAM, specifically including:

[0055] The attitude of the AR device is represented by Euler angles or quaternions to describe the rotation relationship of the sensor coordinate system relative to the AR device coordinate system. According to the Euler angle sequence Z-Y-X, the rotation matrix is the product of three basic rotation matrices: R = R z (ψ)·R y (θ)·R x (φ), where the basic rotation matrix is:

[0056]

[0057] Let the magnetic field vector in the sensor coordinate system be B s =[B sx ,B sy ,B sz T , and the vector converted to the device coordinate system is B d =[B dx ,B dy ,B dz T , B d =R·B s ;

[0058] Given the rotation matrix R w of the AR device in the world coordinate system, the conversion from the AR device coordinate system to the world coordinate system is: B w =R w ·B d ; among them, R w is calibrated by SLAM, and B w is the magnetic field data in the world coordinate system.​​

[0059] Furthermore, the magnetic field direction calculation module is used to calculate the magnetic field direction using the converted world coordinate system data, specifically including:

[0060] The converted three-axis magnetic field data (B wx ,B wy ,B wz ) Calculate the azimuth α and inclination β of the magnetic field;

[0061] α=arctan2(B wy ,B wx )

[0062]

[0063] Among them, α represents the projection of the magnetic field on the horizontal plane and x w The angle between the x and x axes ranges from -π to π; β represents the angle between the magnetic field and the horizontal plane, which is positive when it is upward and negative when it is downward. w Axis east, y w North, z w up.

[0064] Furthermore, the user interaction module includes a gesture control unit and a voice feedback unit. The gesture control unit is used to set the magnetic field strength threshold by sliding and pinching to switch the display mode, including vectors and magnetic lines of force; the voice feedback unit is used to trigger a voice warning when the magnetic field exceeds the safety threshold.

[0065] The beneficial effects of the present invention are:

[0066] The present invention significantly improves the accuracy and visualization efficiency of omnidirectional magnetic field monitoring through multi-module collaborative innovation: the data filtering module adopts CEEMDAN threshold filtering to improve the signal-to-noise ratio of the magnetic field signal, effectively suppresses power frequency noise, and solves the signal distortion problem in a non-stationary environment; AR environment modeling is combined with the SLAM algorithm to realize real-time solution of equipment posture and construction of environmental grid, improve the overlap between the virtual magnetic field vector and the real space, and eliminate the virtual and real misalignment in dynamic movement; the coordinate conversion module reduces the three-dimensional magnetic field direction error and improves the azimuth and inclination accuracy; the visualization module is optimized through the Unity engine to achieve real-time synchronization of magnetic field changes and arrow pointing; the effective working time of the present invention is improved in complex scenes such as strong magnetic interference and weak texture, and the efficiency of magnetic field distribution interpretation is improved by coordinating three-dimensional dynamic arrows with gesture interaction, providing a high-precision, low-latency, and highly robust real-time monitoring solution for industrial detection, scientific research analysis and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a structural schematic diagram of the omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality of the present invention.

[0068] The realization, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0069] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] As Figure 1 shown, the present invention provides an omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality, including a data acquisition and transmission module, a data filtering module, an AR environment modeling module, a coordinate conversion module, a magnetic field direction calculation module, a visualization module, and a user interaction module that are connected in sequence.

[0071] (1) Data acquisition and transmission module

[0072] The data acquisition and transmission module is used to collect the magnetic field signals output by a magnetic field measuring instrument (NARDA ELT-400 magnetic field measuring instrument) and transmit the magnetic field data to the data filtering module.

[0073] In the data acquisition and transmission module, the RS-232 interface of the magnetic field measuring instrument is connected to Arduino Uno through a MAX3232 level conversion module, the serial port data is parsed in Arduino, and then an HC-05 Bluetooth module is used to transmit it to the data filtering module in JSON format via Bluetooth. In the present invention, an AR glasses supporting SLAM (such as HoloLens2 or Magic Leap2) is adopted and developed through the AR Foundation framework of Unity. The sensor synchronously enables the IMU and camera of the AR device, and uses the SLAM algorithm to obtain the device attitude (quaternion) in real time.

[0074] (2) Data filtering module

[0075] The data filtering module is used to remove the noise in the magnetic field signals by using a denoising method based on CEEMDAN threshold filtering to obtain the denoised magnetic field data.

[0076] Applying the CEEMDAN threshold filtering method to the denoising of magnetic field signals, the complete decomposition of the noisy geomagnetic signal is carried out, and the signal after threshold filtering processing and the effective signal are reconstructed to obtain the denoised signal. Specifically, it includes:

[0077] S1. Perform complete decomposition on the magnetic field signals by the CEEMDAN method, and determine the boundary between the decomposed noise components and effective components by using the autocorrelation coefficient and Fourier transform.

[0078] CEEMDAN adaptively adds different Gaussian white noises to it according to the signal characteristics, obtains the IMF components of the next level through the calculated residual signals, and decomposes a set of IMF components from the noisy signal from high frequency to low frequency, which can effectively solve the mode mixing problem that occurs when using EMD and improve the completeness and decomposition efficiency of the decomposition process. Define an operator E j (·), which represents the jth IMF generated by EMD, and ω i (n) is white noise of N(0,1), and ε i is the amplitude coefficient of the white noise, and x(n) is the original magnetic field signal;

[0079] S101. First, add white noise ε0ω i (n) to the original magnetic field signal x(n) to obtain the signal X i (n) as:

[0080] X i (n) = x i (n) + ε0ω i (n)

[0081] where i is the number of times of adding Gaussian white noise, i = 1, 2,..., N; first obtain the first-order IMF component IMF1. For X i (n) with white noise added for the ith time, perform EMD decomposition to obtain IMF i1 , repeat the calculation N times, and the first-order IMF can be obtained by integrating and averaging it as:

[0082]

[0083] The first-order residual is obtained from the original magnetic field signal x(n) and the first-order IMF as:

[0084] r1(n) = x(n) - IMF1

[0085] S102. Add white noise ε1ω i (n) on the basis of the first-order residual r1(n) to obtain the signal r1(n) + ε1ω i (n), and also perform EMD decomposition to obtain IMF i2 , repeat the calculation N times, and the second-order IMF can be obtained by integrating and averaging it as:

[0086]

[0087] The second-order residual is obtained from the first-order residual r1(n) and the second-order IMF as:

[0088] r2(n) = r1(n) - IMF2

[0089] S103. On the residual rk-1 Add white noise ε on the basis of (n) k-1 ω i (n) to obtain signal r k-1 (n) + ε k-1 ω i (n) is subjected to EMD decomposition to obtain IMF ik Repeat the calculation N times, then the k-th order IMF is:[[]]

[0090]

[0091] The decomposed k-th order residual is:[[]]

[0092] r k (n) = r k-1 (n) - IMF k

[0093] S104. Return to step S103 to continue the next decomposition until the residual cannot be decomposed. The final residual is R(n);

[0094] S105. Obtain the final decomposition result. The original magnetic field signal x(n) is expressed as:[[]]

[0095]

[0096] After the final signal is decomposed by CEEMDAN, a series of IMF components from high frequency to low frequency are obtained. CEEMDAN can accurately and completely reconstruct the original signal by using the characteristics of noise adaptability.

[0097] S106. After the magnetic field signal containing random noise is completely decomposed by the CEEMDAN method, the obtained main components are the IMF with noise, the IMF with effective magnetic signal and noise aliasing, and the IMF of the effective magnetic signal. In order to filter out the noise signal in the IMF component containing noise and reconstruct the remaining effective signal IMF component to achieve signal denoising. Therefore, it is necessary to accurately find the boundary between the noise IMF component and the useful signal IMF component, which directly determines the denoising effect of the CEEMDAN filtering method. Specifically include:[[]]

[0098] Set the original magnetic field signal x(n) as x(t), and its autocorrelation function expression is:[[]]

[0099] R x (t1, t2) = E[x(t1), x(t2)]

[0100] Calculate the autocorrelation function values of the noise and the signal modal components by using the normalized autocorrelation function. The expression of the normalized function is:[[]]

[0101]

[0102] The geomagnetic signal containing random noise is decomposed by CEEMDAN to obtain IMF components. According to the above characteristics, the autocorrelation function of each IMF is calculated, and then the boundary between the noisy IMF components and the effective signal IMF is determined. However, due to the complexity of the noise and the uncertainty of the autocorrelation function curve, Fourier analysis can be performed on each IMF component to analyze the signal in the frequency domain, and the demarcation point can be further determined based on the low-frequency characteristics of the geomagnetic signal.

[0103] S2. The wavelet soft threshold method is used to extract the useful signal from the signal with noisy components, and finally signal reconstruction is performed to obtain the denoised magnetic field signal.

[0104] Due to the continuity of general signals in the time domain and the discontinuity of random noise in the time domain, in the wavelet domain, the wavelet coefficients generated by the effective signal have larger modulus values, while Gaussian white noise still shows strong randomness after wavelet transform and its corresponding coefficients are very small. According to this characteristic, denoising processing needs to be carried out by setting a threshold function. The coefficients greater than the threshold are retained, and the decomposed coefficients less than the threshold are eliminated by setting them to zero. CEEMDAN reduces the reconstruction error by adding a finite number of adaptive white noises. However, the high-frequency IMF components obtained after decomposition may contain effective component signals. Therefore, according to the EMD threshold setting method, the wavelet soft threshold processing method is used to perform threshold processing on the high-frequency IMF, and then the filtered components and the useful components are reconstructed to achieve the purpose of signal denoising. Specifically, it includes:

[0105] Threshold filtering is performed on the first (k - 1) IMF noise components, and the soft threshold method is used for the threshold processing form. The expression is:

[0106]

[0107] where, imf′ i is the denoised i-th order IMF component, T i is the threshold of the i-th order IMF component. The method for determining the threshold adopts the threshold calculation model in the EMD method:

[0108]

[0109] where, σ is a unique constant, E i is the energy corresponding to the i-th order IMF, and its value is estimated by the following formula:

[0110]

[0111] After performing wavelet soft thresholding on the first k IMFs, the first k filtered IMF components are obtained as IMF′, and the reconstructed signal can be obtained from the k-th order and subsequent IMF components as follows:

[0112]

[0113] Among them, x′ is the reconstructed magnetic field signal.

[0114] (3) AR environment modeling module

[0115] The AR environment modeling module is used to adopt the SLAM algorithm to calculate the pose of the AR device in the environment in real time through the camera and IMU, and construct a map to provide a coordinate reference for the spatial anchoring of virtual objects.

[0116] In the AR device, the SLAM (Simultaneous Localization and Mapping) algorithm is used to determine the 6DoF (3D position + orientation) of the device in real time and construct an environmental map, which is the core technology for achieving accurate anchoring of virtual objects.

[0117] (4) Coordinate transformation module

[0118] The coordinate transformation module is used to convert the filtered magnetic field data into the world coordinate system of the AR device by integrating the device pose output by SLAM.

[0119] In coordinate transformation, the application of rotation matrices or quaternions is involved to convert the original three-axis magnetic field data into a vector in the target coordinate system. Specifically, it includes:

[0120] Sensor coordinate system (S system): The origin is the center of the sensor; the coordinate axes are usually defined as x s , y s , z s (such as the length, width, and height directions of the sensor).

[0121] Device coordinate system (D system): Origin: The geometric center of the AR device; the coordinate axes are aligned with the physical directions of the device (when the device screen faces forward, x d points to the right, y d points upward, z d points forward).

[0122] World coordinate system (W system): Global reference system (such as the northeast celestial coordinate system: x w points east, y w points north, z w points upward).

[0123] The attitude of the AR device is represented by Euler angles or quaternions to describe the rotation relationship of the sensor coordinate system relative to the AR device coordinate system. The Euler angle definition (taking the Z-Y-X rotation order as an example): Yaw angle ψ: Rotation around the z-axis; Pitch angle θ: Rotation around the rotated y-axis; Roll angle φ: Rotation around the rotated x-axis.

[0124] According to the Euler angle order Z-Y-X, the rotation matrix is the product of three basic rotation matrices: R = R z (ψ)·R y (θ)·R x (φ), where the basic rotation matrices are:

[0125]

[0126] Let the magnetic field vector in the sensor coordinate system be B s =[B sx , B sy , B sz T , and the vector converted to the device coordinate system is B d =[B dx , B dy , B dz T , B d =R·B s ;

[0127] Given the rotation matrix R w of the AR device in the world coordinate system, the conversion from the AR device coordinate system to the world coordinate system is: R w =R w ·B d ; where, R w is obtained by SLAM calibration, and B w is the magnetic field data in the world coordinate system.

[0128] (5) Magnetic field direction calculation module

[0129] The magnetic field direction calculation module is used to calculate the magnetic field direction by using the converted world coordinate system data for AR visualization; specifically including:

[0130] Using the converted three-axis magnetic field data (B wx , B wy , B wz ) to calculate the azimuth angle α and dip angle β of the magnetic field;

[0131] α = arctan2(B wy , B wx )

[0132] ​​

[0133] Among them, α represents the angle between the projection of the magnetic field on the horizontal plane and the x w axis, with a range from -π to π; β represents the angle between the magnetic field and the horizontal plane, positive upward and negative downward; among them, the x w axis points east, the y w axis points north, and the z w axis points upward.

[0134] (6) Visualization module

[0135] The visualization module uses the API of Unity to receive magnetic field data and renders magnetic field direction indicator arrows in the AR scene.

[0136] Unity development:

[0137] Project settings: Import AR Foundation and ARCore / ARKit plugins. Configure Bluetooth serial communication and use the Serial Port Utility Pro plugin to receive data in real time.

[0138] Add a 3D arrow prefab in the scene, set the material color to change with the magnetic field strength, and optimize it using URP (Universal Render Pipeline); process data and update the arrow state in the Update function.

[0139] Provide a visualization calibration tool to guide the user to scan three reference points, automatically calculate conversion parameters, display the calibration error in real time, and support manual fine-tuning.

[0140] (7) User interaction module

[0141] The user interaction module includes a gesture control unit and a voice feedback unit. The gesture control unit is used to set the magnetic field strength threshold to be adjusted by sliding and to switch the display mode, including vectors and magnetic induction lines, by pinching.

[0142] The voice feedback unit is used to trigger a voice warning when the magnetic field exceeds the safety threshold and integrates Windows Mixed Reality voice recognition.

[0143] Through multi-module collaborative innovation, the present invention significantly improves the accuracy and visualization efficiency of omnidirectional magnetic field monitoring: the data filtering module adopts CEEMDAN threshold filtering to improve the signal-to-noise ratio of magnetic field signals, effectively suppress power frequency noise, and solve the problem of signal distortion in non-stationary environments; the combination of AR environment modeling and SLAM algorithm realizes real-time calculation of device pose and construction of environmental grids, improves the coincidence degree of virtual magnetic field vectors and real space, and eliminates the virtual-real misalignment during dynamic movement; the coordinate conversion module reduces the three-dimensional magnetic field direction error and improves the accuracy of azimuth and inclination angles; the visualization module is optimized through the Unity engine to achieve real-time synchronization of magnetic field changes and arrow directions; the effective working time of the present invention in complex scenarios such as strong magnetic interference and weak texture is improved, and together with three-dimensional dynamic arrows and gesture interaction, the efficiency of magnetic field distribution interpretation is improved, providing a real-time monitoring solution with high precision, low latency, and strong robustness for industrial inspection, scientific research analysis and other fields.

[0144] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, device, article or method including that element.

[0145] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality, characterized in that, It includes a data acquisition and transmission module, a data filtering module, an AR environment modeling module, a coordinate conversion module, a magnetic field direction calculation module, and a visualization module, which are connected in sequence; The data acquisition and transmission module is used to collect the magnetic field signals output by the magnetic field measuring instrument and transmit the magnetic field data to the data filtering module; The data filtering module is used to remove the noise in the magnetic field signals by using a denoising method based on CEEMDAN threshold filtering to obtain the denoised magnetic field data; The AR environment modeling module is used to adopt the SLAM algorithm to calculate the pose of the AR device in the environment in real time through the camera and IMU, and construct a map to provide a coordinate reference for the spatial anchoring of virtual objects; The coordinate conversion module is used to convert the filtered magnetic field data into the world coordinate system of the AR device by integrating the device pose output by SLAM; The magnetic field direction calculation module is used to calculate the magnetic field direction by using the converted world coordinate system data for AR visualization; The visualization module uses the API of Unity to receive the magnetic field data and renders magnetic field direction indication arrows in the AR scene.

2. The omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality according to claim 1, characterized in that, In the data acquisition and transmission module, the RS-232 interface of the magnetic field measuring instrument is connected to the Arduino Uno through the MAX3232 level conversion module, the serial port data is parsed in the Arduino, and then the HC-05 Bluetooth module is used to transmit it to the data filtering module in JSON format through Bluetooth.

3. The omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality according to claim 1, characterized in that, The data filtering module uses a denoising method based on CEEMDAN threshold filtering to remove the noise in the magnetic field signals, specifically including: S1. Perform complete decomposition on the magnetic field signals by the CEEMDAN method, and use the autocorrelation coefficient and Fourier transform to determine the boundary between the decomposed noise components and effective components; S2. Adopt the wavelet soft threshold method to extract the useful signals in the signals containing noise components, and finally perform signal reconstruction to obtain the denoised magnetic field signals.

4. The omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality according to claim 3, characterized in that The specific steps of S1 include: Define an operator E j (·), which represents the j-th IMF generated by EMD, ω i (n) is white noise of N(0,1), ε i is the amplitude coefficient of white noise, and x(n) is the original magnetic field signal; S101. First, add white noise ε0ω to the original magnetic field signal x(n) to obtain signal X i (n), where: i (n) is: X i y(n) = x i y(n) + ε0ω i y(n) It should be noted that in the original text, there may be some unclear or incorrect notations. For example, the "ε0ω" part seems rather ambiguous without further context. The above translation is based on the best understanding of the provided text. where \(i\) is the number of times of adding Gaussian white noise, \(i = 1, 2, \cdots, N\); first, obtain the first-order IMF component IMF1. For \(X^{(n)}\) with white noise added for the \(i\)-th time i , perform EMD decomposition to obtain IMF i1 . Repeat the calculation \(N\) times, and the first-order IMF can be obtained by its ensemble average as follows: The first-order residual is obtained from the original magnetic field signal x(n) and the first-order IMF: r1(n) = x(n) - IMF1 S102. Add white noise ε1ω i (n) to the first-order residual r1(n) to obtain the signal r1(n) + ε1ω i (n), and perform EMD decomposition on it as well to obtain IMF i2 . Repeat the calculation N times, and the second-order IMF can be obtained by integrating and averaging them as follows: The second-order residual is obtained from the first-order residual r1(n) and the second-order IMF: r2(n) = r1(n) - IMF2 S103. Add white noise ε k―1 ω(n) to the residual r k―1 ω i (n) to obtain the signal r k―1 (n) + ε k―1 ω i (n), perform EMD decomposition to obtain IMF ik . Repeat the calculation N times, then the k-th order IMF is: The k-order residual is decomposed as: r k (n) = r k―1 (n) - IMF k S104. Return to step S103 to continue the next decomposition until the residual cannot be decomposed, and the final residual is R(n); S105. Obtain the final decomposition result, and the original magnetic field signal x(n) is expressed as: S106. Set the original magnetic field signal x(n) as x(t), and its autocorrelation function expression is: R x (t1,t2) = E x (t1), x (t2)] The autocorrelation function values of the noise and signal modal components are calculated by using the normalized autocorrelation function, and the expression of the normalized function is: Perform CEEMDAN decomposition on the geomagnetic signals containing random noise to obtain IMF components. According to the above characteristics, the autocorrelation functions of each IMF are rooted, and then the boundary between the IMF components containing noise and the effective signal IMF is judged. Fourier analysis is performed on each IMF component to analyze the signal in the frequency domain, and the demarcation point is further determined according to the low-frequency characteristics of the geomagnetic signals.

5. The omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality according to claim 4, characterized in that, The specific steps of step S2 include: Threshold filtering is performed on the first (k-1) IMF noise components, and the threshold processing form adopts the soft threshold method. The expression is: Among them, imf′ i is the i-th order IMF component after denoising, and T i is the threshold of the i-th order IMF component. The method for determining the threshold adopts the threshold calculation model in the EMD method: where σ is a unique constant, and E i is the energy corresponding to the i-th order IMF, and its value is estimated by the following formula: After performing wavelet soft threshold processing on the first k-order IMFs, the first k-order filtered IMF components are obtained as IMF′, and the reconstructed signal can be obtained from the k-order and subsequent IMF components as: Among them, x ′ is the reconstructed magnetic field signal.

6. The omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality according to claim 1, wherein The coordinate transformation module converts the filtered magnetic field data into the world coordinate system of the AR device by combining the device poses output by SLAM, specifically including: The attitude of the AR device is represented by Euler angles or quaternions to describe the rotation relationship of the sensor coordinate system relative to the AR device coordinate system. According to the Euler angle sequence Z-Y-X, the rotation matrix is the product of three basic rotation matrices: R = R z (ψ)·R y (θ)·R x (φ), where the basic rotation matrix is: Let the magnetic field vector in the sensor coordinate system be B s = [B sx , B sy , B sz T , and the vector in the device coordinate system after transformation be B d = [B dx , B dy , B dz T , where B d = R·B s ;​​ The known rotation matrix R of the AR device in the world coordinate system w , then the conversion from the AR device coordinate system to the world coordinate system is: B w = R w · B d ; where R w is obtained by SLAM calibration, and B w is the magnetic field data in the world coordinate system.

7. The omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality according to claim 6, characterized in that The magnetic field direction calculation module is used to calculate the magnetic field direction by using the converted world coordinate system data, specifically including: Using the converted three-axis magnetic field data (B wx , B wy , B wz ), calculate the azimuth angle α and dip angle β of the magnetic field; α = arctan2(B wy , B wx ) where α represents the angle between the projection of the magnetic field on the horizontal plane and the x w axis, with a range from -π to π; β represents the angle between the magnetic field and the horizontal plane, positive upward and negative downward; where the x w axis points eastward, the y w axis points northward, z and w points upward.

8. The omnidirectional magnetic field real-time monitoring and visualization system based on augmented reality according to claim 1, characterized in that It further includes a user interaction module including a gesture control unit and a voice feedback unit. The gesture control unit is used to set the magnetic field intensity threshold to be adjusted by sliding, and pinching to switch the display mode, including vectors and magnetic induction lines; the voice feedback unit is used to trigger a voice warning when the magnetic field exceeds the safety threshold.

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