Magnetic field measurement method and system based on multi-sensor fusion technology

Abstract

The invention discloses a magnetic field measurement method and system based on a multi-sensor fusion technology, and relates to the technical field of sensor fusion and magnetic field measurement, and the method comprises the steps: deploying a multi-sensor data array, carrying out the adaptive initialization of a bistable SR parameter range, defining an SR system differential equation, and carrying out the iterative optimization through employing an MPA population. Carrying out Hilbert transform edge detection on enhanced signal component data, calculating an array inclination angle, carrying out abbe error and bidirectional projection error compensation, and carrying out metasurface grid coordinate quantization mapping; the collected and cross-scale magnetic field data set is preprocessed and packaged into data cells, quality evaluation and weight distribution are carried out on the data cells, and extended Kalman filtering data fusion is carried out; by introducing a bistable stochastic resonance system and an MPA population optimization algorithm, a weak magnetic field signal is obviously enhanced, and by calculating an array inclination angle and compensating an Abbe error and a bidirectional projection error, the space consistency of a measurement result is improved.

Description

Technical Field

[0001] The present invention relates to the field of sensor fusion and magnetic field measurement technology, and in particular to a magnetic field measurement method and system based on multi-sensor fusion technology. Background Art

[0002] With the continuous development of magnetic field measurement technology, magnetic field measurement methods based on multi-sensor data fusion have gradually emerged. While providing a more comprehensive and accurate analysis of magnetic field information from multiple dimensions, the introduction of advanced technologies such as artificial intelligence, swarm intelligence optimization, and nonlinear system modeling has also provided new solutions for magnetic field signal enhancement and error compensation.

[0003] Current multi-sensor magnetic field measurement technology still has many shortcomings. The existing technology is that the extraction and enhancement methods of weak signal components are still relatively simple, and the potential of stochastic resonance mechanism in low signal-to-noise ratio signal amplification has not been fully explored. The error compensation link mostly relies on linear estimation models, which is difficult to deal with the problem of nonlinear error accumulation, and cannot take into account the comprehensive compensation of Abbe error and projection error. For the expression and reconstruction of cross-scale magnetic field data, there is currently no unified and efficient coordinate mapping mechanism, which restricts the structured management and visual analysis of data. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a magnetic field measurement method and system based on multi-sensor fusion technology to solve the problems that the existing technology is still relatively simple in the extraction and enhancement methods of weak signal components, has not fully explored the potential of the stochastic resonance mechanism in low signal-to-noise ratio signal amplification, the error compensation link mostly relies on linear estimation models, it is difficult to deal with the problem of nonlinear error accumulation, and cannot take into account the comprehensive compensation of Abbe error and projection error. For the expression and reconstruction of cross-scale magnetic field data, there is currently no unified and efficient coordinate mapping mechanism, which restricts the structured management and visual analysis of data.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a magnetic field measurement method based on multi-sensor fusion technology, which includes:

[0008] Deployment of multi-sensor data array, adaptive initialization of bistable SR parameter range, definition of SR system differential equation, iterative optimization using MPA population, and output of enhanced signal component data;

[0009] Perform Hilbert transform edge detection on the enhanced signal component data, calculate the array tilt angle, perform Abbe error and bidirectional projection error compensation, perform metasurface grid coordinate quantization mapping, and combine them into a cross-scale magnetic field data set;

[0010] Preprocess the collected and cross-scale magnetic field data sets and encapsulate them into data cells, perform quality assessment and weight assignment on the data cells, and perform extended Kalman filter data fusion;

[0011] Perform abnormality detection and real-time correction on data cells, store, collect and analyze the generated magnetic field measurement data, and build a visual interface to display the magnetic field strength data.

[0012] As a preferred solution of the magnetic field measurement method based on multi-sensor fusion technology described in the present invention, wherein: the multi-sensor data array deployment, the bistable SR parameter range adaptive initialization, the definition of the SR system differential equation, the use of MPA population for iterative optimization, and the output of enhanced signal component data include:

[0013] In the magnetic field measurement area, a sensor array is arranged on the optical metasurface substrate using an equilateral triangle geometry, and a PT1000 temperature sensor is arranged to collect temperature data around the sensor array.

[0014] Normalizing the collected magnetic field data components;

[0015] Initialize the stochastic resonance system, calculate the statistical characteristics of each axis of the normalized magnetic field signal, and screen out the largest standard deviation, maximum peak value, and maximum signal energy among the three axes;

[0016] Use fast Fourier transform to calculate the power spectrum of each axis signal and calculate the statistical characteristics of the power spectrum of each axis;

[0017] The standard form of the bistable state function is defined, the potential well depth and width of the bistable state function are calculated, and the potential well depth is set to adapt to the maximum signal energy of the three axes, and the potential well width is set to adapt to the maximum peak value of the three axes. The simultaneous equations are solved to obtain the range D of the nonlinear coefficient range, the linear coefficient range, and the noise intensity range. The maximum signal-to-noise ratio of the three axes is used to adaptively narrow the linear coefficient range, the nonlinear coefficient range, and the noise intensity range.

[0018] Initialize the MPA population, define the SR system differential equation for each individual, use the fourth-order Runge-Kutta method to solve the SR system differential equation, calculate the output signal-to-noise ratio, select the individual with the largest output signal-to-noise ratio as the optimal individual, set the maximum number of iterations, evenly divide the maximum number of iterations into three parts, and set three stages to update the individuals in the population;

[0019] The first stage based on Brownian motion random vector update;

[0020] The second stage is based on the update of Lévy flight random vector and Brownian motion random vector;

[0021] The third stage is based on the random vector update of Lévy flight;

[0022] When the maximum number of iterations is reached, the iteration is stopped, the parameter vector of the optimal individual of the last iteration is output, and input into the SR system differential equation to obtain the enhanced signal, and the enhanced signal is denormalized to obtain the magnetic field enhancement signal component data.

[0023] As a preferred solution of the magnetic field measurement method based on multi-sensor fusion technology described in the present invention, the method includes performing Hilbert transform edge detection on the enhanced signal component data, calculating the array tilt angle, performing Abbe error and bidirectional projection error compensation, and performing metasurface grid coordinate quantization mapping to form a cross-scale magnetic field data set, including:

[0024] Use fast Fourier transform (FFT) to perform discrete Hilbert transform on the magnetic field enhancement signal component data of each axis, calculate the signal amplitude of each axis signal of the discrete Hilbert transform, calculate the first-order difference sequence of the signal amplitude of each axis, calculate the standard deviation of the first-order difference sequence, adaptively calculate the edge detection threshold based on differential statistics, use discrete wavelet transform to perform multi-scale decomposition on the signal amplitude of each axis signal, obtain the wavelet coefficient of each axis, normalize the edge detection threshold, and mark the edge of each axis;

[0025] Calculate the weight of each scale, calculate the edge score of each axis, construct the three-axis edge scores into a feature matrix, calculate the covariance matrix of the feature matrix, calculate the comprehensive edge score, calculate the mean of the comprehensive edge score, and mark the comprehensive edge score greater than the mean of the comprehensive edge score as an edge, otherwise it is marked as a non-edge;

[0026] Calculate the tilt angle of the sensor array based on acceleration, calculate the tilt angle, use the Abbe error compensation algorithm to calculate the Abbe corrected position for each axis, perform forward and reverse projection scans along the three axes, and use the bidirectional projection error compensation algorithm to calculate the bidirectional corrected position of each axis;

[0027] Calculate the error estimates between the Abbe correction position and the bidirectional correction position and the original position measured by the laser interferometer respectively, define the Abbe correction weight, and calculate the final corrected position using the weighted summation method based on the Abbe correction weight and the bidirectional correction weight;

[0028] Map the final corrected position to the hypersurface grid coordinates, calculate the error between the grid coordinates and the final corrected position, set error thresholds for edge and non-edge regions respectively, and mark errors exceeding the error thresholds for edge and non-edge regions as abnormal, otherwise mark them as normal;

[0029] The magnetic field intensity data marked as normal grid coordinates, the final corrected position magnetic field data and the edge markers are combined into a cross-scale magnetic field data set.

[0030] As a preferred solution of the magnetic field measurement method based on multi-sensor fusion technology described in the present invention, the pre-processing of the collected and cross-scale magnetic field data set and packaging it into data cells includes:

[0031] The cross-scale magnetic field data set and collected data were denoised using low-pass filtering, outliers were detected and removed using the median absolute deviation (MAD), and linear temperature compensation was performed on each magnetic field intensity.

[0032] An active vibration isolation platform is used to monitor vibration acceleration in real time and calculate a vibration stability score. If the vibration stability score is less than a preset universal threshold, the active vibration isolation platform is triggered to suppress vibration through piezoelectric ceramic feedback control, and the sensor array attitude is corrected using inertial sensor data.

[0033] Encapsulate the preprocessed data into data cells.

[0034] As a preferred solution of the magnetic field measurement method based on multi-sensor fusion technology described in the present invention, the quality assessment and weight distribution of data cells and the extended Kalman filter data fusion include:

[0035] Calculate the environmental stability score, use the reward-penalty model to calculate the data cell quality score, use the difference method to calculate the normalized change amplitude of temperature and vibration, and use the normalized change amplitude of temperature and vibration to calculate the environmental adaptability factor. Based on expert advice, set the task relevance score of different sensors, and use the environmental adaptability factor and cell quality score to calculate the fusion weight.

[0036] The magnetic field strength data is extracted from the preprocessed data cell set, and the discrete wavelet transform is used to decompose the magnetic field strength data into high-frequency and low-frequency components. The extended Kalman filter (EKF) is used in combination with the fusion weight to perform data fusion and output the final fused magnetic field value.

[0037] As a preferred solution of the magnetic field measurement method based on multi-sensor fusion technology described in the present invention, the abnormality detection and real-time correction of data cells include:

[0038] Cross-validation is performed on multi-sensor data cells with the same timestamp and metasurface coordinates. The magnetic field deviation of each data cell is calculated. If the magnetic field deviation is greater than twice the standard deviation of the historical deviation, the data cell is marked as abnormal, otherwise it is marked as normal.

[0039] If an abnormal data cell appears, calculate the standardized deviation values ​​of noise, temperature, vibration and acceleration of the abnormal data cell, arrange them in descending order, select the largest standardized deviation value as the cause of the abnormality, and perform magnetic field data correction based on the abnormal cause.

[0040] As a preferred embodiment of the magnetic field measurement method based on multi-sensor fusion technology described in the present invention, the method of storing, collecting and analyzing the generated magnetic field measurement data and constructing a visual interface to display the magnetic field strength data includes:

[0041] Storing the data collected and analyzed means storing the magnetic field measurement data collected and analyzed in a database and setting up security access measures. The database will back up the stored data to the cloud and regularly perform integrity checks on the stored data and backup data. After the test is completed, an integrity test record will be generated and stored synchronously in the database. WebGL will be used to build a visualization interface, generate a 3D heat map to display the spatial distribution of magnetic field intensity, and generate a 2D time series graph to display the change of magnetic field intensity over time.

[0042] In a second aspect, the present invention provides a magnetic field measurement system based on multi-sensor fusion technology, comprising:

[0043] Deployment iteration module for multi-sensor data array deployment, adaptive initialization of bistable SR parameter range, definition of SR system differential equation, iterative optimization using MPA population, and output of enhanced signal component data;

[0044] The calculation and mapping module is used to perform Hilbert transform edge detection on the enhanced signal component data, calculate the array tilt angle, perform Abbe error and bidirectional projection error compensation, perform metasurface grid coordinate quantization mapping, and combine them into a cross-scale magnetic field data set;

[0045] The preprocessing and fusion module is used to preprocess the collected and cross-scale magnetic field data sets and encapsulate them into data cells, perform quality assessment and weight assignment on the data cells, and perform extended Kalman filter data fusion;

[0046] Detection and correction module, used to detect abnormalities and correct data cells in real time;

[0047] The storage visualization module is used to store the magnetic field measurement data collected and analyzed, and to build a visualization interface to display the magnetic field strength data.

[0048] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the magnetic field measurement method based on multi-sensor fusion technology as described in the first aspect of the present invention is implemented.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the magnetic field measurement method based on multi-sensor fusion technology as described in the first aspect of the present invention is implemented.

[0050] The beneficial effects of the present invention are as follows: the present invention deploys a multi-sensor data array, adaptively initializes the bistable SR parameter range, defines the SR system differential equation, uses the MPA population for iterative optimization, and outputs enhanced signal component data; performs Hilbert transform edge detection on the enhanced signal component data, calculates the array tilt angle, performs Abbe error and bidirectional projection error compensation, performs metasurface grid coordinate quantization mapping, and combines them into a cross-scale magnetic field data set; pre-processes the collected and cross-scale magnetic field data sets and encapsulates them into data cells, performs quality assessment and weight allocation on the data cells, and performs extended Kalman filter data fusion; performs anomaly detection and real-time correction on the data cells, stores the collected and analyzed magnetic field measurement data, and constructs a visual interface to display the magnetic field intensity data, thereby improving the system's adaptability in complex magnetic field environments, improving the spatial consistency and numerical accuracy of the measurement results, and enhancing the system's sensitivity and responsiveness to weak magnetic field signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a flow chart of a magnetic field measurement method based on multi-sensor fusion technology in Example 1.

[0053] Figure 2 Schematic diagram of a magnetic field measurement system based on multi-sensor fusion technology in Example 1.

[0054] Figure 3 This is a schematic diagram of the sensor array deployment in Example 1.

[0055] Figure 4 This is a flow chart of position error compensation and grid mapping in Example 1. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0059] Example 1, with reference to Figures 1 to 4 , which is the first embodiment of the present invention, provides a magnetic field measurement method based on multi-sensor fusion technology, comprising the following steps:

[0060] S1, multi-sensor data array deployment, bistable SR parameter range adaptive initialization, definition of SR system differential equation, use MPA population for iterative optimization, and output enhanced signal component data;

[0061] Specifically, in the magnetic field measurement area, a sensor array is arranged on the optical metasurface substrate using an equilateral triangle geometry, and a PT1000 temperature sensor is arranged to collect temperature data around the sensor array.

[0062] The equilateral triangle geometric sensor array includes three NV color center magnetometers arranged at the three vertices of the equilateral triangle to collect magnetic field intensity component data, two laser interferometers, one arranged at the center of the triangle for global positioning reference, and the other arranged near a random vertex to assist in local high-precision positioning, and a MEMS inertial sensor combination (three-axis accelerometer + three-axis gyroscope) arranged at the center of the triangle to monitor the overall posture and vibration of the sensor array;

[0063] Normalizing the collected magnetic field data components;

[0064] Initialize the stochastic resonance system, including setting the linear coefficient range, nonlinear coefficient range and noise intensity range;

[0065] Calculate the statistical characteristics (mean, standard deviation, maximum peak value, and signal energy) of each axis of the normalized magnetic field signal, sort them in descending order, and select the ones with the largest standard deviation, maximum peak value, and maximum signal energy among the three axes;

[0066] Use fast Fourier transform to calculate the power spectrum of each axis signal and calculate the statistical characteristics of the power spectrum of each axis (power spectrum mean and power spectrum standard deviation);

[0067] Define the standard form of the bistable state function, formula:

[0068]

[0069] Where V(s) is the bistable state function, s is the function value to be evaluated, a is the linear coefficient, and b is the nonlinear coefficient;

[0070] Calculate the potential well depth and width of the bistable potential function, set the potential well depth to match the maximum signal energy of the three axes, and the potential well width to match the maximum peak value of the three axes, and solve the simultaneous equations to find the range of a and b. The formula is:

[0071]

[0072] Among them, [a min ,a max ] is the initial linear coefficient range, a min and a max are the minimum and maximum boundaries of the preliminary linear coefficient range, [b min ,b max ] is the initial nonlinear coefficient range, b min and b max are the minimum and maximum boundaries of the initial nonlinear coefficient range, E max is the maximum signal energy of the three axes, A peak is the maximum peak value of the three axes;

[0073] Set the range of D, formula:

[0074]

[0075] Among them, [D min ,D max ] is the initial noise intensity range, D min and D max are the minimum and maximum boundaries of the preliminary noise intensity range;

[0076] Using the maximum signal-to-noise ratio of the three axes, the linear coefficient range, nonlinear coefficient range and noise intensity range are adaptively narrowed for anti-interference SR enhancement;

[0077]

[0078] Among them, b' max 、a' max and D' max are the maximum value boundaries of the narrowed nonlinear coefficient range, linear coefficient range, and noise intensity range respectively;

[0079] Based on the narrowed maximum value boundary, update the final nonlinear coefficient range [b min ,b' max ]、Linear coefficient range [a min ,a' max ] and noise intensity range [D min ,D' max ];

[0080] Initialize the MPA population, including setting the population size, and randomly generate the initial population using a pseudo-random number generator within the range of linear coefficients, nonlinear coefficients, and noise intensity. Each individual in the population contains a linear coefficient, nonlinear coefficient, and noise intensity value.

[0081] For each individual in the population, define the SR system differential equation:

[0082]

[0083] Among them, s is the solution output by the SR system, a i 、b i and D i are the linear coefficient, nonlinear coefficient and noise intensity parameter of the i-th population individual, B s,norm (t) is the pre-processed magnetic field input signal of the x-axis input at time t. There are three axes in total, corresponding to x, y, and z in space. Each axis is calculated using the x-axis as an example. ξ(t) is the Gaussian white noise at time t, which simulates random interference in the environment and promotes weak signals to cross the potential barrier in the SR system.

[0084] The fourth-order Runge-Kutta method is used to solve the SR system differential equation and calculate the output signal-to-noise ratio. The formula is:

[0085]

[0086] Among them, fit i is the maximum output signal-to-noise ratio of the i-th population individual, SNR out is the output signal-to-noise ratio of the SR system, is the output signal power, N' is the number of sampling points, c is the sampling point index, s c is the population individual obtained by solving the input time series data, which is also time series data. A time series contains multiple sampling points, s c is the cth sampling point, is the mean of the individuals in the population, P no,out is the output noise power, obtained based on the square of the standard deviation;

[0087] Select the population individual with the largest output signal-to-noise ratio as the optimal individual, set the maximum number of iterations, evenly divide the maximum number of iterations into three parts, and set three stages to update the population individuals;

[0088] The first stage of random vector update based on Brownian motion, formula:

[0089]

[0090] in, is the parameter vector of the i-th individual at the k+1-th iteration (including linear coefficients, nonlinear coefficients and noise intensity parameters), is the parameter vector of the i-th individual at the k-th iteration, R B is a Brownian motion random vector, uniformly distributed in [0,1], is the optimal individual of the kth iteration;

[0091] The second stage based on the update of the Levy flight random vector and the Brownian motion random vector is as follows:

[0092]

[0093] in, is the parameter vector of the i-th individual at the k'+1th iteration, R L is a Levy flight random vector, generated by Levy distribution, is the optimal individual of the k'th iteration, is the parameter vector of individual i at the k'th iteration, and N is the population size;

[0094] The third stage of random vector update based on Levy flight, formula:

[0095]

[0096] in, is the parameter vector of the i-th individual at the k'+1th iteration, is the optimal individual of the k'th iteration, is the parameter vector of the i-th individual at the k'th iteration;

[0097] When the maximum number of iterations is reached, the iteration is stopped, the parameter vector of the optimal individual of the last iteration is output, and input into the SR system differential equation to obtain the enhanced signal, and the enhanced signal is denormalized to obtain the magnetic field enhancement signal component data.

[0098] By adopting an equilateral triangle geometric structure in the magnetic field test area, multi-dimensional synchronous perception of magnetic field intensity, position and posture, and environmental disturbances is achieved, ensuring that data acquisition has spatial stability and environmental adaptability. By presetting the linear coefficient, nonlinear coefficient and noise intensity range of the SR system, a three-dimensional parameter space with controllable parameters is constructed, providing a boundary framework for searching for optimal enhancement conditions. By establishing a potential function form and linking its parameters with the maximum signal energy and the maximum peak value, the physical matching of the potential well depth and width to the target signal is achieved. By setting the noise intensity range in combination with the three-axis signal-to-noise ratio and the maximum energy value, the noise is transformed from a disturbance source to a signal enhancement factor. By dynamically narrowing the parameter boundary based on the signal-to-noise ratio feedback mechanism, the system can adaptively adjust the optimal enhancement interval. By combining the Brownian motion and the Lévy flight mechanism in stages and applying them to individual parameter updates, a dynamic balance between exploration and utilization is achieved, taking into account both global search and local mining.

[0099] S2. Perform Hilbert transform edge detection on the enhanced signal component data, calculate the array tilt angle, perform Abbe error and bidirectional projection error compensation, perform metasurface grid coordinate quantization mapping, and combine them into a cross-scale magnetic field data set;

[0100] Specifically, a fast Fourier transform (FFT) is used to perform discrete Hilbert transform on the magnetic field enhancement signal component data of each axis, the signal amplitude of each axis signal of the discrete Hilbert transform is calculated, the first-order difference sequence of the signal amplitude of each axis is calculated, and the standard deviation of the first-order difference sequence is calculated;

[0101] The edge detection threshold is adaptively calculated based on differential statistics. The formula is:

[0102]

[0103] Among them, A threshold,x is the x-axis edge detection threshold, μ x and σ x are the mean and standard deviation of the x-axis signal amplitude, is the ratio of the difference standard deviation to the global mean (dimensionless), reflecting the relative strength of the edge mutation, σD x is the standard deviation of the x-axis difference sequence;

[0104] Use discrete wavelet transform to perform multi-scale decomposition on the signal amplitude of each axis signal (the maximum decomposition level is J), obtain the wavelet coefficient of each axis, normalize the edge detection threshold, and mark the edge of each axis. The formula is:

[0105]

[0106] Among them, F x(j, t') is the edge mark of the x-axis signal amplitude at scale j and position t' (1 indicates edge, 0 indicates non-edge), j is the decomposition level of discrete wavelet transform, t' is the time position index and different from t, t' represents discrete time, t is continuous time, corresponding to the sampling point on the time axis, W x (j, t') is the wavelet coefficient of the x-axis at scale j and position t', A' threshold,x is the normalized x-axis edge detection threshold;

[0107] Calculate the weight of each scale, formula:

[0108]

[0109] Among them, w j,x is the energy weight of the x-axis at scale j, and J is the maximum number of decomposition scales;

[0110] Based on the weight of each scale, calculate the edge score of each axis, formula:

[0111]

[0112] Among them, S x [t'] is the edge score of the x-axis at position t';

[0113] Construct the three-axis edge scores into a feature matrix, calculate the covariance matrix of the feature matrix, and calculate the comprehensive edge score. The formula is:

[0114]

[0115] Among them, S[t'] is the comprehensive edge score at position t', u T is the transpose of the eigenvector corresponding to the maximum eigenvalue of the covariance matrix, [S x [t'],S y [t'],S z [t']] is the three-axis edge score at position t';

[0116] Calculate the mean of the comprehensive edge scores. The comprehensive edge scores greater than the mean of the comprehensive edge scores are marked as edges, otherwise they are marked as non-edges.

[0117] Calculate the tilt angle of the sensor array using the formula:

[0118]

[0119] Where θ is the array tilt angle, ω x 、ω y and ω z The angular velocities of the x, y, and z axes, respectively, are the rotational speeds measured by the gyroscopes, reflecting the dynamic attitude of the array;

[0120] Based on the acceleration, calculate the tilt angle ∝, formula:

[0121]

[0122] Among them, a z is the z-axis acceleration, g is the acceleration due to gravity;

[0123] Use the Abbe error compensation algorithm to calculate the Abbe correction position for each axis. The formula is:

[0124] ABx=x mea -θ·L·sin(∝),

[0125] Where ABx is the Abbe-corrected position of the x-axis, x mea is the x-axis position measured by the laser interferometer, and L is the distance from the sensor to the reference point;

[0126] Bidirectional projection error compensation: forward and reverse projection scanning is performed along the three axes respectively, and the bidirectional correction position of each axis is calculated. The formula is:

[0127]

[0128] Among them, SXx is the bidirectional correction position of the x-axis, x forward and x backward are the forward projection position and the reverse projection position respectively;

[0129] Calculate the error estimates e between the Abbe-corrected position and the bidirectionally corrected position and the original position measured by the laser interferometer abbe and e proj , define the Abbe correction weight formula:

[0130]

[0131] Among them, w ab is the Abbe correction weight, e max is the maximum running error, F is the edge mark;

[0132] Based on the Abbe correction weight and the bidirectional correction weight (the sum of the two weights is one), the final correction position is calculated using the weighted summation method;

[0133] Map the final corrected position to the hypersurface grid coordinates, formula:

[0134]

[0135] Among them, x grid,i' is the i'th x-axis grid coordinate, x finalis the final corrected position along the x-axis, i' is the grid index along the x-axis, and p is the metasurface period, which represents the repetition interval of the periodic nanostructure on the optical metasurface substrate. It determines the grid resolution and affects the accuracy of cross-scale positioning. The period range is set based on empirical rules.

[0136] The metasurface grid coordinates refer to scanning the metasurface using a laser interferometer to generate a three-dimensional spatial reference grid;

[0137] Calculate the error between the grid coordinates and the final corrected position, set the error thresholds for the edge area and the non-edge area respectively, and mark the error exceeding the error thresholds for the edge area and the non-edge area as abnormal, otherwise mark it as normal;

[0138] The magnetic field intensity data marked as normal grid coordinates, the final corrected position magnetic field data and the edge markers are combined into a cross-scale magnetic field data set.

[0139] Through FFT, the frequency domain of the original magnetic field data is first enhanced to improve signal stability and anti-interference ability. By introducing the ratio of differential standard deviation to mean, the threshold does not rely on fixed settings when detecting edges, but is dynamically adjusted according to the degree of local signal change, thereby solving the problem of poor adaptability of fixed thresholds in different scenarios. By integrating edge information of each scale through energy weighting, the interference of high-frequency noise on edge recognition is avoided, while retaining key multi-scale information. The dominant direction in the edge score is extracted using the principal component of the covariance matrix, which can significantly improve the representativeness and information compression efficiency of cross-axis fusion, avoid redundant information interference, form the most representative edge expression on three axes, and realize the optimal fusion representation of edge features in the spatial coordinate system. Through weighted fusion, the complementary characteristics of Abbe error compensation and bidirectional projection error compensation are maximized to improve the credibility of the final position information and achieve highly accurate multi-source fusion correction.

[0140] S3, pre-processing the collected and cross-scale magnetic field data sets and encapsulating them into data cells, performing quality assessment and weight assignment on the data cells, and performing extended Kalman filter data fusion;

[0141] Specifically, low-pass filtering is used to denoise the cross-scale magnetic field data set and the collected data, the median absolute deviation (MAD) is used to detect and delete outliers, and linear temperature compensation is performed on each magnetic field intensity;

[0142] An active vibration isolation platform is used to monitor vibration acceleration in real time and calculate a vibration stability score. If the vibration stability score is less than a preset universal threshold, the active vibration isolation platform is triggered to suppress vibration through piezoelectric ceramic feedback control, and the sensor array attitude is corrected using inertial sensor data (accelerometer and gyroscope).

[0143] Encapsulate the preprocessed data into data cells;

[0144] The data cells include sensor type, timestamp, metasurface coordinates, magnetic field strength, environmental parameters (temperature, vibration and attitude) and signal-to-noise ratio.

[0145] By building a complete set of processing procedures from data denoising, anomaly detection, temperature compensation, vibration control to attitude correction and data structure encapsulation, high-precision and stable acquisition and management of magnetic field signals in dynamic environments are achieved.

[0146] Furthermore, the temperature and vibration intensity data of the environmental data cells are collected from the PT1000 temperature sensor and MEMS inertial sensor combination, and the environmental stability score is calculated using the formula:

[0147]

[0148] Among them, S env is the environmental stability score (0 to 1), T is the current temperature, which comes from the PT1000 sensor, T0 is the calibration temperature (general value 25℃), T max is the maximum temperature deviation, Vib is the vibration intensity, from the MEMS sensor, Vib max is the maximum vibration intensity, w T and w V are the weights of temperature and vibration intensity respectively, with the same initial values, and their sum is 1;

[0149] Based on the pre-processed cross-scale magnetic field data cell ensemble data, the reward-penalty model is used to calculate the data cell quality score. The formula is:

[0150]

[0151] Among them, ZR g is the mass fraction of the g-th cell, S env,g and Edge g',g are the signal-to-noise ratio of the g-th cell, S env,g The environmental stability score of the g-th cell and the reliability of the sensor g' (set as the normalized value estimated based on the regression of historical data) are 1 for the edge area and 0 for the non-edge area;

[0152] The standardized variation of temperature and vibration is calculated using the difference method, and the environmental adaptability factor is calculated using the standardized variation of temperature and vibration. The formula is:

[0153]

[0154] Among them, λ g is the environmental adaptability factor of the g-th cell, ΔT norm,g and ΔVibnorm,g is the normalized variation of the temperature and vibration of the g-th cell;

[0155] Based on expert recommendations, task relevance scores for different sensors were set, and the fusion weight was calculated using the environmental adaptability factor and cell quality score. The formula is:

[0156]

[0157] Among them, sw g is the fusion weight of the g-th cell, M' is the total number of cells, α is the moderate adjustment task impact factor, which is set to a fixed value based on expert advice, TA g',g Score the task relevance of the g'th sensor to the g'th cell;

[0158] Extract magnetic field intensity data from the preprocessed data cell set and decompose the magnetic field intensity data into high-frequency (nanometer-level dynamic changes) and low-frequency (millimeter-level static background) components using discrete wavelet transform;

[0159] Based on the high-frequency and low-frequency components, the state vector, covariance matrix, state transfer matrix and process noise covariance of the extended Kalman filter EKF are initialized;

[0160] Calculate the weighted observation value, formula:

[0161]

[0162] Among them, ZK t' is the weighted observation value at the current time t', [B x ,B y ,B z ] g is the magnetic field strength of the g-th data cell;

[0163] Calculate the observation noise covariance matrix, formula:

[0164]

[0165] Among them, R t' is the observation noise covariance matrix, reflecting the observation uncertainty, is the noise variance of the magnetic field measurement of sensor g';

[0166] Use Kalman filter EKF to estimate the predicted state vector of the magnetic field and rate of change at the current moment, use the difference method to calculate the observation residual between the weighted observation value and the predicted state vector, and calculate the Kalman gain. The formula is:

[0167] K t' =P t'|t'-1 H t (HP t'|t'-1 HT +R t' ) -1 ,

[0168] Among them, K t' is the Kalman gain, p t'|t'-1 is the prediction covariance matrix at time t' based on the previous time t'-1, H is the observation matrix, mapping the state to the observation space, R t' is the observation noise covariance matrix, reflecting the observation uncertainty;

[0169] Use the Kalman gain to update the state vector, formula:

[0170]

[0171] in, is the updated state vector, is the predicted state vector, ω t' is the observation residual;

[0172] Based on the operation process of calculating weighted observation values, calculating observation noise covariance matrix and updating state vector using Kalman gain, the high-frequency and low-frequency components are updated respectively, and the updated high-frequency and low-frequency components are extracted from the updated high-frequency and low-frequency state vectors for fusion, and the final fused magnetic field value is output [B x ,B y ,B z ]' g .

[0173] By introducing three factors, namely signal-to-noise ratio, environmental stability score and sensor reliability, and using a weighted penalty mechanism, a refined evaluation of the quality of data cells in different spatial regions (such as edge / non-edge) is achieved. The dynamic response of environmental variables is measured by the difference method after standardized processing, and a quantitative expression of the ability of each data cell to adapt to the current environmental changes is achieved. By introducing task-relevance parameters set by expert experience and the automatically generated environmental adaptability factors and quality factors, the knowledge-driven and data-driven combination of weight allocation is achieved. By initializing the state vector, state transfer matrix and covariance terms based on high and low frequency components, parameterized preparation for multi-scale magnetic field modeling is achieved. By fusing the magnetic field observations of multiple data cells and considering the difference in noise intensity, uncertainty modeling and dynamic adjustment of the observation data are achieved. By introducing the dynamically calculated Kalman gain to adjust the predicted state vector, the optimal estimate of the magnetic field state at each moment is achieved, thereby dynamically correcting the system deviation.

[0174] S4, perform abnormality detection and real-time correction on data cells, store, collect and analyze the generated magnetic field measurement data, and build a visual interface to display the magnetic field intensity data;

[0175] Specifically, the basic calibration frequency is set using an empirical method, and abnormal data cells are calibrated during each execution cycle of the basic calibration frequency;

[0176] Cross-validation is performed on multi-sensor data cells with the same timestamp and metasurface coordinates. The magnetic field deviation of each data cell is calculated. If the magnetic field deviation is greater than twice the standard deviation of the historical deviation, the data cell is marked as abnormal, otherwise it is marked as normal.

[0177] If an abnormal data cell appears, calculate the standardized deviation values ​​of noise, temperature, vibration and acceleration of the abnormal data cell, sort them in descending order, and select the largest standardized deviation value as the cause of the abnormality;

[0178] If the anomaly is caused by noise, collect baseline data for each sensor in a non-magnetic field environment (simulated by a standard magnetic field generator), calculate the zero-point deviation, and use the difference method to calculate the difference between the final fused magnetic field value and the zero-point deviation as the corrected fused magnetic field value.

[0179] If the abnormality is caused by temperature, calculate the temperature correction factor, and use the difference method to calculate the difference between the final fusion magnetic field value and the temperature correction factor as the corrected fusion magnetic field value;

[0180] If the abnormality is caused by vibration and acceleration, calculate the vibration-artifact correction factor, and use the difference method to calculate the difference between the final fusion magnetic field value and the vibration-artifact correction factor as the corrected fusion magnetic field value;

[0181] The temperature correction factor and the vibration-artifact correction factor are calculated as follows:

[0182] CT b' =K ct ·(T b' -T0),

[0183] CV b' =K Vib Vib b' +K Acc Acc b' ,

[0184] Among them, CT b' and CV b' are the temperature correction factor and vibration-artifact correction factor of abnormal data cell b', K ct , K Vib and K Acc are the correction coefficients for temperature, vibration, and acceleration (experimental calibration, general settings), T b' 、Vib b' and Acc b' are the temperature, vibration and acceleration data of abnormal data cell b' respectively;

[0185] Use the maximum standardized deviation value to dynamically adjust the calibration frequency, formula:

[0186] f cal,b' =f base ·(1+Z factor,b' ),

[0187] Among them, f cal,b' is the calibration frequency of abnormal data cell b', f base is the basic calibration frequency, Z factor,b' is the standardized deviation value of the abnormal factors.

[0188] By using the empirical method to set the basic calibration frequency, a reasonable initial configuration of the calibration period is achieved. By cross-validating multi-source data cells at the same time and space point, the introduction of spatial consistency and redundant fault tolerance mechanisms is achieved. By quantitatively analyzing and sorting the standardized deviations of various physical interference factors, quantitative identification and priority determination of anomaly causes are achieved. By dynamically adjusting the basic calibration frequency through the introduction of standardized deviation values, an adaptive calibration mechanism based on anomaly intensity is implemented, thereby enhancing the resource scheduling flexibility of the system.

[0189] Furthermore, storing the data generated by collection and analysis means storing the data generated by collection and analysis in a database and setting up security access measures. The database will back up the stored data to the cloud and regularly perform integrity checks on the stored data and backup data. After the test is completed, an integrity test record will be generated and stored synchronously in the database.

[0190] A visualization interface was constructed using WebGL to generate a 3D heat map to show the spatial distribution of magnetic field intensity, and a 2D time series graph to show how magnetic field intensity changes over time.

[0191] By integrating data storage, security control, backup mechanisms, and integrity verification processes, a secure, highly available, and traceable data storage system has been built, providing a high-quality, reliable data foundation for subsequent visualization analysis and intelligent processing. Through WebGL-driven visualization technology, abstract and complex magnetic field data can be presented in a dynamic, interactive, and intuitive manner, significantly enhancing data comprehension and operability. This system is particularly suitable for scenarios requiring spatial-temporal multidimensional analysis, such as engineering monitoring, scientific research, and industrial control.

[0192] This embodiment also provides a magnetic field measurement system based on multi-sensor fusion technology, including:

[0193] Deployment iteration module for multi-sensor data array deployment, adaptive initialization of bistable SR parameter range, definition of SR system differential equation, iterative optimization using MPA population, and output of enhanced signal component data;

[0194] The calculation and mapping module is used to perform Hilbert transform edge detection on the enhanced signal component data, calculate the array tilt angle, perform Abbe error and bidirectional projection error compensation, perform metasurface grid coordinate quantization mapping, and combine them into a cross-scale magnetic field data set;

[0195] The preprocessing and fusion module is used to preprocess the collected and cross-scale magnetic field data sets and encapsulate them into data cells, perform quality assessment and weight assignment on the data cells, and perform extended Kalman filter data fusion;

[0196] Detection and correction module, used to detect abnormalities and correct data cells in real time;

[0197] The storage visualization module is used to store the magnetic field measurement data collected and analyzed, and to build a visualization interface to display the magnetic field strength data.

[0198] This embodiment also provides a computer device, which is suitable for a magnetic field measurement method based on multi-sensor fusion technology, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a magnetic field measurement method based on multi-sensor fusion technology proposed in the above embodiment.

[0199] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0200] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a magnetic field measurement method based on multi-sensor fusion technology as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0201] In summary, the present invention adopts the following methods: multi-sensor data array deployment, bistable SR parameter range adaptive initialization, definition of SR system differential equations, iterative optimization using MPA population, and output of enhanced signal component data; Hilbert transform edge detection is performed on the enhanced signal component data, array tilt angle is calculated, Abbe error and bidirectional projection error compensation are performed, and metasurface grid coordinate quantization mapping is performed to combine them into a cross-scale magnetic field data set; the collected and cross-scale magnetic field data sets are preprocessed and encapsulated into data cells, quality assessment and weight assignment are performed on the data cells, and extended Kalman filter data fusion is performed; anomaly detection and real-time correction are performed on the data cells, the magnetic field measurement data generated by the collection and analysis are stored, and a visual interface is constructed to display the magnetic field intensity data, thereby improving the system's adaptability in complex magnetic field environments, improving the spatial consistency and numerical accuracy of the measurement results, and enhancing the system's sensitivity and responsiveness to weak magnetic field signals.

[0202] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A magnetic field measurement method based on multi-sensor fusion technology, characterized by: include, Deployment of multi-sensor data array, adaptive initialization of bistable SR parameter range, definition of SR system differential equation, iterative optimization using MPA population, and output of enhanced signal component data; Perform Hilbert transform edge detection on the enhanced signal component data, calculate the array tilt angle, perform Abbe error and bidirectional projection error compensation, perform metasurface grid coordinate quantization mapping, and combine them into a cross-scale magnetic field data set; Preprocess the collected and cross-scale magnetic field data sets and encapsulate them into data cells, perform quality assessment and weight assignment on the data cells, and perform extended Kalman filter data fusion; Perform abnormality detection and real-time correction on data cells, store, collect and analyze the generated magnetic field measurement data, and build a visual interface to display the magnetic field strength data.

2. The magnetic field measurement method based on multi-sensor fusion technology according to claim 1, characterized in that: The multi-sensor data array deployment, bistable SR parameter range adaptive initialization, definition of SR system differential equations, iterative optimization using MPA population, and output of enhanced signal component data include: In the magnetic field measurement area, a sensor array is arranged on the optical metasurface substrate using an equilateral triangle geometry, and a PT1000 temperature sensor is arranged to collect temperature data around the sensor array. Normalizing the collected magnetic field data components; Initialize the stochastic resonance system, calculate the statistical characteristics of each axis of the normalized magnetic field signal, and screen out the largest standard deviation, maximum peak value, and maximum signal energy among the three axes; Use fast Fourier transform to calculate the power spectrum of each axis signal and calculate the statistical characteristics of the power spectrum of each axis; The standard form of the bistable state function is defined, the potential well depth and width of the bistable state function are calculated, and the potential well depth is set to adapt to the maximum signal energy of the three axes, and the potential well width is set to adapt to the maximum peak value of the three axes. The simultaneous equations are solved to obtain the range D of the nonlinear coefficient range, the linear coefficient range, and the noise intensity range. The maximum signal-to-noise ratio of the three axes is used to adaptively narrow the linear coefficient range, the nonlinear coefficient range, and the noise intensity range. Initialize the MPA population, define the SR system differential equation for each individual, use the fourth-order Runge-Kutta method to solve the SR system differential equation, calculate the output signal-to-noise ratio, select the individual with the largest output signal-to-noise ratio as the optimal individual, set the maximum number of iterations, evenly divide the maximum number of iterations into three parts, and set three stages to update the individuals in the population; The first stage based on Brownian motion random vector update; The second stage is based on the update of Lévy flight random vector and Brownian motion random vector; The third stage is based on the random vector update of Lévy flight; When the maximum number of iterations is reached, the iteration is stopped, the parameter vector of the optimal individual of the last iteration is output, and input into the SR system differential equation to obtain the enhanced signal, and the enhanced signal is denormalized to obtain the magnetic field enhancement signal component data.

3. The magnetic field measurement method based on multi-sensor fusion technology according to claim 2, characterized in that: The enhanced signal component data is subjected to Hilbert transform edge detection, array tilt angle calculation, Abbe error and bidirectional projection error compensation, and hypersurface grid coordinate quantization mapping to form a cross-scale magnetic field data set, including: Use fast Fourier transform (FFT) to perform discrete Hilbert transform on the magnetic field enhancement signal component data of each axis, calculate the signal amplitude of each axis signal of the discrete Hilbert transform, calculate the first-order difference sequence of the signal amplitude of each axis, calculate the standard deviation of the first-order difference sequence, adaptively calculate the edge detection threshold based on differential statistics, use discrete wavelet transform to perform multi-scale decomposition on the signal amplitude of each axis signal, obtain the wavelet coefficient of each axis, normalize the edge detection threshold, and mark the edge of each axis; Calculate the weight of each scale, calculate the edge score of each axis, construct the three-axis edge scores into a feature matrix, calculate the covariance matrix of the feature matrix, calculate the comprehensive edge score, calculate the mean of the comprehensive edge score, and mark the comprehensive edge score greater than the mean of the comprehensive edge score as an edge, otherwise it is marked as a non-edge; Calculate the tilt angle of the sensor array based on acceleration, calculate the tilt angle, use the Abbe error compensation algorithm to calculate the Abbe corrected position for each axis, perform forward and reverse projection scans along the three axes, and use the bidirectional projection error compensation algorithm to calculate the bidirectional corrected position of each axis; Calculate the error estimates between the Abbe correction position and the bidirectional correction position and the original position measured by the laser interferometer respectively, define the Abbe correction weight, and calculate the final corrected position using the weighted summation method based on the Abbe correction weight and the bidirectional correction weight; Map the final corrected position to the hypersurface grid coordinates, calculate the error between the grid coordinates and the final corrected position, set error thresholds for edge and non-edge regions respectively, and mark errors exceeding the error thresholds for edge and non-edge regions as abnormal, otherwise mark them as normal; The magnetic field intensity data marked as normal grid coordinates, the final corrected position magnetic field data and the edge markers are combined into a cross-scale magnetic field data set.

4. The magnetic field measurement method based on multi-sensor fusion technology according to claim 3, characterized in that: The pre-processing of the collected and cross-scale magnetic field data sets and packaging into data cells includes: The cross-scale magnetic field data set and collected data were denoised using low-pass filtering, outliers were detected and removed using the median absolute deviation (MAD), and linear temperature compensation was performed on each magnetic field intensity. An active vibration isolation platform is used to monitor vibration acceleration in real time and calculate a vibration stability score. If the vibration stability score is less than a preset universal threshold, the active vibration isolation platform is triggered to suppress vibration through piezoelectric ceramic feedback control, and the sensor array attitude is corrected using inertial sensor data. Encapsulate the preprocessed data into data cells.

5. The magnetic field measurement method based on multi-sensor fusion technology according to claim 4, characterized in that: The quality assessment and weight allocation of data cells and the extended Kalman filter data fusion include: Calculate the environmental stability score, use the reward-penalty model to calculate the data cell quality score, use the difference method to calculate the normalized change amplitude of temperature and vibration, and use the normalized change amplitude of temperature and vibration to calculate the environmental adaptability factor. Based on expert advice, set the task relevance score of different sensors, and use the environmental adaptability factor and cell quality score to calculate the fusion weight. The magnetic field strength data is extracted from the preprocessed data cell set, and the discrete wavelet transform is used to decompose the magnetic field strength data into high-frequency and low-frequency components. The extended Kalman filter (EKF) is used in combination with the fusion weight to perform data fusion and output the final fused magnetic field value.

6. The magnetic field measurement method based on multi-sensor fusion technology according to claim 5, characterized in that: The abnormality detection and real-time correction of data cells include: Cross-validation is performed on multi-sensor data cells with the same timestamp and metasurface coordinates. The magnetic field deviation of each data cell is calculated. If the magnetic field deviation is greater than twice the standard deviation of the historical deviation, the data cell is marked as abnormal, otherwise it is marked as normal. If an abnormal data cell appears, calculate the standardized deviation values ​​of noise, temperature, vibration and acceleration of the abnormal data cell, arrange them in descending order, select the largest standardized deviation value as the cause of the abnormality, and perform magnetic field data correction based on the abnormal cause.

7. The magnetic field measurement method based on multi-sensor fusion technology according to claim 6, characterized in that: The storage, collection and analysis of the generated magnetic field measurement data and the construction of a visual interface to display the magnetic field strength data include: Storing the data collected and analyzed means storing the magnetic field measurement data collected and analyzed in a database and setting up security access measures. The database will back up the stored data to the cloud and regularly perform integrity checks on the stored data and backup data. After the test is completed, an integrity test record will be generated and stored synchronously in the database. WebGL will be used to build a visualization interface, generate a 3D heat map to display the spatial distribution of magnetic field intensity, and generate a 2D time series graph to display the change of magnetic field intensity over time.

8. A magnetic field measurement system based on multi-sensor fusion technology, based on the magnetic field measurement method based on multi-sensor fusion technology according to any one of claims 1 to 7, characterized in that: include, Deployment iteration module for multi-sensor data array deployment, adaptive initialization of bistable SR parameter range, definition of SR system differential equation, iterative optimization using MPA population, and output of enhanced signal component data; The calculation and mapping module is used to perform Hilbert transform edge detection on the enhanced signal component data, calculate the array tilt angle, perform Abbe error and bidirectional projection error compensation, perform metasurface grid coordinate quantization mapping, and combine them into a cross-scale magnetic field data set; The preprocessing and fusion module is used to preprocess the collected and cross-scale magnetic field data sets and encapsulate them into data cells, perform quality assessment and weight assignment on the data cells, and perform extended Kalman filter data fusion; Detection and correction module, used to detect abnormalities and correct data cells in real time; The storage visualization module is used to store the magnetic field measurement data collected and analyzed, and to build a visualization interface to display the magnetic field strength data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the magnetic field measurement method based on multi-sensor fusion technology according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the magnetic field measurement method based on multi-sensor fusion technology according to any one of claims 1 to 7 are implemented.

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