Magnetic field real-time vector projection method, device, equipment, medium and product

Abstract

The invention discloses a magnetic field real-time vector projection method, device and equipment, a medium and a product, and relates to the field of magnetic field measurement, and the method comprises the steps: obtaining a real-time original signal of a magnetic field; the real-time original signal is a magnetic field time domain signal detected by an NV color center magnetic sensor; performing envelope analysis on the real-time original signal to obtain a real-time signal envelope; according to the real-time signal envelope, utilizing a sliding window to carry out feature region identification to obtain a peak region and a non-peak region; respectively constructing signal trend envelopes for the peak region and the off-peak region by using an overlapped sliding window; taking the signal trend envelope as a reference trend, performing region estimation according to the real-time original signal, and determining a mean envelope; according to the real-time original signal, vector projection noise reduction is carried out in a projection space by using a sliding window technology, and a denoised signal is obtained; the projection space is constructed according to the mean value envelope. The method can overcome the noise influence, and improves the real-time measurement precision of the magnetic field.

Description

Technical Field

[0001] The present application relates to the field of magnetic field measurement, and in particular to a magnetic field real-time vector projection method, device, equipment, medium and product. Background Art

[0002] With the rapid development of quantum technology, sensors based on diamond nitrogen vacancy (NV) color centers have become an important tool for high-precision magnetic field detection. NV color centers have excellent optical and electronic properties at room temperature. Through optical pumping and microwave technology, they can initialize, manipulate and read out spin states, thereby achieving high-sensitivity detection of physical quantities such as magnetic fields, electric fields, temperature and strain. In the fields of biological magnetic field detection, geological exploration, quantum metrology, etc., NV color center sensors are widely used in various scientific research and engineering technologies due to their excellent sensitivity, large detection range and good adaptability at room temperature.

[0003] However, despite the significant advantages of NV color center systems, their performance is still significantly affected by environmental noise. Noise sources such as environmental nuclear spin interference and external bias fields cause signal frequency offset and distortion, which in turn affects the sensitivity and stability of the sensor. In particular, the non-uniform broadening of the signal caused by electromagnetic interference, temperature fluctuations, detector thermal noise, and white noise of electronic components significantly weakens the sensor's signal-to-noise ratio, thereby affecting the measurement accuracy. In addition, intrinsic noise such as laser power fluctuations, microwave frequency instability, and spin state decoherence caused by electromagnetic interference limits the application of NV color center systems in complex environments.

[0004] To address these challenges, researchers have proposed a variety of noise suppression methods, such as dynamic decoupling (DD) technology and numerical optimization schemes based on smooth pulse design. These methods extend the decoherence time and improve the pulse control fidelity by reducing the impact of environmental noise, thereby enhancing the anti-noise performance to a certain extent. However, these technologies usually require precise hardware support or a large amount of computing resources, and the processing time is long, which makes it difficult to meet the needs of real-time applications. Destructive noise reduction technology uses differential and dual-path photodetectors (PDs) to effectively suppress the noise caused by laser power fluctuations. Although its effect is significant, it is highly complex and difficult to integrate. In addition, noise filtering methods based on deep learning can identify complex nonlinear noise characteristics and perform well in prediction and compensation, but the demand for training data and computing resources limits its use in real-time applications. In summary, NV color center vector projection technology has great application potential in the field of quantum sensing, but how to effectively overcome the influence of noise and improve real-time measurement accuracy is still an important problem that needs to be solved in this technical field. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment, medium and product for real-time vector projection of magnetic fields, which can overcome the influence of noise and improve the real-time measurement accuracy of magnetic fields.

[0006] To achieve the above object, this application provides the following solutions:

[0007] In the first aspect, this application provides a method for real-time vector projection of magnetic fields, including:

[0008] Obtain the real-time original signal of the magnetic field; the real-time original signal is the magnetic field time-domain signal detected by the NV color center magnetic sensor;

[0009] Perform envelope analysis on the real-time original signal to obtain the real-time signal envelope;

[0010] Use a sliding window to identify the characteristic regions based on the real-time signal envelope, and obtain the peak region and the non-peak region;

[0011] Construct signal trend envelopes for the peak region and the non-peak region respectively using overlapping sliding windows;

[0012] Take the signal trend envelope as the reference trend, and perform regional estimation based on the real-time original signal to determine the mean envelope;

[0013] Perform vector projection denoising on the real-time original signal in the projection space using the sliding window technique to obtain the denoised signal; the projection space is constructed based on the mean envelope.

[0014] Optionally, performing envelope analysis on the real-time original signal to obtain the real-time signal envelope specifically includes:

[0015] Determine the peak valleys, valley values, and secondary valley value points according to the extreme values of the real-time original signal;

[0016] Determine the real-time signal envelope according to the peak valleys, the valley values, and the secondary valley value points.

[0017] Optionally, using a sliding window to identify the characteristic regions based on the real-time signal envelope to obtain the peak region and the non-peak region specifically includes:

[0018] Determine the sliding window according to the secondary valley value points of the real-time signal envelope;

[0019] Calculate the baseline of the first and last data points within the window using the sliding window;

[0020] Calculate the range and the absolute value of the change between two consecutive data points according to the baseline; the range is the range between the data points within the sliding window and the baseline;

[0021] Determine the peak region and the non-peak region according to the range, the absolute value of the change, the first slope, and the second slope; the first slope is the slope of the straight line connecting the first data point of the sliding window and the central data point of the sliding window; the second slope is the slope of the straight line connecting the last data point of the sliding window and the central data point of the sliding window.

[0022] Optionally, determining the peak region and the non-peak region according to the range, the absolute value of the change, the first slope, and the second slope specifically includes:

[0023] Judge whether the sliding window satisfies the first condition, the second condition, and the third condition respectively to obtain a first judgment result; the first condition is that the absolute value of the change is less than or equal to the range; the second condition is that the first slope is greater than zero; the second condition is that the second slope is less than zero;

[0024] If the first judgment result is yes, then the sliding window is the peak region;

[0025] If the first judgment result is no, then the sliding window is the non-peak region.

[0026] Optionally, construct signal trend envelopes for the peak region and the non-peak region respectively using overlapping sliding windows, specifically including:

[0027] Select the data points that are only located in the peak region within the overlapping sliding window for mean processing;

[0028] Select all data points within the overlapping sliding window for mean processing;

[0029] The sliding window traverses step by step, and the signal trend envelope is determined according to all the data points after mean processing.

[0030] Optionally, use the signal trend envelope as a reference trend, and perform region estimation according to the real-time original signal to determine the mean envelope, specifically including:

[0031] Use the signal trend envelope as a reference trend, and perform real-time comparison with the peak information and valley information of the real-time original signal respectively to determine the trend difference;

[0032] Judge whether the trend difference is greater than the trend threshold to obtain a second judgment result;

[0033] If the second judgment result is yes, then the size of the overlapping sliding window, and return to the step "Use the signal trend envelope as a reference trend, and perform real-time comparison with the peak information and valley information of the real-time original signal respectively to determine the trend difference";

[0034] If the second judgment result is yes, then determine the mean envelope according to the signal trend envelope.

[0035] In a second aspect, the present application provides a magnetic field real-time vector projection device, including:

[0036] A real-time raw signal acquisition module, configured to acquire a real-time raw signal of a magnetic field; the real-time raw signal is a magnetic field time-domain signal detected by an NV color center magnetic sensor;

[0037] An envelope analysis module, configured to perform envelope analysis on the real-time raw signal to obtain a real-time signal envelope;

[0038] A feature region recognition module, configured to perform feature region recognition on the real-time signal envelope by using a sliding window to obtain a peak region and a non-peak region;

[0039] A construction module, configured to respectively construct signal trend envelopes for the peak region and the non-peak region by using overlapping sliding windows;

[0040] A region estimation module, configured to use the signal trend envelope as a reference trend, and perform region estimation according to the real-time raw signal to determine a mean envelope;

[0041] A vector projection module, configured to perform vector projection denoising on the real-time raw signal in a projection space by using a sliding window technique to obtain a denoised signal; the projection space is constructed according to the mean envelope.

[0042] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the magnetic field real-time vector projection method described in any one of the above.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the magnetic field real-time vector projection method described in any one of the above is implemented.

[0044] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the magnetic field real-time vector projection method described in any one of the above is implemented.

[0045] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0046] The present application provides a method, apparatus, device, medium and product for real-time vector projection of magnetic fields. The magnetic field time-domain signal detected by the NV color center magnetic sensor can achieve high-sensitivity weak magnetic field measurement at room temperature, thereby improving the accuracy of magnetic field measurement. Through the cooperation of envelope analysis, characteristic region identification and region estimation, real-time perception, feedback adjustment and construction of a dynamic projection space of multi-source noise signals and real-time projection noise reduction are realized, significantly reducing the noise level and effectively suppressing multi-source noise, thereby further overcoming the influence of noise and improving the accuracy of magnetic field measurement. Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0048] Figure 1 It is an application environment diagram of a method for real-time vector projection of magnetic fields in an embodiment of the present application;

[0049] Figure 2 It is a schematic diagram of the principle of a method for real-time vector projection of magnetic fields provided in an embodiment of the present application;

[0050] Figure 3 It is a schematic diagram of the pump excitation principle of the NV color center of a method for real-time vector projection of magnetic fields provided in an embodiment of the present application;

[0051] Figure 4 It is a schematic diagram of a method for real-time vector projection of magnetic fields in an embodiment of the present application;

[0052] Figure 5 It is a schematic diagram of the comparison before and after noise reduction of the time-domain signal of a method for real-time vector projection of magnetic fields in an embodiment of the present application;

[0053] Figure 6 It is a flowchart of a method for real-time vector projection of magnetic fields in an embodiment of the present application;

[0054] Figure 7 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application.

[0055] Reference Signs:

[0056] 1 - 532nm laser; 2 - objective lens; 3 - microwave antenna; 4 - diamond; 5 - fluorescence signal; 6 - photodetector; 7 - lock-in amplifier; 8 - envelope analysis module; 9 - characteristic region identification module; 10 - region estimation and feedback system; 11 - vector projection noise reduction module. Detailed Embodiments

[0057] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0058] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0059] The real-time magnetic field vector projection method provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the original signal to be processed to the server 104. After receiving the original signal to be processed, for the original signal to be processed, the server 104 performs envelope analysis on the real-time original signal to obtain a real-time signal envelope; uses a sliding window to identify feature regions according to the real-time signal envelope to obtain a peak region and a non-peak region; respectively constructs signal trend envelopes for the peak region and the non-peak region using overlapping sliding windows; uses the signal trend envelope as a reference trend, and performs region estimation according to the real-time original signal to determine a mean envelope; performs vector projection denoising on the projection space using the sliding window technology according to the real-time original signal to obtain a denoised signal. The server 104 can feedback the obtained denoised signal to the terminal 102. In addition, in some embodiments, the real-time magnetic field vector projection method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform vector projection processing on the original signal to be processed, or the server 104 can obtain the original signal to be processed from the data storage system and perform vector projection processing on the original signal to be processed.

[0060] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0061] In an exemplary embodiment, asFigure 6 As shown, a real-time vector projection method for magnetic fields is provided. This method is executed by a computer device, which can be specifically executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of this application, taking this method applied to Figure 1 the server 104 in

[0062] Step 201: Obtain the real-time original signal of the magnetic field; the real-time original signal is the magnetic field time-domain signal detected by an NV color center magnetic sensor.

[0063] Step 202: Perform envelope analysis on the real-time original signal to obtain the real-time signal envelope.

[0064] Step 203: Use a sliding window to identify the characteristic regions based on the real-time signal envelope, and obtain the peak region and the non-peak region.

[0065] Step 204: Use overlapping sliding windows to construct signal trend envelopes for the peak region and the non-peak region respectively.

[0066] Step 205: Use the signal trend envelope as a reference trend, and perform region estimation based on the real-time original signal to determine the mean envelope.

[0067] Step 206: Use the sliding window technique to perform vector projection noise reduction on the real-time original signal in the projection space to obtain the denoised signal; the projection space is constructed based on the mean envelope.

[0068] Implementing the above steps 201 to 206 can improve the real-time measurement accuracy of the magnetic field. In an exemplary embodiment of this application, performing envelope analysis on the real-time original signal to obtain the real-time signal envelope specifically includes:

[0069] Determine the peak-valley, valley value, and secondary valley value points according to the extreme values of the real-time original signal.

[0070] Determine the real-time signal envelope according to the peak-valley, the valley value, and the secondary valley value points.

[0071] In an exemplary embodiment of this application, using a sliding window to identify the characteristic regions based on the real-time signal envelope to obtain the peak region and the non-peak region specifically includes:

[0072] Determine the sliding window according to the secondary valley value points of the real-time signal envelope.

[0073] Use the sliding window to calculate the baseline of the first and last data points within the window.

[0074] Calculate the range and the absolute value of the change between two consecutive data points based on the reference line; the range is the range between the data points within the sliding window and the reference line.

[0075] Determine the peak area and the non-peak area based on the range, the absolute value of the change, the first slope, and the second slope; the first slope is the slope of the line connecting the first data point in the sliding window and the central data point in the sliding window; the second slope is the slope of the line connecting the last data point in the sliding window and the central data point in the sliding window.

[0076] In practical applications, determining the peak area and the non-peak area based on the range, the absolute value of the change, the first slope, and the second slope specifically includes:

[0077] Judge whether the sliding window satisfies the first condition, the second condition, and the third condition respectively to obtain the first judgment result; the first condition is that the absolute value of the change is less than or equal to the range; the second condition is that the first slope is greater than zero; the second condition is that the second slope is less than zero.

[0078] If the first judgment result is yes, the sliding window is the peak area.

[0079] If the first judgment result is no, the sliding window is the non-peak area.

[0080] In an exemplary embodiment of the present application, signal trend envelopes are constructed for the peak area and the non-peak area respectively by using overlapping sliding windows, specifically including:

[0081] Select the data points only located in the peak area within the overlapping sliding window for mean processing.

[0082] Select all data points within the overlapping sliding window for mean processing.

[0083] The sliding window traverses step by step, and the signal trend envelope is determined according to all the data points after mean processing.

[0084] In an exemplary embodiment of the present application, taking the signal trend envelope as the reference trend, perform region estimation according to the real-time original signal to determine the mean envelope, specifically including:

[0085] Taking the signal trend envelope as the reference trend, compare it with the peak information and the valley information of the real-time original signal in real time to determine the trend difference;

[0086] Judge whether the trend difference is greater than the trend threshold to obtain the second judgment result;

[0087] If the second judgment result is yes, determine the size of the overlapping sliding window, and return to the step of "using the signal trend envelope as the reference trend, and comparing it with the peak information and valley information of the real-time original signal in real time to determine the trend difference".

[0088] If the second judgment result is yes, determine the mean envelope according to the signal trend envelope.

[0089] This application first extracts the signal trend through envelope analysis and identifies the characteristic regions according to the signal fluctuations. Then, it combines local estimation and feedback optimization to adjust the noise reduction parameters in real time to sense the signal changes and make corrections, so as to significantly reduce the noise level without affecting the signal characteristics and effectively suppress multi-source noise. Compared with the conventional vector projection method that separates the signal from the noise by projecting the signal into the noise orthogonal space composed of basis vectors, this method can analyze and process the signal and reduce noise in real time, significantly reduce the noise impact, improve the signal-to-noise ratio, reduce the delay while maintaining the signal fidelity, and has strong stability in a dynamic noise environment. It realizes the improvement of the signal processing effect and the signal-to-noise ratio in a multi-source noise environment, especially the enhancement of the signal fidelity, sensitivity and stability in a dynamic noise environment, and at the same time effectively reduces the delay time of signal processing.

[0090] To further prove the effectiveness of this application, a corresponding analysis of the related technologies is carried out now. The related technologies currently have the following disadvantages:

[0091] Multi-source and multi-band noise interference: During the magnetic field measurement of the object to be detected, it is prone to be interfered by various noises (such as environmental noise, electronic noise, laser noise, microwave noise, etc.), resulting in decreased sensitivity, reduced signal-to-noise ratio and low measurement accuracy.

[0092] Although the dynamic decoupling (DD) technology and the optimization scheme based on smooth pulse design can effectively extend the decoherence time and improve the pulse control fidelity, they rely on precise hardware support and complex algorithms and are difficult to meet the real-time requirements.

[0093] The cancellation noise reduction technology (such as the application of differential and dual-channel photodetectors) has a significant effect in suppressing laser power noise, but it is highly complex and not easy to be integrated into a compact system.

[0094] The noise filtering method based on deep learning can effectively identify non-linear noise characteristics and perform well in prediction and compensation, but its dependence on large-scale training data and high computing resources limits its application in real-time measurement.

[0095] Limited real-time application: The existing noise suppression technologies generally have problems of high computational complexity or excessive hardware requirements, and cannot adapt to the actual scenarios with high requirements for real-time performance and high integration.

[0096] In view of the disadvantages of the above related technologies, the present application is used to solve the problems that when the prior art measures the magnetic field of a detection object, it is affected by multi-source and multi-band noise, resulting in low sensitivity, small signal-to-noise ratio, and difficulty in achieving high-precision measurement. The present application is mainly applied to the signal processing after the magnetic field is measured by an NV color center magnetic sensor, such as Figure 2 and Figure 4 As shown, the method includes the following steps:

[0097] Perform envelope analysis on the time-domain signal of the magnetic sensor, follow and extract the characteristic points and trend information of the signal in real time and store them. The time-domain signal obtained by demodulating the optoelectronic signal of the NV color center magnetic sensor by a lock-in amplifier is first subjected to envelope analysis:

[0098] During the signal detection process, envelope analysis is used to perceive the main trend in the original signal in real time, and the overall change of the signal is reflected by identifying the peak and valley values of the signal. The peak and valley values are determined by calculating the difference between each data point and its adjacent data point. When the difference is positive, it is identified as a peak, and when the difference is negative, it is identified as a valley. Based on these points, after real-time storage and comparison, the secondary valley points of the peak are further identified as the basis for subsequent interval division. This method helps to improve the accuracy of signal trend estimation, especially in the case of strong noise. Noise usually appears as high-frequency fluctuations, which may lead to misjudgment of peaks or valleys, thus affecting the accuracy of interval division. The secondary valley points are located between the main peaks and valleys, which can effectively filter out noise interference, provide a smoother and more stable trend representation, and thus ensure the accurate construction of the signal envelope. In addition, this method helps to avoid overfitting, capture the long-term change law of the signal, and improve the robustness of envelope analysis. Based on the set of peaks and valleys obtained by real-time analysis, the envelope of the real-time signal is obtained, so as to construct the overall change trend of the signal.

[0099] Identify and divide the characteristic regions of the signal through a sliding window, obtain the regional characteristic information of the signal, and perform a summary calculation on the signal based on the extracted characteristic points, so as to generate a preliminary peak envelope and valley envelope. Perform characteristic region identification and summary calculation on the time-domain signal:

[0100] The sliding window updates the characteristic values within the window in real time each time it moves, and selects the secondary valley point of the peak as the demarcation point to determine the boundary of the characteristic region. During the real-time progress of the signal, the sliding window is established following the identification of the secondary valley point of the peak. In the window, first calculate the reference line L Baseline (i) of the first and last data points within the window as the lowest threshold of the data within the window, and calculate the range ΔH max between the data point and the reference line and the absolute value of the change ΔCon of two consecutive data points:

[0101] ΔH max= |N(x i ) - L Baseline (i)| max

[0102] ΔCon = |N(x i ) - N(x i+1 )| max

[0103] Where N(x i ) is the amplitude of the data point calculated within the current sliding window, N(x i+1 ) is the amplitude of the next data point of the currently calculated data point, x i is the time point corresponding to the currently calculated data point, x i+1 is the time point corresponding to the next data point, and i is the count.

[0104] Calculate the slopes from the central data point to both ends within the calculation window, i.e., the first - end slope Slope left and the second - end slope Slope right . If the following conditions are met:

[0105] ΔCon ≤ ΔH max ,

[0106] Slope left > 0, Slope right < 0.

[0107] Then this window is determined to be a peak area; otherwise, it is determined to be a non - peak area. Using the overlapping sliding window method, its size can be manually adjusted so that each window has partial overlap, maximizing the retention of signal features. The overlapping sliding window steps in real time with the signal, splitting the peak and valley trend information, so that in each sliding window, a peak estimate value and a valley estimate value are obtained respectively as the central summary statistics for that window. The summary statistic principle is as follows: If this window is a peak area, then select the data points that are only within the peak area in the overlapping sliding window for mean processing; if this window is a non - peak area, then select all the data points in the overlapping sliding window for mean processing. Through the step - by - step traversal of the sliding window, each central summary statistic point obtained thus constructs the upper and lower envelope lines that describe the signal fluctuation range and trend. By generating the upper and lower edge lines of the signal, the construction of the signal trend envelope is realized, thereby describing the signal fluctuation range and trend change. These envelope lines not only help identify the main trend of the signal but also effectively filter out high - frequency noise, providing a stable basis for subsequent vector projection noise reduction.

[0108] Compare the generated peak envelope and valley envelope with the characteristic points of the signal and provide real - time feedback information to change the size of the sliding window, thereby dynamically adjusting the envelope trend line. Based on the initial signal envelope trend, perform regional estimation on it and provide feedback:

[0109] In the initial sliding window processing, information loss may occur in some regions. To solve this problem, a region estimation module is set. The upper and lower envelope lines obtained from the initial estimation are used as the reference trend for region estimation, and are compared with the peak and valley information in the original signal in real time to ensure that the system can accurately capture signal changes. The trend difference T max The smaller it is, the higher the estimation accuracy and the more sensitive the perception of signal fluctuations; if the trend difference is too large, the system will automatically feedback information to the feature region recognition module and dynamically adjust the size of the overlapping sliding window method to improve the ability to capture the trend of the original signal. It should be noted that the larger the overlapping sliding window, that is, the more peak and valley information it contains, the more significant the signal noise reduction effect, because a larger window can more comprehensively summarize the region information. This method overcomes the limitation of the static basis vector in the traditional vector projection method, enabling the noise reduction process to be adjusted in real time with the dynamic changes of the signal and noise.

[0110] Project the original signal onto the projection space composed of the envelope trend lines adjusted in real time, so as to achieve efficient real-time noise reduction. Based on the projection space constructed by the envelope trend of the real-time signal, perform vector projection noise reduction operation on the signal:

[0111] In the signal processing of the NV color center magnetic sensor, vector projection is an effective technique for signal noise reduction and feature extraction. The peak and valley envelopes obtained through envelope analysis reflect the main change trend and perturbation trend of the signal. Based on these extracted trend signals, use the vector projection of data points to construct the corresponding vector space to ensure the effectiveness and accuracy of the projection operation. First, use the sliding window technique to segment the signal. In each sliding window, the algorithm picks the point on the mean envelope line closest to the distance as the first point of the sliding window, that is, the noise reduction point reference, and projects all the original signal points in the window onto the corresponding vector space to obtain a vector Detect whether the vector is completely located in the noise reduction space composed of the peak and valley envelopes to identify valid vectors and invalid vectors. The invalid vectors will be eliminated, while the valid vectors are retained. Subsequently, calculate the modulus value of each valid vector in the sliding window, and further calculate the average valid vector modulus value as the vector estimation value of the noise reduction reference in this window:

[0112]

[0113]

[0114] where λ is the number of valid vectors. V k(i) is the slope of each original signal point with respect to the reference noise reduction point, V wk(i)is the average effective vector value. According to the previous noise reduction reference point, the original signal point in the sliding window is dynamically adjusted and projected into the noise reduction space. The specific adjustment formula is as follows:

[0115] N (i+1) =N (i) +V wk(i) ×(x (i+1) -x (k) )

[0116] N (i+1) is the trend point of the signal in the sliding window. Finally, by calculating the difference between the original signal point and the trend point, differential mode suppression is performed and then superimposed back to the trend point, thereby effectively suppressing noise interference. By comparing the signals before and after denoising, it can be found that the denoised signal is smoother and can more accurately reflect the main trend of the original signal. This denoising method shows significant advantages in complex dynamic environments. Figure 5 ,Comparing the signals before and after denoising, it can be found that the denoised signal is smoother and can more accurately reflect the main trend of the original signal.,This denoising method shows significant advantages in complex dynamic environments.

[0117] The core mechanism of difference suppression.

[0118] Problems with traditional methods: In many existing signal denoising methods, the denoising process usually ignores local deviations between the signal and its trend, which may lead to the loss of key information or fail to effectively remove complex noise. The present application separates the noise component from the signal by calculating the difference between the original signal point and the trend point, and constrains the absolute value of the difference (differential mode suppression). This difference suppression method can filter out noise more accurately while retaining the true trend characteristics of the signal. It can accurately identify the boundaries between noise components and signal trend components. It has higher adaptability to multi-band noise interference. Significantly reduce the possibility of signal distortion.

[0119] Dynamic adjustment of trend point regression.

[0120] Problems with traditional methods: Static trend extraction methods are usually unable to adapt to changes in signals in a dynamic environment, especially when the signal fluctuates violently or the frequency components are complex, and trend deviations are prone to occur. This application dynamically adjusts the original signal points in the sliding window and constructs trend points using the average modulus of the effective vector. This method "regresses" the signal points to the dynamic trend space to ensure the real-time and accuracy of trend extraction. Real-time: Through dynamic adjustment, trend points can quickly adapt to changes in signals. Accuracy: The calculation of trend points is centered on effective vectors and can reflect the true main trend of the signal.

[0121] The combined effect of difference suppression and trend point regression.

[0122] Difference suppression is used to eliminate invalid noise components, and trend point regression is used to retain the main trend of the signal. The combination of the two makes the signal denoising process have the following characteristics:

[0123] Enhance signal smoothness: After eliminating noise, the signal becomes smoother and the volatility decreases. Highlight the main trend of the signal: Through the regression adjustment of trend points, the main trend of the original signal can be accurately reconstructed.

[0124] Adapt to dynamic environments: Regardless of how complex the frequency band of the noise source is, the method can ensure stable denoising effects.

[0125] Application scenarios and advantages.

[0126] Complex noise environments: It is particularly suitable for environments with multi-band and multi-noise source interferences, such as biomedical signal processing, real-time detection of NV color center magnetic sensors, and denoising of complex industrial signals.

[0127] Scenarios with high-precision requirements: In scenarios that require extremely high trend recognition accuracy (such as the extraction of the main trend of magnetic field signals), the difference suppression and trend point regression methods can significantly improve the analysis effect.

[0128] Dynamic signal processing: For example, signal tracking in the dynamic environment of drones, real-time detection of changes in geomagnetic signals, etc.

[0129] Through the above operations, the influence of the main noise source can be significantly reduced, while maintaining the integrity of key signal features. It overcomes the static dependence of traditional denoising techniques on noise characteristics and also realizes the real-time dynamic suppression of multi-source and multi-band noise. This method provides an efficient solution for improving the measurement accuracy and stability of NV color center magnetic sensors in complex environments and has broad application prospects.

[0130] Compared with the prior art, the NV - center magnetic sensor of the present application uses the nitrogen - vacancy (NV) centers in diamond as the detection unit, enabling high - sensitivity weak magnetic field measurement at room temperature. Therefore, it has broad application prospects in fields such as electric field measurement, geological exploration, and biomagnetic field detection. However, the noise problem remains the main factor limiting its sensitivity and accuracy. Existing noise reduction techniques, such as dynamic decoupling, cancellation noise reduction, and deep - learning - based prediction models, although each has its advantages, still pose challenges in practical applications. The enhanced real - time vector projection technique addresses the noise reduction requirements of NV - center magnetic sensors in complex dynamic environments. Through the coordinated action of three major modules: envelope analysis, characteristic region identification, and region estimation, it realizes real - time perception of multi - source noise signals, feedback adjustment, construction of a dynamic projection space, and real - time projection noise reduction. In the application of NV - center magnetic sensors, this method demonstrates extremely high adaptability and reliability in dealing with complex and frequently changing noise sources and variable signal characteristics. The present application provides an efficient and widely integratable solution for the application of NV - center sensors in complex noise environments. The specific advantages are reflected in the following aspects:

[0131] High - sensitivity detection based on NV centers:

[0132] Using the nitrogen - vacancy (NV) centers in diamond as the detection unit, the NV - center magnetic sensor can achieve high - sensitivity weak magnetic field measurement at room temperature, thereby improving the accuracy of magnetic field measurement. The NV - center sensor can achieve high - sensitivity weak magnetic field measurement at room temperature and is applicable to a wide range of fields such as electric field measurement, geological exploration, and biomagnetic field detection.

[0133] Enhanced real - time noise reduction ability:

[0134] The enhanced real - time vector projection technique proposed in the present application, aiming at the noise reduction requirements of NV - center magnetic sensors in complex dynamic environments, combines three major modules: envelope analysis, characteristic region identification, and region estimation, and effectively suppresses multi - source noise through the following measures.

[0135] Real - time perception of multi - source noise: Real - time monitoring and analysis of the characteristics of different noise sources.

[0136] Feedback adjustment and construction of dynamic projection space: Through adaptive adjustment, efficient noise isolation is achieved.

[0137] Real - time projection noise reduction: Ensure the stability and accuracy of the measured signal in a dynamic environment.

[0138] Environmental adaptability and reliability:

[0139] NV color center magnetic sensors can maintain high measurement accuracy in various noise environments, including complex and harsh conditions, demonstrating excellent environmental adaptability. This adaptability is achieved by effectively suppressing the interference of environmental noise on the signal, making it suitable for diverse application scenarios. The technology can maintain stable performance in a complex and frequently changing noise source environment, has a high adaptability to variable signal characteristics, and improves the reliability of measurement.

[0140] Integration potential:

[0141] It provides an efficient and real-time solution with good system integration potential, paving the way for the wide application of NV color center magnetic sensors.

[0142] This application also provides an application scenario that applies the above-mentioned real-time magnetic field vector projection method. Specifically: The real-time magnetic field vector projection method provided in this embodiment can be applied in magnetic field detection. Magnetic field detection includes magnetic field measurement, measurement data processing, and magnetic field determination; measurement data enters the measurement data processing from the length measurement, obtains corresponding content features through a human-machine collaboration method, and enters the downstream magnetic field determination link. The real-time magnetic field vector projection method provided in this embodiment belongs to the signal denoising link in measurement data processing. Specifically, in the process of measurement data processing for measurement data, noise reduction can be performed on the measurement data based on the collaboration of machine noise reduction and artificial noise reduction, that is, noise reduction is performed on the real-time original signal.

[0143] Based on the same inventive concept, the embodiment of this application also provides a real-time magnetic field vector projection device for implementing the above-mentioned real-time magnetic field vector projection method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following real-time magnetic field vector projection device can refer to the limitations on the real-time magnetic field vector projection method in the above text, and will not be repeated here.

[0144] In an exemplary embodiment, as Figure 2 and Figure 3 shown, a real-time magnetic field vector projection device is provided, including:

[0145] A real-time original signal acquisition module for acquiring the real-time original signal of the magnetic field; the real-time original signal is the magnetic field time-domain signal detected by the NV color center magnetic sensor;

[0146] An envelope analysis module for performing envelope analysis on the real-time original signal to obtain a real-time signal envelope;

[0147] A feature region recognition module for using a sliding window to perform feature region recognition based on the real-time signal envelope to obtain a peak region and a non-peak region;

[0148] A building block for constructing signal trend envelopes for the peak region and the non-peak region respectively using overlapping sliding windows;

[0149] A region estimation module for using the signal trend envelope as a reference trend and performing region estimation based on the real-time original signal to determine the mean envelope;

[0150] A vector projection module for performing vector projection noise reduction on the real-time original signal in the projection space using the sliding window technique to obtain the denoised signal; the projection space is constructed based on the mean envelope.

[0151] The real-time magnetic field vector projection device further includes a lock-in amplifier 7 for demodulating the original signal to extract the signal components of the target frequency; a sliding window adjustment module 10 for dynamically adjusting the window size to adapt to the signal change characteristics; and an envelope analysis module 8 for extracting the upper and lower envelope lines of the signal to construct the mean envelope to support the construction of the projection space.

[0152] The 532 nm laser 1 is the light source of the magnetic field real-time vector projection device. It generates a laser beam to excite the NV color centers in the diamond. After passing through the objective lens 2, the laser will be focused on the surface of the diamond 4. This step is to ensure that the laser beam effectively excites the NV color centers in the diamond 4, thereby generating a fluorescence signal. The function of the objective lens 2 is to focus the laser beam and accurately irradiate the laser onto the designated area on the diamond 4. By focusing the laser beam, the objective lens 2 ensures that the energy of the laser can effectively excite the NV color centers in the diamond 4. The microwave antenna 3 is used to apply microwave radiation to the NV color centers in the diamond 4. These microwave signals will affect the quantum states of the NV color centers, thereby changing their fluorescence characteristics. Microwave radiation is one of the key factors driving the NV color centers to sense the external magnetic field. The diamond 4 is the core part of the NV color center magnetic sensor, and the NV color centers are distributed in the diamond crystal. When an external magnetic field acts on these NV color centers, their fluorescence properties change. By monitoring these changes, the magnetic field real-time vector projection device can sense the intensity and direction of the external magnetic field. When the NV color centers in the diamond 4 are excited by the laser, they will emit a fluorescence signal. These fluorescence signals carry information about the external magnetic field and are the core data for measuring the magnetic field. The photodetector 6 is used to collect the fluorescence signals from the NV color centers. It converts the fluorescence signals into electrical signals and then transmits this signal to the subsequent lock-in amplifier 7 for processing. The working process is that the light emitted by the laser is focused on the diamond 4 through the objective lens 2 to excite the NV color centers in the diamond. The microwave antenna 3 applies microwave radiation to the NV color centers in the diamond, driving the NV color centers to produce fluorescence changes related to the magnetic field. The fluorescence signal 5 is emitted by the NV color centers, and the photodetector 6 collects these fluorescence signals and converts them into electrical signals. The converted electrical signals are sent to the lock-in amplifier 7, and the signal is demodulated by the lock-in amplifier 7 to extract the signal components of the target frequency.

[0153] The NV color center magnetic sensor is in Figure 2 the following diamond structure below, and this structure excites the NV centers through a 532 nm laser, generates a fluorescence signal, and detects it. Figure 3 It further demonstrates the working principle of the NV color center magnetic sensor. Specifically, the 532 nm laser 1 is used to excite the NV color centers in the diamond 4 to generate a fluorescence signal 5, which is highly sensitive to changes in the external magnetic field. In addition, by applying an external magnetic field, the electron energy levels of the NV color centers are regulated to achieve highly sensitive detection of magnetic field information. At the same time, environmental noise and related interferences will also affect the stability of the fluorescence signal. Therefore, noise reduction processing needs to be carried out through the vector projection module to improve the signal quality and detection accuracy.

[0154] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store real-time magnetic field vector projection data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for real-time magnetic field vector projection.

[0155] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the above method embodiments are implemented.

[0156] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the above method embodiments are implemented.

[0157] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the above method embodiments are implemented.

[0158] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0159] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0160] The databases involved in the various embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0161] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0162] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A magnetic field real-time vector projection method, characterized in that: The magnetic field real-time vector projection method comprises: Acquire a real-time original signal of the magnetic field; the real-time original signal is a time domain signal of the magnetic field detected by the NV color center magnetic sensor; Performing envelope analysis on the real-time original signal to obtain a real-time signal envelope; According to the real-time signal envelope, a sliding window is used to identify a characteristic region to obtain a peak region and a non-peak region; The signal trend envelope is constructed using overlapping sliding windows for the peak area and the non-peak area respectively; Taking the signal trend envelope as a reference trend, performing regional estimation based on the real-time original signal, and determining a mean envelope; According to the real-time original signal, vector projection denoising is performed in the projection space using a sliding window technology to obtain a denoised signal; the projection space is constructed according to the mean envelope.

2. The magnetic field real-time vector projection method according to claim 1, characterized in that: Performing envelope analysis on the real-time original signal to obtain a real-time signal envelope specifically includes: Determine peak-valley, valley-value and secondary valley-value points according to the extreme value of the real-time original signal; A real-time signal envelope is determined according to the peak-to-valley, the valley value and the secondary valley value point.

3. The magnetic field real-time vector projection method according to claim 1, characterized in that: According to the real-time signal envelope, a sliding window is used to identify the feature area to obtain the peak area and the non-peak area, which specifically includes: Determine a sliding window according to the secondary valley point of the real-time signal envelope; Calculate the baseline of the first and last data points in the sliding window using the sliding window; Calculate the range and the absolute value of the change of two consecutive data points according to the baseline; the range is the range between the data point in the sliding window and the baseline; The peak area and the non-peak area are determined according to the range, the absolute value of the change, the first slope and the second slope; the first slope is the slope of the straight line connecting the first data point of the sliding window and the central data point of the sliding window; the second slope is the slope of the straight line connecting the last data point of the sliding window and the central data point of the sliding window.

4. The magnetic field real-time vector projection method according to claim 3, characterized in that: Determining the peak area and the non-peak area according to the range, the absolute value of the change, the first slope and the second slope specifically includes: Determine whether the sliding window satisfies the first condition, the second condition and the third condition, and obtain a first judgment result; the first condition is that the absolute value of the change is less than or equal to the range; the second condition is that the first slope is greater than zero; the second condition is that the second slope is less than zero; If the first judgment result is yes, then the sliding window is a peak area; If the first judgment result is no, then the sliding window is a non-peak area.

5. The magnetic field real-time vector projection method according to claim 1, characterized in that: The signal trend envelope is constructed using overlapping sliding windows for the peak area and the non-peak area, including: Selecting data points in the overlapping sliding window that are only located in the peak area for mean processing; Select all data points in the overlapping sliding window for mean processing; The sliding window is traversed step by step to determine the signal trend envelope based on all data points after mean processing.

6. The magnetic field real-time vector projection method according to claim 1, characterized in that: Taking the signal trend envelope as a reference trend, performing region estimation according to the real-time original signal, and determining the mean envelope specifically include: The signal trend envelope is used as a reference trend, and is compared with the peak value information and the valley value information of the real-time original signal in real time to determine the trend difference; Determine whether the trend difference is greater than a trend threshold, and obtain a second determination result; If the second judgment result is yes, the size of the overlapping sliding window is increased, and the process returns to the step of "using the signal trend envelope as a reference trend, and comparing it with the peak value information and valley value information of the real-time original signal in real time to determine the trend difference"; If the second judgment result is yes, the mean envelope is determined according to the signal trend envelope.

7. A magnetic field real-time vector projection device, characterized in that: The magnetic field real-time vector projection device comprises: A real-time original signal acquisition module is used to acquire a real-time original signal of a magnetic field; the real-time original signal is a time domain signal of a magnetic field detected by an NV color center magnetic sensor; An envelope analysis module, used for performing envelope analysis on the real-time original signal to obtain a real-time signal envelope; A feature region identification module, used to identify feature regions using a sliding window according to the real-time signal envelope to obtain peak regions and non-peak regions; A construction module is used to construct a signal trend envelope using overlapping sliding windows for the peak area and the non-peak area respectively; A region estimation module, used to use the signal trend envelope as a reference trend, perform region estimation according to the real-time original signal, and determine a mean envelope; The vector projection module is used to perform vector projection noise reduction in the projection space according to the real-time original signal using the sliding window technology to obtain a denoised signal; the projection space is constructed according to the mean envelope.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the real-time vector projection method for a magnetic field according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the real-time vector projection method of the magnetic field according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the real-time vector projection method of the magnetic field according to any one of claims 1 to 6 is implemented.

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