System and method for detecting magnetization state of hard magnetic material

Abstract

The invention provides a magnetization state detection system and method for a hard magnetic material, and relates to the technical field of magnetization state detection. Identifying a target magnetic flux density area from the magnetic flux density distribution diagram, and positioning the output end of the local excitation probe to a physical feature point corresponding to the target magnetic flux density area on the to-be-detected surface of the hard magnetic material; applying a local reverse magnetic field to the physical feature points, and obtaining a local magnetization reversal curve at the physical feature points; extracting a characteristic parameter representing the coercive force of the physical characteristic point from the local magnetization inversion curve, and if the attenuation percentage of the characteristic parameter relative to the reference coercive force parameter exceeds a second preset threshold value, determining that the area where the physical characteristic point is located is a demagnetized area; and outputting a magnetization state detection map of the hard magnetic material based on the demagnetized region. According to the invention, the precise positioning of the demagnetization area can be realized when the magnetic domain structure of the hard magnetic material generates local irreversible change.

Description

Technical Field

[0001] This application relates to the field of magnetization state detection technology, and more specifically, to a magnetization state detection system and method for hard magnetic materials. Background Technology

[0002] Magnetization state detection involves measuring the remanent magnetic induction intensity. The strength of the remanent magnetic induction intensity directly affects the subsequent use of materials and the quality of processes. For example, in magnetic particle testing, appropriate remanence can effectively attract magnetic particles and highlight defects, while excessive or insufficient remanent magnetic induction intensity may lead to detection failure or interference. Currently, this detection mainly adopts the magnetic field measurement method, which uses a gaussmeter / magnetometer to measure the magnetic induction intensity on the surface of the material, thereby determining whether its magnetization state meets the requirements.

[0003] In existing magnetization state detection methods, the focus is on directly measuring the residual magnetic field formed on the surface of a hard magnetic material after magnetization. During detection, a Hall effect-based magnetometer is typically used. When the magnetometer approaches the material surface, the internal semiconductor wafer generates a voltage signal proportional to the magnetic field strength due to the external magnetic field. By quantifying this voltage signal, the instrument can directly read the magnetic induction intensity at a specified location on the material surface, thereby assessing the strength of the hard magnetic material's magnetization state. However, in the magnetization state detection of hard magnetic materials, factors such as external alternating magnetic fields, high-temperature environments, or mechanical shocks during service can affect the magnetization state of the material. The magnetic domain structure of hard magnetic materials is prone to local irreversible changes, which manifest as a decrease in the coercivity parameter of a specific micro-region. However, the magnetic flux density distribution is masked by the compensation effect of the magnetic field in the adjacent region. Traditional detection methods (such as Hall probe full-field scanning or magnetic flux imaging) can only obtain the spatial distribution of surface magnetic flux density and cannot distinguish whether the decrease in magnetic flux density is due to the decrease in the coercivity of the material itself or due to the demagnetization caused by the geometry. Therefore, it is difficult to accurately locate the demagnetization area of ​​hard magnetic materials. Thus, how to accurately locate the demagnetization area when the magnetic domain structure of hard magnetic materials undergoes local irreversible changes has become a problem faced by the industry. Summary of the Invention

[0004] This application provides a magnetization state detection system and method for hard magnetic materials, which can accurately locate the demagnetization region when the magnetic domain structure of hard magnetic materials undergoes local irreversible changes.

[0005] In a first aspect, this application provides a method for detecting the magnetization state of a hard magnetic material, comprising the following steps: Obtain the magnetic flux density distribution map of the surface of the hard magnetic material to be tested; Identify the target magnetic flux density region whose normal magnetic flux density value is lower than the first preset threshold from the magnetic flux density distribution map, and position the output end of the local excitation probe to the physical feature point corresponding to the target magnetic flux density region on the surface of the hard magnetic material to be tested; At the physical feature point, the local excitation probe is controlled to apply a local reverse magnetic field that increases linearly from zero, and the change in normal magnetic flux density at the physical feature point is monitored to obtain a local magnetization reversal curve. The characteristic parameters characterizing the coercivity of the physical feature point are extracted from the local magnetization reversal curve, and the characteristic parameters are compared with the nominal reference coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds the second preset threshold, the area where the physical feature point is located is determined to be a demagnetized area. The demagnetized region is marked and mapped in the magnetic flux density distribution map, thereby outputting a magnetization state detection spectrum of the hard magnetic material.

[0006] In some embodiments, identifying a target magnetic flux density region whose normal magnetic flux density value is lower than a first preset threshold from the magnetic flux density distribution map specifically includes: The magnetic flux density distribution map is preprocessed by normalization to obtain the preprocessed magnetic flux density distribution map. A first preset threshold is set based on the nominal magnetic property parameters of the hard magnetic material; The normal magnetic flux density value is extracted point by point from the preprocessed magnetic flux density distribution map, and the normal magnetic flux density value of each point is compared with the first preset threshold to filter out discrete pixels with normal magnetic flux density values ​​lower than the first preset threshold. Connectivity analysis is performed on the selected discrete pixels, and the obtained connected regions are then used as the target magnetic flux density region.

[0007] In some embodiments, positioning the output end of the local excitation probe to a physical feature point on the surface of the hard magnetic material to be tested that corresponds to the target magnetic flux density region specifically includes: Establish the coordinate mapping relationship between the pixel coordinates of the magnetic flux density distribution map and the physical coordinates of the hard magnetic material surface under test; Extract the pixel coordinate information of the target magnetic flux density region, and substitute the pixel coordinate information of the target magnetic flux density region into the coordinate mapping relationship to calculate the physical coordinate region corresponding to the target magnetic flux density region. In the physical coordinate region corresponding to the target magnetic flux density region, a physical feature point is determined, and a positioning command is sent to the local excitation probe based on the physical coordinates of the physical feature point to control the output end of the local excitation probe to move to the physical feature point.

[0008] In some embodiments, determining physical feature points within the physical coordinate region corresponding to the target magnetic flux density region specifically includes: The magnetic field gradient amplitude is calculated for the physical coordinate region corresponding to the target magnetic flux density region to obtain the magnetic field gradient amplitude distribution of the target magnetic flux density region. Based on the magnetic field gradient amplitude distribution, the physical coordinate region corresponding to the target magnetic flux density region is divided into a first gradient sub-region and a second gradient sub-region. The magnetic flux density dispersion is calculated for the first gradient sub-region and the second gradient sub-region respectively. Then, the magnetic field gradient extreme points are extracted as candidate physical feature points in the gradient sub-region with the largest magnetic flux density dispersion. Spatial density clustering is used to remove duplicates from all extracted candidate physical feature points in order to determine the physical feature points.

[0009] In some embodiments, controlling the local excitation probe to apply a local reverse magnetic field that increases linearly from zero at the physical feature point specifically includes: The linear relationship between the excitation current and the output reverse magnetic field strength of the local excitation probe was calibrated. Based on the aforementioned linear correspondence, an excitation current control curve is generated that increases linearly from zero. The excitation current control curve is imported into the excitation drive module of the local excitation probe, and the excitation current matching the excitation current control curve is input to the local excitation probe through the excitation drive module. The local excitation probe generates a local reverse magnetic field at a physical feature point based on the input excitation current, which increases linearly from zero and corresponds to the excitation current control curve.

[0010] In some embodiments, extracting characteristic parameters representing the coercivity of the physical feature point from the local magnetization reversal curve specifically includes: The local magnetization reversal curve is smoothed and denoised to obtain a smoothed magnetization reversal curve; Extract the characteristic intersections where the normal magnetic flux density is zero on the smoothed magnetization reversal curve; Extract the reverse magnetic field strength value corresponding to the feature intersection and verify its validity. Use the reverse magnetic field strength value that has passed the validity verification as the feature parameter characterizing the coercivity of the physical feature point.

[0011] In some embodiments, a Hall probe array is used to obtain a magnetic flux density distribution map of the surface of the hard magnetic material to be tested.

[0012] Secondly, this application provides a magnetization state detection system for hard magnetic materials, used to perform a method for detecting the magnetization state of hard magnetic materials, the system comprising: The acquisition module is used to acquire the magnetic flux density distribution map of the hard magnetic material surface under test; The processing module is used to identify the target magnetic flux density region whose normal magnetic flux density value is lower than a first preset threshold from the magnetic flux density distribution map, and to position the output end of the local excitation probe to the physical feature point corresponding to the target magnetic flux density region on the surface of the hard magnetic material to be tested. The processing module is also used to control the local excitation probe to apply a local reverse magnetic field that increases linearly from zero at the physical feature point, and to monitor the change in normal magnetic flux density at the physical feature point, thereby obtaining a local magnetization reversal curve. The processing module is further configured to extract characteristic parameters representing the coercivity of the physical feature point from the local magnetization reversal curve, and compare the characteristic parameters with the nominal reference coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds a second preset threshold, the region where the physical feature point is located is determined to be a demagnetized region. The execution module is used to mark and map the demagnetized region in the magnetic flux density distribution map, and then output the magnetization state detection spectrum of the hard magnetic material.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described method for detecting the magnetization state of hard magnetic materials.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting the magnetization state of hard magnetic materials.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The magnetization state detection system and method for hard magnetic materials provided in this application firstly acquires a magnetic flux density distribution map of the surface of the hard magnetic material to be tested; secondly, a target magnetic flux density region with a normal magnetic flux density value lower than a first preset threshold is identified from the magnetic flux density distribution map, and the output end of the local excitation probe is positioned at a physical feature point on the surface of the hard magnetic material to be tested corresponding to the target magnetic flux density region; further, at the physical feature point, the local excitation probe is controlled to apply a local reverse magnetic field that increases linearly from zero, and the change in normal magnetic flux density at the physical feature point is monitored to obtain a local magnetization reversal curve; then, a characteristic parameter characterizing the coercivity of the physical feature point is extracted from the local magnetization reversal curve, and the characteristic parameter is compared with the nominal reference coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds a second preset threshold, the region where the physical feature point is located is determined to be a demagnetized region; finally, the demagnetized region is marked and mapped in the magnetic flux density distribution map to output a magnetization state detection spectrum of the hard magnetic material.

[0016] Therefore, this application can accurately locate demagnetized regions when the magnetic domain structure of hard magnetic materials undergoes local irreversible changes. First, by acquiring the magnetic flux density distribution map of the surface of the hard magnetic material under test, a preliminary collection and visualization of the overall magnetic performance state of the hard magnetic material can be achieved, providing magnetic performance data support for the subsequent accurate identification of potential abnormal regions. Second, by identifying the target magnetic flux density region and locating physical feature points from the magnetic flux density distribution map, potential demagnetized regions with abnormal normal magnetic flux density can be effectively identified, eliminating invalid detection in areas with normal magnetic performance, achieving targeted focusing of detection points, and avoiding the inefficiency and resource waste caused by indiscriminate detection. Furthermore, by applying a local reverse magnetic field that increases linearly from zero at the physical feature points and monitoring the change in magnetic flux density to obtain a local magnetization reversal curve, the local magnetic domain magnetization reversal can be precisely triggered by a controllable linearly increasing local magnetic field, avoiding secondary magnetic interference from the global reverse magnetic field on the normal region of the material, while effectively identifying the change in magnetic performance at the physical feature points. The continuous reversal pattern of field changes provides data support that fits the local reality for the extraction of coercivity characteristic parameters. Then, coercivity characteristic parameters are extracted from the local magnetization reversal curve, and the demagnetized area is determined by comparing the attenuation percentage. The local magnetic performance state is transformed into quantifiable coercivity parameters. With the benchmark parameter as a reference and the attenuation percentage as the quantitative judgment basis, the determination of the demagnetized area has an objective and accurate quantitative standard. This effectively avoids the situation where traditional detection methods can only obtain the spatial distribution of surface magnetic flux density under local irreversible changes in magnetic domain structure, and cannot distinguish whether the decrease in magnetic flux density is due to the demagnetization problem caused by the decrease in coercivity of the material itself. Finally, the demagnetized area is marked and mapped and a magnetization state detection spectrum is output, realizing the transformation of quantitative detection results into a visual spectrum, which can intuitively present the spatial position of the demagnetized area on the surface of the material to be tested. In summary, the technical solution provided by this application can achieve accurate positioning of the demagnetized area under local irreversible changes in the magnetic domain structure of hard magnetic materials. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a method for detecting the magnetization state of hard magnetic materials according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of a target magnetic flux density region according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of a hard magnetic material magnetization state detection system according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device for implementing a method for detecting the magnetization state of hard magnetic materials according to some embodiments of this application. Detailed Implementation

[0018] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a method for detecting the magnetization state of hard magnetic materials according to some embodiments of this application. The figure mainly includes the following steps: In step S101, the magnetic flux density distribution map of the hard magnetic material surface to be tested is obtained.

[0020] In practice, the magnetic flux density distribution map of the hard magnetic material surface under test is obtained through a Hall probe array. That is, on a plane parallel to the hard magnetic material surface under test, the Hall probe array performs synchronous measurements to collect the normal magnetic flux density value of the hard magnetic material surface under test directly below each measurement point. At the same time, the spatial coordinates corresponding to each measurement point are recorded, and an interpolation algorithm is used to reconstruct a continuous two-dimensional data matrix from the discrete normal magnetic flux density values ​​to obtain the magnetic flux density distribution map of the hard magnetic material surface under test. Alternatively, a magnetometer can also be used to obtain the magnetic flux density distribution map of the hard magnetic material surface under test, which is not limited here.

[0021] It should be noted that, in this application, hard magnetic materials refer to a class of magnetic materials with high coercivity and high remanence, which are widely used in motors, sensors, data storage, and acoustic devices. In this application, the magnetic flux density distribution map refers to a visualized two-dimensional image that reflects the magnitude and direction of the magnetic induction intensity at each point on a specified spatial plane above the surface of the hard magnetic material. This magnetic flux density distribution map converts the magnetic flux density numerical matrix measured at discrete points into an intuitive spatial intensity distribution shape through image processing technology, so as to clearly show the strength changes, polarity distribution, and possible abnormal areas of the magnetic field on the material surface. It is the initial spatial magnetic field data basis for subsequent demagnetization area location and analysis.

[0022] In step S102, a target magnetic flux density region with a normal magnetic flux density value lower than a first preset threshold is identified from the magnetic flux density distribution map, and the output end of the local excitation probe is positioned at the physical feature point corresponding to the target magnetic flux density region on the surface of the hard magnetic material to be tested.

[0023] In some embodiments, reference Figure 2 As shown in the figure, this is an exemplary flowchart of determining a target magnetic flux density region according to some embodiments of this application. In this embodiment, identifying a target magnetic flux density region with a normal magnetic flux density value lower than a first preset threshold from the magnetic flux density distribution map can be achieved by the following steps: In step S1021, the magnetic flux density distribution map is normalized and preprocessed to obtain the preprocessed magnetic flux density distribution map. In step S1022, a first preset threshold is set based on the nominal magnetic property parameters of the hard magnetic material; In step S1023, the normal magnetic flux density value is extracted point by point from the preprocessed magnetic flux density distribution map, and the normal magnetic flux density value of each point is compared with the first preset threshold to filter out discrete pixels with normal magnetic flux density values ​​lower than the first preset threshold. In step S1024, connected component analysis is performed on the selected discrete pixels, and the obtained connected regions are then used as the target magnetic flux density region.

[0024] In specific implementation, firstly, a linear normalization algorithm is used to preprocess the magnetic flux density distribution map. By linearly mapping the normal magnetic flux density value corresponding to each pixel in the magnetic flux density distribution map to the [0,1] interval, a preprocessed magnetic flux density distribution map is obtained. The preprocessed magnetic flux density distribution map refers to the magnetic flux density distribution map after normalization. Secondly, based on the nominal magnetic performance parameters of hard magnetic materials (e.g., nominal remanence) and the preliminary judgment requirements for magnetic performance attenuation in engineering applications, a first preset threshold is set according to expert knowledge. The first preset threshold refers to the critical normal magnetic flux density value that distinguishes between normal magnetic flux density areas and potentially abnormal magnetic flux density areas. Then, the preprocessed magnetic flux density distribution map is processed... A point-by-point scan is performed to extract the normal magnetic flux density value corresponding to each pixel. Simultaneously, the extracted normal magnetic flux density value of each point is compared with the first preset threshold one by one to filter out all discrete pixels whose normal magnetic flux density value is lower than the first preset threshold. The discrete pixels refer to single pixels without spatial correlation. Finally, the eight-neighbor connected component labeling algorithm is used to perform connected component analysis on the filtered discrete pixels. By traversing all discrete pixels, adjacent pixels (i.e., within the eight-neighbor range) are marked and grouped into the same region. Then, each independent connected region is used as the target magnetic flux density region. The connected component analysis refers to an image processing method that identifies independent regions composed of adjacent pixels in the image.

[0025] It should be noted that, in this application, the target magnetic flux density region refers to the region where the normal magnetic flux density value is lower than the first preset threshold. This target magnetic flux density region characterizes the region where there is magnetic anomaly. Determining the target magnetic flux density region can provide a targeted detection range for the detection of the magnetization state of hard magnetic materials. By locking the region where the normal magnetic flux density value is lower than the first preset threshold from the magnetic flux density distribution map, the region with normal magnetic properties can be effectively excluded, avoiding indiscriminate detection of the entire surface to be tested, and greatly improving detection efficiency and targeting. At the same time, the target magnetic flux density region provides a clear spatial basis for the physical point positioning of the subsequent local excitation probe, ensuring that the application of the local reverse magnetic field, the monitoring of changes in normal magnetic flux density, and the extraction of coercive force characteristic parameters are all focused on the potential magnetic anomaly region.

[0026] In some embodiments, positioning the output end of the local excitation probe to a physical feature point on the surface of the hard magnetic material to be tested that corresponds to the target magnetic flux density region is achieved by the following steps: Establish the coordinate mapping relationship between the pixel coordinates of the magnetic flux density distribution map and the physical coordinates of the hard magnetic material surface under test; Extract the pixel coordinate information of the target magnetic flux density region, and substitute the pixel coordinate information of the target magnetic flux density region into the coordinate mapping relationship to calculate the physical coordinate region corresponding to the target magnetic flux density region. In the physical coordinate region corresponding to the target magnetic flux density region, a physical feature point is determined, and a positioning command is sent to the local excitation probe based on the physical coordinates of the physical feature point to control the output end of the local excitation probe to move to the physical feature point.

[0027] In specific implementation, firstly, the Zhang Zhengyou camera calibration method is used to establish a coordinate mapping relationship between the pixel coordinates of the magnetic flux density distribution map and the physical coordinates of the hard magnetic material surface to be tested. This involves placing a standard calibration plate on the hard magnetic material surface, collecting feature points with known physical coordinates on the calibration plate and their corresponding pixel coordinates in the magnetic flux density distribution map, and substituting these into a perspective projection model to calculate the coordinate mapping relationship, which includes rotation, translation, and scaling parameters. This coordinate mapping relationship refers to the mathematical relationship that enables the mutual conversion between pixel coordinates and physical coordinates. Then, based on this coordinate mapping relationship, an image contour extraction algorithm from image processing is used. The pixel coordinate information of the target magnetic flux density region is extracted, and the extracted pixel coordinate information of the target magnetic flux density region is successively substituted into the coordinate mapping relationship. The physical coordinate region corresponding to the target magnetic flux density region is obtained by inverse calculation. The physical coordinate region refers to the actual spatial range occupied by the target magnetic flux density region on the hard magnetic material to be tested surface. Finally, physical feature points are determined in the physical coordinate region corresponding to the target magnetic flux density region. Based on the physical coordinates of the physical feature points, pulse control commands are generated and sent to the motion controller of the local excitation probe. The motion controller drives the servo motor to move the output end of the local excitation probe to the physical feature point.

[0028] In some embodiments, determining physical feature points within the physical coordinate region corresponding to the target magnetic flux density region is achieved using the following steps: The magnetic field gradient amplitude is calculated for the physical coordinate region corresponding to the target magnetic flux density region to obtain the magnetic field gradient amplitude distribution of the target magnetic flux density region. Based on the magnetic field gradient amplitude distribution, the physical coordinate region corresponding to the target magnetic flux density region is divided into a first gradient sub-region and a second gradient sub-region. The magnetic flux density dispersion is calculated for the first gradient sub-region and the second gradient sub-region respectively. Then, the magnetic field gradient extreme points are extracted as candidate physical feature points in the gradient sub-region with the largest magnetic flux density dispersion. Spatial density clustering is used to remove duplicates from all extracted candidate physical feature points in order to determine the physical feature points.

[0029] In specific implementation, firstly, the finite difference method is used to calculate the magnetic field gradient amplitude of the physical coordinate region corresponding to the target magnetic flux density region. By performing neighborhood difference operations on the normal magnetic flux density values ​​of each physical coordinate point within this region, the magnetic field gradient amplitude of each point is solved, and a global distribution is constructed, resulting in the magnetic field gradient amplitude distribution of the target magnetic flux density region. This magnetic field gradient amplitude distribution refers to the spatial distribution of the magnetic field gradient amplitude at each point within the physical coordinate range of the target magnetic flux density region, reflecting the degree of drastic change in magnetic field strength. Secondly, based on the magnetic field gradient amplitude distribution, an adaptive threshold segmentation algorithm (i.e., Otsu's method) in image processing is used to determine the gradient segmentation threshold. Regions with gradient amplitudes greater than or equal to this threshold are divided into first gradient sub-regions, and regions with gradient amplitudes less than this threshold are divided into second gradient sub-regions. The first and second gradient sub-regions refer to the regions of the physical coordinate region of the target magnetic flux density region based on the difference in magnetic field gradient amplitude. The domain is divided into two sub-regions, corresponding to regions with drastic and gradual magnetic field changes, respectively. Then, the variance calculation method is used to calculate the magnetic flux density dispersion for the first and second gradient sub-regions. By comparing the magnetic flux density dispersion values ​​of the two sub-regions, the gradient sub-region with the largest magnetic flux density dispersion is extracted. The magnetic field gradient amplitude distribution within the extracted gradient sub-region is then scanned, and points where the gradient amplitude reaches its peak are extracted as candidate physical feature points. These candidate physical feature points refer to points with potential magnetic anomaly characteristics selected from the gradient sub-region with the largest magnetic flux density dispersion. Finally, a density clustering algorithm is used to perform spatial density clustering to remove duplicates from all extracted candidate physical feature points. The spatial neighborhood radius and minimum number of cluster points are set according to actual needs. Candidate physical feature points with a distance smaller than the spatial neighborhood radius are grouped into one class, and the cluster center point is retained. Redundant points are eliminated to determine the physical feature points. Further details are omitted here.

[0030] It should be noted that, in this application, physical feature points refer to the points used for subsequent application of local reverse magnetic fields and detection of magnetic property parameters. Determining physical feature points can provide precise targeted detection points for the detection of the magnetization state of hard magnetic materials, ensuring that the subsequent application of local reverse magnetic fields and extraction of coercivity parameters are focused on the areas with the most significant anomalies in magnetic properties. Existing technologies often use indiscriminate traversal point selection or only use the geometric center of the region as the detection point, which is prone to missing weak demagnetization areas and misjudging normal areas due to insufficient point targeting. This scheme uses magnetic field gradient amplitude calculation to identify areas with drastic magnetic field changes, combines magnetic flux density dispersion to screen anomaly concentration sub-regions, and then uses spatial density clustering to remove duplicate points, which can ensure a strong correlation between physical feature points and magnetic property anomalies and avoid point waste. It provides a reliable point benchmark for subsequent demagnetization area determination and spectrum generation, making the overall detection results more consistent with the actual magnetization state of hard magnetic materials and meeting the needs of high-precision magnetic property detection.

[0031] In step S103, at the physical feature point, the local excitation probe is controlled to apply a local reverse magnetic field that increases linearly from zero, and the change in normal magnetic flux density at the physical feature point is monitored to obtain a local magnetization reversal curve.

[0032] In some embodiments, controlling the local excitation probe to apply a local reverse magnetic field that increases linearly from zero at the physical feature point is achieved by the following steps: The linear relationship between the excitation current and the output reverse magnetic field strength of the local excitation probe was calibrated. Based on the aforementioned linear correspondence, an excitation current control curve is generated that increases linearly from zero. The excitation current control curve is imported into the excitation drive module of the local excitation probe, and the excitation current matching the excitation current control curve is input to the local excitation probe through the excitation drive module. The local excitation probe generates a local reverse magnetic field at a physical feature point based on the input excitation current, which increases linearly from zero and corresponds to the excitation current control curve.

[0033] In specific implementation, firstly, the linear correspondence between the excitation current and the output reverse magnetic field strength of the local excitation probe is calibrated using the standard magnetometer calibration method. This involves placing the local excitation probe and the standard magnetometer coaxially aligned, adjusting the input current of the excitation circuit, and simultaneously recording the reverse magnetic field strength detected by the standard magnetometer. The least squares method is then used to fit the linear correlation equation between the two, yielding the linear correspondence between the excitation current and the output reverse magnetic field strength of the local excitation probe. This linear correspondence refers to the mathematical relationship that characterizes the proportional relationship between the magnitude of the excitation current and the output reverse magnetic field strength of the probe. Secondly, based on this linear correspondence, a linear interpolation method is used to plan the current time-series variation law. With time as the horizontal axis and the corresponding excitation current value as the vertical axis, an excitation current control curve is generated that starts from zero and increases linearly at a preset rate (the specific rate can be set according to actual needs and is not limited here), ensuring smooth current changes without abrupt changes. The magnetocurrent control curve is a time-series curve describing the linear increase of the excitation current from zero over time. Then, the excitation current control curve is imported into the excitation drive module of the local excitation probe. The built-in digital-to-analog converter of the excitation drive module converts the digital signal of the excitation current control curve into an analog drive signal. The excitation current matching the excitation current control curve is input to the local excitation probe through the excitation drive module, which is a circuit module that receives the control curve signal and outputs the corresponding excitation current to drive the local excitation probe. Finally, the excitation coil of the local excitation probe generates an induced magnetic field based on the input continuous excitation current. Utilizing the principle of coil magnetic field superposition, an induced magnetic field is formed at the physical feature point with a direction opposite to the initial magnetization direction of the hard magnetic material and an intensity that increases linearly with the input current. This, in turn, excites a local reverse magnetic field at the physical feature point that corresponds to the excitation current control curve and increases linearly from zero.

[0034] It should be noted that the local reverse magnetic field in this application refers to the magnetic field generated by the probe at the physical feature point, which is opposite in direction to the initial magnetization direction of the hard magnetic material and whose intensity increases linearly with the current. Determining the local reverse magnetic field can specifically affect the magnetic domain structure of the potential demagnetization area, causing the magnetic domains to gradually reverse as the magnetic field strength increases. Then, by monitoring the corresponding change in normal magnetic flux density, a local magnetization reversal curve is generated, providing effective data support for the subsequent extraction of coercivity characteristic parameters. Compared with the problems of global reverse magnetic fields in the prior art, which are prone to causing secondary interference to the overall magnetic properties of the material and uncontrollable magnetic field strength leading to parameter extraction deviations, the local reverse magnetic field of this solution can effectively avoid the magnetization effect on the normal area of ​​the hard magnetic material, and can identify the critical state of magnetization reversal through linearly increasing intensity changes, ensuring the accuracy of coercivity parameter extraction. This provides a reliable basis for the magnetic performance response for subsequent comparison with benchmark parameters to determine the demagnetization area, ensuring the pertinence and accuracy of the entire magnetization state detection process.

[0035] In some embodiments, monitoring the change in normal magnetic flux density at the physical feature point to obtain the local magnetization reversal curve is achieved through the following steps: Acquire the normal magnetic flux density variation data at the physical feature points; The normal magnetic flux density variation data is filtered and denoised to obtain effective normal magnetic flux density data. By fitting the data with the real-time intensity of the local reverse magnetic field on the horizontal axis and the effective normal magnetic flux density data on the vertical axis, a local magnetization reversal curve is obtained.

[0036] In specific implementation, firstly, a Hall probe array is used to collect data on the change in normal magnetic flux density at the physical feature point during the application of a local reverse magnetic field. This data refers to a time-series dataset showing the change in normal magnetic flux density at the physical feature point with the intensity of the local reverse magnetic field. Then, the data is filtered and denoised using a moving average filter to obtain effective normal magnetic flux density data. Alternatively, other filtering and denoising methods can be used to denoise the data; no specific method is specified here. The effective normal magnetic flux density data refers to the data reflecting the change in magnetic flux density during domain reversal after denoising. Finally, with the real-time intensity of the local reverse magnetic field on the horizontal axis and the effective normal magnetic flux density data on the vertical axis, the least squares method is used for data fitting to construct a continuous correlation curve between magnetic field intensity and magnetic flux density, thereby obtaining the local magnetization reversal curve.

[0037] It should be noted that the local magnetization reversal curve in this application refers to the characteristic curve representing the magnetization reversal of hard magnetic materials at physical feature points as the local reverse magnetic field strength changes. Determining the local magnetization reversal curve can provide data support for extracting characteristic parameters representing coercivity from physical feature points, and at the same time lay an accurate magnetic performance data foundation for subsequent demagnetization region determination. The local magnetization reversal curve is a characteristic representation of the magnetization reversal of magnetic domains of hard magnetic materials at physical feature points as the local reverse magnetic field strength increases. This curve effectively identifies the continuous law of magnetic flux density changing with the reverse magnetic field strength and the critical state of magnetization reversal, avoiding systematic deviations in the extraction of coercivity parameters and ensuring that the extracted characteristic parameters can truly reflect the actual magnetic performance state of the physical feature points.

[0038] In step S104, characteristic parameters representing the coercivity of the physical feature point are extracted from the local magnetization reversal curve, and the characteristic parameters are compared with the nominal reference coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds a second preset threshold, the area where the physical feature point is located is determined to be a demagnetized area.

[0039] In some embodiments, extracting characteristic parameters representing the coercivity of the physical feature point from the local magnetization reversal curve is achieved through the following steps: The local magnetization reversal curve is smoothed and denoised to obtain a smoothed magnetization reversal curve; Extract the characteristic intersections where the normal magnetic flux density is zero on the smoothed magnetization reversal curve; Extract the reverse magnetic field strength value corresponding to the feature intersection and verify its validity. Use the reverse magnetic field strength value that has passed the validity verification as the feature parameter characterizing the coercivity of the physical feature point.

[0040] In specific implementation, firstly, Gaussian filtering is used to smooth and denoise the local magnetization reversal curve, resulting in a smoothed magnetization reversal curve. This smoothed magnetization reversal curve refers to the local magnetization reversal curve that, after Gaussian filtering and denoising, accurately reflects the magnetization reversal pattern. Then, linear interpolation is used to analyze the smoothed magnetization reversal curve, locating two adjacent discrete data points on the smoothed magnetization reversal curve where the normal magnetic flux density changes from positive to negative. By constructing a linear function between the discrete data points and solving for the abscissa value where the normal magnetic flux density is zero, characteristic intersection points on the smoothed magnetization reversal curve with zero normal magnetic flux density are obtained. These characteristic intersection points are the coordinate points on the smoothed magnetization reversal curve where the normal magnetic flux density is zero. That is, the coordinate point corresponding to the critical state of magnetization reversal; finally, the reverse magnetic field strength value corresponding to the feature intersection is extracted (that is, the quantized value of the horizontal axis coordinate mapped by the feature intersection with zero normal magnetic flux density in the local magnetization reversal curve coordinate system constructed with the real-time strength of the local reverse magnetic field as the horizontal axis and the effective normal magnetic flux density as the vertical axis). The validity of the reverse magnetic field strength value is verified by the rationality verification method. That is, the local reverse magnetic field strength value is first compared with the reasonable fluctuation range of the nominal coercivity of the hard magnetic material, and then the magnetization reversal monotonicity of the curve around the feature intersection is checked. Invalid values ​​that exceed the reasonable range and the curve monotonicity are eliminated. The reverse magnetic field strength value that has passed the validity verification is used as the characteristic parameter characterizing the coercivity of the physical feature point.

[0041] It should be noted that the characteristic parameters characterizing the coercivity of physical feature points in this application refer to parameters reflecting the demagnetization resistance of hard magnetic materials at physical feature points. Determining the characteristic parameters characterizing the coercivity of physical feature points is a quantitative indicator for obtaining the actual demagnetization resistance of hard magnetic materials at physical feature points. It provides direct data basis for subsequent comparison with the nominal reference coercivity parameters of hard magnetic materials and calculation of the attenuation percentage of characteristic parameters. It is also the key quantitative core for determining whether the area where the physical feature point is located is a demagnetized area. At the same time, it provides a point-level magnetic performance judgment basis for subsequent marking and mapping of demagnetized areas and output of magnetization state detection spectrum, ensuring the objectivity and accuracy of demagnetization area judgment.

[0042] In some embodiments, the characteristic parameter is compared with the nominal coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds a second preset threshold, the region where the physical feature point is located is determined to be a demagnetized region by the following steps: Obtain the nominal reference coercivity parameters of hard magnetic materials; The attenuation percentage of the characteristic parameter relative to the reference coercivity parameter is calculated based on the characteristic parameter and the reference coercivity parameter. A second preset threshold is set according to the application scenario and magnetic performance requirements of the hard magnetic material. The attenuation percentage is compared with the second preset threshold. If the attenuation percentage exceeds the second preset threshold, the area where the physical feature point is located is determined to be a demagnetized area.

[0043] In practice, firstly, nominal magnetic property parameters that perfectly match the model, specifications, and batch of the hard magnetic material under test are retrieved from the manufacturer's technical parameter database. Then, the nominal reference coercivity parameter of the hard magnetic material is obtained from these nominal magnetic property parameters. The reference coercivity parameter refers to the standardized coercivity value of the hard magnetic material's resistance to demagnetization under normal conditions, providing a benchmark for determining magnetic property attenuation. Next, based on the characteristic parameters and the reference coercivity parameter, a difference ratio calculation method is used. First, the difference between the reference coercivity parameter and the characteristic parameters is calculated. Then, this difference is compared with the reference coercivity parameter and converted into a percentage. Finally, based on the characteristic parameters and the reference... The coercivity parameter is calculated to obtain the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter. The attenuation percentage refers to the index characterizing the degree of attenuation of the actual coercivity at the physical feature point relative to the nominal reference value, and is used to reflect the attenuation magnitude of local magnetic properties. Finally, a second preset threshold is set according to the application scenario and magnetic performance requirements of the hard magnetic material. The second preset threshold is set by expert knowledge, and then the attenuation percentage is compared with the second preset threshold using a direct numerical comparison method. If the attenuation percentage exceeds the second preset threshold, the area where the physical feature point is located is determined to be a demagnetized area. The second preset threshold is the critical attenuation percentage value that distinguishes between normal attenuation of local magnetic properties and failure demagnetization.

[0044] It should be noted that, in this application, the demagnetized region refers to the area in the hard magnetic material where the physical feature point is located where the local coercivity decay exceeds the critical value and the magnetic properties can no longer meet the application requirements. Identifying the demagnetized region enables a qualitative judgment from the coercivity parameters of the physical feature point to the failure state of the region's magnetic properties. This provides a clear abnormal region judgment result for subsequent marking and mapping of the region on the magnetic flux density distribution map and outputting a visualized magnetization state detection spectrum. At the same time, it provides a precise spatial region and magnetic property failure basis for the overall magnetization state assessment of the hard magnetic material.

[0045] In step S105, the demagnetized region is marked and mapped in the magnetic flux density distribution map, thereby outputting the magnetization state detection spectrum of the hard magnetic material.

[0046] In some embodiments, marking and mapping the demagnetized region in the magnetic flux density distribution map to output the magnetization state detection spectrum of the hard magnetic material is achieved through the following steps: Obtain the physical coordinate range and magnetic flux density distribution map corresponding to the demagnetized area; Based on a preset coordinate mapping relationship, the physical coordinate range of the demagnetized area is mapped to the pixel coordinate range of the magnetic flux density distribution map; According to the preset differential visualization marking rules, the corresponding pixel coordinate range in the magnetic flux density distribution map is marked to generate a magnetization state detection spectrum of hard magnetic materials.

[0047] In specific implementation, firstly, the physical coordinate range and magnetic flux density distribution map corresponding to the demagnetized region are obtained. The physical coordinate range and magnetic flux density distribution map corresponding to the demagnetized region refer to the set of coordinate boundary information characterizing the actual spatial position of the demagnetized region and the original image data reflecting the global normal magnetic flux density distribution of the material under test. Then, based on the coordinate mapping relationship between the pixel coordinates of the magnetic flux density distribution map and the physical coordinates of the hard magnetic material surface under test, a coordinate transformation algorithm is used to substitute each boundary point of the physical coordinate range of the demagnetized region into the spatial coordinate mapping relationship for coordinate calculation. The pixel coordinates corresponding to each boundary point are obtained and fitted to form a closed region, thus mapping the physical coordinate range of the demagnetized region to the pixel coordinate range of the magnetic flux density distribution map. The pixel coordinate range of the magnetic flux density distribution map refers to the closed image region formed by all the pixels corresponding to the physical coordinates of the demagnetized region after spatial mapping. Finally, differentiated visualization marking rules are pre-defined according to the visualization requirements of the magnetic properties detection of hard magnetic materials (i.e., the normal magnetic property region in the magnetic flux density distribution map is filled with a light blue gradient based on the normal amplitude of the magnetic flux density, without additional...). Contour marking: For the determined demagnetized areas, they are first divided into three levels—mild, moderate, and severe—based on the degree of coercivity attenuation. Mildly demagnetized areas are filled with solid orange-yellow and outlined with a 1px black dashed line; moderately demagnetized areas are filled with solid orange-red and outlined with a 2px black solid line; and severely demagnetized areas are filled with solid dark red and outlined with a 3px thick black solid line. A visual distinction standard between demagnetized areas and normal magnetic flux density areas is established. An image pixel rendering algorithm is used to apply this differential visualization marking rule to the magnetic flux density distribution map. Pixel-level visual markings are performed within the pixel coordinate range to visually distinguish between demagnetized and normal areas, thereby generating a magnetization state detection spectrum for hard magnetic materials. The degree of coercivity attenuation is calculated based on the characteristic parameters representing the coercivity of physical feature points and the nominal reference coercivity parameters of hard magnetic materials. First, the specific percentage attenuation of the coercivity at the physical feature point relative to the nominal standard is calculated using the known quantification formula (reference coercivity parameter - characteristic parameter) ÷ reference coercivity parameter × 100%. This percentage attenuation is then used as the degree of coercivity attenuation, which will not be elaborated further here.

[0048] It should be noted that the magnetization state detection spectrum of hard magnetic materials in this application refers to a magnetic performance state visualization spectrum that integrates the global magnetic flux density distribution data of the hard magnetic material under test with the visualization marking information of the demagnetized area, which can intuitively reflect the overall magnetization state of the material and the location of abnormal areas.

[0049] Furthermore, in another aspect of this application, in some embodiments, this application provides a magnetization state detection system for hard magnetic materials, referring to... Figure 3The figure is a schematic diagram of a magnetization state detection system for hard magnetic materials according to some embodiments of this application. The magnetization state detection system for hard magnetic materials includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire the magnetic flux density distribution map of the surface of the hard magnetic material to be tested; Processing module 202, in this application, is mainly used to identify the target magnetic flux density region whose normal magnetic flux density value is lower than the first preset threshold from the magnetic flux density distribution map, and to position the output end of the local excitation probe to the physical feature point corresponding to the target magnetic flux density region on the surface of the hard magnetic material to be tested. The processing module 202 is also used to control the local excitation probe to apply a local reverse magnetic field that increases linearly from zero at the physical feature point, and to monitor the change in normal magnetic flux density at the physical feature point, thereby obtaining a local magnetization reversal curve. In addition, the processing module 202 is also used to extract characteristic parameters representing the coercivity of the physical feature point from the local magnetization reversal curve, and compare the characteristic parameters with the nominal reference coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds a second preset threshold, the area where the physical feature point is located is determined to be a demagnetized area. The execution module 203 in this application is mainly used to mark and map the demagnetized region in the magnetic flux density distribution map, and then output the magnetization state detection spectrum of the hard magnetic material.

[0050] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described method for detecting the magnetization state of hard magnetic materials.

[0051] In some embodiments, reference Figure 4 The figure is a schematic diagram of the structure of a computer device for implementing a method for detecting the magnetization state of hard magnetic materials according to some embodiments of this application. The method for detecting the magnetization state of hard magnetic materials in the above embodiments can be achieved through... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0052] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the magnetization state detection method for the hard magnetic material in this application.

[0053] The communication bus 302 can be used to transmit information between the aforementioned components.

[0054] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via the communication bus 302. The memory 303 may also be integrated with the processor 301.

[0055] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the magnetization state detection method of hard magnetic materials can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0056] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0057] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0058] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0059] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting the magnetization state of hard magnetic materials.

[0060] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0061] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for detecting the magnetization state of a hard magnetic material, characterized in that, Includes the following steps: Obtain the magnetic flux density distribution map of the surface of the hard magnetic material to be tested; Identify the target magnetic flux density region whose normal magnetic flux density value is lower than the first preset threshold from the magnetic flux density distribution map, and position the output end of the local excitation probe to the physical feature point corresponding to the target magnetic flux density region on the surface of the hard magnetic material to be tested; At the physical feature point, the local excitation probe is controlled to apply a local reverse magnetic field that increases linearly from zero, and the change in normal magnetic flux density at the physical feature point is monitored to obtain a local magnetization reversal curve. The characteristic parameters characterizing the coercivity of the physical feature point are extracted from the local magnetization reversal curve, and the characteristic parameters are compared with the nominal reference coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds the second preset threshold, the area where the physical feature point is located is determined to be a demagnetized area. The demagnetized region is marked and mapped in the magnetic flux density distribution map, thereby outputting a magnetization state detection spectrum of the hard magnetic material.

2. The method as described in claim 1, characterized in that, Specifically, the target magnetic flux density regions whose normal magnetic flux density values ​​are lower than a first preset threshold are identified from the magnetic flux density distribution map, including: The magnetic flux density distribution map is preprocessed by normalization to obtain the preprocessed magnetic flux density distribution map. A first preset threshold is set based on the nominal magnetic property parameters of the hard magnetic material; The normal magnetic flux density value is extracted point by point from the preprocessed magnetic flux density distribution map, and the normal magnetic flux density value of each point is compared with the first preset threshold to filter out discrete pixels with normal magnetic flux density values ​​lower than the first preset threshold. Connectivity analysis is performed on the selected discrete pixels, and the obtained connected regions are then used as the target magnetic flux density region.

3. The method as described in claim 1, characterized in that, Positioning the output end of the local excitation probe to a physical feature point on the surface of the hard magnetic material to be tested, corresponding to the target magnetic flux density region, specifically includes: Establish the coordinate mapping relationship between the pixel coordinates of the magnetic flux density distribution map and the physical coordinates of the hard magnetic material surface under test; Extract the pixel coordinate information of the target magnetic flux density region, and substitute the pixel coordinate information of the target magnetic flux density region into the coordinate mapping relationship to calculate the physical coordinate region corresponding to the target magnetic flux density region. In the physical coordinate region corresponding to the target magnetic flux density region, a physical feature point is determined, and a positioning command is sent to the local excitation probe based on the physical coordinates of the physical feature point to control the output end of the local excitation probe to move to the physical feature point.

4. The method as described in claim 3, characterized in that, Determining physical feature points within the physical coordinate region corresponding to the target magnetic flux density region specifically includes: The magnetic field gradient amplitude is calculated for the physical coordinate region corresponding to the target magnetic flux density region to obtain the magnetic field gradient amplitude distribution of the target magnetic flux density region. Based on the magnetic field gradient amplitude distribution, the physical coordinate region corresponding to the target magnetic flux density region is divided into a first gradient sub-region and a second gradient sub-region. The magnetic flux density dispersion is calculated for the first gradient sub-region and the second gradient sub-region respectively. Then, the magnetic field gradient extreme points are extracted as candidate physical feature points in the gradient sub-region with the largest magnetic flux density dispersion. Spatial density clustering is used to remove duplicates from all extracted candidate physical feature points in order to determine the physical feature points.

5. The method as described in claim 1, characterized in that, At the aforementioned physical feature point, controlling the local excitation probe to apply a local reverse magnetic field that increases linearly from zero specifically includes: The linear relationship between the excitation current and the output reverse magnetic field strength of the local excitation probe was calibrated. Based on the aforementioned linear correspondence, an excitation current control curve is generated that increases linearly from zero. The excitation current control curve is imported into the excitation drive module of the local excitation probe, and the excitation current matching the excitation current control curve is input to the local excitation probe through the excitation drive module. The local excitation probe generates a local reverse magnetic field at a physical feature point based on the input excitation current, which increases linearly from zero and corresponds to the excitation current control curve.

6. The method as described in claim 1, characterized in that, Specifically, extracting characteristic parameters representing the coercivity of the physical feature point from the local magnetization reversal curve includes: The local magnetization reversal curve is smoothed and denoised to obtain a smoothed magnetization reversal curve; Extract the characteristic intersections where the normal magnetic flux density is zero on the smoothed magnetization reversal curve; Extract the reverse magnetic field strength value corresponding to the feature intersection and verify its validity. Use the reverse magnetic field strength value that has passed the validity verification as the feature parameter characterizing the coercivity of the physical feature point.

7. The method as described in claim 1, characterized in that, The magnetic flux density distribution map of the hard magnetic material surface under test is obtained by using a Hall probe array.

8. A magnetization state detection system for hard magnetic materials, used to perform the magnetization state detection method for hard magnetic materials as described in any one of claims 1 to 7, characterized in that, The system includes: The acquisition module is used to acquire the magnetic flux density distribution map of the hard magnetic material surface under test; The processing module is used to identify the target magnetic flux density region whose normal magnetic flux density value is lower than a first preset threshold from the magnetic flux density distribution map, and to position the output end of the local excitation probe to the physical feature point corresponding to the target magnetic flux density region on the surface of the hard magnetic material to be tested. The processing module is also used to control the local excitation probe to apply a local reverse magnetic field that increases linearly from zero at the physical feature point, and to monitor the change in normal magnetic flux density at the physical feature point, thereby obtaining a local magnetization reversal curve. The processing module is further configured to extract characteristic parameters representing the coercivity of the physical feature point from the local magnetization reversal curve, and compare the characteristic parameters with the nominal reference coercivity parameter of the hard magnetic material. If the attenuation percentage of the characteristic parameter relative to the reference coercivity parameter exceeds a second preset threshold, the region where the physical feature point is located is determined to be a demagnetized region. The execution module is used to mark and map the demagnetized region in the magnetic flux density distribution map, and then output the magnetization state detection spectrum of the hard magnetic material.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the method for detecting the magnetization state of a hard magnetic material as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for detecting the magnetization state of hard magnetic materials as described in any one of claims 1 to 7.

Images