Medium-high frequency magnetic element data analysis system

Abstract

The invention discloses a medium-high frequency magnetic element data analysis system, and particularly relates to the field of nanocrystalline strip detection, which comprises a radiation environment simulation module, a multi-dimensional characterization module, a primary detection module, a secondary analysis module and a reliability evaluation module, by establishing a multi-dimensional detection and analysis model, the performance evaluation method of the nanocrystalline strip in the radiation environment is optimized; radiation environments of different doses are generated through a radiation environment simulation module and are combined with physical structures, magnetic properties and high-frequency response characteristics, and a reliability evaluation system based on actual radiation conditions is constructed; the evaluation system is used for identifying the key problems of microstructure change, magnetic attenuation, frequency response distortion and the like of the material in an extreme environment, so that the reliability and performance stability of the nanocrystalline strip under long-time and intense radiation conditions are effectively predicted, and the adaptability of the material in a high-requirement and high-frequency environment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nanocrystalline strip detection, and more specifically, to a medium- and high-frequency magnetic element data analysis system. Background Art

[0002] In the existing technology, most studies on changes in magnetic properties of magnetic materials in strong radiation environments are limited to low-frequency applications, and the understanding of the relationship between radiation-induced microstructural changes and performance attenuation mechanisms is still incomplete. However, the sensitivity of medium and high frequency magnetic components to radiation is particularly critical, especially in extreme scenarios such as aerospace, nuclear energy, etc.

[0003] Nanocrystalline ribbons have significant advantages in the application of medium and high frequency magnetic components due to their high magnetic permeability and low loss characteristics. However, there is a lack of systematic research and data support on their magnetic permeability attenuation, loss increase and frequency response characteristics under strong radiation conditions, which has become a problem restricting their widespread application. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a medium and high frequency magnetic component data analysis system, which solves the problems raised in the above-mentioned background technology through a radiation environment simulation module, a multi-dimensional characterization module, a primary detection module, a secondary analysis module, and a reliability assessment module.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a medium- and high-frequency magnetic component data analysis system, comprising a radiation environment simulation module, a multi-dimensional characterization module, a primary detection module, a secondary analysis module, and a reliability assessment module;

[0006] The radiation environment simulation module simulates radiation environments of different doses through radiation sources. The simulated radiation environments are used to place nanocrystalline ribbons in the simulation scene.

[0007] The multi-dimensional characterization module is used to characterize and detect the irradiated nanocrystalline strip in three dimensions, namely the first dimension, the second dimension, and the third dimension;

[0008] The first-level detection module establishes a first-level detection model based on the characterization detection of the first dimension, the second dimension, and the third dimension; the first-level detection module obtains the first-level model detection value based on the first-level detection model; if the first-level model detection value deviates from the first-level model detection threshold range preset by the system, it is listed as an abnormal material, otherwise it is normal;

[0009] The secondary analysis module conducts data analysis by passing the nanocrystalline ribbons classified as abnormal materials through a secondary detection model, which includes dynamic testing of the magnetic properties of the nanocrystalline ribbons and evaluation of the frequency response characteristics;

[0010] The reliability assessment module is used to conduct reliability assessment on nanocrystalline strips that have completed the secondary detection model data analysis, establish a radiation environment based on the primary detection model and the secondary detection model, and provide material optimization suggestions based on the reliability assessment obtained in the radiation environment.

[0011] In a preferred embodiment, the first dimension contained in the first-level detection model of the first-level detection module includes physical structure characterization detection; the second dimension contained in the first-level detection model of the first-level detection module includes magnetic property characterization detection; the second dimension contained in the first-level detection model of the first-level detection module includes high-frequency response characteristic characterization detection.

[0012] In a preferred embodiment, the required radiation environment is generated by the radiation environment simulation module to test the response of the nanocrystalline ribbon under different doses and frequencies; the radiation dose of the proposed radiation source is D, and the energy distribution of the radiation source is E(ω), where ω is the frequency of the radiation source; the radiation environment response R simulated by the radiation environment simulation module sim It is expressed by the following formula:

[0013]

[0014] Where R sim (D,ω) is the response of the simulated radiation environment, which represents the environmental characteristics of the material under the radiation dose D and frequency ω; D0 is the standard dose; E(ω) is the energy distribution function of the radiation source; is the ratio of radiation dose to standard dose; ω is the frequency of the radiation source; It means integrating all frequencies to obtain the total radiation intensity of the radiation source at each frequency.

[0015] In a preferred embodiment, the multi-dimensional representation module assumes that the representation result is represented as a vector containing three dimensions: X1 represents the measurement result of physical structure characterization; X2 represents the measurement result of magnetic property characterization; X3 represents the measurement result of high frequency response characteristic characterization;

[0016] The comprehensive evaluation of X1, X2, and X3 is expressed as the following formula:

[0017] S=w1·f1(X1)+w2·f2(X2)+w3·f3(X3)

[0018] Among them, X1 includes the physical property data of the geometric size, shape, and thickness of the test material; X2 includes the magnetic permeability and saturation flux density of the test material; X3 includes the loss and frequency response characteristic data of the test material at high frequency; w1, w2, and w3 are the weight coefficients of each dimension respectively; f1(X1), f2(X2), and f3(X3) are the data processing functions of each dimension; S is the comprehensive evaluation result.

[0019] In a preferred embodiment, the primary detection module uses the characterization results To calculate the first-level model detection value S1, and compare it with the first-level model detection threshold T1 to determine whether the material is abnormal. The output S1 of the first-level detection model is expressed as:

[0020]

[0021] If S1 exceeds the preset threshold T1, it is judged as abnormal;

[0022]

[0023] where w i is the weight coefficient for each dimension; f i (X i ) is the detection function of each dimension; T1 is the detection threshold of the first-level model.

[0024] In a preferred embodiment, for abnormal materials, the secondary analysis module further performs data analysis through dynamic magnetic performance testing and frequency response characteristic evaluation; assuming that the result of the secondary analysis is S2;

[0025] S2=α·f mag (B,H)+β·f freq (f,P)

[0026] where f mag (B,H) represents the dynamic test function of magnetic performance, B is the magnetic flux density, H is the magnetic field intensity; f frcq (f,P) represents the frequency response characteristic evaluation function, f is the frequency, P is the power loss; α and β are weight coefficients; S2 is the output value of the secondary detection model; α, β are two weight coefficients.

[0027] In a preferred embodiment, the reliability assessment module performs reliability assessment of the material based on the results of the primary detection model and the secondary detection model; and proposes a reliability assessment value of R;

[0028] R=γ1·S1+γ2·S2

[0029] Where S1 and S2 are the output values ​​of the first-level detection model and the second-level detection model respectively; γ1 and γ2 are weight coefficients used to reflect the importance of the result analysis of the first-level detection model and the second-level detection model in reliability assessment; the optimization suggestion O is composed of the evaluation value R and the preset threshold R thrcshold Compare to get;

[0030]

[0031] Where R is the reliability assessment value of the material; S1 and S2 are the results of primary detection and secondary analysis respectively; γ1 and γ2 are weight coefficients.

[0032] In a preferred embodiment, X1 includes the physical property data of the geometric size, shape, and thickness of the detection material; X2 includes the magnetic permeability and saturation flux density magnetic performance data of the detection material; X3 includes the loss and frequency response characteristic data of the detection material at high frequency;

[0033] In X1, it is assumed that the volume V of the material is determined by the thickness d, width w, and length l; X2 includes the detection of magnetic permeability μ, saturation flux density B s ; X3 represents the loss and frequency response of the material at high frequencies, assuming that the relationship between the loss P and the frequency f follows the Pearson distribution;

[0034]

[0035]

[0036] Where X1 represents the measurement result of physical structure characterization; f geometry (d,w,l) is the geometric shape function; d is the thickness; w is the width; l is the length; η is the geometric shape coefficient;

[0037] Where X2 represents the measurement result of magnetic properties characterization; f mag (μ,B s ,H) is the magnetic performance function; μ is the magnetic permeability; B s is the saturation magnetic flux density; H is the magnetic field intensity; H sat is the saturation magnetic field strength;

[0038] Where X3 represents the measurement result of the high frequency response characteristic; f freq (f,P) is the high-frequency response characteristic function; f is the frequency, which indicates the frequency of the change of electromagnetic waves or magnetic fields; P is the power loss; α is the frequency attenuation index, which is used to reflect the power relationship between loss and frequency; β is the exponential attenuation factor.

[0039] Technical effects and advantages of the present invention:

[0040] 1. By establishing a multi-dimensional detection and analysis model, the performance evaluation method of nanocrystalline strips in radiation environment is optimized; radiation environments of different doses are generated through the radiation environment simulation module and combined with physical structure, magnetic properties and high-frequency response characteristics to build a reliability evaluation system based on actual radiation conditions;

[0041] This evaluation system is used to identify key issues such as microstructural changes, magnetic attenuation and frequency response distortion of materials under extreme environments, and effectively predict the reliability and performance stability of nanocrystalline ribbons under long-term, strong radiation conditions. Through this technology, the adaptability of materials in high-demand, high-frequency environments is improved, providing important support for the design and application of high-performance magnetic components.

[0042] 2. This solution uses a multi-dimensional characterization module to comprehensively characterize the physical structure, magnetic properties and frequency response characteristics of the material, avoiding errors or omissions that may be caused by single-dimensional analysis; by separately analyzing the changes in physical size, surface integrity, chemical composition and the dynamic changes in magnetic properties, loss and frequency response, it effectively captures the multiple effects of the radiation environment on the material, thereby providing accurate data support for the subsequent optimization and reliability prediction of the material;

[0043] 3. In addition, this solution builds an efficient fault diagnosis effect based on the collaborative work of the primary detection model and the secondary analysis model; the primary detection model can quickly screen out normal or abnormal samples based on the characterization data of physical structure and magnetic properties. If the material is abnormal, the secondary analysis module conducts in-depth analysis of the abnormal material through more detailed dynamic magnetic property testing and frequency response characteristic evaluation, providing data basis for the final material optimization; this system makes the high-frequency magnetic component detection process in a radiation environment more systematic and efficient, and has a strong breadth of application;

[0044] 4. By introducing a comprehensive reliability assessment module, this solution effectively improves the application safety and long-term stability of nanocrystalline strips. The reliability assessment module not only combines the results of the first and second level tests, but also evaluates the long-term tolerance of the material based on the experimental data under different radiation environments, and derives targeted material optimization suggestions. Through this mechanism, the problem of material performance degradation in a radiation environment is avoided, and it can provide strong support for fault prevention and material selection in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Refer to the instruction manual Figure 1 , a medium and high frequency magnetic component data analysis system according to an embodiment of the present invention comprises a radiation environment simulation module, a multi-dimensional characterization module, a primary detection module, a secondary analysis module, and a reliability assessment module;

[0048] The radiation environment simulation module simulates radiation environments of different doses through radiation sources. The simulated radiation environments are used to place nanocrystalline ribbons in the simulation scene.

[0049] The multi-dimensional characterization module is used to characterize and detect the irradiated nanocrystalline strip in three dimensions, namely the first dimension, the second dimension, and the third dimension;

[0050] The first-level detection module establishes a first-level detection model based on the characterization detection of the first dimension, the second dimension, and the third dimension; the first-level detection module obtains the first-level model detection value based on the first-level detection model; if the first-level model detection value deviates from the first-level model detection threshold range preset by the system, it is listed as an abnormal material, otherwise it is normal;

[0051] The secondary analysis module conducts data analysis by passing the nanocrystalline ribbons classified as abnormal materials through a secondary detection model, which includes dynamic testing of the magnetic properties of the nanocrystalline ribbons and evaluation of the frequency response characteristics;

[0052] The reliability assessment module is used to conduct reliability assessment on nanocrystalline strips that have completed the secondary detection model data analysis, establish a radiation environment based on the primary detection model and the secondary detection model, and provide material optimization suggestions based on the reliability assessment obtained in the radiation environment.

[0053] The first dimension included in the first-level detection model of the first-level detection module includes physical structure characterization detection; the second dimension included in the first-level detection model of the first-level detection module includes magnetic property characterization detection; the second dimension included in the first-level detection model of the first-level detection module includes high-frequency response characteristic characterization detection.

[0054] The required radiation environment is generated by the radiation environment simulation module to test the response of the nanocrystalline strip under different doses and frequencies; the radiation dose of the proposed radiation source is D, and the energy distribution of the radiation source is E(ω), where ω is the frequency of the radiation source; the radiation environment response R simulated by the radiation environment simulation module sim It is expressed by the following formula:

[0055]

[0056] Where R sim (D, ω) is the response of the simulated radiation environment, which represents the environmental characteristics of the material under the radiation dose D and frequency ω; D is the radiation dose, which represents the intensity of the radiation; D0 is the standard dose, which is set as the reference value of the conventional radiation dose and is used to standardize the radiation dose; E(ω) is the energy distribution function of the radiation source, which represents the radiation intensity under different frequencies ω; is the ratio of radiation dose to standard dose, which is used to calibrate the relative change of radiation intensity; ω is the frequency of the radiation source, which indicates the frequency distribution of radiation; It means integrating all frequencies to obtain the total radiation intensity of the radiation source at each frequency; the goal of the radiation environment simulation module is to simulate the radiation environment under different frequency and dose conditions and provide basic data for subsequent tests.

[0057] Through the multi-dimensional representation module, it is assumed that the representation result is represented as a vector containing three dimensions X1 represents the measurement result of physical structure characterization; X2 represents the measurement result of magnetic performance characterization; X3 represents the measurement result of high frequency response characteristic characterization;

[0058] The comprehensive evaluation of X1, X2, and X3 is expressed as the following formula:

[0059] S=w1·f1(X1)+w2·f2(X2)+w3·f3(X3)

[0060] in The characterization data vector used to represent three dimensions contains different material property data; X1 includes the physical property data of the test material, such as geometric size, shape, thickness, etc.; X2 includes the magnetic property data of the test material, such as magnetic permeability and saturation flux density; X3 includes the characteristic data of the test material at high frequency, such as loss and frequency response; w1, w2, w3 are the weight coefficients of each dimension, which are used to reflect the importance of each dimension in the overall evaluation or determined by engineering experience; f1 (X1), f2 (X2), f3 (X3) are the data processing functions of each dimension, which convert the physical structure, magnetic properties and frequency response characteristics into comparable values; S is the comprehensive evaluation result, which indicates the overall performance of the material after radiation; a higher S value indicates that the material has better performance, which is in line with expectations. Through these three-dimensional characterization tests, the performance changes of the material after radiation can be comprehensively evaluated.

[0061] Characterization results of the primary detection module To calculate the first-level model detection value S1, and compare it with the first-level model detection threshold T1 to determine whether the material is abnormal. The output S1 of the first-level detection model is expressed as:

[0062]

[0063] If S1 exceeds the preset threshold T1, it is judged as abnormal;

[0064]

[0065] Where S1 is the first-level model test value, which represents the comprehensive test value and is used to evaluate whether the material meets the standard; w i is the weight coefficient of each dimension, which is used to reflect the importance of physical structure, magnetic properties and high-frequency response characteristics in the total detection value; f i (X i ) is the detection function for each dimension, which converts the characterization data of each dimension into digital performance results; T1 is the detection threshold of the first-level model, which is set to a certain standard value. When this value is exceeded, the material is judged to be abnormal; S1>T1 means that when the calculation result S1 exceeds the threshold, the material is judged to be abnormal; otherwise, the material is normal; the purpose of this step is to screen out materials that meet the requirements through comprehensive detection values. If the performance of the material exceeds the standard threshold, further analysis is required.

[0066] For abnormal materials, the secondary analysis module further performs data analysis through dynamic magnetic performance testing and frequency response characteristic evaluation; assuming that the result of the secondary analysis is S2;

[0067] S2=α·f mag (B,H)+β·f freq (f,P)

[0068] where f mag (B,H) represents the dynamic test function of magnetic performance, B is the magnetic flux density, H is the magnetic field intensity; f frcq (f,P) represents the frequency response characteristic evaluation function, f is the frequency, P is the power loss; α and β are weight coefficients used to determine the impact of magnetic performance test and frequency response evaluation on the overall result; S2 is the output value of the secondary detection model, which represents the detailed performance evaluation result of the material; f mag (B,H) is a dynamic test based on magnetic properties, which indicates the relationship between magnetic flux density B and magnetic field strength H. B is magnetic flux density, which indicates the number of magnetic lines per unit area of ​​the material under the action of the magnetic field. H is magnetic field strength, which indicates the strength of the magnetic field in which the material is located and affects the magnetic response of the material. frcq(f,P) is the frequency response characteristic evaluation, which indicates the relationship between frequency f and power loss P, and mainly evaluates the loss characteristics of materials at high frequencies; f is frequency, which indicates the frequency of radiation or electrical signals; P is power loss, which indicates the energy loss of materials at a specific frequency; α and β are two weight coefficients used to adjust the contribution of magnetic properties and frequency response characteristics in the results. The secondary analysis module conducts more accurate performance analysis of materials through magnetic properties and frequency response characteristic evaluation.

[0069] The reliability assessment module conducts reliability assessment of materials based on the results of the primary detection model and the secondary detection model; the proposed reliability assessment value is R;

[0070] R=γ1·S1+γ2·S2

[0071] Where S1 and S2 are the output values ​​of the first-level detection model and the second-level detection model respectively; γ1 and γ2 are weight coefficients used to reflect the importance of the result analysis of the first-level detection model and the second-level detection model in reliability assessment; the optimization suggestion O is composed of the evaluation value R and the preset threshold R thrcshold Compare to get;

[0072]

[0073] Where R is the reliability evaluation value of the material, indicating its adaptability and long-term stability in the radiation environment; S1 and S2 are the results of the primary test and secondary analysis, respectively, which comprehensively evaluate the various properties of the material; γ1 and γ2 are weight coefficients, representing the importance of the primary test and secondary analysis in the evaluation; O is the optimization suggestion, indicating whether the material needs to be optimized according to the evaluation results; R thrcshold is the reliability assessment threshold set. If R is less than R thrcshold , indicating the need to optimize the material reliability assessment module by comprehensively evaluating the various test results of the materials and proposing targeted optimization suggestions to ensure the long-term stability of the materials in radiation environments.

[0074] The X1 includes the physical property data of the geometric size, shape and thickness of the detection material; X2 includes the magnetic permeability and saturation flux density of the detection material; X3 includes the loss and frequency response characteristic data of the detection material at high frequency;

[0075] In X1, it is assumed that the volume V of the material is determined by the thickness d, width w, and length l; X2 includes the detection of magnetic permeability μ, saturation flux density B s ; X3 represents the loss and frequency response of the material at high frequencies, assuming that the relationship between the loss P and the frequency f follows the Pearson distribution;

[0076]

[0077] Where X1 represents the measurement result of physical structure characterization; f geometry (d,w,l) is the geometric shape function, which combines the geometric dimensions d,w,l of the material; d is the thickness, which indicates the dimension of the material in the vertical direction; w is the width, which indicates the lateral dimension of the material; l is the length, which indicates the longitudinal dimension of the material; η is the geometric shape coefficient, which is used to adjust the volume calculation under different shapes and is suitable for complex geometric shapes (such as supplementary factors for non-rectangular and irregular shapes); the overall geometric dimensions of the material are obtained through the formula of X1, and are used to evaluate the structural stability and space occupancy of the material;

[0078] Where X2 represents the measurement result of magnetic property characterization, indicating the magnetic response ability of the material; f mag (μ,B s ,H) is the magnetic performance function, taking into account the magnetic permeability μ, saturation flux density B s and magnetic field intensity H; μ is magnetic permeability, which indicates the magnetic response ability of the material under an external magnetic field; B s is the saturation magnetic flux density, which indicates the magnetic flux density of the material in the saturated magnetization state; H is the magnetic field intensity, which indicates the intensity of the externally applied magnetic field; H sat is the saturation magnetic field intensity, the magnetic field intensity when the material reaches saturation magnetization; the formula of X2 combines the characteristics of magnetic permeability and magnetic flux density, and introduces the nonlinear effect of magnetic field intensity on the magnetic properties of the material;

[0079] Where X3 represents the measurement result of high frequency response characterization, indicating the loss and response of the material at different frequencies; f freq (f,P) is the high-frequency response characteristic function, which indicates the change in material loss at different frequencies; f is frequency, which indicates the frequency of electromagnetic wave or magnetic field change; P is power loss, which indicates the energy loss of the material at a specific frequency; α is the frequency attenuation index, which is used to reflect the power relationship between loss and frequency; β is the exponential attenuation factor, which indicates how the material loss increases exponentially with the increase of frequency; the X3 formula reflects the loss characteristics of the material in a high-frequency electromagnetic environment, comprehensively considers the relationship between frequency and loss, and introduces the nonlinear behavior of frequency attenuation.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A medium and high frequency magnetic component data analysis system, comprising a radiation environment simulation module, a multi-dimensional characterization module, a primary detection module, a secondary analysis module, and a reliability assessment module, characterized in that: The radiation environment simulation module simulates radiation environments of different doses through radiation sources. The simulated radiation environments are used to place nanocrystalline ribbons in the simulation scene. The multi-dimensional characterization module is used to characterize and detect the irradiated nanocrystalline strip in three dimensions, namely the first dimension, the second dimension, and the third dimension; The first-level detection module establishes a first-level detection model based on the characterization detection of the first dimension, the second dimension, and the third dimension; the first-level detection module obtains the first-level model detection value based on the first-level detection model; if the first-level model detection value deviates from the first-level model detection threshold range preset by the system, it is listed as an abnormal material, otherwise it is normal; The secondary analysis module conducts data analysis by passing the nanocrystalline ribbons classified as abnormal materials through a secondary detection model, which includes dynamic testing of the magnetic properties of the nanocrystalline ribbons and evaluation of the frequency response characteristics; The reliability assessment module is used to conduct reliability assessment on nanocrystalline strips that have completed the secondary detection model data analysis, establish a radiation environment based on the primary detection model and the secondary detection model, and provide material optimization suggestions based on the reliability assessment obtained in the radiation environment.

2. A medium and high frequency magnetic component data analysis system according to claim 1, characterized in that: The first dimension included in the first-level detection model of the first-level detection module includes physical structure characterization detection; the second dimension included in the first-level detection model of the first-level detection module includes magnetic property characterization detection; the second dimension included in the first-level detection model of the first-level detection module includes high-frequency response characteristic characterization detection.

3. A medium and high frequency magnetic component data analysis system according to claim 2, characterized in that: The required radiation environment is generated by the radiation environment simulation module to test the response of the nanocrystalline strip under different doses and frequencies; the radiation dose of the proposed radiation source is D, and the energy distribution of the radiation source is E(ω), where ω is the frequency of the radiation source; the radiation environment response R simulated by the radiation environment simulation module sim It is expressed by the following formula: Where R sim (D,ω) is the response of the simulated radiation environment, which represents the environmental characteristics of the material under the radiation dose D and frequency ω; D0 is the standard dose; E(ω) is the energy distribution function of the radiation source; is the ratio of radiation dose to standard dose; ω is the frequency of the radiation source; It means integrating all frequencies to obtain the total radiation intensity of the radiation source at each frequency.

4. A medium and high frequency magnetic component data analysis system according to claim 3, characterized in that: Through the multi-dimensional representation module, it is assumed that the representation result is represented as a vector containing three dimensions X1 represents the measurement result of physical structure characterization; X2 represents the measurement result of magnetic property characterization; X3 represents the measurement result of high frequency response characteristic characterization; The comprehensive evaluation of X1, X2, and X3 is expressed as the following formula: S=w1·f1(X1)+w2·f2(X2)+w3·f3(X3) Among them, X1 includes the physical property data of the geometric size, shape, and thickness of the test material; X2 includes the magnetic permeability and saturation flux density of the test material; X3 includes the loss and frequency response characteristic data of the test material at high frequency; w1, w2, and w3 are the weight coefficients of each dimension respectively; f1(X1), f2(X2), and f3(X3) are the data processing functions of each dimension; S is the comprehensive evaluation result.

5. A medium and high frequency magnetic component data analysis system according to claim 4, characterized in that: The first-level detection module uses the characterization results To calculate the first-level model detection value S1, and compare it with the first-level model detection threshold T1 to determine whether the material is abnormal. The output S1 of the first-level detection model is expressed as: If S1 exceeds the preset threshold T1, it is judged as abnormal; where w i is the weight coefficient for each dimension; f i (X i ) is the detection function of each dimension; T1 is the detection threshold of the first-level model.

6. A medium and high frequency magnetic component data analysis system according to claim 5, characterized in that: For abnormal materials, the secondary analysis module further performs data analysis through dynamic magnetic performance testing and frequency response characteristic evaluation; assuming that the result of the secondary analysis is S2; S2=α·f mag (B,H)+β·f freq (f,P) where f mag (B,H) represents the dynamic test function of magnetic performance, B is the magnetic flux density, H is the magnetic field intensity; f frcq (f,P) represents the frequency response characteristic evaluation function, f is the frequency, P is the power loss; α and β are weight coefficients; S2 is the output value of the secondary detection model; α, β are two weight coefficients.

7. A medium and high frequency magnetic component data analysis system according to claim 6, characterized in that: The reliability assessment module conducts reliability assessment of materials based on the results of the primary detection model and the secondary detection model; the proposed reliability assessment value is R; R=γ1·S1+γ2·S2 Where S1 and S2 are the output values ​​of the first-level detection model and the second-level detection model respectively; γ1 and γ2 are weight coefficients used to reflect the importance of the result analysis of the first-level detection model and the second-level detection model in reliability assessment; the optimization suggestion O is composed of the evaluation value R and the preset threshold R thrcshold Compare to get; Where R is the reliability assessment value of the material; S1 and S2 are the results of primary detection and secondary analysis respectively; γ1 and γ2 are weight coefficients.

8. A medium and high frequency magnetic component data analysis system according to claim 7, characterized in that: The X1 includes the physical property data of the geometric size, shape and thickness of the detection material; X2 includes the magnetic permeability and saturation flux density of the detection material; X3 includes the loss and frequency response characteristic data of the detection material at high frequency; In X1, it is assumed that the volume V of the material is determined by the thickness d, width w, and length l; X2 includes the detection of magnetic permeability μ, saturation flux density B s ; X3 represents the loss and frequency response of the material at high frequencies, assuming that the relationship between the loss P and the frequency f follows the Pearson distribution; Where X1 represents the measurement result of physical structure characterization; f geometry (d,w,l) is the geometric shape function; d is the thickness; w is the width; l is the length; η is the geometric shape coefficient; Where X2 represents the measurement result of magnetic properties characterization; f mag (μ,B s ,H) is the magnetic performance function; μ is the magnetic permeability; B s is the saturation magnetic flux density; H is the magnetic field intensity; H sat is the saturation magnetic field strength; Where X3 represents the measurement result of the high frequency response characteristic; f freq (f,P) is the high-frequency response characteristic function; f is the frequency, which indicates the frequency of the change of electromagnetic waves or magnetic fields; P is the power loss; α is the frequency attenuation index, which is used to reflect the power relationship between loss and frequency; β is the exponential attenuation factor.

Images