A wireless charging device magnetic element monitoring system

Abstract

The application discloses a kind of wireless charging device magnetic element monitoring system, specifically relates to the field of high-frequency electromagnetic phenomenon monitoring of magnetic nanocrystalline material, including the key parameters such as acquisition operating frequency, magnetic permeability, surface current density, resistance change rate, judge whether element is in high-frequency electromagnetic state, when operating frequency exceeds preset threshold, and surface current density and resistance change rate significantly change, system will automatically send high-frequency electromagnetic state signal, and mark the area as abnormal state, the advantage of the present scheme is that by real-time judgment of high-frequency electromagnetic state, the working stability of magnetic element can be effectively improved, the safety and reliability of system in high-frequency environment are ensured, potential failure is prevented from occurring, and the performance of wireless charging device is optimized.

Description

Technical Field

[0001] This invention relates to the field of high-frequency electromagnetic phenomenon monitoring technology for magnetic nanocrystalline materials, and more specifically, to a magnetic component monitoring system for a wireless charging device. Background Technology

[0002] Nanocrystalline ribbons are advanced soft magnetic materials that have attracted much attention due to their high saturation magnetic induction, low loss, and excellent permeability. Compared with traditional materials such as ferrites and silicon steel, nanocrystalline ribbons exhibit superior performance in high-frequency applications, making them particularly suitable for magnetic shielding sheets in high-frequency transformers, inductors, and wireless charging. This material is formed using alloy melting and rapid cooling technology, resulting in extremely small grain sizes of approximately 10-20 nanometers. This significantly reduces energy loss in high-frequency electromagnetic environments.

[0003] In the development of wireless charging devices, the selection of magnetic components plays a crucial role in charging efficiency and stability. Traditional materials often exhibit significant losses in high-frequency electromagnetic environments, making it difficult to maintain efficient and stable operation under the high-frequency conditions required for wireless charging. Therefore, nanocrystalline ribbon materials, with their low loss and high permeability, have become a preferred material, especially in dealing with high-frequency electromagnetic phenomena, where they can significantly improve energy transmission efficiency. However, as the frequency increases, the complex changes in the electromagnetic field place higher demands on the performance of nanocrystalline materials. Specifically, how to effectively monitor the performance of nanocrystalline ribbon materials under high-frequency electromagnetic phenomena has become a key issue for long-term stable operation. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a magnetic component monitoring system for a wireless charging device. This system uses multi-dimensional sensors to collect multiple key parameters such as operating frequency, permeability, and current density to identify whether the magnetic nanocrystal component is in a high-frequency electromagnetic state, thereby solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a magnetic component monitoring system for a wireless charging device, comprising: a primary information acquisition module, a primary state judgment module, a high-frequency electromagnetic state judgment module, a spatial location information management module, a high-frequency influence coefficient generation module, and a high-frequency electromagnetic state linear regression judgment module;

[0006] The primary information acquisition module collects primary judgment information of the magnetic nanocrystalline element through multi-dimensional sensors, and determines whether the current area is in a primary high-frequency electromagnetic state based on the collected primary judgment information. The primary judgment information includes operating frequency, permeability, current density of conductor surface, current carrying capacity, resistivity change rate and material frequency response value.

[0007] The primary state judgment module is used to judge the primary abnormal state. When the operating frequency exceeds the system's preset operating frequency threshold and the magnetic permeability is less than the system's preset magnetic permeability, it sends a primary abnormal state signal to the system and lists the area as a primary abnormal state; otherwise, it is a normal state.

[0008] The high-frequency electromagnetic state judgment module is used to judge the primary high-frequency electromagnetic state. When the current density on the conductor surface exceeds the system's preset surface current carrying capacity threshold and the resistance change rate exceeds the system's preset material frequency response threshold, it sends a primary high-frequency electromagnetic state signal to the system and lists the area as the primary high-frequency electromagnetic state; otherwise, it is in a normal state.

[0009] The spatial location information management module is used to record the spatial location information of the primary high-frequency electromagnetic state and obtain the monitoring points. The spatial location of each monitoring point is recorded by the eccentric distance. The spatial location information is recorded together with each measuring point of the multi-dimensional sensor, and the spatial location information is managed by combining the spatial location information with the location information matrix.

[0010] The high-frequency influence coefficient generation module is used to generate a first high-frequency influence coefficient and a second high-frequency influence coefficient for each monitoring point;

[0011] The high-frequency electromagnetic state linear regression judgment module introduces the first high-frequency influence coefficient and the second high-frequency influence coefficient into the linear regression model, and linearly combines the first high-frequency influence coefficient and the second high-frequency influence coefficient. If the current monitoring point exceeds the system's preset final high-frequency electromagnetic state threshold, then the current monitoring point is classified as a high-frequency electromagnetic state.

[0012] In a preferred embodiment, parameters of the magnetic nanocrystalline element are acquired using a multidimensional sensor, and the results of the initial information acquisition are represented as set X:

[0013] X={f,μ,J s C I ,ΔR,f r ,ρ e ,η}

[0014] Where f is the operating frequency, μ is the permeability, and J s It is the surface current density, C I It is the current carrying capacity, ΔR is the rate of change of resistance, and f is the current carrying capacity. r It is the material's frequency response value, ρ e η is the conductivity, and η is the hysteresis loss factor of the material.

[0015] The primary state determination module is used to determine whether there is a primary abnormal state in the current area. The determination conditions are as follows:

[0016]

[0017] Where Sprimary This is the output signal for the initial state, where 1 indicates an abnormal state and 0 indicates a normal state; f threshold This represents the system's set operating frequency threshold, measured in Hertz (Hz); μ threshold This indicates the system's set permeability threshold, expressed in Henry per meter; when the operating frequency f exceeds the set threshold f... threshold And the permeability μ is lower than the set permeability threshold μ threshold When this happens, the system will issue a primary abnormal status signal.

[0018] In a preferred embodiment, the high-frequency electromagnetic state determination module is based on the conductor surface current density J. s The primary high-frequency electromagnetic state is determined by the resistance change rate ΔR, and S is proposed. high-freq The output signal for the primary high-frequency electromagnetic state:

[0019]

[0020] J s,threshold It is the surface current density threshold; R threshold It is the threshold of the rate of change of resistance, when J s When ΔR exceeds the system threshold, the system is determined to be in a primary high-frequency electromagnetic state.

[0021] In a preferred embodiment, the spatial location information management module records the three-dimensional spatial position of each monitoring point by measuring the eccentricity distance of the monitoring points, and determines the eccentricity distance d. i Let represent the spatial distance between monitoring point i and reference point (x0, y0, z0). The actual distance between each monitoring point and the reference point is calculated using the distance formula in a three-dimensional coordinate system.

[0022]

[0023] Where d i It is the three-dimensional spatial distance between monitoring point i and reference point; (x i ,y i ,z i (x0, y0, z0) are the three-dimensional spatial coordinates of monitoring point i; (x0, y0, z0) are the three-dimensional spatial coordinates of the reference point; the goal of the spatial location information management module is to create a three-dimensional spatial information matrix M by recording the specific location of each monitoring point and combining it with set X. pos .

[0024] In a preferred embodiment, the first high-frequency influence coefficient is determined to be C. highl The first high-frequency influence coefficient is generated based on eddy current effect information, which includes eddy current loss P. eddy Hysteresis loss P hyst Electric field strength E and material microstructure effect λ;

[0025] Eddy current loss P eddy :

[0026] P eddy =k1·ρ e ·f 2 ·ΔR

[0027] Where ρ e ΔR is the conductivity, f is the operating frequency, ΔR is the rate of change of resistance, and k1 is a constant.

[0028] Hysteresis loss P hyst :

[0029] P hyst =k2·H c ·f·μ·ρ·η

[0030] Where H c η is the coercivity, μ is the permeability, ρ is the material density, η is the hysteresis loss factor, and k2 is a constant.

[0031] Electric field strength E:

[0032] E = J s ·R

[0033] J s R is the surface current density, and R is the resistance.

[0034] Material microstructure effect λ:

[0035]

[0036] Where λ reflects the influence of the material's microstructure on its electromagnetic behavior;

[0037] Combined with eddy current loss P from eddy current effect information eddy Hysteresis loss P hyst The first high-frequency influence coefficient is obtained by considering the electric field strength E and the material microstructure effect λ. The first high-frequency influence coefficient is:

[0038] C high1 =α1·P eddy +α2·P hyst +α3·E+α4·λ

[0039] Where α1, α2, α3, and α4 are the eddy current losses P eddy Hysteresis loss P hyst The weighting coefficients for electric field strength E and material microstructure effect λ.

[0040] In a preferred embodiment, the second high-frequency influence coefficient is determined to be C. high2The second high-frequency influence coefficient is generated based on skin effect information, which includes surface current density J. s Surface effect depth δ, surface energy density U s and surface thermal effect T s ;

[0041] Surface current density J s :

[0042]

[0043] Among them I total It is the total current in the conductor, A s It is the surface area of ​​the conductor;

[0044] Surface effect depth δ:

[0045]

[0046] Where δ is the surface effect depth, f is the operating frequency, μ is the permeability, and ρ is the magnetic permeability. e π is electrical conductivity, and π is pi (circular diameter).

[0047] Surface energy density U s for:

[0048]

[0049] J s is the surface current density, and μ is the magnetic permeability;

[0050] Surface thermal effect T s for:

[0051]

[0052] Where T s J is used to reflect the thermal effect caused by surface current density. s It is the surface current density, R s It is surface resistance;

[0053] By combining the surface current density J in the skin effect information s Surface effect depth δ, surface energy density U s and surface thermal effect T s Generate the second high-frequency influence coefficient C high2 The second high-frequency influence coefficient C high2 for:

[0054]

[0055] Where β1, β2, β3, and β4 are the surface current densities J, respectively. sSurface effect depth δ, surface energy density U s and surface thermal effect T s The weighting coefficients.

[0056] In a preferred embodiment, the high-frequency electromagnetic state linear regression judgment module combines the first high-frequency influence coefficient and the second high-frequency influence coefficient through a linear regression model to determine the total judgment value of the high-frequency electromagnetic state as S. final The total judgment value S of the high-frequency electromagnetic state final for:

[0057] S final =γ1·C highl +γ2·C high2

[0058] Where S final It is the final total judgment value of the high-frequency electromagnetic state; γ1 and γ2 are the weight parameters of the first high-frequency influence coefficient and the second high-frequency influence coefficient in the linear regression, respectively.

[0059] When S final Exceeding threshold S threshold At that time, the monitoring point was determined to be in a high-frequency electromagnetic state:

[0060]

[0061] Where S high-freq-final This is the final high-frequency electromagnetic state output signal; 1 indicates that the monitoring point is in a high-frequency electromagnetic state, and 0 indicates a normal state; S threshold It is the system relative to S final The set final high-frequency electromagnetic state threshold.

[0062] In a preferred embodiment, the effect back-calculation module is designed to incorporate a first high-frequency influence coefficient C. highl Second high frequency influence coefficient C high2 The effect inversion module is used to distinguish between eddy current effect and skin effect in high-frequency electromagnetic conditions by inverting the proportion of influence of eddy current effect and skin effect in the monitoring points; the effect inversion module distinguishes between the two by determining the contribution value of eddy current effect and skin effect to the system respectively.

[0063] The total high-frequency effect of the proposed system is E total E total The formula used to represent the combined contribution of the eddy current effect and the skin effect is as follows:

[0064] E total =ω1·C high1 +ω2·C high2

[0065] Among them, E totalC represents the total high-frequency effect of the system; ω1 and ω2 are the weighting factors for the eddy current effect and the skin effect, respectively, and ω1 and ω2 satisfy ω1+ω2=1, used to determine the proportion of the two effects in the total contribution; highl C is the first high-frequency influence coefficient. highl The calculation results are based on the eddy current effect; C high2 C is the second most frequent influence coefficient. high2 The results are based on calculations using the skin effect;

[0066] An error minimization model is introduced through the effect back-calculation module. This model is used to find ω1 and ω2, and an error function is introduced. Through the error function To measure the theoretical model E total Compared with the actual measured effect E actual Error between:

[0067]

[0068] in E is the error function used to represent the deviation between the theoretical and actual effects. actual It is the actual high-frequency effect value obtained based on sensor data and monitoring results; by minimizing ω1 and ω2 are calculated in this way to determine the contribution ratio of eddy current effect and skin effect;

[0069] When ω1>0.5, the eddy current effect dominates; when ω2>0.5, the skin effect dominates; when ω1≈ω2, the two effects are roughly equal.

[0070] The technical effects and advantages of this invention are as follows:

[0071] 1. Distinguish the high-frequency electromagnetic state of magnetic nanocrystal components: By collecting multiple key parameters such as operating frequency, permeability, and current density through multi-dimensional sensors, the system can identify whether the magnetic nanocrystal components are in a high-frequency electromagnetic state. When the operating frequency and current density exceed the preset threshold, the system can judge and send a status signal in real time, thereby realizing effective monitoring of the components and ensuring the stable operation of the equipment.

[0072] 2. Differentiating between eddy current effect and skin effect based on high-frequency electromagnetic state: After determining that the component is in a high-frequency electromagnetic state, the system further distinguishes between eddy current effect and skin effect by analyzing the first high-frequency influence coefficient and the second high-frequency influence coefficient; by introducing weights and optimization algorithms, the influence ratio of the two effects is quantified, providing data support for subsequent performance optimization and fault detection. Attached Figure Description

[0073] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] Refer to the instruction manual appendix Figure 1 A magnetic component monitoring system for a wireless charging device according to an embodiment of the present invention includes: a primary information acquisition module, a primary state judgment module, a high-frequency electromagnetic state judgment module, a spatial location information management module, a high-frequency influence coefficient generation module, and a high-frequency electromagnetic state linear regression judgment module.

[0076] The primary information acquisition module collects primary judgment information of the magnetic nanocrystalline element through multi-dimensional sensors, and determines whether the current area is in a primary high-frequency electromagnetic state based on the collected primary judgment information. The primary judgment information includes operating frequency, permeability, current density of conductor surface, current carrying capacity, resistivity change rate and material frequency response value.

[0077] The primary state judgment module is used to judge the primary abnormal state. When the operating frequency exceeds the system's preset operating frequency threshold and the magnetic permeability is less than the system's preset magnetic permeability, it sends a primary abnormal state signal to the system and lists the area as a primary abnormal state; otherwise, it is a normal state.

[0078] The high-frequency electromagnetic state judgment module is used to judge the primary high-frequency electromagnetic state. When the current density on the conductor surface exceeds the system's preset surface current carrying capacity threshold and the resistance change rate exceeds the system's preset material frequency response threshold, it sends a primary high-frequency electromagnetic state signal to the system and lists the area as the primary high-frequency electromagnetic state; otherwise, it is in a normal state.

[0079] The spatial location information management module is used to record the spatial location information of the primary high-frequency electromagnetic state and obtain the monitoring points. The spatial location of each monitoring point is recorded by the eccentric distance. The spatial location information is recorded together with each measuring point of the multi-dimensional sensor, and the spatial location information is managed by combining the spatial location information with the location information matrix.

[0080] The high-frequency influence coefficient generation module is used to generate a first high-frequency influence coefficient and a second high-frequency influence coefficient for each monitoring point;

[0081] The high-frequency electromagnetic state linear regression judgment module introduces the first high-frequency influence coefficient and the second high-frequency influence coefficient into the linear regression model, and linearly combines the first high-frequency influence coefficient and the second high-frequency influence coefficient. If the current monitoring point exceeds the system's preset final high-frequency electromagnetic state threshold, then the current monitoring point is classified as a high-frequency electromagnetic state.

[0082] The parameters of the magnetic nanocrystalline element are collected using a multi-dimensional sensor. The results of the initial information collection are represented as set X:

[0083] X={f,μ,J s C I ,ΔR,f r ,ρ e ,η}

[0084] Where f is the operating frequency, μ is the permeability, and J s It is the surface current density, C I It is the current carrying capacity, ΔR is the rate of change of resistance, and f is the current carrying capacity. r It is the material's frequency response value, ρ e η is the conductivity, and η is the hysteresis loss factor of the material.

[0085] The primary state determination module is used to determine whether there is a primary abnormal state in the current area. The determination conditions are as follows:

[0086]

[0087] Where S primary This is the output signal for the initial state, where 1 indicates an abnormal state and 0 indicates a normal state; f threshold This represents the system's set operating frequency threshold, measured in Hertz (Hz); μ threshold This indicates the system's set permeability threshold, expressed in Henry per meter; when the operating frequency f exceeds the set threshold f... threshold And the permeability μ is lower than the set permeability threshold μ threshold When this happens, the system will issue a primary abnormal status signal.

[0088] The high-frequency electromagnetic state determination module is based on the conductor surface current density J. s The primary high-frequency electromagnetic state is determined by the resistance change rate ΔR, and S is proposed. high-freq The output signal for the primary high-frequency electromagnetic state:

[0089]

[0090] J s,threshold It is the surface current density threshold; R threshold It is the threshold of the rate of change of resistance, when J s When ΔR exceeds the system threshold, the system is determined to be in a primary high-frequency electromagnetic state.

[0091] The spatial location information management module records the three-dimensional spatial position of each monitoring point by measuring the eccentricity distance d. i Let represent the spatial distance between monitoring point i and reference point (x0, y0, z0). The actual distance between each monitoring point and the reference point is calculated using the distance formula in a three-dimensional coordinate system.

[0092]

[0093] Where d i It is the three-dimensional spatial distance between monitoring point i and reference point; (x i ,y i ,z i (x0, y0, z0) are the three-dimensional spatial coordinates of monitoring point i; (x0, y0, z0) are the three-dimensional spatial coordinates of the reference point; the goal of the spatial location information management module is to create a three-dimensional spatial information matrix M by recording the specific location of each monitoring point and combining it with set X. pos ; Three-dimensional spatial information matrix M pos This is used to help the system manage and track the spatial location of all monitoring points in a unified manner, ensuring that high-frequency electromagnetic state changes and effects can be monitored in different spatial areas, thereby providing spatial distribution information support for subsequent state analysis;

[0094] Three-dimensional spatial information matrix M pos The formation is based on the specific coordinates of each monitoring point in three-dimensional space, and each monitoring point has unique coordinates (x, y, y) in three-dimensional space. i ,y i ,z i These coordinates represent the position of a point along the X, Y, and Z axes. By recording the coordinates of each monitoring point and arranging them in a certain order, a three-dimensional spatial information matrix can be constructed. Each row of this matrix represents the spatial position of a monitoring point, while the columns represent the coordinates along the X, Y, and Z axes, respectively. Assuming the system has N monitoring points, then the three-dimensional spatial information matrix M... pos The dimension is N×3, representing the three-dimensional spatial coordinate information of N points. The principle of matrix construction is as follows:

[0095]

[0096] Each element x in the matrix i ,y i ,z iThese are the coordinates of monitoring point i. By collecting data from multiple monitoring points, the system can obtain a complete spatial distribution information matrix. This matrix helps the system manage the spatial location of each monitoring point and, combined with other monitored electromagnetic information, provides an accurate spatial location reference for subsequent status analysis and anomaly detection.

[0097] The proposed first high-frequency influence coefficient is C. highl The first high-frequency influence coefficient is generated based on eddy current effect information, which includes eddy current loss P. eddy Hysteresis loss P hyst Electric field strength E and material microstructure effect λ;

[0098] Eddy current loss P eddy :

[0099] P eddy =k1·ρ e ·f 2 ·ΔR

[0100] Where ρ e ΔR is the conductivity, f is the operating frequency, ΔR is the rate of change of resistance, and k1 is a constant.

[0101] Hysteresis loss P hyst :

[0102] P hyst =k2·H c ·f·μ·ρ·η

[0103] Where H c η is the coercivity, μ is the permeability, ρ is the material density, η is the hysteresis loss factor, and k2 is a constant.

[0104] Electric field strength E:

[0105] E = J s ·R

[0106] J s R is the surface current density, and R is the resistance.

[0107] Material microstructure effect λ:

[0108]

[0109] Where λ reflects the influence of the material's microstructure on its electromagnetic behavior;

[0110] Combined with eddy current loss P from eddy current effect information eddy Hysteresis loss P hyst The first high-frequency influence coefficient is obtained by considering the electric field strength E and the material microstructure effect λ. The first high-frequency influence coefficient is:

[0111] Chigh1 =α1·P eddy +α2·P hyst +α3·E+α4·λ

[0112] Where α1, α2, α3, and α4 are the eddy current losses P eddy Hysteresis loss P hyst The weighting coefficients for electric field strength E and material microstructure effect λ.

[0113] The proposed second high-frequency influence coefficient is C. high2 The second high-frequency influence coefficient is generated based on skin effect information, which includes surface current density J. s Surface effect depth δ, surface energy density U s and surface thermal effect T s ;

[0114] Surface current density J s :

[0115]

[0116] Among them I total It is the total current in the conductor, A s It is the surface area of ​​the conductor;

[0117] Surface effect depth δ:

[0118]

[0119] Where δ is the surface effect depth, f is the operating frequency in Hertz, μ is the permeability, and ρ is the magnetic permeability. e π is electrical conductivity, and π is pi (circular diameter).

[0120] Surface energy density U s for:

[0121]

[0122] J s is the surface current density, and μ is the magnetic permeability;

[0123] Surface thermal effect T s for:

[0124]

[0125] Where T s J is used to reflect the thermal effect caused by surface current density. s It is the surface current density, R s It is surface resistance;

[0126] By combining the surface current density J in the skin effect informations Surface effect depth δ, surface energy density U s and surface thermal effect T s Generate the second high-frequency influence coefficient C high2 The second high-frequency influence coefficient C high2 for:

[0127]

[0128] Where β1, β2, β3, and β4 are the surface current densities J, respectively. s Surface effect depth δ, surface energy density U s and surface thermal effect T s The weighting coefficients.

[0129] The high-frequency electromagnetic state linear regression judgment module combines the first and second high-frequency influence coefficients through a linear regression model to determine the total judgment value of the high-frequency electromagnetic state as S. final The total judgment value S of the high-frequency electromagnetic state final for:

[0130] S final =γ1·C highl +γ2·C high2

[0131] Where S final γ1 is the final high-frequency electromagnetic state determination value, representing the result calculated by the system through linear regression, used to determine whether the current monitoring point is in the final high-frequency electromagnetic state; γ1 and γ2 are the weight parameters of the first high-frequency influence coefficient and the second high-frequency influence coefficient in the linear regression, respectively. The values ​​of γ1 and γ2 depend on the system's weighting of different effects, reflecting the relative importance of these two coefficients in the final determination.

[0132] When S final Exceeding threshold S threshold At that time, the monitoring point was determined to be in a high-frequency electromagnetic state:

[0133]

[0134] Where S high-freq-final This is the final high-frequency electromagnetic state output signal; 1 indicates that the monitoring point is in a high-frequency electromagnetic state, and 0 indicates a normal state; S threshold It is the system relative to S final The set final high-frequency electromagnetic state threshold, when S final When this value is exceeded, the system will determine it to be a high-frequency electromagnetic state.

[0135] The effect back-calculation module aims to combine the first high-frequency influence coefficient C highl Second high frequency influence coefficient C high2The effect inversion module is used to distinguish between eddy current effect and skin effect in high-frequency electromagnetic conditions by inverting the proportion of influence of eddy current effect and skin effect in the monitoring points; the effect inversion module distinguishes between the two by determining the contribution value of eddy current effect and skin effect to the system respectively.

[0136] The total high-frequency effect of the proposed system is E total E total The formula used to represent the combined contribution of the eddy current effect and the skin effect is as follows:

[0137] E total =ω1·C high1 +ω2·C high2

[0138] Among them, E total The total high-frequency effect of the system is dimensionless, representing the sum of the contributions of different effects; ω1 and ω2 are the weighting factors for the eddy current effect and the skin effect, respectively, and are dimensionless, satisfying ω1 + ω2 = 1, used to determine the proportion of the two effects in the total contribution; C highl C is the first high-frequency influence coefficient. highl The calculation results are based on the eddy current effect; C high2 C is the second most frequent influence coefficient. high2 The results are based on calculations using the skin effect;

[0139] An error minimization model is introduced through the effect back-calculation module. This model is used to find the optimal ω1 and ω2, and an error function is introduced. Through the error function To measure the theoretical model E total Compared with the actual measured effect E actual Error between:

[0140]

[0141] in E is the error function used to represent the deviation between the theoretical and actual effects. actual It is the actual high-frequency effect value obtained based on sensor data and monitoring results; by minimizing In this way, by using optimization algorithms, such as gradient descent, we can calculate the most suitable ω1 and ω2, thereby determining the contribution ratio of the eddy current effect and the skin effect;

[0142] Once the weights ω1 and ω2 are calculated, the system performs a quantitative analysis of the eddy current effect and skin effect based on the weights.

[0143] When ω1>0.5, the eddy current effect dominates; when ω2>0.5, the skin effect dominates; when ω1≈ω2, the two effects are roughly equal. Through the effect back-calculation module, the system can dynamically determine the actual impact of different effects on the monitoring points, thereby further optimizing the electromagnetic monitoring and management of the system.

[0144] Further explanation is needed regarding the above solution. The primary information acquisition module monitors the electromagnetic properties of the magnetic nanocrystal components through multi-dimensional sensors, collecting key parameters in real time, including operating frequency, permeability, current density on the conductor surface, current carrying capacity, rate of change of resistance, and frequency response of the material. The collected primary judgment information can reflect the changes in the electromagnetic environment in which the component operates. This module ensures that the entire system can be based on accurate real-time data to help subsequent analysis and judgment of high-frequency electromagnetic states, and is especially suitable for scenarios in wireless charging devices that require precise monitoring of magnetic components.

[0145] The primary state judgment module is responsible for analyzing primary information to identify whether the magnetic component is in an abnormal state. The system sets preset thresholds for operating frequency and permeability. When the operating frequency exceeds the threshold and the permeability is lower than the set value, the system will send an abnormal signal and mark the area as a primary abnormal state. The operation of this module ensures that the operating state of the component under excessively high frequency or abnormal permeability conditions can be quickly determined, avoiding equipment failure due to abnormal electromagnetic conditions. It is mainly used to realize real-time monitoring of components in high-frequency electromagnetic environments.

[0146] The high-frequency electromagnetic state judgment module further refines the analysis of the current density and resistance change rate of the conductor surface; when the surface current density exceeds the system's preset threshold and the resistance change rate also exceeds the set value of the material's frequency response, the system determines that the area is in a high-frequency electromagnetic state and issues a warning signal; the core of this step is to monitor whether the component is in a high-frequency abnormal state through precise analysis of high-frequency electromagnetic parameters, ensuring that the equipment can maintain stable operation under such conditions and reducing potential risks caused by high-frequency electromagnetic anomalies.

[0147] The spatial location information management module records and manages spatial location information under primary high-frequency electromagnetic conditions. By calculating the eccentric distance between each monitoring point and the reference point, it accurately determines the positional changes of each monitoring point. The system integrates this data into a spatial information matrix, and combined with the electromagnetic sampling points of each sensor, it can clearly reflect the spatial distribution of magnetic components under high-frequency electromagnetic conditions. This module not only helps to locate the specific location of abnormal electromagnetic phenomena, but also provides important spatial data for subsequent optimization of system spatial configuration.

[0148] The high-frequency influence coefficient generation module is responsible for generating two key coefficients that reflect the effects of eddy current and skin effects, respectively. The first high-frequency influence coefficient is generated by calculating eddy current loss, hysteresis loss, electric field strength, and material microstructure effects. The second high-frequency influence coefficient is generated by analyzing surface current density, surface effect depth, surface energy density, and surface thermal effects. This module further optimizes the dynamic performance analysis of components in high-frequency electromagnetic environments by quantifying the specific impact of different high-frequency effects on components, and is especially suitable for complex electromagnetic environments where different effects need to be distinguished.

[0149] The linear regression judgment module combines the first and second high-frequency influence coefficients and uses a linear regression model to derive the final high-frequency electromagnetic state judgment value. Based on this result, the system determines whether the current monitoring point is in a high-frequency electromagnetic state and compares it with the system's preset state threshold. When the judgment value exceeds the threshold, the system automatically issues an alarm signal and marks the point as an abnormal high-frequency state. By integrating the influence of two different high-frequency effects, this module ensures that the system can make accurate judgments in a multi-dimensional electromagnetic environment, greatly improving the system's monitoring accuracy.

[0150] The effect inference module infers the specific impacts of eddy current effects and skin effects on high-frequency electromagnetic states by analyzing the weight ratio of the first and second high-frequency influence coefficients. The system minimizes the error between the theoretical model and actual data through optimization algorithms, dynamically adjusts the weight value of each effect, and determines its contribution to the system state. This module ensures that the system can accurately distinguish the influence of different high-frequency effects, which helps to make more accurate judgments on the system state and provides more targeted guidance for equipment fault prediction and performance optimization.

[0151] The above description is merely 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 within the protection scope of the present invention.

Claims

1. A magnetic component monitoring system for a wireless charging device, characterized in that, include: The module includes a primary information acquisition module, a primary state judgment module, a high-frequency electromagnetic state judgment module, a spatial location information management module, a high-frequency influence coefficient generation module, and a high-frequency electromagnetic state linear regression judgment module. The primary information acquisition module collects primary judgment information of the magnetic nanocrystalline element through multi-dimensional sensors, and determines whether the current area is in a primary high-frequency electromagnetic state based on the collected primary judgment information. The primary judgment information includes operating frequency, permeability, current density of conductor surface, current carrying capacity, resistivity change rate and material frequency response value. The primary state judgment module is used to judge the primary abnormal state. When the operating frequency exceeds the system's preset operating frequency threshold and the magnetic permeability is less than the system's preset magnetic permeability, it sends a primary abnormal state signal to the system and lists the area as a primary abnormal state; otherwise, it is a normal state. The high-frequency electromagnetic state judgment module is used to judge the primary high-frequency electromagnetic state. When the current density on the conductor surface exceeds the system's preset surface current carrying capacity threshold and the resistance change rate exceeds the system's preset material frequency response threshold, it sends a primary high-frequency electromagnetic state signal to the system and lists the area as the primary high-frequency electromagnetic state; otherwise, it is in a normal state. The spatial location information management module is used to record the spatial location information of the primary high-frequency electromagnetic state and obtain the monitoring points. The spatial location of each monitoring point is recorded by the eccentric distance. The spatial location information is recorded together with each measuring point of the multi-dimensional sensor, and the spatial location information is managed by combining the spatial location information with the location information matrix. The high-frequency influence coefficient generation module is used to generate a first high-frequency influence coefficient and a second high-frequency influence coefficient for each monitoring point. The first high-frequency influence coefficient is generated based on eddy current effect information, which includes eddy current loss, hysteresis loss, electric field strength and material microstructure effect. The second high-frequency influence coefficient is generated based on skin effect information, which includes surface current density, surface effect depth, surface energy density and surface thermal effect. The high-frequency electromagnetic state linear regression judgment module introduces the first high-frequency influence coefficient and the second high-frequency influence coefficient into the linear regression model, and linearly combines the first high-frequency influence coefficient and the second high-frequency influence coefficient. If the current monitoring point exceeds the system's preset final high-frequency electromagnetic state threshold, then the current monitoring point is classified as a high-frequency electromagnetic state.

2. The magnetic component monitoring system for a wireless charging device according to claim 1, characterized in that: The parameters of the magnetic nanocrystalline element are collected using a multi-dimensional sensor. The results of the initial information collection are represented as set X: X={f,μ,J s ,C I ,ΔR,f r ,r e ,or} Where f is the operating frequency, μ is the permeability, and J s It is the surface current density, C I It is the current carrying capacity, ΔR is the rate of change of resistance, and f is the current carrying capacity. r It is the material's frequency response value, ρ e η is the conductivity, and η is the hysteresis loss factor of the material. The primary state determination module is used to determine whether there is a primary abnormal state in the current area. The determination conditions are as follows: Where S primary This is the output signal for the initial state, where 1 indicates an abnormal state and 0 indicates a normal state; f threshold This represents the system's set operating frequency threshold, measured in Hertz (Hz); μ threshold This indicates the system's set permeability threshold, expressed in Henry per meter; when the operating frequency f exceeds the set threshold f... threshold And the permeability μ is lower than the set permeability threshold μ threshold When this happens, the system will issue a primary abnormal status signal.

3. The magnetic component monitoring system for a wireless charging device according to claim 2, characterized in that: The high-frequency electromagnetic state determination module is based on the conductor surface current density J. s The primary high-frequency electromagnetic state is determined by the resistance change rate ΔR, and S is proposed. high-freq The output signal for the primary high-frequency electromagnetic state: J s,threshold It is the surface current density threshold; R threshold It is the threshold of the rate of change of resistance, when J s When ΔR exceeds the system threshold, the system is determined to be in a primary high-frequency electromagnetic state.

4. The magnetic component monitoring system for a wireless charging device according to claim 3, characterized in that: The spatial location information management module records the three-dimensional spatial position of each monitoring point by measuring the eccentricity distance d. i Let represent the spatial distance between monitoring point i and reference point (x0, y0, z0). The actual distance between each monitoring point and the reference point is calculated using the distance formula in a three-dimensional coordinate system. Where d i It is the three-dimensional spatial distance between monitoring point i and reference point; (x i ,y i ,z i (x0, y0, z0) are the three-dimensional spatial coordinates of monitoring point i; (x0, y0, z0) are the three-dimensional spatial coordinates of the reference point; the goal of the spatial location information management module is to create a three-dimensional spatial information matrix M by recording the specific location of each monitoring point and combining it with the set X. pos .

5. The magnetic component monitoring system for a wireless charging device according to claim 4, characterized in that: The proposed first high-frequency influence coefficient is C. highl ; Eddy current loss P eddy : P eddy =k1·ρ e ·f 2 ·ΔR Where ρ e Here, f is the conductivity, f is the operating frequency, ΔR is the rate of change of resistance, and k1 is a constant. Hysteresis loss P hyst : P hyst =k2·H c ·f·m·r·h Where H c It is the coercivity, μ is the permeability, ρ is the material density, η is the hysteresis loss factor, and k2 is a constant; Electric field strength E: E=J s ·R J s R is the surface current density, and R is the resistance. Material microstructure effect λ: Where λ reflects the influence of the material's microstructure on its electromagnetic behavior; Combined with eddy current loss P from eddy current effect information eddy Hysteresis loss P hyst The first high-frequency influence coefficient is obtained by considering the electric field intensity E and the material microstructure effect λ. The first high-frequency influence coefficient is: C high1 =α1·P eddy +α2·P hyst +α3·E+α4·λ Where α1, α2, α3, and α4 are the eddy current losses P eddy Hysteresis loss P hyst The weighting coefficients for electric field strength E and material microstructure effect λ.

6. The magnetic component monitoring system for a wireless charging device according to claim 5, characterized in that: The proposed second high-frequency influence coefficient is C. high2 ; Surface current density J s : Among them I total It is the total current in the conductor, A s It is the surface area of ​​the conductor; Surface effect depth δ: Where δ is the surface effect depth, f is the operating frequency, μ is the permeability, and ρ is the magnetic permeability. e π is electrical conductivity, and π is pi (circular diameter). Surface energy density U s for: J s is the surface current density, and μ is the magnetic permeability; Surface thermal effect T s for: Where T s J is used to reflect the thermal effect caused by surface current density. s It is the surface current density, R s It is surface resistance; By combining the surface current density J in the skin effect information s Surface effect depth δ, surface energy density U s and surface thermal effect T s Generate the second high-frequency influence coefficient C high2 The second high-frequency influence coefficient C high2 for: Where β1, β2, β3, and β4 are the surface current densities J, respectively. s Surface effect depth δ, surface energy density U s and surface thermal effect T s The weighting coefficients.

7. A magnetic component monitoring system for a wireless charging device according to claim 6, characterized in that: The high-frequency electromagnetic state linear regression judgment module combines the first and second high-frequency influence coefficients through a linear regression model to determine the total judgment value of the high-frequency electromagnetic state as S. final The total judgment value S of the high-frequency electromagnetic state final for: S final =γ1·C highl +γ2·C high2 Where S final It is the final total judgment value of the high-frequency electromagnetic state; γ1 and γ2 are the weight parameters of the first high-frequency influence coefficient and the second high-frequency influence coefficient in the linear regression, respectively. When S final Exceeding threshold S threshold At that time, the monitoring point was determined to be in a high-frequency electromagnetic state: Where S high-freq-final This is the final high-frequency electromagnetic state output signal; 1 indicates that the monitoring point is in a high-frequency electromagnetic state, and 0 indicates a normal state; S threshold It is the system relative to S final The set final high-frequency electromagnetic state threshold.

8. A magnetic component monitoring system for a wireless charging device according to claim 7, characterized in that: It also includes an effect inference module; The effect back-calculation module aims to combine the first high-frequency influence coefficient C highl Second high frequency influence coefficient C high2 The effect inversion module is used to distinguish between eddy current effect and skin effect in high-frequency electromagnetic conditions by inverting the proportion of influence of eddy current effect and skin effect in the monitoring points; the effect inversion module distinguishes between the two by determining the contribution value of eddy current effect and skin effect to the system respectively. The total high-frequency effect of the proposed system is E total E total The formula used to represent the combined contribution of the eddy current effect and the skin effect is as follows: E total =ω1·C high1 +ω2·C high2 Among them, E total C represents the total high-frequency effect of the system; ω1 and ω2 are the weighting factors for the eddy current effect and the skin effect, respectively, and ω1 and ω2 satisfy ω1+ω2=1, used to determine the proportion of the two effects in the total contribution; highl C is the first high-frequency influence coefficient. highl The calculation results are based on the eddy current effect; C high2 C is the second most frequent influence coefficient. high2 The results are based on calculations using the skin effect; An error minimization model is introduced through the effect back-calculation module. This model is used to find ω1 and ω2, and an error function is introduced. Through the error function To measure the theoretical model E total Compared with the actual measured effect E actual Error between: in E is the error function used to represent the deviation between the theoretical and actual effects. actual It is the actual high-frequency effect value obtained based on sensor data and monitoring results; by minimizing ω1 and ω2 are calculated in this way to determine the contribution ratio of eddy current effect and skin effect; When ω1>0.5, the eddy current effect dominates; when ω2>0.5, the skin effect dominates; when ω1≈ω2, the two effects are roughly equal.

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