Wireless charging device magnetic element monitoring system

Abstract

The invention discloses a wireless charging device magnetic element monitoring system, and particularly relates to the field of magnetic nanocrystalline material high-frequency electromagnetic phenomenon monitoring, which comprises the steps of acquiring key parameters such as working frequency, magnetic conductivity, surface current density, resistance change rate and the like, judging whether an element is in a high-frequency electromagnetic state, and when the working frequency exceeds a preset threshold value, judging whether the element is in a high-frequency electromagnetic state. When the area is in an abnormal state and the surface current density and the resistance change rate are obviously changed, the system can automatically send out a high-frequency electromagnetic state signal and mark the area in the abnormal state, and the scheme has the advantages that the working stability of the magnetic element can be effectively improved through real-time judgment on the high-frequency electromagnetic state, and the reliability of the magnetic element is improved. The safety and reliability of the system in a high-frequency environment are ensured, potential faults are prevented, and the performance of the wireless charging device is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of high-frequency electromagnetic phenomenon monitoring of magnetic nanocrystalline materials, and more specifically, to a magnetic element monitoring system for a wireless charging device. Background Art

[0002] Nanocrystalline ribbon is an advanced soft magnetic material that has attracted much attention due to its high saturation magnetic induction intensity, low loss and excellent magnetic permeability. Compared with traditional ferrite, silicon steel and other materials, nanocrystalline ribbon shows better performance in high-frequency applications, and is particularly suitable for high-frequency transformers, inductors and magnetic shielding sheets in wireless charging. This material is formed by alloy smelting and rapid cooling technology, and has the characteristics of extremely small grain size of about 10-20 nanometers, which significantly reduces its energy loss in high-frequency electromagnetic environments.

[0003] In the research and development process of wireless charging devices, the selection of magnetic components plays a vital role in charging efficiency and stability. Traditional materials often show large losses in high-frequency electromagnetic environments and it is difficult to maintain efficient and stable working conditions under the high-frequency conditions required for wireless charging. Therefore, nanocrystalline ribbon materials have become a preferred material due to their low loss and high magnetic permeability. Especially when dealing with high-frequency electromagnetic phenomena, they can significantly improve energy transmission efficiency. However, with the increase of frequency, the complex changes of the electromagnetic field put forward higher requirements 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] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a magnetic component monitoring system for a wireless charging device, which collects multiple key parameters such as operating frequency, magnetic permeability, current density, etc. through a multi-dimensional sensor, so as to identify whether the magnetic nanocrystalline component is in a high-frequency electromagnetic state, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: A wireless charging device magnetic component monitoring system, comprising: a primary information acquisition module, a primary state judgment module, a high-frequency electromagnetic state judgment module, a spatial position 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 nanocrystal element through a multi-dimensional sensor, and judges whether the current area is in a primary high-frequency electromagnetic state through the collected primary judgment information; the primary judgment information includes operating frequency, magnetic permeability, current density on the conductor surface, current carrying capacity, resistance 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 operating frequency threshold preset by the system and the magnetic permeability is less than the magnetic permeability preset by the system, a primary abnormal state signal is sent to the system and the area is listed 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 surface of the conductor exceeds the surface current carrying threshold preset by the system, and the resistance change rate exceeds the material frequency response threshold preset by the system, a primary high-frequency electromagnetic state signal is sent to the system and the area is listed as the primary high-frequency electromagnetic state. Otherwise, it is in a normal state.

[0009] The spatial position information management module is used to record the spatial position information of the primary high-frequency electromagnetic state and obtain the monitoring points, record the spatial position of each monitoring point by the eccentric distance, record the spatial position information together with each measuring point of the multi-dimensional sensor, and manage the spatial position information in combination with the position 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, linearly combines the first high-frequency influence coefficient and the second high-frequency influence coefficient, and if the current monitoring point exceeds the final high-frequency electromagnetic state threshold preset by the system, the current monitoring point is listed as a high-frequency electromagnetic state.

[0012] In a preferred embodiment, the parameters of the magnetic nanocrystal element are collected by a multi-dimensional sensor, and the result of the primary information collection is represented as a 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 magnetic permeability, J s is the surface current density, C I is the current carrying capacity, ΔR is the resistance change rate, f r is the material frequency response value, ρ e is the electrical conductivity, η is the hysteresis loss factor of the material;

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

[0016]

[0017] Where Sprimary is the output signal of the primary state, 1 indicates an abnormal state, and 0 indicates a normal state; f threshold Indicates the operating frequency threshold set by the system, in Hertz; μ threshold Indicates the magnetic permeability threshold set by the system, in Henry per meter; when the operating frequency f exceeds the set threshold f threshold , and the magnetic permeability μ is lower than the set magnetic permeability threshold μ threshold When the system is in a state of abnormality, it will send out a primary abnormal status signal.

[0018] In a preferred embodiment, the high-frequency electromagnetic state judgment module is based on the conductor surface current density J s and resistance change rate ΔR to determine the primary high-frequency electromagnetic state, and propose S high-freq Output signal of the primary high-frequency electromagnetic state:

[0019]

[0020] Among them J s,threshold is the surface current density threshold; R threshold is the resistance change rate threshold, when J s When and ΔR exceed the system threshold, the system determines it to be in the primary high-frequency electromagnetic state.

[0021] In a preferred embodiment, the spatial position information management module records the three-dimensional spatial position of each monitoring point through the eccentric distance of the monitoring point, and proposes the eccentric distance d i Represents the spatial distance of monitoring point i relative to the reference point (x0, y0, z0). The actual distance of each monitoring point relative to the reference point is calculated using the distance formula in the three-dimensional space coordinate system:

[0022]

[0023] where d i is the three-dimensional space distance between monitoring point i and the reference point; (x i ,y i ,z i ) is the three-dimensional spatial coordinate of monitoring point i; (x0, y0, z0) is the three-dimensional spatial coordinate of the reference point; the goal of the spatial position 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 .

[0024] In a preferred embodiment, the first high frequency influence coefficient is set to be C highl The first high-frequency influence coefficient is generated based on the eddy current effect information, and the eddy current effect information includes the 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 is the conductivity, f is the operating frequency, ΔR is the resistance change rate, and k1 is a constant;

[0028] Hysteresis loss P hyst :

[0029] P hyst =k2·H c ·f·μ·ρ·η

[0030] Among them, H c is the coercive force, μ is the magnetic 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] Among them J s is the surface current density, R is the resistance;

[0034] Material microstructure effect λ:

[0035]

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

[0037] Combined with the eddy current effect information, the eddy current loss P eddy , hysteresis loss P hyst , electric field strength E and material microstructure effect λ to obtain the first high-frequency influence coefficient, which is:

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

[0039] Among them, α1, α2, α3, and α4 are eddy current losses P eddy , hysteresis loss P hyst , the weight coefficient of the electric field strength E and the material microstructure effect λ.

[0040] In a preferred embodiment, the second high frequency influence coefficient is set to C high2The second high-frequency influence coefficient is generated based on the skin effect information, which includes the 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 is the total current in the conductor, A s 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 magnetic permeability, ρ e is the conductivity, π is the circumference of a circle;

[0047] Surface energy density U s for:

[0048]

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

[0050] Surface thermal effect T s for:

[0051]

[0052] Where T s Used to reflect the thermal effect caused by surface current density, J s is the surface current density, R s is the 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 s, surface effect depth δ, surface energy density U s and surface thermal effect T s The weight coefficient of .

[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 propose a 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 is the total judgment value of the final high-frequency electromagnetic state; γ1 and γ2 are weight parameters of the first high-frequency influence coefficient and the second high-frequency influence coefficient in linear regression respectively;

[0059] When S final Exceeding the threshold S threshold When , the monitoring point is judged to be in high-frequency electromagnetic state:

[0060]

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

[0062] In a preferred embodiment, the effect back-estimation module is designed to combine the first high-frequency influence coefficient C highl and the second high frequency influence coefficient C high2 , by inferring the influence proportion of eddy current effect and skin effect in the monitoring point, the effect inference module is used to distinguish eddy current effect and skin effect under high-frequency electromagnetic state; the effect inference module distinguishes the eddy current effect and skin effect by determining their contribution values ​​to the system respectively;

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

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

[0065] Among them, E totalis the total high-frequency effect of the system; ω1 and ω2 are the weight factors of the eddy current effect and the skin effect, respectively, and ω1 and ω2 satisfy ω1+ω2=1, which are used to determine the proportion of the two effects in the total contribution; C highl is the first high frequency influence coefficient, C highl is the calculation result based on eddy current effect; C high2 is the second high frequency influence coefficient, C high2 It is the calculation result based on the skin effect;

[0066] The error minimization model is introduced through the effect backstepping module. The error minimization model is used to find ω1 and ω2 and introduce the error function Through the error function To measure the theoretical model E total Compared with the actual measured effect E actual The error between:

[0067]

[0068] in is the error function, which is used to represent the deviation between theoretical and actual effects; E actual 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 is dominant; when ω2>0.5, the skin effect is dominant; when ω1≈ω2, the influence of the two effects is equivalent.

[0070] Technical effects and advantages of the present invention:

[0071] 1. Distinguish the high-frequency electromagnetic state of magnetic nanocrystal components: Through the multi-dimensional sensor to collect multiple key parameters such as working frequency, magnetic permeability, current density, etc., to identify whether the magnetic nanocrystal components are in a high-frequency electromagnetic state; when the working frequency and current density exceed the preset threshold, the system can judge and send the status signal in real time, so as to realize the effective monitoring of the components and ensure the stable operation of the equipment;

[0072] 2. Distinguish eddy current effect and skin effect according to high-frequency electromagnetic state: After determining that the component is in a high-frequency electromagnetic state, the system further distinguishes 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0074] 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.

[0075] Refer to the instruction manual Figure 1 , a wireless charging device magnetic component monitoring system according to an embodiment of the present invention comprises: a primary information acquisition module, a primary state judgment module, a high-frequency electromagnetic state judgment module, a spatial position 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 nanocrystal element through a multi-dimensional sensor, and judges whether the current area is in a primary high-frequency electromagnetic state through the collected primary judgment information; the primary judgment information includes operating frequency, magnetic permeability, current density on the conductor surface, current carrying capacity, resistance 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 operating frequency threshold preset by the system and the magnetic permeability is less than the magnetic permeability preset by the system, a primary abnormal state signal is sent to the system and the area is listed 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 surface of the conductor exceeds the surface current carrying threshold preset by the system, and the resistance change rate exceeds the material frequency response threshold preset by the system, a primary high-frequency electromagnetic state signal is sent to the system and the area is listed as the primary high-frequency electromagnetic state. Otherwise, it is in a normal state.

[0079] The spatial position information management module is used to record the spatial position information of the primary high-frequency electromagnetic state and obtain the monitoring points, record the spatial position of each monitoring point by the eccentric distance, record the spatial position information together with each measuring point of the multi-dimensional sensor, and manage the spatial position information in combination with the position 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, linearly combines the first high-frequency influence coefficient and the second high-frequency influence coefficient, and if the current monitoring point exceeds the final high-frequency electromagnetic state threshold preset by the system, the current monitoring point is listed as a high-frequency electromagnetic state.

[0082] The parameters of the magnetic nanocrystal element are collected by multi-dimensional sensors, and the results of primary information collection are expressed as a 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 magnetic permeability, J s is the surface current density, C I is the current carrying capacity, ΔR is the resistance change rate, f r is the material frequency response value, ρ e is the electrical conductivity, η is the hysteresis loss factor of the material;

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

[0086]

[0087] Where S primary is the output signal of the primary state, 1 indicates an abnormal state, and 0 indicates a normal state; f threshold Indicates the operating frequency threshold set by the system, in Hertz; μ threshold Indicates the magnetic permeability threshold set by the system, in Henry per meter; when the operating frequency f exceeds the set threshold f threshold , and the magnetic permeability μ is lower than the set magnetic permeability threshold μ threshold When the system is in a state of abnormality, it will send out a primary abnormal status signal.

[0088] The high-frequency electromagnetic state judgment module is based on the conductor surface current density J s and resistance change rate ΔR to determine the primary high-frequency electromagnetic state, and propose S high-freq Output signal of the primary high-frequency electromagnetic state:

[0089]

[0090] Among them J s,threshold is the surface current density threshold; R threshold is the resistance change rate threshold, when J s When and ΔR exceed the system threshold, the system determines it to be in the primary high-frequency electromagnetic state.

[0091] The spatial position information management module records the three-dimensional spatial position of each monitoring point through the eccentric distance of the monitoring point, and proposes the eccentric distance d i Represents the spatial distance of monitoring point i relative to the reference point (x0, y0, z0). The actual distance of each monitoring point relative to the reference point is calculated using the distance formula in the three-dimensional space coordinate system:

[0092]

[0093] where d i is the three-dimensional space distance between monitoring point i and the reference point; (x i ,y i ,z i ) is the three-dimensional spatial coordinate of monitoring point i; (x0, y0, z0) is the three-dimensional spatial coordinate of the reference point; the goal of the spatial position 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 ; Three-dimensional space information matrix M pos It is used to help the system to uniformly manage and track the spatial locations of all monitoring points, ensuring that the 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 space information matrix M pos The formation is based on the specific coordinates of each monitoring point in three-dimensional space. Each monitoring point has a unique coordinate (x i ,y i ,z i ), these coordinates represent the position of the point in the X, Y, and Z axis directions. 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 the matrix represents the spatial position of a monitoring point, and the columns represent the coordinates of the X, Y, and Z axes respectively. Assuming that the system has N monitoring points, then the three-dimensional spatial information matrix M pos The dimension is N×3, which represents the three-dimensional spatial coordinate information of N points. The composition principle of the matrix is ​​as follows:

[0095]

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

[0097] The first high frequency influence coefficient is proposed to be C highl The first high-frequency influence coefficient is generated based on the eddy current effect information, and the eddy current effect information includes the 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 is the conductivity, f is the operating frequency, ΔR is the resistance change rate, and k1 is a constant;

[0101] Hysteresis loss P hyst :

[0102] P hyst =k2·H c ·f·μ·ρ·η

[0103] Among them, H c is the coercive force, μ is the magnetic 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] Among them J s is the surface current density, R is the resistance;

[0107] Material microstructure effect λ:

[0108]

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

[0110] Combined with the eddy current effect information, the eddy current loss P eddy , hysteresis loss P hyst , electric field strength E and material microstructure effect λ to obtain the first high-frequency influence coefficient, which is:

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

[0112] Among them, α1, α2, α3, and α4 are eddy current losses P eddy , hysteresis loss P hyst , the weight coefficient of the electric field strength E and the material microstructure effect λ.

[0113] The second high frequency influence coefficient is proposed to be C high2 The second high-frequency influence coefficient is generated based on the skin effect information, which includes the 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 is the total current in the conductor, A s 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 magnetic permeability, ρ e is the conductivity, π is the circumference of a circle;

[0120] Surface energy density U s for:

[0121]

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

[0123] Surface thermal effect T s for:

[0124]

[0125] Where T s Used to reflect the thermal effect caused by surface current density, J s is the surface current density, R s is the 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 s , surface effect depth δ, surface energy density U s and surface thermal effect T s The weight coefficient of .

[0129] 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, and proposes a 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 It is the total judgment value of the final high-frequency electromagnetic state, representing the result of the system through linear regression calculation, and is 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 weight configuration for different effects, reflecting the relative importance of these two coefficients in the total judgment;

[0132] When S final Exceeding the threshold S threshold When , the monitoring point is judged to be in high-frequency electromagnetic state:

[0133]

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

[0135] The effect back-estimation module is designed to combine the first high-frequency influence coefficient C highl and the second high frequency influence coefficient C high2, by inferring the influence proportion of eddy current effect and skin effect in the monitoring point, the effect inference module is used to distinguish eddy current effect and skin effect under high-frequency electromagnetic state; the effect inference module distinguishes the eddy current effect and skin effect by determining their contribution values ​​to the system respectively;

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

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

[0138] Among them, E total is the total high-frequency effect of the system, in dimensionless units, representing the sum of contributions of different effects; ω1 and ω2 are the weighting factors of the eddy current effect and the skin effect, respectively, which are dimensionless, and ω1 and ω2 satisfy ω1+ω2=1, which are used to determine the proportion of the two effects in the total contribution; C highl is the first high frequency influence coefficient, C highl is the calculation result based on eddy current effect; C high2 is the second high frequency influence coefficient, C high2 It is the calculation result based on the skin effect;

[0139] The error minimization model is introduced through the effect back-stepping module. The error minimization model is used to find the optimal ω1 and ω2 and introduce the error function Through the error function To measure the theoretical model E total Compared with the actual measured effect E actual The error between:

[0140]

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

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

[0143] When ω1>0.5, the eddy current effect is dominant; when ω2>0.5, the skin effect is dominant; when ω1≈ω2, the influence of the two effects is equivalent; through the effect back-calculation module, the system can dynamically judge the actual impact of different effects on the monitoring points, thereby further optimizing the system's electromagnetic monitoring and management.

[0144] It is necessary to further explain the above scheme that the primary information acquisition module monitors the electromagnetic characteristics of the magnetic nanocrystal components through multi-dimensional sensors and collects key parameters in real time, including operating frequency, magnetic permeability, current density on the conductor surface, current carrying capacity, resistance change rate and frequency response of the material. The collected primary judgment information can reflect the changes in the electromagnetic environment of the component during operation. 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, especially suitable for scenarios where magnetic components need to be precisely monitored in wireless charging devices.

[0145] The primary state judgment module is responsible for analyzing the primary information to identify whether the magnetic component is in an abnormal state. The system sets preset thresholds for the operating frequency and magnetic permeability. When the operating frequency exceeds the threshold and the magnetic 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 working state of the component under excessively high frequency or abnormal magnetic permeability can be quickly determined to avoid equipment failure due to abnormal electromagnetic state. 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 on the conductor surface; when the surface current density exceeds the system preset threshold and the resistance change rate also exceeds the set value of the material 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 accurate analysis of high-frequency electromagnetic parameters, to ensure that the equipment can maintain stable operation under such conditions, and to reduce potential risks caused by high-frequency electromagnetic anomalies;

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

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

[0149] The linear regression judgment module combines the first high-frequency influence coefficient and the second high-frequency influence coefficient, and obtains the final high-frequency electromagnetic state judgment value through the linear regression model; based on this result, the system judges whether the current monitoring point is in a high-frequency electromagnetic state, and compares it with the state threshold preset by the system; when the judgment value exceeds the threshold, the system automatically sends an alarm signal and marks the point as an abnormal high-frequency state; this module ensures that the system can make accurate judgments in a multi-dimensional electromagnetic environment by integrating the influence of two different high-frequency effects, greatly improving the monitoring accuracy of the system;

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

[0151] 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 wireless charging device magnetic component monitoring system, characterized in that: include: Primary information collection module, primary state judgment module, high-frequency electromagnetic state judgment module, spatial position information management module, high-frequency influence coefficient generation module and high-frequency electromagnetic state linear regression judgment module; The primary information acquisition module collects primary judgment information of the magnetic nanocrystal element through a multi-dimensional sensor, and judges whether the current area is in a primary high-frequency electromagnetic state through the collected primary judgment information; the primary judgment information includes operating frequency, magnetic permeability, current density on the conductor surface, current carrying capacity, resistance 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 operating frequency threshold preset by the system and the magnetic permeability is less than the magnetic permeability preset by the system, a primary abnormal state signal is sent to the system and the area is listed 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 surface of the conductor exceeds the surface current carrying threshold preset by the system, and the resistance change rate exceeds the material frequency response threshold preset by the system, a primary high-frequency electromagnetic state signal is sent to the system and the area is listed as the primary high-frequency electromagnetic state. Otherwise, it is in a normal state. The spatial position information management module is used to record the spatial position information of the primary high-frequency electromagnetic state and obtain the monitoring points, record the spatial position of each monitoring point by the eccentric distance, record the spatial position information together with each measuring point of the multi-dimensional sensor, and manage the spatial position information in combination with the position 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 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, linearly combines the first high-frequency influence coefficient and the second high-frequency influence coefficient, and if the current monitoring point exceeds the final high-frequency electromagnetic state threshold preset by the system, the current monitoring point is listed as a high-frequency electromagnetic state.

2. A wireless charging device magnetic component monitoring system according to claim 1, characterized in that: The parameters of the magnetic nanocrystal element are collected by multi-dimensional sensors, and the results of primary information collection are expressed as a set X: X={f,μ,J s ,C I ,ΔR,f r ,r e ,or} Where f is the operating frequency, μ is the magnetic permeability, J s is the surface current density, C I is the current carrying capacity, ΔR is the resistance change rate, f r is the material frequency response value, ρ e is the electrical conductivity, η is the hysteresis loss factor of the material; The primary state judgment module is used to determine whether there is a primary abnormal state in the current area. The judgment conditions are as follows: Where S primary is the output signal of the primary state, 1 indicates an abnormal state, and 0 indicates a normal state; f threshold Indicates the operating frequency threshold set by the system, in Hertz; μ threshold Indicates the magnetic permeability threshold set by the system, in Henry per meter; when the operating frequency f exceeds the set threshold f threshold , and the magnetic permeability μ is lower than the set magnetic permeability threshold μ threshold When the system is in a state of abnormality, it will send out a primary abnormal status signal.

3. A wireless charging device magnetic component monitoring system according to claim 2, characterized in that: The high-frequency electromagnetic state judgment module is based on the conductor surface current density J s and resistance change rate ΔR to determine the primary high-frequency electromagnetic state, and propose S high-freq The output signal of the primary high-frequency electromagnetic state: Among them J s,threshold is the surface current density threshold; R threshold is the resistance change rate threshold, when J s When and ΔR exceed the system threshold, the system determines it to be in the primary high-frequency electromagnetic state.

4. A wireless charging device magnetic component monitoring system according to claim 3, characterized in that: The spatial position information management module records the three-dimensional spatial position of each monitoring point through the eccentric distance of the monitoring point, and proposes the eccentric distance d i Represents the spatial distance of monitoring point i relative to the reference point (x0, y0, z0). The actual distance of each monitoring point relative to the reference point is calculated using the distance formula in the three-dimensional space coordinate system: where d i is the three-dimensional space distance between monitoring point i and the reference point; (x i ,y i ,z i ) is the three-dimensional spatial coordinate of monitoring point i; (x0, y0, z0) is the three-dimensional spatial coordinate of the reference point; the goal of the spatial position 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. A wireless charging device magnetic component monitoring system according to claim 4, characterized in that: The first high frequency influence coefficient is proposed to be C highl The first high-frequency influence coefficient is generated based on the eddy current effect information, and the eddy current effect information includes the eddy current loss P eddy , hysteresis loss P hyst , electric field strength E and material microstructure effect λ; Eddy current loss P eddy : P eddy =k1·ρ e ·f 2 ·ΔR where ρ e is the conductivity, f is the operating frequency, ΔR is the resistance change rate, and k1 is a constant; Hysteresis loss P hyst : P hyst =k2·H c ·f·m·r·h Among them, H c is the coercive force, μ is the magnetic 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 Among them J s is the surface current density, R is the resistance; Material microstructure effect λ: Where λ reflects the influence of the material microstructure on the electromagnetic behavior; Combined with the eddy current loss P in the eddy current effect information eddy , hysteresis loss P hyst , electric field strength E and material microstructure effect λ to obtain the first high-frequency influence coefficient, which is: C high1 =α1·P eddy +α2·P hyst +α3·E+α4·λ Among them, α1, α2, α3, and α4 are eddy current losses P eddy , hysteresis loss P hyst , the weight coefficient of the electric field strength E and the material microstructure effect λ.

6. A wireless charging device magnetic component monitoring system according to claim 5, characterized in that: The second high frequency influence coefficient is proposed to be C high2 The second high-frequency influence coefficient is generated based on the skin effect information, which includes the surface current density J s , surface effect depth δ, surface energy density U s and surface thermal effect T s ; Surface current density J s : Among them I total is the total current in the conductor, A s 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 magnetic permeability, ρ e is the conductivity, π is the circumference; Surface energy density U s for: Among them J s is the surface current density, μ is the magnetic permeability; Surface thermal effect T s for: Where T s Used to reflect the thermal effect caused by surface current density, J s is the surface current density, R s is the 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 s , surface effect depth δ, surface energy density U s and surface thermal effect T s The weight coefficient of .

7. A wireless charging device magnetic component monitoring system according to claim 6, characterized in that: 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, and proposes a 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 is the total judgment value of the final high-frequency electromagnetic state; γ1 and γ2 are weight parameters of the first high-frequency influence coefficient and the second high-frequency influence coefficient in linear regression respectively; When S final Exceeding the threshold S threshold When , the monitoring point is judged to be in high-frequency electromagnetic state: Where S high-freq-final is the final high-frequency electromagnetic state output signal, 1 indicates that the monitoring point is in the high-frequency electromagnetic state, and 0 indicates the normal state; S threshold is the system relative to S final Set the final high-frequency electromagnetic state threshold.

8. A wireless charging device magnetic component monitoring system according to claim 7, characterized in that: Also includes effect backpropagation module; The effect back-estimation module is designed to combine the first high-frequency influence coefficient C highl and the second high frequency influence coefficient C high2 , by inferring the influence proportion of eddy current effect and skin effect in the monitoring point, the effect inference module is used to distinguish eddy current effect and skin effect under high-frequency electromagnetic state; the effect inference module distinguishes the eddy current effect and skin effect by determining their contribution values ​​to the system respectively; The total high frequency effect of the proposed system is E total , E total The formula used to express the combined contribution of eddy current effect and skin effect is as follows: E total =ω1·C high1 +ω2·C high2 Among them, E total is the total high-frequency effect of the system; ω1 and ω2 are the weight factors of the eddy current effect and the skin effect, respectively, and ω1 and ω2 satisfy ω1+ω2=1, which are used to determine the proportion of the two effects in the total contribution; C highl is the first high frequency influence coefficient, C highl is the calculation result based on eddy current effect; C high2 is the second high frequency influence coefficient, C high2 It is the calculation result based on the skin effect; The error minimization model is introduced through the effect backstepping module. The error minimization model is used to find ω1 and ω2 and introduce the error function Through the error function To measure the theoretical model E total Compared with the actual measured effect E actual The error between: in is the error function, which is used to represent the deviation between theoretical and actual effects; E actual 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 is dominant; when ω2>0.5, the skin effect is dominant; when ω1≈ω2, the influence of the two effects is equivalent.

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