A magnetic attraction detection device and method for wireless charging devices

Abstract

This application relates to the technical field of wireless charging technology and performance testing of smart terminal components, and in particular to a magnetic attraction detection device and method for wireless charging devices. The method includes setting test modes through a software system, wherein the test modes include at least a single static distribution test mode and a multi-cycle durability test mode; controlling a clamping component to move the sample under test and the magnetic target along a trajectory matching the test mode; during the movement, simultaneously acquiring normal separation force data and magnetic field strength data using a sensing measurement system; and, according to the set test mode, calling the corresponding data processing flow to analyze the acquired data, generating a feature spectrum and outputting the test results. This application improves upon the shortcomings of existing detection modes, such as their singularity, fragmented force and magnetic data, and lack of intelligent diagnostics, thereby enhancing the comprehensiveness and accuracy of magnetic attraction performance evaluation.

Description

Technical Field

[0001] This application relates to the technical field of wireless charging technology and performance testing of smart terminal components, and in particular to a magnetic attraction detection device and detection method for wireless charging devices. Background Technology

[0002] With the popularization of wireless charging technology, magnetic structures are widely used in the consumer electronics field due to their convenient alignment. The strength of the magnetic force and the uniformity of the magnetic field distribution are directly related to the reliability of the product. Insufficient magnetic force can easily cause the device to fall off, while excessive force increases the difficulty of separation. Therefore, a standardized quantitative testing method is required.

[0003] In related technologies, detection mainly relies on manual judgment, simple spring balances, or large universal testing machines. Manual assessment is highly subjective and cannot be quantified; spring balances struggle to standardize testing speed and angle, and cannot simulate dynamic processes. While large testing machines offer acceptable accuracy, they are costly, cumbersome to operate, and only support single static tests. Some automated equipment achieves automatic proximity, but is limited to intermittent peak measurements and lacks the ability to monitor continuous dynamic adsorption processes.

[0004] Regarding the aforementioned technologies, firstly, the testing mode is too simplistic, failing to simultaneously assess both static distribution accuracy and dynamic cyclic durability. Secondly, the data dimensions are fragmented, lacking simultaneous acquisition and coupled analysis of mechanical properties and magnetic field strength, making it difficult to identify latent defects such as magnetic circuit nonlinearity anomalies. Thirdly, there is a lack of intelligent diagnostic mechanisms, making it impossible to automatically determine specific failure mechanisms through attenuation trends or spatial offsets, thus hindering effective guidance for process improvement. Summary of the Invention

[0005] To improve the shortcomings of existing detection modes, such as their singularity, fragmented force and magnetic data, and lack of intelligent diagnostics, and to enhance the comprehensiveness and accuracy of magnetic attraction performance evaluation, this application provides a magnetic attraction detection device and method for wireless charging devices.

[0006] In a first aspect, this application provides a magnetic attraction detection device for wireless charging devices, which adopts the following technical solution: A magnetic attraction detection device for a wireless charging device includes a base for providing a support platform, a clamping assembly disposed on the base, a driving assembly connected to the clamping assembly and used to drive the clamping assembly to move, a sensing and measurement system for detecting a sample to be tested, an electronic control system electrically connected to the driving assembly and the sensing and measurement system respectively and used to control the movement of the driving assembly and collect data from the sensing and measurement system, and a software system communicatively connected to the electronic control system and used to display test data, plot test curves, and export test reports. The clamping assembly includes an adjustable clamp for fixing the sample to be tested and a magnetic sample stage for placing a magnetic target. The clamping assembly moves relative to the base via the driving assembly, thereby driving the magnetic target to move relative to the sample to be tested. The sensing and measurement system includes a high-precision tensile sensor for real-time monitoring of the normal separation force between the sample and the magnetic target, and a Hall effect magnetic field sensor for synchronously recording changes in magnetic field strength.

[0007] By adopting the above technical solution, the equipment integrates a tensile sensor and a Hall magnetic field sensor to achieve simultaneous acquisition of normal separation force and magnetic field strength, solving the problem of the separation between mechanical and magnetic field data. Adjustable fixtures, in conjunction with the drive components, simulate the actual dynamic adsorption and separation process, adapting to samples of different specifications. The electronic control system and software system work together to automate the motion control, data acquisition, curve plotting, and report export processes, reducing human error. In a single test, the solution can simultaneously acquire force change curves and magnetic field distribution data, providing multi-dimensional quantitative evidence to ensure objective test results, thereby meeting the accuracy and efficiency requirements of R&D verification and production line quality control.

[0008] Secondly, this application provides a magnetic attraction detection method for a wireless charging device, which adopts the following technical solution: A magnetic attraction detection method for a wireless charging device includes: The test mode is set through the software system, which includes at least a single static distribution test mode and a multiple cycle durability test mode. The control clamping components drive the sample under test and the magnetic target to execute a motion trajectory that matches the test mode; During the motion, the normal separation force data and magnetic field strength data are collected simultaneously using a sensing and measurement system; Based on the set test mode, the corresponding data processing flow is invoked to analyze the collected data, generate feature maps, and output the detection results.

[0009] By adopting the above technical solution, a dual-mode approach—single static distribution and multiple-cycle durability—is implemented, overcoming the limitations of single static peak measurement. Software-defined test trajectories execute differentiated motion strategies for the evaluation target, scanning the spatial distribution of the magnetic field while simulating long-term insertion and removal fatigue conditions. Force and magnetic field data are simultaneously acquired during the motion, and corresponding processing flows are invoked to generate feature maps, achieving the transformation from raw data to performance indicators. Multi-mode compatibility covers the verification needs of the entire product lifecycle. Standardized data processing eliminates human interference, improves the repeatability and comparability of test results, and provides support for product quality control.

[0010] Optionally, when the test mode is a multi-cycle endurance test mode, the corresponding data processing steps include: The control drive component drives the sample under test to perform N repeated cyclic movements with the magnetic target, and records the key mechanical feature values ​​in each cycle to form the original sequence dataset; Denoising and standardization were performed on the original sequence dataset to remove outlier data points and construct a standard sequence that reflects the true trend of magnetic attraction force. Generate a decay trend curve characterizing the magnetic attraction force as a function of the number of cycles based on the standard sequence, and extract characteristic parameters characterizing the performance degradation rate. Generate a dynamic change map of magnetic attraction force based on characteristic parameters; The durability level of the sample under test is evaluated based on the dynamic change spectrum of magnetic attraction force.

[0011] By employing the above technical solution, the original sequence dataset is denoised, standardized, and trend-fitted to remove environmental interference and transient outliers, constructing a standard sequence reflecting the true decay law of magnetic attraction force. Based on this sequence, a decay trend curve is generated and performance degradation rate characteristic parameters are extracted, transforming discrete cyclic data into a continuous performance evolution spectrum to capture subtle performance degradation trends. This solution can quantitatively assess the durability level of the tested sample, demonstrating the dynamic process of magnetic attraction force changing with the number of uses, and determining whether the product meets long-term reliability requirements. Compared to methods that only record initial and final values, this solution provides process data, providing a basis for optimizing magnetic circuit design and material selection.

[0012] Optionally, assessing the durability level of a sample based on a dynamic change spectrum of magnetic attraction force also includes: The characteristic parameters representing the rate of performance degradation are matched with a pre-defined feature library of multiple failure modes. Analyze the morphological characteristics of the attenuation trend curve and the displacement variation law corresponding to the key mechanical characteristic values; If a nonlinear abrupt change in the decay rate is detected, accompanied by significant fluctuations in displacement characteristics, it is determined to be a Type I failure mode. If the decay rate is detected to be linearly gradual and the displacement characteristics remain relatively stable, it is determined to be a type II failure mode. The system automatically labels potential failure mechanism types based on the judgment results.

[0013] By adopting the above technical solution, a failure mode feature library is established and matched with measured feature parameters, realizing the transformation from data recording to fault diagnosis. The morphological characteristics of attenuation curves and displacement fluctuation patterns are analyzed to distinguish between sudden failures caused by structural loosening and magnet breakage, and gradual failures caused by natural demagnetization of magnetic materials. Potential failure mechanisms are automatically marked, shortening the fault analysis cycle and enabling technicians to quickly locate the root cause of the problem. This solution enhances the intelligence level of the detection system, transforming detection results into process improvement suggestions to guide the production end in solving specific types of quality defects and improving product reliability and yield.

[0014] Optionally, constructing a standard sequence that reflects the actual changing trend of magnetic attraction force also includes: Extract the synchronous magnetic field strength value at the moment when the key mechanical characteristic value is reached in each cycle; Construct a force-magnetic coupling index and calculate the stability coefficient of the force-magnetic coupling index in N cycles; If the stability coefficient exceeds the preset allowable fluctuation range, it is determined that the data acquisition system has drift or the magnetic circuit of the sample under test has nonlinear anomalies. Mark the data in the current loop or trigger a recalibration process.

[0015] By employing the above technical solution, a force-magnetic coupling relationship index is constructed, and the stability coefficient during the cyclic process is calculated to verify the status of the data acquisition system and the magnetic circuit characteristics of the sample under test. Synchronous magnetic field values ​​at key mechanical moments are extracted to identify data distortion caused by sensor drift, equipment vibration, or abnormal nonlinearity of the sample's magnetic circuit. When the stability coefficient exceeds the preset range, a marking or recalibration process is triggered to ensure the authenticity of the analytical data. This mechanism incorporates a self-checking function during the detection process to prevent misjudgments due to poor equipment condition or sample abnormalities, thus ensuring data quality.

[0016] Optionally, when the test mode is a single static distribution detection mode, the corresponding data processing steps include: The control drive component drives the sample under test to perform multi-dimensional scanning motion or discrete point stepping motion relative to the magnetic target; At each sampling point along the motion path, the three-dimensional magnetic field components and the composite magnetic field intensity are collected to form a discrete magnetic field dataset. Based on discrete magnetic field datasets, a continuous spatial distribution model of magnetic field intensity is reconstructed. Based on the spatial distribution model of magnetic field intensity, the geometric center of magnetic field distribution, the effective adsorption area range and uniformity index are calculated. Generate a magnetic field distribution map of the product.

[0017] By employing the aforementioned technical solutions, multi-dimensional scanning or discrete-point stepping motion, combined with three-dimensional magnetic field component acquisition, a continuous spatial distribution model of magnetic field intensity is reconstructed. This solution overcomes the limitations of single-point measurement, presenting a complete picture of the magnetic field distribution on the surface of the sample under test. Based on the reconstructed model, the geometric center, effective adsorption area, and uniformity index are calculated, transforming the magnetic field data into a product performance spectrum, intuitively demonstrating the advantages and disadvantages of the magnetic circuit design. The equivalent magnetic pole center is extracted to calculate the geometric center, accurately locating the core area of ​​the actual magnetic field, providing spatial coordinates for evaluating the accuracy of magnetic adsorption alignment. Spatial analysis capabilities help identify local magnetic field distortions or design defects, guiding the optimization of magnetic array layout and improving alignment smoothness and adsorption stability.

[0018] Optionally, the steps following the generation of the product's magnetic field distribution map also include: Extract the feature contours representing the effective magnetic attraction boundary from the reconstructed spatial distribution model of magnetic field strength; Calculate the geometric center point of the magnetic field distribution of the feature profile; Obtain the theoretical design center point of the sample to be tested; Calculate the spatial offset vector between the geometric center point of the magnetic field distribution and the theoretical design center point to obtain the offset amount and offset direction; The offset is compared with the preset alignment tolerance threshold. If it exceeds the threshold, the offset information is visualized on the product magnetic field distribution map, and the magnetic circuit alignment process is determined to be unqualified.

[0019] By employing the above technical solution, a vector comparison is performed between the geometric center of the magnetic field distribution and the theoretical design center, enabling quantitative and visual evaluation of the magnetic circuit alignment accuracy. The spatial offset vector is calculated, and a tolerance threshold is set to automatically determine whether the magnetic circuit alignment process is qualified, displaying the offset direction and magnitude on a graph. This solution simplifies the complex magnetic field distribution problem into a process qualification conclusion, reducing the difficulty of interpretation for quality inspectors. For abnormal products exceeding the threshold, the visualized offset information indicates the specific deviation direction in the production process, providing guidance for process adjustments and ensuring the consistency of the magnetic circuit center in mass production.

[0020] Thirdly, this application provides a magnetic attraction detection system for a wireless charging device, which adopts the following technical solution: A magnetic attraction detection system for a wireless charging device includes: The acquisition module is used to acquire the test modes set by the software system. The test modes include at least a single static distribution test mode and a multiple cycle durability test mode. During the motion, it acquires the normal separation force data and magnetic field strength data synchronously collected by the sensing and measurement system. A memory for storing a program for a magnetic attraction detection method for a wireless charging device as described above; The processor and the program in the memory can be loaded and executed by the processor to implement the magnetic detection method of the wireless charging device as described above.

[0021] By adopting the above technical solution, the testing method is solidified into a modular architecture combining hardware and software, achieving standardized deployment of testing capabilities. The acquisition module, memory, and processor work together to ensure the operation of test mode settings, synchronous acquisition of multi-source data, and algorithm processing, enabling non-professionals to operate high-precision testing equipment. The system inherits the advantages of multi-mode switching, synchronous force and magnetic analysis, and intelligent diagnostics, possessing scalability and compatibility to adapt to different production line environments and testing scales. Through programmed loading and execution, the consistency of test results across different batches and different operators is guaranteed, eliminating the uncertainty of human operation and providing a platform for enterprises to establish a standardized, digital magnetic performance quality control system.

[0022] Fourthly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described above.

[0023] Fifthly, this application provides a computer storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the methods described above.

[0024] In summary, this application includes at least one of the following beneficial technical effects: This application solves the problem of the separation between mechanical performance and magnetic field distribution data in traditional testing by integrating a high-precision tensile sensor and a Hall effect magnetic field sensor, along with a timestamp synchronization mechanism in the electronic control system. The software system defines two modes: "single static distribution" and "multiple cycle durability." These modes can reconstruct the magnetic field spatial distribution model through multi-dimensional scanning to evaluate the uniformity and alignment accuracy of the magnetic circuit design, and can also simulate repeated insertion and removal conditions in actual use to monitor long-term reliability. This upgrade in testing dimensions from "single-point static peak value" to "spatiotemporal dynamic evolution" provides multi-dimensional quantitative evidence for the R&D verification and production line quality control of wireless charging devices. This application not only records data but also effectively eliminates misjudgments caused by sensor drift or environmental interference by constructing a "force-magnetic coupling relationship index" and its stability coefficient, and incorporating the self-testing and calibration functions of the built-in data acquisition system. By matching the measured attenuation trend curve shape and displacement change law with a preset failure mode feature library, the system can automatically distinguish between "sudden structural failure" (Type I failure) and "gradual material degradation" (Type II failure), and automatically mark potential failure mechanisms (such as magnet breakage, adhesive layer debonding, or demagnetization). This transforms the detection process into an intelligent diagnostic process, shortening the fault analysis cycle and thus guiding process improvement and material selection. To address the high sensitivity of wireless charging to coil alignment, this application proposes a precise alignment evaluation method based on a reconstructed magnetic field model. By extracting the characteristic contours of the effective magnetic attraction boundary to calculate the measured geometric center, and comparing it with the theoretically designed center via vector, the spatial offset and direction are accurately calculated. If the deviation exceeds a preset tolerance threshold, the system not only visualizes the deviation information on the graph but also directly determines the process is unqualified and locks out defective products. This method simplifies the complex magnetic field distribution problem into an intuitive conclusion of process conformity, reducing the difficulty of interpretation for quality inspectors and realizing a transformation from "experience-based judgment" to "data-driven" quality control, ensuring the consistency of the magnetic circuit center and assembly yield in mass production. Attached Figure Description

[0025] Figure 1 This is a flowchart of a magnetic attraction detection method for a wireless charging device according to an embodiment of this application.

[0026] Figure 2 This is a flowchart that calls the corresponding data processing steps when the test mode is a multi-cycle durability test mode.

[0027] Figure 3 This is a flowchart for evaluating the durability level of a sample based on a dynamic change spectrum of magnetic attraction force.

[0028] Figure 4 It is a flowchart for constructing a standard sequence that reflects the true changing trend of magnetic attraction force.

[0029] Figure 5 This is a flowchart of the corresponding data processing steps when the test mode is a single static distribution detection mode.

[0030] Figure 6 This is a flowchart of the steps following the generation of the product's magnetic field distribution map.

[0031] Figure 7 This is a module diagram of a magnetic attraction detection system for a wireless charging device. Detailed Implementation

[0032] The present application will be further described in detail below with reference to the accompanying drawings.

[0033] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0034] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the appendices in the embodiments of this application will be described below. Figure 1-7The technical solutions in the embodiments of this application are clearly and completely described. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] This application discloses a magnetic attraction detection device for wireless charging devices. The device includes a base, a clamping assembly, a driving assembly, a sensing and measurement system, an electronic control system, and a software system. The base provides a support platform, the clamping assembly is positioned above the base, and the driving assembly is connected to the clamping assembly and drives its movement. The clamping assembly includes an adjustable clamp for fixing the sample to be tested and a magnetic sample stage for placing a magnetic target. The clamping assembly moves relative to the base via the driving assembly, thereby driving the magnetic target to move relative to the sample to be tested. The driving assembly is a multi-axis motion platform capable of driving the clamping assembly to perform controllable trajectory movement in a plane. The adjustable clamp can change the clamping distance via a lead screw mechanism or a cylinder to accommodate wireless charging device samples of different sizes.

[0036] The sensing and measurement system is used to detect the sample to be tested. The sensing and measurement system includes a high-precision tensile sensor for real-time monitoring of the normal separation force between the sample to be tested and the magnetic target, and a Hall effect magnetic field sensor for synchronously recording changes in magnetic field strength.

[0037] The electronic control system controls the movement of the drive components and collects data from the sensing and measurement system. The electronic control system is electrically connected to both the drive components and the sensing and measurement system. The software system communicates with the electronic control system and is used to display test data, plot test curves, and export test reports.

[0038] This application also discloses a magnetic attraction detection method for a wireless charging device, which is applied to the magnetic attraction detection device as described above.

[0039] Reference Figure 1 The magnetic attraction detection method for wireless charging devices includes: Step S100: Set the test mode through the software system. The test mode includes at least a single static distribution test mode and a multiple cycle durability test mode.

[0040] The software system refers to the human-computer interaction and data processing platform running on the host computer of the electronic control system. It has functions such as parameter configuration, motion planning, data visualization and report generation, and supports users to select specific test strategies according to their testing needs.

[0041] The single static distribution detection mode is a testing strategy that focuses on spatial dimension analysis. It can be used to obtain the distribution law of magnetic field strength in three-dimensional space and the peak distribution of normal separation force of the sample under test in a static or quasi-static scanning state, and is used to evaluate the uniformity and alignment accuracy of magnetic circuit design.

[0042] The multiple-cycle durability testing mode is a testing strategy that focuses on the time dimension and fatigue characteristics. It aims to simulate the dynamic process of repeated adsorption and separation of a product during its actual use life, monitor the attenuation trend of magnetic attraction as the number of cycles increases, and evaluate the long-term reliability and structural stability of the product.

[0043] The general process is as follows: The operator starts the software system and selects the test mode. If it is necessary to evaluate the rationality of the magnetic circuit design of the newly developed sample, the single static distribution detection mode is selected, and the scanning range, scanning step distance, and sampling frequency are set. If it is necessary to verify the durability of mass-produced samples, the multi-cycle durability test mode is selected, and the number of cycles, movement speed, and pause time are set. After receiving the instruction, the software system verifies the validity of the parameters. If the parameters exceed the equipment safety threshold, a warning pops up and a request for re-entry is made. If the parameters are valid, the corresponding motion control code sequence is generated and prepared to be sent to the electronic control system.

[0044] Step S101: Control the clamping assembly to drive the sample to be tested and the magnetic target to perform a motion trajectory that matches the test mode.

[0045] The clamping assembly includes an adjustable clamp for fixing the sample to be tested and a magnetic sample stage for placing a standard magnetic target. The two are respectively installed on the movable end and the fixed end of the drive assembly, forming a relative kinematic pair.

[0046] The motion trajectory refers to the spatial displacement path of the clamping component relative to the base under the drive of the driving component. In static distribution mode, it is usually a multi-dimensional raster scanning path or a discrete point stepping path, while in cyclic endurance mode, it is a motion path with a specific stroke.

[0047] The general process is as follows: The electronic control system parses the control code and sends signals to the drive component. In the single static distribution detection mode, the drive component moves the sample to be tested relative to a fixed magnetic target in a zigzag scanning motion, controlling the sample to move point by point in the XY plane with a set step distance. At each coordinate point, it pauses for a preset time to maintain stability, ensuring that the sensor readings are not affected by inertia. In the multi-cycle durability detection mode, the drive component moves the sample to be tested and the magnetic target in a high-speed reciprocating motion, controlling the sample to quickly approach from the separation position to the contact position, maintain adsorption for a preset time, and then quickly separate back to the initial position, repeating this cycle. During the motion execution, the grating ruler built into the drive component provides real-time feedback on the actual position information. If the deviation between the actual position and the theoretical trajectory exceeds the preset tolerance or the motor current exceeds the overload threshold, it indicates mechanical jamming. The electronic control system triggers an emergency stop command, stopping all motion and displaying a motion trajectory abnormality alarm on the software interface to prevent damage to the sample or sensor. If there are no abnormalities in the motion process, execution continues until the predetermined trajectory is completed.

[0048] Step S102: During the motion, the normal separation force data and magnetic field strength data are collected synchronously using the sensing measurement system.

[0049] The sensing and measurement system integrates a high-precision tension sensor and a triaxial Hall effect magnetic field sensor. Both are rigidly connected and fixed near the key stress points and magnetic circuit center of the clamping assembly, ensuring a high degree of spatial overlap. Synchronous acquisition refers to using a high-frequency timer in the electronic control system to simultaneously read the analog voltage signal from the tension sensor and the digital signal from the magnetic field sensor at the same timestamp, eliminating data misalignment caused by time delays.

[0050] The general process is as follows: When the clamping component starts to move, the electronic control system activates the data acquisition card. In the single static distribution detection mode, the system continuously acquires multiple sets of force and magnetic field data at each scanning dwell point. After removing the maximum and minimum values, the average is taken as the final data for that spatial point, forming a discrete position-force-magnetic dataset. In the multi-cycle durability detection mode, the system records data in a continuous flow mode, focusing on capturing the peak impact force at the moment of adsorption, the steady-state force during the holding phase, and the maximum separation force at the moment of separation. At the same time, the corresponding magnetic field strength change curve is recorded, forming a dynamic sequence data that changes over time. During the acquisition process, the system monitors the sensor signal quality in real time. If a drastic change in the signal is detected, such as the force value instantly dropping to zero or the magnetic field value exceeding the physical limit, it is determined that the sensor has poor contact or is subject to external strong magnetic interference. The system automatically marks this segment of data as invalid data and attempts to re-acquire it in the next cycle. If the data for multiple consecutive cycles is invalid, the test is paused and a prompt is made to check the sensor connection status. If the data is normal, the data packet with a timestamp is written to the memory in real time.

[0051] Step S103: According to the set test mode, call the corresponding data processing flow to analyze the collected data, generate feature maps and output the detection results.

[0052] The data processing flow refers to the set of algorithm modules pre-installed in the software system. For static mode, it includes spatial interpolation algorithm, contour line drawing algorithm and center point fitting algorithm. For durability mode, it includes noise reduction filtering algorithm, trend fitting algorithm and failure mode identification algorithm.

[0053] Feature maps refer to graphical results that visualize abstract data, including magnetic field distribution cloud maps, force-magnetic coupling curves, durability decay trend maps, etc.

[0054] The test results refer to the judgment on the compliance of the sample based on the preset standards, including specific quantitative indicators and the status of compliance or non-compliance.

[0055] The general process is as follows: When the test mode is a single static distribution detection mode, the system calls the spatial reconstruction algorithm to fit the discrete dataset into a continuous three-dimensional magnetic field distribution model, calculates the offset vector between the geometric center of the magnetic field distribution and the theoretical design center, and calculates the magnetic field uniformity within the effective adsorption area. If the test passes, i.e., the offset is less than or equal to the preset tolerance threshold and the uniformity is greater than or equal to the preset standard, the system generates a magnetic field distribution qualification report, highlights the effective area in green on the spectrum, automatically archives the data, and indicates that the sample's magnetic circuit alignment process is qualified and can proceed to the next process. If the test fails, i.e., the offset exceeds the standard or the uniformity is insufficient, the system generates a magnetic field distribution anomaly report, marks the area with the largest deviation in red on the spectrum, calculates the specific offset direction and coordinates of the non-uniformity points, and the system further calls the process diagnosis module to analyze possible causes such as misalignment of the magnetizing mold or deviation of the magnetic sheet size, and provides correction suggestions in the report, such as suggesting adjustment of the position of the magnetizing fixture positioning pin, while marking the sample as a rework or scrap product to prevent it from flowing into subsequent processes.

[0056] When the test mode is a multi-cycle durability test, the system denoises the dynamic sequence data, extracts the maximum separation force of each cycle to construct a cycle count minus suction force decay curve, calculates the performance degradation rate, and matches it with a preset failure mode feature library. If the test passes (i.e., the suction force decay rate after N cycles is less than or equal to a preset threshold and no abrupt failure is identified), the system generates a durability pass report, plots a smooth decay trend graph, marks the final remaining suction force percentage, determines that the sample meets long-term service life requirements, and automatically unlocks the production line release signal. If the test fails (i.e., the decay rate exceeds the standard or nonlinear abrupt changes and early fractures are identified), the system generates a durability failure report, extracts the specific cycle count node where the abrupt change occurs, replays the force-magnetic synchronization waveform at that node, and automatically determines the failure mechanism type, such as Type I failure (structural fracture of the magnet) or Type II failure (aging and debonding of the adhesive layer). The system clearly indicates the cycle interval in which the failure occurred in the report, and simultaneously triggers a root cause analysis process, linking current environmental temperature and humidity data and equipment operation logs to assist engineers in locating whether the problem is a material batch issue or a process curing issue, locking the batch of samples and prohibiting them from leaving the factory.

[0057] Reference Figure 2 When the test mode is a multi-cycle endurance test mode, the corresponding data processing steps include: In step S200, the control drive component drives the sample to be tested and the magnetic target to perform N repeated cyclic movements, and records the key mechanical feature values ​​in each cycle to form the original sequence dataset.

[0058] Key mechanical characteristic values ​​refer to representative mechanical indicators in a single adsorption-separation cycle, mainly including the peak value of maximum adsorption force, the average force value during the holding phase, the maximum normal separation force at the moment of separation, and the work area under the force-displacement curve.

[0059] The original sequence dataset refers to a collection of key mechanical characteristic values ​​mentioned above in N cycles, arranged in chronological order, and is usually stored in the form of an array or database table.

[0060] The general process is as follows: The electronic control system drives the clamping component to move according to the preset number of cycles N, such as 5000 or 10000 times. At specific phase points in each cycle, such as the contact end point or separation start point, the sensing and measurement system captures the force signal at a high-frequency sampling rate and extracts the maximum separation force F of that cycle through an extreme value search algorithm. max-i and average holding force F avg-i And set the corresponding loop number i and timestamp t iThe ambient temperature and humidity data are packaged and stored in a temporary buffer. As the loop continues, the buffer data accumulates to form an original sequence dataset containing N records. If a power outage or communication interruption occurs during recording, the system automatically saves the collected partial dataset and marks it as an "incomplete sequence." After recovery, the user is prompted to choose to continue adding data or restart the test. If the process is successful, a complete original sequence dataset is generated after N loops for subsequent processing.

[0061] Step S201: Perform denoising and standardization on the original sequence dataset, remove outlier data points, and construct a standard sequence that reflects the true trend of magnetic attraction.

[0062] Noise reduction refers to the use of digital filtering algorithms to eliminate high-frequency noise, mechanical vibration interference, and data spikes caused by electromagnetic pulses from sensors.

[0063] Standardization refers to converting data with different dimensions or different initial benchmarks to a unified comparable scale, eliminating systematic errors caused by sensor zero-point drift or minor differences in initial clamping.

[0064] Abnormal data points refer to erroneous data whose values ​​deviate significantly from statistical patterns due to external interference such as sudden impacts or voltage fluctuations.

[0065] A standard sequence refers to a clean data sequence that, after being cleaned and corrected, can objectively reflect the evolution of magnetic attraction performance with the number of cycles.

[0066] The general process is as follows: The software system reads the original sequence dataset and first smooths the force curve using a wavelet threshold denoising algorithm or a moving average filtering algorithm to filter out noise signals with frequencies higher than the mechanical response frequency band. Then, it calculates the mean and standard deviation of the dataset and uses the 3σ principle (mean plus or minus three times the standard deviation) to identify outliers. If a sudden change in force value in a cycle exceeds the physically possible range (e.g., a momentary drop to zero followed by immediate recovery), it is identified as an outlier and removed. For removed data points, if the preceding and following data are continuous and the changes are gradual, linear interpolation is used to fill in the missing data. If multiple outliers appear consecutively, the interval is marked as a "missing data area." Next, the data is standardized, using the force value of the first cycle as a benchmark to normalize all subsequent data, eliminating the influence of initial clamping preload fluctuations. If the proportion of valid data points is found to be lower than a preset threshold (e.g., 80%) during data processing, the test data is deemed unusable, a "data quality unqualified" message is output, and step S200 is requested to be re-executed. If the data quality meets the standard, the constructed standard sequence is output.

[0067] Step S202: Generate a decay trend curve representing the change of magnetic attraction force with the number of cycles based on the standard sequence, and extract characteristic parameters representing the performance degradation rate.

[0068] The decay trend curve is a continuous function curve plotted with the number of cycles as the horizontal axis and the normalized magnetic attraction force as the vertical axis. It visually demonstrates the dynamic process of product performance decreasing as the frequency of use increases.

[0069] The characteristic parameters of performance degradation rate refer to mathematical indicators used to quantify the rate and pattern of degradation, including but not limited to linear degradation slope k, exponential degradation coefficient λ, and inflection point position N. turn And the predicted remaining lifespan.

[0070] The general process is as follows: The system calls a least squares fitting algorithm or a nonlinear regression algorithm, such as a power function model or an exponential model, to perform curve fitting on discrete data points in the standard sequence, generating a smooth decay trend curve. Based on the fitted curve, the first derivative is calculated to obtain the instantaneous decay rate, and key feature parameters are extracted. For example, the average slope of the first 10% cycle interval is calculated as the "initial break-in decay rate," and the average slope of the last 10% cycle interval is calculated as the "steady-state decay rate." If the curve exhibits obvious piecewise characteristics, the inflection point is automatically identified and the corresponding cycle number is recorded. If the goodness of fit R² is lower than the preset value, it indicates that the data fluctuation is too large or does not conform to the conventional decay model. The system automatically switches to piecewise fitting mode or marks it as "atypical decay mode." If the fitting is successful, a set of feature parameters including slope, decay coefficient, and confidence interval is output.

[0071] Step S203: Generate a dynamic change map of magnetic attraction force based on the characteristic parameters.

[0072] The dynamic change spectrum of magnetic attraction force is a multi-dimensional visualization chart that includes a decay trend curve, overlaid with confidence interval bands, decay rate histograms, and key failure threshold lines, to comprehensively demonstrate the durability performance of a sample.

[0073] The general process is as follows: The software graphics engine receives the feature parameter set and standard sequence data. In the main plotting area, it draws a central trend line showing the normalized magnetic force changing with the number of cycles. Above and below, it draws semi-transparent confidence interval bands based on the standard deviation to display the data dispersion. Simultaneously, in the secondary plotting area, it generates a bar chart or line chart showing the decay rate changing with the number of cycles, visually displaying the intervals of accelerated or decelerated decay. A preset failure threshold line, such as 70% or 80% of the initial force value, is horizontally marked on the chart. If the trend curve intersects with the failure threshold line, the intersection coordinates are automatically marked and highlighted, representing the theoretical number of failure cycles. If the trend curve remains above the threshold line and the trend is stable, the plot area is rendered with a green background. If the trend curve crosses the threshold line or exhibits violent fluctuations, a red or yellow background is used as a warning. Finally, the multiple layers are combined into a complete dynamic change map of the magnetic force and cached in memory.

[0074] Step S204: Evaluate the durability level of the sample under test based on the dynamic change spectrum of magnetic attraction force.

[0075] Durability rating refers to the different quality levels into which the fatigue resistance performance of a sample is classified according to industry standards or company specifications, such as excellent, qualified, critical, and failure grades, which are used to guide the classification and warehousing or shipment determination of products.

[0076] The general process is as follows: The system compares the characteristic parameters with a preset level determination rule base. The rule base defines the quantitative thresholds for each level. For example, "Excellent" requires an attenuation rate of less than 5% after N cycles and no abrupt change points; "Acceptable" requires an attenuation rate of less than 15% and not reaching the failure threshold; "Critical" refers to an attenuation rate between 15% and 20% or just reaching the threshold; and "Failure" refers to an attenuation rate exceeding 20% ​​or premature fracture failure. If the evaluation result is Excellent or Acceptable, the system adds a "PASS" electronic stamp to the upper right corner of the graph, automatically generates a qualified report containing the specific level conclusion, and sends an instruction to the production line control system to allow the batch of samples to flow into the next process or be packaged and stored. If the evaluation result is Critical, the system generates a "Conditional Acceptance" report, marks items that require special attention, such as suggesting shortening the warranty period or strengthening sampling inspections, and pushes it to the quality engineer for manual review. If the assessment result is a failure level, the system generates a "FAIL" non-conformance report, circles the failure range in the spectrum, automatically locks the batch of samples and prohibits them from leaving the factory, triggers an alarm signal to notify the production line to stop and investigate, and calls the root cause analysis module to associate with the current process parameters, such as curing temperature and adhesive batch, to help locate the root cause of insufficient durability, thus forming a complete closed loop from data analysis to quality decision-making.

[0077] Reference Figure 3 The assessment of the durability level of a test sample based on the dynamic change spectrum of magnetic attraction force also includes: Step S300: Match the feature parameters characterizing the performance degradation rate with a preset library of multiple failure mode features.

[0078] The failure mode feature library is a pre-built digital fingerprint database containing various typical magnetic attraction failure mechanisms, such as brittle fracture of magnets, fatigue debonding of adhesive layers, irreversible demagnetization of magnetic domains, and plastic deformation of structural components. The library stores the feature parameter vectors corresponding to different failure modes, including the location of the abrupt change point of the decay slope, the variance of the decay rate, the eigenvalues ​​of higher-order derivatives, and the statistical distribution morphological parameters.

[0079] The general process is as follows: The system extracts performance degradation rate feature parameters, such as instantaneous slope sequence and rate of change of curvature, and converts them into standard feature vectors. Using a cosine similarity algorithm or Euclidean distance algorithm, this vector is compared with each standard template vector in the failure mode feature library, and a matching score is calculated. If the highest matching score exceeds a preset confidence threshold, the suspected failure mode type is initially identified. If all matching scores are below the threshold, it is determined to be an "unknown novel failure mode" or a "compound failure mode." The system will automatically mark the sample and prompt for manual intervention analysis, while recording the current feature vector to the database to expand the sample library. If a match is successful, a preliminary list of failure mode candidates and their corresponding confidence probabilities are output for further verification in subsequent steps.

[0080] Step S301: Analyze the morphological characteristics of the attenuation trend curve and the displacement change law corresponding to the key mechanical characteristic values.

[0081] Morphological characteristics refer to the geometric shape of the decay trend curve, such as linearity, concavity / convexity, number of inflection points, and oscillation frequency.

[0082] The displacement change law refers to the evolution of the mechanical displacement or stroke required to achieve the same target suction force during the cycle, reflecting the changes in the physical state of the air gap or contact interface in the magnetic circuit.

[0083] The general process is as follows: The system calls a morphological analysis algorithm to calculate the second derivative of the decay trend curve, identify the concave and convex inflection points of the curve, and determine whether the curve exhibits a smooth decline, a step-like drop, or a violent oscillation. Simultaneously, it retrieves the force-displacement (FD) curve data from the original data and analyzes the displacement value Z corresponding to the point of maximum separation force. max The trend of change with the number of cycles. If the decrease in magnetic attraction is due to the degradation of the magnet's own performance, the displacement value usually remains relatively stable or only has a slight elastic change. If the decrease in magnetic attraction is due to the increase in air gap caused by structural loosening, cracking of the adhesive layer, or wear, it is usually accompanied by a significant increase in displacement value or a change in the hysteresis loop area. The system performs spatiotemporal alignment correlation analysis on the morphological characteristics of the force curve and the changing law of the displacement curve to construct a "force-displacement coupling feature matrix". If a decoupling phenomenon of decreased force value but unchanged displacement is found, or a coupling phenomenon of decreased force value accompanied by a sharp increase in displacement is found, different feature labels are assigned respectively, providing multi-dimensional physical basis for the final determination of failure mode.

[0084] In step S302, if a nonlinear abrupt change in the decay rate is detected, accompanied by significant fluctuations in displacement characteristics, it is determined to be a type I failure mode.

[0085] Nonlinear abrupt changes refer to a rapid change in the decay rate within an extremely short cycle range, such as an instantaneous increase in slope several times, manifested as a vertical drop or a precipitous decline on the curve; significant fluctuations in displacement characteristics refer to an irreversible step increase or violent, irregular oscillation in the displacement required to achieve the same attractive force before and after the abrupt change. Type I failure modes typically correspond to sudden structural damage, such as brittle fracture of permanent magnets, instantaneous and complete detachment of the adhesive interface, or catastrophic failures like the collapse of snap-fit ​​structures.

[0086] The general process is as follows: The system monitors the coupling feature matrix in real time. When the algorithm detects that the absolute value of the first derivative of the decay rate exceeds the preset abrupt change threshold, for example, when the suction loss exceeds 5% between two adjacent cycles, and simultaneously detects the corresponding displacement feature value Z... max If a step change exceeding the preset tolerance or a sharp increase in standard deviation occurs at the same time point, the logic judgment module immediately triggers the first type of failure judgment logic, confirming that the sample has experienced structural fracture or interface peeling. The system then generates a diagnostic conclusion of "First Type Failure: Sudden Structural Damage" and marks the specific cycle number of the abrupt change with a red lightning bolt symbol on the dynamic change graph. Simultaneously, the current test process is frozen to prevent further movement that could cause complete sample pulverization or equipment damage. If the synchronous characteristics of the aforementioned nonlinear abrupt change and displacement fluctuation are not detected, the system proceeds to the next step to investigate the second type of failure mode.

[0087] In step S303, if the decay rate is detected to be linearly gradual and the displacement characteristics remain relatively stable, it is determined to be a second type of failure mode.

[0088] Linear gradual decay refers to a slow, approximately linear decrease in decay rate over a relatively long cycle period, with a relatively constant rate of change.

[0089] The displacement characteristics remain relatively stable, meaning that the displacement amount at maximum attraction changes very little throughout the entire attenuation process, indicating that the air gap structure and contact state of the magnetic circuit have not undergone significant physical changes.

[0090] Type II failure modes typically correspond to the gradual degradation of material properties, such as thermal demagnetization of magnets, irreversible flipping of magnetic domain structures, or weakening of magnetism due to the accumulation of micro-fatigue within the material, rather than the destruction of macroscopic structures.

[0091] The general process is as follows: After excluding the first type of failure mode, the system further analyzes the goodness of fit of the attenuation curve. If the goodness of fit R² between the data points and the linear regression line is higher than the preset value, and the calculated variance of the attenuation rate is low, and the displacement characteristic value Z is detected... maxIf the total change over the entire N cycles is less than the preset micro-motion threshold, the logic judgment module determines that the sample meets the "gradual performance degradation" characteristic, triggering the second type of failure judgment logic, confirming that the sample is mainly affected by the degradation of the intrinsic properties of the material. The system generates a diagnostic conclusion of "Second Type Failure: Gradual Degradation of Material Performance", and selects the entire decay range with a yellow dashed line on the dynamic change spectrum, marking the average decay slope, indicating that the failure belongs to normal lifespan wear or improper material selection. If the decay rate is neither nonlinear nor abrupt, or if the displacement characteristics show other complex changes such as stabilization followed by abrupt change, it is judged as a mixed failure mode or an undefined mode, requiring comprehensive judgment based on more sensor data.

[0092] Step S304: Automatically mark potential failure mechanism types based on the judgment results.

[0093] Failure mechanism type is a deeper explanation of the physicochemical causes behind the failure mode, such as "demagnetization caused by oxidation of NdFeB grain boundaries", "debonding caused by fatigue crack propagation of epoxy resin", and "magnet chipping caused by mechanical stress concentration".

[0094] Automatic labeling refers to the system retrieving the corresponding mechanism description text and code tags from the knowledge base based on the judgment conclusions of the previous steps, and writing them into the test report and sample digital file.

[0095] The general process is as follows: Based on the judgment result of step S302 or S303, the system executes the mapping logic. If it is determined to be a first-type failure mode, the specific mechanism type is further refined by combining the number of cycles and environmental data at the time of the mutation. For example, if the mutation occurs in the early cycle and is accompanied by huge displacement fluctuations, it is marked as "process defect: interface peeling caused by incomplete bonding and curing". If the mutation occurs in the late stage, it is marked as "fatigue accumulation: brittle fracture caused by long-term alternating stress of structural components". If it is determined to be a second-type failure mode, it is marked as "material aging: irreversible retreat of magnetic domains under high temperature and high humidity environment" or "intrinsic characteristics: local demagnetization caused by excessively low magnet operating point design" based on the magnitude of the decay rate. The system automatically fills the marked failure mechanism type into the "failure analysis" section of the test report and generates corresponding improvement suggestions, such as "suggestion to optimize adhesive formulation" and "suggestion to improve magnet coercivity index". At the same time, the marked data package is uploaded to the quality management system (QMS) for subsequent big data statistics and process iteration optimization. If the failure mode cannot be clearly determined in the preceding steps, it will be marked as "Pending manual review: Complex coupling failure" and the abnormal data segment will be highlighted for expert diagnosis.

[0096] Reference Figure 4 Constructing a standard sequence that reflects the true changing trend of magnetic attraction also includes: Step S400: Extract the synchronous magnetic field strength value at the moment when the key mechanical characteristic value is reached in each cycle.

[0097] The critical mechanical characteristic value moment specifically refers to the moment when the normal separation force F reaches its peak value in a single cycle of the waveform. max Or the precise time point t of the trough value peak .

[0098] Synchronous magnetic field strength value refers to the value at the same timestamp t. peak Below, the magnetic field vector magnitude value (Bsync) or specific axial component value is acquired by a triaxial Hall sensor. The core is to ensure strict alignment of force and magnetic data in the time domain, eliminating phase errors caused by asynchronous sampling or signal transmission delays.

[0099] The general process is as follows: The data processing module iterates through each loop record in the original sequence dataset. First, it uses an extreme value search algorithm to locate the peak point of the normal separation force curve in the i-th loop and records the corresponding timestamp t. i-peak and force value F i-max Then immediately index the same timestamp t in the magnetic field strength data stream. i-peak If there is a slight deviation in the sampling clock, a linear interpolation method is used to obtain the estimated magnetic field value at that moment, and the corresponding magnetic field strength B is read. isync , will F i-max B i-sync The data is bound into a "force-magnetic coupling data pair". As the number of iterations N progresses, the system sequentially extracts and stores N such data pairs, forming the original force-magnetic synchronization sequence. If, during the extraction process, it is found that the force peak moment of a certain iteration is missing the corresponding magnetic field data, that iteration is marked as "incomplete data", and an attempt is made to interpolate and complete the data using the magnetic field data from adjacent moments. If the data cannot be completed, that point is temporarily discarded for further processing.

[0100] Step S401: Construct the force-magnetic coupling relationship index and calculate the stability coefficient of the force-magnetic coupling relationship index in N cycles.

[0101] Force-magnetic coupling index η i Defined as the ratio of the maximum separation force to the synchronous magnetic field strength in the i-th cycle, η i =F i-max / B i-sync Or, based on the coupling coefficient fitted by the physical model, the index is used to reflect the actual mechanical adsorption efficiency generated by the sample under a specific magnetic field excitation, and theoretically should remain relatively constant when the sample structure is stable and the sensor is drift-free.

[0102] The stability coefficient S is a statistic used to quantify the degree of fluctuation of the indicator over N cycles. It is usually expressed as the coefficient of variation (CV), which is the ratio of the standard deviation to the mean, the variance of the sliding window, or the slope of the trend.

[0103] The general process is as follows: Based on N "force-magnetic coupling data pairs", the system calculates the coupling relationship index η for each cycle in turn. i This forms the index sequence {1,2,...,}{η1,η2,...,η N Next, calculate the arithmetic mean of the sequence. and standard deviation σ η The stability coefficient S = σ is obtained. η / This refers to the coefficient of variation. The system can also use the least squares method to perform linear fitting on the index sequence and calculate the slope k. η To assess whether there is a monotonic trend over time, if the S value is extremely small and k η A value close to zero indicates a highly stable relationship between force and magnetism, suggesting high reliability of the data acquisition; if the S value is large or k... η If the value is significantly non-zero, it indicates a deviation in the relationship between the two, and the system will record the specific S value and k. η The value serves as the basis for subsequent judgments.

[0104] In step S402, if the stability coefficient exceeds the preset allowable fluctuation range, it is determined that the data acquisition system has drift or the magnetic circuit of the sample under test has nonlinear anomalies.

[0105] The preset allowable fluctuation range is an empirical threshold range derived from the sensor accuracy level, sample material characteristics, and historical test data. For example, the coefficient of variation S should be less than 1% or the absolute value of the trend slope should be less than a certain small value.

[0106] Data acquisition system drift refers to systematic errors caused by zero-point drift of the tension sensor, gain drift, or changes in the sensitivity of the magnetic field sensor due to temperature / time.

[0107] The presence of nonlinear anomalies in the magnetic circuit of the sample under test refers to the propagation of microcracks inside the sample, irreversible flipping of magnetic domains, or local debonding of the adhesive layer, which leads to unexpected abrupt or gradual changes in the force-magnetic conversion efficiency, disrupting the original linear or quasi-linear coupling relationship.

[0108] The general process is as follows: The system will use the stability coefficient S and the trend slope k η Compared with the threshold S preset in the software configuration limit and k limit Compare them; if S>S limit or |k η |>k limitThe logic judgment module immediately triggers the anomaly judgment mechanism. At this time, the system further analyzes the distribution pattern of the indicator sequence: if the indicator sequence shows an overall unidirectional monotonic drift, such as continuous increase or decrease, and all samples show a similar trend, it is preferentially judged as "data acquisition system drift", such as sensor temperature drift; if the indicator sequence shows irregular violent oscillation, or only changes abruptly after a specific number of cycles, and other samples in the same batch are normal, it is judged as "magnetic circuit nonlinearity anomaly of the sample under test", such as internal structural damage. If the judgment result is unclear, the system will mark it as "suspected composite anomaly" and retrieve real-time ambient temperature data to assist in judging whether the sensor drift is caused by temperature.

[0109] Step S403: Mark the data of the current loop or trigger the recalibration process.

[0110] Labeling refers to assigning specific tags to outlier data points in a dataset, such as "sensor drift effect", "sample structural anomaly", or "data questionable", so that they can be weighted or removed when generating standard sequences later.

[0111] The recalibration process refers to pausing the current test and performing operations such as sensor zero-point reset, gain correction, or introducing standard weights / standard magnetic sources for online calibration.

[0112] The general process is as follows: Based on the judgment result of step S402, if it is determined that "the magnetic circuit nonlinearity of the sample under test is abnormal", the system does not adjust the equipment, but directly marks the affected data segment of this cycle and subsequent cycles in the original dataset as "sample abnormal feature area", retains the original data for failure analysis, but when constructing the final standard sequence, a robust filtering algorithm is used to reduce the weight of these abnormal points to prevent them from distorting the overall decay trend, and the report specifically notes that "abnormal force-magnetic coupling relationship was detected, indicating that there may be hidden damage inside the sample". If the system determines that the data acquisition system is drifting, it immediately pauses the test and displays a "Sensor Drift Warning" dialog box, automatically triggering a recalibration process: the control drive component moves the sample to a safe position, unloads all loads, reads the zero point of the tensile sensor and automatically corrects it. If an online calibration module is provided, the system controls the calibration mechanism to apply a standard force value or move a standard magnetic field source to recalibrate the sensor gain coefficient. After calibration, the system asks the user whether to discard the data before calibration or perform data compensation correction. If the user chooses compensation, the system performs reverse correction on the historical data based on the drift curve. If the calibration fails or the drift exceeds the hardware's allowable limit, the test is terminated and a "Replace Sensor" message is displayed, ensuring that the final constructed standard sequence truly reflects the sample's performance changes rather than measurement errors.

[0113] Reference Figure 5 When the test mode is a single static distribution detection mode, the corresponding data processing steps include: In step S500, the control drive component drives the sample to be tested to perform multi-dimensional scanning motion or discrete point stepping motion relative to the magnetic target.

[0114] Multidimensional scanning motion refers to continuous or quasi-continuous movement in three-dimensional space along the X, Y, and Z axes, following a preset grating path, spiral path, or custom trajectory, thereby covering the entire space of the area to be measured.

[0115] Discrete point step motion refers to an intermittent motion method in which the driving component moves the sample to a preset coordinate grid point, stops and stabilizes at each point for measurement, and then moves to the next point.

[0116] The general process is as follows: The software system generates a detailed sequence of motion control commands based on the user-defined scanning range, such as a ±20mm area with the center of the magnetic target as the origin, the scanning step distance, and the scanning height. Upon receiving the commands, the electronic control system drives a high-precision servo motor, causing the sample to be tested on the clamping assembly to move relative to the fixed magnetic target. If a discrete point stepping mode is used, the sample quickly moves to the first coordinate point (x1, y1, z1), then enters a "stabilization waiting period," typically 0.5 to 2 seconds, to eliminate mechanical vibration and inertial effects. After confirming the position is locked and the speed is zero, the acquisition signal is triggered. After acquisition, the sample quickly moves to the next coordinate point, repeating this cycle until all preset grid points have been traversed. If a continuous scanning mode is used, the sample moves along the grating path at a constant low speed. The system synchronously records the position information and sensor readings with high-frequency timestamps, and later reconstructs the grid data through position interpolation. If, during the movement, the actual trajectory deviates from the theoretical path beyond the tolerance or a collision risk occurs, the system immediately stops and reports an error. If the movement is successfully completed, the data acquisition and processing stage begins.

[0117] Step S501: At each sampling point along the motion path, collect the three-dimensional magnetic field components and the composite magnetic field intensity to form a discrete magnetic field dataset.

[0118] The three-dimensional magnetic field components refer to the magnetic field strength values ​​B measured by the triaxial Hall sensor in the three orthogonal directions of X, Y, and Z. x B y B z .

[0119] Synthetic magnetic field strength B total According to the vector composition formula The calculated scalar value.

[0120] Discrete magnetic field datasets refer to datasets containing the spatial coordinates (x, y) of each sampling point. i ,y i ,z i ) and the set of corresponding magnetic field vector data.

[0121] The general process is as follows: Once the drive components reach each sampling point and stabilize, the sensing and measurement system immediately initiates a high-speed acquisition program, simultaneously reading data from the tension sensor and the triaxial magnetic field sensor. It performs multiple rapid samplings of the magnetic field signal along each axis, such as acquiring data 10 times consecutively and averaging the results to suppress random noise. Simultaneously, it records the current precise spatial coordinates, calculates the synthetic magnetic field strength at that point, and packages the coordinates and magnetic field data into a single data record. As the scanning motion progresses, the system continuously accumulates these records, ultimately forming a discrete magnetic field dataset covering the entire scanning area. If a magnetic field value exceeding the sensor's range or an abnormal jump is detected at a certain point, the system automatically marks that point as "invalid data" and can choose to perform a second retest at that point or skip it directly, depending on the settings, ensuring the integrity and reliability of the dataset.

[0122] Step S502: Based on the discrete magnetic field dataset, reconstruct a continuous spatial distribution model of magnetic field intensity.

[0123] Reconstruction refers to the use of mathematical interpolation or fitting algorithms to transform discrete grid point data into a continuous spatial function, thereby enabling the prediction of magnetic field strength at any location.

[0124] Spatial distribution models are typically represented as a three-dimensional scalar field B(x,y,z) or a two-dimensional distribution map on a specific plane.

[0125] The general process is as follows: The data processing module reads the discrete magnetic field dataset and first checks the sparsity and uniformity of the data distribution. If there are a few missing points, the nearest neighbor weighted average method is used to fill them in. Then, a suitable spatial interpolation algorithm is selected, such as bicubic spline interpolation, Kriging interpolation, or radial basis function (RBF) interpolation. Based on the known magnetic field strength values ​​of the grid points, a continuous magnetic field distribution function is constructed for the entire scanning area. This function can smoothly connect the discrete points, eliminate the sawtooth effect caused by measurement noise, and generate a high-resolution magnetic field strength cloud map data matrix. For three-dimensional scanning data, the system can also construct a voxel model to show the attenuation law of the magnetic field in the spatial depth direction. After reconstruction, the system will verify the smoothness and fidelity of the model to ensure that the position of the extreme point after reconstruction does not deviate from the original data within the allowable error, thereby obtaining a high-precision continuous magnetic field strength spatial distribution model.

[0126] Step S503: Based on the spatial distribution model of magnetic field strength, calculate the geometric center of magnetic field distribution, the effective adsorption area range, and the uniformity index.

[0127] In this embodiment, the geometric center of the magnetic field distribution is defined as the effective adsorption region, that is, the geometric centroid of the region where the magnetic field strength is greater than a preset threshold. The effective adsorption region range refers to the area or volume of a continuous region where the magnetic field strength is greater than the preset threshold, such as the minimum field strength B that can generate sufficient adsorption force.threshold The uniformity index is used to quantify the consistency of the magnetic field distribution within a region, and is typically defined as (B... max -B min ) / (B max +B min (or the ratio of standard deviation to mean)

[0128] The general process is as follows: The system traverses the reconstructed spatial distribution model data matrix, first searching for the global maximum point or its neighborhood high-value region, and calculating the centroid coordinates of this high-value region as the geometric center of the magnetic field distribution (X). c ,Y c ), and correlate this coordinate with the design theory center (X). design ,Y design The alignment is compared, and the offset vector Δd is calculated to evaluate the alignment accuracy. Then, a magnetic field strength threshold B is set. threshold Extract all points exceeding the threshold, calculate the area or projected range of their connected regions to determine the effective adsorption region, and check whether this region completely covers the required charging coil range. Subsequently, statistically analyze the maximum magnetic field strength B within the effective adsorption region. max_eff Minimum value B min_eff and average value B avg The uniformity index is calculated by substituting the values ​​into the formula. The smaller the uniformity value, the more uniform the magnetic field distribution. If the calculated offset Δd exceeds the preset tolerance or the uniformity index fails to meet the standard, the system will record the specific deviation value and the coordinates of the non-uniform area to provide a quantitative basis for subsequent process adjustments.

[0129] Step S504: Generate the magnetic field distribution map of the product.

[0130] The product magnetic field distribution map is a visual report that intuitively displays the above analysis results. It usually includes a two-dimensional contour map, a three-dimensional surface map, a heat map, and a data panel of key indicators.

[0131] The general process is as follows: The graphics rendering engine calls the continuous model data generated in step S502 and the key indicators calculated in step S503 to draw various forms of graphs: First, a two-dimensional planar heat map is generated, using different colors to represent the distribution of magnetic field strength, and contour lines are superimposed on the map to clearly show the gradient changes. At the same time, the calculated magnetic field distribution geometric center crosshair and the effective adsorption area boundary box are marked on the map. Then, a three-dimensional surface map is generated to three-dimensionally display the fluctuation shape of the magnetic field strength and intuitively present the sharpness or flatness of the magnetic poles. Finally, a data panel is attached next to the graph, listing the specific values ​​of geometric center coordinates, center offset, effective area, maximum / minimum / average magnetic field strength, and uniformity index, and judging the passability of each index according to preset standards. If the center offset is too large, the graph will use an arrow to indicate the offset direction. If the uniformity is insufficient, the abnormal areas of low or high field strength will be circled with dashed lines. Finally, all the graphs and data are integrated into a complete PDF or image format test report, which can be printed and archived or uploaded to the quality management system to complete the entire process of a single static distribution test.

[0132] Reference Figure 6 The steps following the generation of the product's magnetic field distribution map also include: Step S600: Extract the feature contours representing the effective magnetic attraction boundary from the reconstructed spatial distribution model of magnetic field strength.

[0133] The effective magnetic attraction boundary refers to the magnetic field strength value in the spatial distribution model of magnetic field strength that is equal to or slightly higher than the system-defined "minimum effective adsorption threshold" B. threshold The contour lines of a 2D plane or the isosurfaces of a 3D space define the physical range within which the sample can generate sufficient suction to maintain a stable connection or charging function.

[0134] Feature profiles refer to closed curves or polygonal boundaries that have been smoothed and topology optimized, removing minute burrs or discontinuous segments caused by measurement noise.

[0135] The general process is as follows: The data processing module reads the spatial distribution model of the continuous magnetic field intensity generated in step S502, and first sets the minimum effective adsorption threshold B according to the specifications of the product to be tested or industry standards. thresholdFor example, in wireless charging applications, this value might be set as the minimum field strength to ensure energy transfer efficiency, such as 20mT. Using contour extraction algorithms, such as MarchingSquares for 2D slicing or MarchingCubes for 3D volume data, the system searches for all points in the model with a magnetic field strength equal to Bthreshold, connecting these discrete points to form an initial closed contour. Then, spline curve fitting or the Douglas-Puk simplification algorithm is applied to smooth the initial contour, eliminating jagged fluctuations caused by high-frequency sensor noise, resulting in a smooth, continuous feature contour with a complete topological structure. If a break or multiple nesting is detected in the contour, indicating an abnormally complex magnetic field distribution, the system automatically attempts to repair the break or extract the outermost main contour as the final effective magnetic attraction boundary, storing the coordinate sequence of this contour for subsequent calculations.

[0136] Step S601: Calculate the geometric center point of the magnetic field distribution of the feature profile.

[0137] The geometric center of the magnetic field distribution refers to the centroid coordinates (Xgeo, Ygeo) calculated by mathematical integration or the centroid formula within the geometric region enclosed by the feature contour extracted in step S600, representing the physical center of the actual effective magnetic field region. In this embodiment, to improve the accuracy of the assessment of the alignment precision of the effective adsorption region, the centroid based on the feature contour is used as the geometric center of the magnetic field distribution; in other embodiments, the center of the region with the highest magnetic field strength can also be directly selected as the geometric center.

[0138] The general process is as follows: The system treats the feature contour as a closed planar polygon or a polygon on a 3D projection plane, calls the centroid calculation algorithm, divides the internal region of the contour into several micro-element triangles or trapezoids, calculates the area of ​​each micro-element and its local centroid coordinates, and then uses the weighted average formula X... geo =∑(A i ⋅x i ) / ∑A i and Y geo =∑(A i ⋅y i ) / ∑A i ∑ represents the product of the corresponding elements (i=1 to n) (A i ⋅x i Or A i ⋅y i Add them up, then divide by the total area of ​​all infinitesimal elements ∑A i This yields the geometric centroid coordinates of the entire feature contour region. Here, i is the element index, n is the total number of elements, and A... i Let x be the area of ​​the i-th infinitesimal element, (x i ,y i) represents the centroid coordinates of the i-th infinitesimal element.

[0139] The geometric center coordinates of the entire effective adsorption area are obtained by integration. If the feature contour is irregular or has voids, such as a ring magnet, the algorithm will automatically process the topology to ensure that the calculated center point accurately reflects the overall distribution center of the effective magnetic adsorption area. After the calculation is completed, the system marks the coordinate point as the "measured magnetic circuit center" and records the position information relative to the origin of the scanning coordinate system.

[0140] Step S602: Obtain the preset theoretical design center point of the sample to be tested.

[0141] Theoretical design center point (X) design ,Y design ) refers to the ideal installation position coordinates of a magnet or coil as defined by the product's CAD drawings, magnetic circuit simulation model, or process specifications. These coordinates are usually fixed values ​​determined relative to the sample's mechanical reference (such as locating pin holes, edge chamfers, or the center of the outer contour).

[0142] The general process is as follows: The system reads the theoretical design parameters of the sample under test from the product configuration database of the current test task or the uploaded CAD file, and parses the theoretical offset of the magnetic circuit component relative to the mechanical reference. Combined with the mechanical coordinate system calibration data of the scanning motion starting point in step S500, the theoretical design position is transformed to the same absolute scanning coordinate system as in step S601 to obtain the coordinates of the theoretical design center point (Xdesign, Ydesign). If the product configuration file is missing or the coordinate definition is ambiguous, the system prompts the operator to manually input the theoretical center coordinates or automatically calculate them after identifying the mechanical reference of the sample through the vision-assisted system, ensuring that the measured center and the theoretical center are in the same reference system for accurate spatial comparison.

[0143] Step S603: Calculate the spatial offset vector between the geometric center point of the magnetic field distribution and the theoretical design center point to obtain the offset amount and offset direction.

[0144] Spatial offset vector It is a two-dimensional vector whose starting point is the theoretical design center point and whose ending point is the geometric center point of the measured magnetic field distribution. It intuitively describes the deviation of the actual position of the magnetic circuit from the design position.

[0145] The offset D is the magnitude of the vector (i.e., the Euclidean distance), and the offset direction θ is the angle between the vector and the reference axis (such as the X-axis).

[0146] The general process is as follows: The system performs vector subtraction to calculate the offset vector. We obtain the X-axis component ΔX and the Y-axis component ΔY, and then use the Pythagorean theorem to calculate the offset. , and use the arctangent function to calculate the offset direction θ = arctan2(ΔY, ΔX), convert the angle to degrees relative to the positive direction of the sample (such as "30 degrees to the upper right"), and the system records the detailed information of this vector at the same time, including the specific millimeter-level offset value and the angle direction. If the offset D is extremely small (close to zero), it indicates that the magnetic circuit alignment is precise; if D is large, it indicates that there are significant assembly errors, magnetization positioning deviations or mold wear problems, and these data serve as the core basis for determining whether the process is qualified.

[0147] Step S604: Compare the offset with the preset alignment tolerance threshold. If it exceeds the threshold, visually display the offset information on the magnetic field distribution map of the product, and determine that the magnetic circuit alignment process is unqualified.

[0148] Alignment tolerance threshold T limit is the maximum allowable offset distance preset according to the product function requirements (such as the sensitivity of wireless charging efficiency to coil alignment) and the assembly process capabilities.

[0149] Visual display means highlighting the deviation graphically on the map, such as drawing offset arrows, highlighting the deviation area or adding warning color blocks.

[0150] The general process is as follows: The system compares the offset D with the alignment tolerance threshold T preset in the system limit for logical comparison. If D ≤ T limit , determine that the magnetic circuit alignment process is qualified. The system slightly marks the coincidence of the center points with a thin green line on the map and marks "PASS" in the report; if D > T limit , the system immediately determines that the magnetic circuit alignment process is unqualified, triggers the "FAIL" state, and performs enhanced visualization processing on the generated magnetic field distribution map of the product: First, use a prominent red arrow on the map to point from the theoretical center point to the measured center point, and the arrow length is enlarged proportionally for easy observation, and the specific offset value (such as "Offset: 1.2mm") and the direction angle are marked beside it; then use a red dashed line box to circle the effective magnetic adsorption boundary, and use a semi-transparent red shadow to cover the non-coincident part between the theoretical effective area and the actual effective area, visually showing the loss of effective adsorption area caused by the alignment deviation; finally, automatically generate a warning message of "Magnetic circuit alignment exceeds the standard" in the conclusion column of the report, suggesting checking the accuracy of the magnetization fixture, the wear condition of the assembly positioning pins or the magnet bonding process, and automatically archiving the test data of this sample to the "non-conforming product library" to prevent it from flowing into the next process, realizing strict closed-loop control of the magnetic circuit alignment quality.

[0151] Refer to Figure 7 , based on the same inventive concept, an embodiment of the present application provides a magnetic adsorption detection system for a wireless charging device, including: The acquisition module is used to acquire the test modes set by the software system. The test modes include at least a single static distribution test mode and a multiple cycle durability test mode. During the motion, it acquires the normal separation force data and magnetic field strength data synchronously collected by the sensing and measurement system. A memory for storing programs for the magnetic attraction detection method of wireless charging devices as described above; The processor and memory programs can be loaded and executed by the processor to implement the magnetic detection method of the wireless charging device described above.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed to perform a magnetic attraction detection method for a wireless charging device.

[0154] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0155] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute a magnetic attraction detection method for a wireless charging device.

[0156] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0157] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A magnetic attraction detection device for a wireless charging device, characterized in that, The system includes a base for providing a support platform, a clamping assembly disposed on the base, a drive assembly connected to the clamping assembly and used to drive the clamping assembly to move, a sensing and measurement system for detecting the sample to be tested, an electronic control system electrically connected to the drive assembly and the sensing and measurement system respectively and used to control the movement of the drive assembly and collect data from the sensing and measurement system, and a software system communicatively connected to the electronic control system and used to display test data, plot test curves, and export test reports. The clamping assembly includes an adjustable clamp for fixing the sample to be tested and a magnetic sample stage for placing a magnetic target. The clamping assembly moves relative to the base via the driving assembly, thereby driving the magnetic target to move relative to the sample to be tested. The sensing and measurement system includes a high-precision tensile sensor for real-time monitoring of the normal separation force between the sample and the magnetic target, and a Hall effect magnetic field sensor for synchronously recording changes in magnetic field strength.

2. A magnetic attraction detection method for a wireless charging device, characterized in that, The magnetic attraction detection device as described in claim 1 includes: The test mode is set through the software system, which includes at least a single static distribution test mode and a multiple cycle durability test mode. The control clamping components drive the sample under test and the magnetic target to execute a motion trajectory that matches the test mode; During the motion, the normal separation force data and magnetic field strength data are collected simultaneously using a sensing and measurement system; Based on the set test mode, the corresponding data processing flow is invoked to analyze the collected data, generate feature maps, and output the detection results.

3. The magnetic attraction detection method for a wireless charging device according to claim 2, characterized in that, When the test mode is a multi-cycle endurance test mode, the corresponding data processing steps include: The control drive component drives the sample under test to perform N repeated cyclic movements with the magnetic target, and records the key mechanical feature values ​​in each cycle to form the original sequence dataset; Denoising and standardization were performed on the original sequence dataset to remove outlier data points and construct a standard sequence that reflects the true trend of magnetic attraction force. Generate a decay trend curve characterizing the magnetic attraction force as a function of the number of cycles based on the standard sequence, and extract characteristic parameters characterizing the performance degradation rate. Generate a dynamic change map of magnetic attraction force based on characteristic parameters; The durability level of the sample under test is evaluated based on the dynamic change spectrum of magnetic attraction force.

4. The magnetic attraction detection method for a wireless charging device according to claim 3, characterized in that, The assessment of the durability level of a test sample based on the dynamic change spectrum of magnetic attraction force also includes: The characteristic parameters representing the rate of performance degradation are matched with a pre-defined feature library of multiple failure modes. Analyze the morphological characteristics of the attenuation trend curve and the displacement variation law corresponding to the key mechanical characteristic values; If a nonlinear abrupt change in the decay rate is detected, accompanied by significant fluctuations in displacement characteristics, it is determined to be a Type I failure mode. If the decay rate is detected to be linearly gradual and the displacement characteristics remain relatively stable, it is determined to be a type II failure mode. The system automatically labels potential failure mechanism types based on the judgment results.

5. The magnetic attraction detection method for a wireless charging device according to claim 3, characterized in that, Constructing a standard sequence that reflects the true changing trend of magnetic attraction also includes: Extract the synchronous magnetic field strength value at the moment when the key mechanical characteristic value is reached in each cycle; Construct a force-magnetic coupling index and calculate the stability coefficient of the force-magnetic coupling index in N cycles; If the stability coefficient exceeds the preset allowable fluctuation range, it is determined that the data acquisition system has drift or the magnetic circuit of the sample under test has nonlinear anomalies. Mark the data in the current loop or trigger a recalibration process.

6. The magnetic attraction detection method for a wireless charging device according to claim 2, characterized in that, When the test mode is a single static distribution detection mode, the corresponding data processing steps include: The control drive component drives the sample under test to perform multi-dimensional scanning motion or discrete point stepping motion relative to the magnetic target; At each sampling point along the motion path, the three-dimensional magnetic field components and the composite magnetic field intensity are collected to form a discrete magnetic field dataset. Based on discrete magnetic field datasets, a continuous spatial distribution model of magnetic field intensity is reconstructed. Based on the spatial distribution model of magnetic field intensity, the geometric center of magnetic field distribution, the effective adsorption area range and uniformity index are calculated. Generate a magnetic field distribution map of the product.

7. The magnetic attraction detection method for a wireless charging device according to claim 6, characterized in that, The steps following the generation of the product's magnetic field distribution map also include: Extract the feature contours representing the effective magnetic attraction boundary from the reconstructed spatial distribution model of magnetic field strength; Calculate the geometric center point of the magnetic field distribution of the feature profile; Obtain the theoretical design center point of the sample to be tested; Calculate the spatial offset vector between the geometric center point of the magnetic field distribution and the theoretical design center point to obtain the offset amount and offset direction; The offset is compared with the preset alignment tolerance threshold. If it exceeds the threshold, the offset information is visualized on the product magnetic field distribution map, and the magnetic circuit alignment process is determined to be unqualified.

8. A magnetic attraction detection system for a wireless charging device, characterized in that, include: The acquisition module is used to acquire the test modes set by the software system. The test modes include at least a single static distribution test mode and a multiple cycle durability test mode. During the motion, it acquires the normal separation force data and magnetic field strength data synchronously collected by the sensing and measurement system. A memory for storing a program for the magnetic attraction detection method of the wireless charging device as described in any one of claims 2 to 7; The processor and the program in the memory can be loaded and executed by the processor to implement the magnetic attraction detection method of the wireless charging device as described in any one of claims 2 to 7.

9. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed according to any one of claims 2 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed according to any one of claims 2 to 7.

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