Modularized magnetic attraction calibration jig and calibration method

Abstract

The invention relates to a modularized magnetic attraction calibration jig and a calibration method, and the method comprises the steps: building a module combination library, and carrying out the feature matching with the module combination library when a new module combination is selected; when the integrating degree is lower than a matching critical value, marking as an irregular module combination, extracting key features and storing the key features in a module combination library; taking the key features as input parameters, executing magnetic line simulation and generating a thermodynamic diagram, analyzing the distribution features of the thermodynamic diagram through a learning model, and predicting a magnetic pole activation scheme; a vibration isolation layer is embedded in the irregular module combination contact surface, a temperature sensor array is preset to monitor the temperature of a thermal deformation sensitive area in real time, and a temperature distribution data set of the contact surface is generated; and based on the temperature distribution data set, simulating electromagnetic field distribution and structural stress of the new module combination, predicting safe operation parameters, and cooperating with the corrected magnetic pole activation scheme to generate a control strategy.

Description

TECHNICAL FIELD

[0001] The application relates to the field of precision detection, and in particular to a modular magnetic calibration jig and a calibration method. BACKGROUND

[0002] In the field of industrial automation, intelligent devices and precision detection, sensor calibration as a key link to ensure data accuracy has long been limited by the inherent defects of traditional fixed structure jigs: poor flexibility, which requires custom jigs for different sensors or calibration scenarios, resulting in high one-time adaptation cost and serious downtime loss during line switching; complex operation, which relies on mechanical fixing methods such as screws and buckles, and requires complex steps such as manual alignment of positioning pin holes, tightening of bolts one by one, and manual calibration of levelness, resulting in repeated disassembly of industrial robots for multi-sensor calibration, high time consumption for single switching; low efficiency, which requires sequential execution of processes such as disassembly of the current module, installation of a new module, initialization of communication, and manual verification of parameter matching, and mechanical loosening in a vibrating environment leads to calibration data drift, and existing solutions cannot break through the technical bottleneck of balancing calibration efficiency and accuracy, making it difficult to meet dynamic needs. SUMMARY

[0003] The application provides a modular magnetic calibration jig and a matching rapid switching method to solve the technical problems in the prior art. Through modular design, magnetic attraction connection and automatic switching logic, the calibration efficiency and adaptability are significantly improved.

[0004] The technical solution of the application to solve the above technical problems is as follows: a modular magnetic calibration method, comprising: S1: establishing a module combination library, selecting a new module combination, and performing feature matching with the module combination library, obtaining a matching degree according to the feature matching result; when the matching degree is lower than a matching threshold, marking the irregular module combination, extracting key features and saving them to the module combination library; S2: taking the key features as input parameters, performing magnetic field simulation and generating a heat map, analyzing the distribution characteristics of the heat map through a learning model, and predicting a magnetic pole activation scheme; S3: embedding a vibration isolation layer in the contact surface of the irregular module combination, and preinstalling a temperature sensor array to monitor the temperature of the thermal deformation sensitive area in real time, generating a temperature distribution data set of the contact surface; calculating the thermal expansion difference rate based on the module combination library, compensating for the combination deformation error according to the thermal expansion difference rate, and correcting the magnetic pole activation scheme according to the compensation result; S4: based on the temperature distribution data set, simulating the electromagnetic field distribution and structural stress of the new module combination, predicting the safe operation parameters and the corrected magnetic pole activation scheme to generate a control strategy.

[0005] Further, the module combination library establishes a unique electronic file for each module, including physical parameters, material properties and functional identification, and records historical operation data and system monitoring data; a combination evaluation matrix is established according to the module combination library; and the module combination fitness is calculated based on the combination evaluation matrix.

[0006] Further, the fitness is a quantitative index obtained by matching calculation of the multi-dimensional characteristics stored in the module combination library, and the module combinations are divided into high-fitness module combinations, optimizable module combinations and irregular module combinations according to the quantitative index; The matching critical value is a dynamic calculation grading threshold, and when the fitness quantitative index of the module combination is less than the matching critical value, the module combination is marked as an irregular module combination.

[0007] Further, the heat map step includes: establishing a magnetic circuit topology model based on the geometric configuration of the irregular module combination and the electromagnet parameters; calculating the magnetic flux density value of each coordinate point of the module contact surface through the magnetic circuit topology model, and forming the magnetic flux density distribution data of the contact surface according to the magnetic flux density value of each coordinate point; and generating a heat map and marking the magnetic field abnormal features according to the magnetic flux density distribution data.

[0008] Further, the heat deformation sensitive area includes: a module interfacial contact area, a cantilever structure end area, a positioning pin hole circumferential area and an electromagnet heat dissipation dead angle area. The temperature distribution data set includes: the real-time coordinate position of each temperature sensor on the module contact surface and its corresponding temperature value, the time stamp and temperature change curve of each temperature sampling point, the material thermal expansion coefficient and thermal conductivity stored in the associated module combination library, and the high-risk type data marked in the sensor coverage area.

[0009] Further, the thermal expansion difference rate is the relative deformation rate between adjacent modules caused by the difference in material thermal expansion coefficient and uneven temperature distribution, and the calculation formula is: , Wherein, is the thermal expansion difference rate, N is the total number of sampling points, is the sum of all temperature sampling points i on the contact surface, is the thermal expansion coefficient of module A, is the thermal expansion coefficient of module B, Ti is the current temperature of the i th sampling point on the contact surface, Tr is the reference temperature of the module combination, K is the magnetic field distribution influence factor, which is 1.5 when the magnetic flux gradient in the heat map is > 0.3T / mm, 0.5 when < 0.1T / mm, and 1.0 otherwise, and B is the normalized magnetic flux density of the center coordinate point i of the heat map, is the reference length of the module contact surface, The characteristic length of the element where the i-th point of the contact surface grid is located.

[0010] Further, the electromagnetic field distribution is generated by dynamically adjusting the magnetic pole activation direction according to the real-time temperature distribution data set of the module combination through the thermal expansion difference rate. The structural stress is the deformation stress caused by the difference in thermal expansion coefficient between modules calculated from the contact surface temperature distribution data set collected by the temperature sensor.

[0011] Further, the control strategy includes: adjusting the magnetic pole activation direction based on the electromagnetic field distribution; adjusting the physical structure of the vibration isolation layer according to the temperature distribution data set; triggering a safety warning action when the predicted structural stress exceeds the safety threshold.

[0012] Further, the vibration isolation layer includes a phononic crystal vibration isolation layer composed of periodically arranged silicon-aluminum composite units; the inter-unit spacing of the phononic crystal vibration isolation layer is dynamically adjusted to block the transmission of cross-module vibration; The magnetic pole activation scheme also includes: the magnetic pole activation direction of adjacent modules is set to be opposite to suppress magnetic field interaction, the magnetic pole activation direction of horizontally adjacent modules is set to be opposite to each other, and the magnetic pole activation phase of vertically adjacent modules is arranged with a 180-degree offset.

[0013] A modular magnetic attraction calibration jig, comprising: A combination management module for establishing a unique electronic file for each module, storing physical parameters, material properties and historical operation data, and calculating the fitness of a new module combination based on a module combination library; A magnetic pole control module for performing magnetic field line simulation to generate a thermal map, analyzing the magnetic field distribution characteristics through a learning model, predicting the magnetic pole activation scheme and dynamically adjusting the magnetic pole current intensity and direction; A temperature monitoring module for preinstalling a distributed temperature sensor array on the module contact surface, collecting temperature data in the thermal deformation sensitive area in real time and generating a temperature distribution data set; A vibration isolation execution module including a periodically arranged silicon-aluminum composite phononic crystal vibration isolation layer for dynamically adjusting the lattice spacing to block cross-module vibration transmission according to the vibration frequency spectrum; A stress prediction module for simulating the electromagnetic field distribution and the structural stress based on the temperature distribution data set and the thermal expansion difference rate, and predicting the safety operation parameters; A collaborative control module for compensating for deformation errors based on the thermal expansion difference rate, generating a control strategy based on the corrected magnetic pole activation scheme, and triggering a safety warning when the structural stress exceeds the threshold.

[0014] The beneficial effects of this invention are: through intelligent matching of module combination library, magnetic line simulation optimization, dynamic compensation for thermal deformation and safety strategy, it can improve accuracy and efficiency in the fields of industrial automation and precision testing. Attached Figure Description

[0015] Figure 1 This is a flowchart of a modular magnetic calibration method according to the present invention; Figure 2 This is a schematic diagram of a modular magnetic calibration fixture according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0018] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0019] Example 1, as Figure 1 As shown, the present invention provides a modular magnetic calibration fixture and calibration method: S1: Establish a module combination library. When selecting a new module combination, perform feature matching with the module combination library and obtain the fit degree based on the feature matching results. When the fit degree is lower than the matching threshold, mark it as an irregular module combination, extract key features and save it to the module combination library. The module combination library establishes a unique electronic file for each module, including physical parameters, material properties, and functional identification, and records historical operation data and system monitoring data; a combination evaluation matrix is established according to the module combination library; and the module combination fit degree is calculated based on the combination evaluation matrix.

[0020] Specifically, each module is assigned a unique electronic file in the module combination library, which serves as the digital identity of the module and includes physical parameters (module size, weight, geometric configuration), material properties (thermal expansion coefficient, thermal conductivity, electromagnetic properties), functional identification, historical operation data, and system detection data (real-time collected temperature, vibration, magnetic field strength, etc.).

[0021] According to the module combination library, a multi-dimensional combination evaluation matrix is constructed. The matrix evaluates the compatibility and performance of the module combination: the rows of the matrix represent module attributes, and the columns represent evaluation indexes, which are dynamically adjusted according to historical operation data and real-time monitoring data; the matrix includes calibration accuracy, stability, and usage frequency.

[0022] The fit degree is a quantitative index obtained by matching and calculating the multi-dimensional features stored in the module combination library, and the module combination is divided into high-fit module combination, optimizable module combination, and irregular module combination according to the quantitative index. Specifically, the combination evaluation matrix calculates the module combination fit degree quantitative index through feature matching, and the calculation formula is: , Where QP is the fit degree quantitative index, Jd is the accuracy coefficient, which is converted according to the measured accuracy and has a value range of [0.3, 1.0]; Wd is the stability coefficient, which is converted according to the failure rate and has a value range of [0.5, 1.0]; Pd is the frequency coefficient, and the formula is , Su is the number of uses; 0.6, 0.3, and 0.1 are the corresponding coefficient weights.

[0023] When Qp>0.9, the module combination is divided into a high-fit module combination; when 0.7 ≤Qp<0.9, it is an optimizable module combination; when Qp<0.7, it is preliminarily determined as an irregular module combination, and further screening is performed according to the matching critical value.

[0024] The matching critical value is a dynamic calculation of the classification threshold, and when the fit degree quantitative index of the module combination is less than the matching critical value, the module combination is marked as an irregular module combination.

[0025] Specifically, the reference threshold value of the matching critical value is 0.7, which is increased by 0.02 when the historical combination data exceeds 100 groups, and is decreased by 0.03 if the high matching combination has a failure rate of more than 1%; when the matching degree quantization index of the module combination is less than the matching critical value dynamically adjusted according to the reference threshold value, the system automatically marks the module combination as an irregular module combination.

[0026] S2: taking the key features as input parameters, performing magnetic field simulation and generating a heat map, analyzing the distribution characteristics of the heat map through a learning model, and predicting a magnetic pole activation scheme; Specifically, the three-dimensional contour data of the irregular module combination is extracted, the contact surface is discretized into 1mm*1mm grid units, and the spatial mapping relationship between the magnetic pole unit and the grid point is established, and the magnetic flux of each point is calculated: , Wherein, is the magnetic flux density of coordinate point i, is the vacuum permeability, and together as a field constant, is the driving current of the magnetic pole unit j, is the effective action area of the magnetic pole unit j, is the angle between the magnetic field line and the normal vector, is the distance from the magnetic pole unit j to the coordinate point i, and the square is the inverse square law of the magnetic field strength.

[0027] The heat map step includes: establishing a magnetic circuit topology model based on the geometric configuration of the irregular module combination and the electromagnet parameters; calculating the magnetic flux density value of each coordinate point of the module contact surface through the magnetic circuit topology model, and forming the magnetic flux density distribution data of the contact surface according to the magnetic flux density value of each coordinate point; generating a heat map and labeling magnetic field anomaly characteristics according to the magnetic flux density distribution data.

[0028] Specifically, the magnetic flux data is normalized to the interval of 0-1.5T based on the magnetic flux density; color spectrum mapping is performed, when the magnetic flux is 0-0.5T, it gradually changes from deep blue to light blue; when the magnetic flux is 0.5-1.0T, it gradually changes from green to yellow; when the magnetic flux is greater than 1.0T, it gradually changes from orange to red. <0.5T, marked as a magnetic force decay area; >1.2T, marked as an oversaturation area; When the rate of change in space is greater than 0.3T / mm, it is marked as a gradient mutation area.

[0029] The learning model is used for intelligently analyzing the distribution characteristics of the thermal map to extract key magnetic field anomaly information. The data of the thermal map is taken as input, including the magnetic flux density value, the labeled anomaly characteristics and the spatial variation rate data. The model learns the correlation between the thermal map distribution and the magnetic pole performance through training, and outputs the prediction parameters of the magnetic pole activation scheme. In predicting the optimal magnetic pole activation scheme, first, the electromagnetic noise frequency generated by the high-power module during operation is detected. When the electromagnetic noise frequency is greater than 30 mV interference, a protection measure is triggered, the electromagnetic shielding net is activated, and a time-sharing operation mode is adopted to reduce interference. Secondly, the stress distribution at the joint of the model prediction module is detected. When a high stress area of 182 MPa is detected, the system vibration frequency is limited to ≤150 Hz, and the magnetic attraction force is enhanced to 150% of the standard value to resist deformation. Finally, the model monitors the temperature in real time. When the temperature difference is >25℃, the standby cooling system is started.

[0030] S3: Embed a vibration isolation layer in the irregular module combination contact surface, and preinstall an array of temperature sensors to monitor the temperature of the thermal deformation sensitive area in real time to generate a temperature distribution data set of the contact surface. Calculate the thermal expansion difference rate based on the module combination library, compensate for the combination deformation error according to the thermal expansion difference rate, and correct the magnetic pole activation scheme according to the compensation result. Specifically, the vibration isolation layer is directly embedded in the module contact interface, covering the thermal deformation sensitive area; used to suppress the calibration drift caused by mechanical vibration and relieve thermal expansion stress conduction.

[0031] The thermal deformation sensitive area includes: the inter-module contact interface area, the cantilever structure end area, the positioning pin hole circumferential area, and the electromagnetic iron heat dissipation dead angle area. Specifically, the preinstalled temperature sensor array has ≥4 sampling points per square centimeter, covering the inter-module contact interface area, the cantilever structure end area, the positioning pin hole circumferential area, and the electromagnetic iron heat dissipation dead angle area.

[0032] The temperature distribution data set includes: the real-time coordinate position of each temperature sensor on the module contact surface and its corresponding temperature value, the timestamp and temperature change curve of each temperature sampling point, the material thermal expansion coefficient and thermal conductivity stored in the associated module combination library, and the high-risk type data labeled by the sensor coverage area.

[0033] Specifically, real-time data acquisition is performed at a frequency of 500 ms / second, and the three-dimensional coordinate position and corresponding temperature value of each sensor are accurately recorded, and a continuous temperature change curve with millisecond-level timestamp is automatically generated. These curves can clearly show the thermal characteristics such as temperature rise rate and steady-state fluctuation. All temperature data are associated with the material property database in the library in real time, and the thermal expansion coefficient and thermal conductivity parameters of the current module material are automatically matched. The sensor coverage area is intelligently risk-labeled: when the local temperature difference exceeds 25°C, it is marked as a red emergency risk area, indicating a high risk of thermal deformation; when the thermal expansion coefficient difference of the heterogeneous material combination exceeds 15ppm / °C, it is marked as a yellow moderate risk area, which has potential deformation conflicts; when the historical failure rate exceeds 5%, it is marked as a blue warning area. The output includes coordinate position, timestamp, temperature change curve, and risk type label.

[0034] The thermal expansion difference rate is the relative deformation rate caused by the difference in thermal expansion coefficient of adjacent modules and uneven temperature distribution, and the calculation formula is: , wherein, is the thermal expansion difference rate, N is the total number of samples, is the sum of all temperature sampling points i on the contact surface, is the thermal expansion coefficient of module A, is the thermal expansion coefficient of module B, Ti is the current temperature of the i-th sampling point on the contact surface, Tr is the reference temperature of the module combination, taken from the historical data of the module combination library, K is the magnetic field distribution influence factor, which is 1.5 when the magnetic flux gradient in the thermal map is >0.3T / mm, 0.5 when <0.1T / mm, and 1.0 otherwise, B is the normalized magnetic flux density of the center coordinate point i of the thermal map, is the reference length of the module contact surface, is the characteristic length of the i-th point on the contact surface after meshing.

[0035] Specifically, for quantifying the expansion coefficient difference of heterogeneous materials; for comparing the local deviation of real-time temperature and historical reference temperature; for representing the enhancement and inhibition effect of magnetic flux density on heat conduction.

[0036] According to the real-time calculated thermal expansion difference rate, <0.003, fine-tune the magnetic attraction force by ±5%; 0.003≤ ≤0.01, piezoelectric ceramic displacement compensation 0.01-0.05mm; >0.01, take liquid cooling system and magnetic pole array reconstruction.

[0037] When the system collects the contact surface temperature distribution data through the temperature sensor array real-time acquisition module, the thermal expansion difference rate is calculated based on the module combination library, and the deformation error compensation is completed through piezoelectric ceramic displacement compensation, magnetic attraction fine adjustment or liquid cooling system start-up, etc., to generate the compensated deformation data set containing key parameters such as displacement and stress peak value; when the displacement deviation > 0.03 mm or the local stress peak value > 150 MPa and other threshold value conditions are detected, the system calls the learning model, combines the magnetic field abnormal area marked in the thermal map, dynamically reconstructs the magnetic pole activation parameters, including reducing the driving current of the magnetic field oversaturation area by 20% to reduce magnetic interference, and increasing the current of the magnetic force attenuation area by 15% to enhance the adsorption force, while adopting the time-sharing working mode of high-frequency module and signal acquisition module to optimize the electromagnetic field timing distribution; the corrected magnetic pole activation instruction is output and executed within <100 ms cycle.

[0038] S4: Based on the temperature distribution data set, the electromagnetic field distribution and structural stress of the new module combination are simulated, the safe operation parameters are predicted, and the control strategy is generated in cooperation with the corrected magnetic pole activation scheme.

[0039] The electromagnetic field distribution is an anti-deformation magnetic field generated by dynamically adjusting the magnetic pole activation direction based on the real-time temperature distribution data set of the module combination; Specifically, for the new module combination in the module combination library with feature extraction, before the first magnetic pole activation, the temperature distribution data set is input to the electromagnetic field model as data, and the magnetic field correction is calculated: , Wherein, is the corrected magnetic flux density, is the initial magnetic flux density, is the material permeability correction coefficient, which is used to quantify the response of material permeability to temperature change, and the value range is [0.5, 1.5].

[0040] The structural stress is the deformation stress caused by the difference in thermal expansion coefficient between modules calculated from the contact surface temperature distribution data set collected by the temperature sensor.

[0041] Specifically, the structural stress caused by the thermal expansion difference in the modular magnetic attraction fixture is predicted, and the calculation formula is: , Wherein, is the structural stress, E is the elastic modulus of the material's resistance to deformation, is the maximum temperature difference of the contact surface, is the structural constraint factor, the value range is [0.05, 0.25], is the current structural characteristic length, is the contact surface reference length.

[0042] The control strategy comprises: adjusting the magnetic pole activation direction based on the electromagnetic field distribution; adjusting the physical structure of the vibration isolation layer according to the temperature distribution dataset; triggering a safety warning action when the predicted structural stress exceeds a safety threshold.

[0043] Specifically, according to the thermal map and the thermal expansion difference rate, when the thermal map marks a magnetic field oversaturation area or a decay area or the thermal expansion difference rate is greater than 0.01, the current intensity is redistributed, the current in the oversaturation area is reduced by 20%, and the current in the decay area is increased by 15%. When the local temperature difference is greater than 25℃, the density of the vibration isolation layer is increased by 30%; when the overall temperature rise is greater than 40℃, the density of the vibration isolation layer is reduced by 20%; and when the temperature difference gradient is greater than 10℃ / cm, the gradient density distribution is performed.

[0044] According to the structural stress prediction value, a hierarchical response is performed, 120MPa ≤150MPa is a yellow warning, and the magnetic attraction force is increased by 30%; >150MPa is a red warning, and the frequency is forcibly reduced and the liquid cooling is started.

[0045] The technical solutions in the embodiments of the present application have at least the following technical effects or advantages: The present application automatically identifies irregular module combinations and extracts features into the database through electronic archives and dynamic fitness calculation of the module combination library, so that the matching accuracy of new modules is more than 98%, the historical data learning efficiency is improved by 37%, and the module switching time is compressed from 45 minutes to 9 minutes; the magnetic field abnormal area is marked on the thermal map, the current intensity and the activation timing are dynamically adjusted, and the magnetic field uniformity is improved to 93%; at the same time, the thermal deformation sensitive area is monitored by relying on the temperature sensor array, and the deformation error is less than or equal to 0.02mm in the new energy calibration by combining the thermal expansion difference rate δ hierarchical compensation, and the aviation blade thermal drift suppression rate is 89%; based on the structural stress prediction model, when the stress is greater than 150MPa, the frequency is forcibly reduced and the liquid cooling system is started, and the failure rate is reduced from 9.6% to 0.3%, and the closed-loop response speed is less than 100ms.

[0046] In embodiment 2, on the basis of the vibration isolation layer in embodiment 1, the calibration data pollution problem caused by inter-module mechanical vibration transmission is further optimized.

[0047] The vibration isolation layer comprises a phononic crystal vibration isolation layer composed of periodically arranged silicon-aluminum composite units; the unit spacing is dynamically adjusted by the phononic crystal vibration isolation layer to block the transmission of cross-module vibration.

[0048] Specifically, the vibration isolation layer adopts a periodic composite structure of a silicon matrix and an aluminum column, and the arrangement mode is a hexagonal close-packed structure. The diameter of the aluminum column unit is 1.2mm, and the acoustic band gap range formed by the structure is 150-800Hz.

[0049] The module incorporates a triaxial accelerometer for sampling, generating a real-time spectrum waterfall plot. The system analyzes the transmission path: when the phase difference between vibrations of adjacent modules is less than 30°, it is determined to be direct vibration transmission; when the phase difference is greater than 150°, it is determined to be weak coupling.

[0050] The system is based on the detected principal vibration frequency Calculate the target lattice spacing: , Where d is the target lattice spacing, c is the propagation speed of sound waves in the silicon substrate, 2 is the half-wavelength matching relationship when sound waves propagate in the medium, and 0.9 is a safety factor used to ensure that the bandgap completely covers the main frequency band. After the target value d is calculated, the piezoelectric ceramic actuator receives voltage commands to compress the silicon substrate to dynamically adjust the lattice spacing and realize the migration of the acoustic bandgap.

[0051] The system implements a differentiated blocking strategy. When the vibrations are in phase and frequency (phase difference between adjacent modules < 30°), a lattice isolation band is generated; when the vibrations are out of phase (phase difference between adjacent modules > 150°), the phase cancellation array is activated; and when the vibrations are wideband (50-1000Hz), three overlapping bandgap regions are divided for processing.

[0052] The system takes corresponding protective measures based on the vibration impact rate: when subjected to a slight impact (vibration impact rate <50μm / s²), the damping layer is thickened by 20%; when subjected to a moderate impact (50-200μm / s²), a dual response strategy is adopted: the magnetic attraction force is instantly increased by 50%, and lattice encryption protection is implemented; when subjected to a severe impact (>200μm / s²), electromechanical combined protection is adopted: the transient magnetic force is increased by 100%, the mechanical lock stop pin is ejected, the system is shut down in an emergency, calibration operations are suspended, and an alarm is triggered.

[0053] The system performs temperature compensation to adapt to environmental changes. When the real-time temperature is <-10℃, low-temperature compensation is adopted, and the compensation formula is as follows: , in, This is the amount of lattice spacing compensation. The coefficient of thermal expansion for silicon-aluminum composite is [4.0, 20.0] × 10⁻ 6 K⁻¹, Ls is the lattice feature length, 10 is the temperature offset reference, Ts is the real-time ambient temperature, and Tre is the basic temperature difference value; When the real-time temperature is >60℃, high-temperature compensation is applied, and the compensation formula is as follows: .

[0054] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: The application adopts a phonon crystal vibration isolation layer, generates a frequency spectrum waterfall chart in real time through a built-in three-axis acceleration sensor, analyzes a vibration transmission path according to a main vibration frequency detected by the three-axis acceleration sensor, and adjusts a lattice spacing of the phonon crystal vibration isolation layer according to the main vibration frequency The target lattice spacing is dynamically calculated, the lattice spacing is adjusted by using a piezoelectric ceramic driver to realize band gap migration, a differentiated blocking strategy is executed, a lattice isolation band is generated when the same frequency and the same phase are generated, a phase cancellation array is activated when the opposite phase vibration is generated, three overlapping band gap regions are divided when the wide frequency vibration is generated, and hierarchical protection is taken according to a vibration impact rate: the damping layer is thickened by 20% when a slight impact is generated, the magnetic attraction force is instantaneously enhanced by 50% and the lattice is encrypted when a moderate impact is generated, the transient magnetic force is instantaneously enhanced by 100% when a heavy impact is generated, the mechanical locking pin is ejected, and the system is emergency stopped; in addition, a temperature compensation mechanism is used to adapt to environmental changes: the compensation lattice spacing is adjusted when the temperature is low, and the expansion is inhibited when the temperature is high, so that the vibration suppression performance is comprehensively optimized.

[0055] In example 3, in order to solve the problem of decreased positioning accuracy caused by overlapping of magnetic field lines of adjacent modules in example 2, further optimization is performed.

[0056] The magnetic pole activation scheme further includes: the magnetic pole activation direction of the adjacent module is set to be opposite to suppress the magnetic field interaction, the magnetic pole activation direction of the horizontally adjacent module is set to be opposite to each other, and the magnetic pole activation phase of the vertically adjacent module is kept 180 degrees staggered arrangement.

[0057] Specifically, for the relative position relationship between the modules, a differentiated magnetic pole activation strategy is adopted: the magnetic pole activation direction of the horizontally adjacent module is set to be opposite to each other; the magnetic pole activation phase of the vertically adjacent module is kept 180 degrees staggered arrangement; the diagonal adjacent module adopts a magnetic pole strength gradient distribution design, the magnetic pole strength of the center region is kept at 80% of the reference value, and the edge region is enhanced to 120% of the reference value.

[0058] According to the heat map and the vibration signal data, when the heat map shows the magnetic field oversaturation region, the magnetic pole current of the region is reduced by 20%, when the magnetic force decay region is detected, the magnetic pole current of the region is increased by 15%, when high frequency vibration (>200Hz) is detected, the edge magnetic pole adsorption force is instantaneously enhanced by 30%, the phase difference of the vertically adjacent module is fine-tuned from 180° to 170°, and anti-resonance optimization is performed.

[0059] The system scans the three-dimensional topological structure of the module combination, identifies the horizontally adjacent, vertically stacked and diagonally adjacent relationship. Based on the electromagnetic parameters and historical data in the module combination library, a magnetic pole activation scheme is automatically generated: the horizontally adjacent modules are forced to be arranged in opposite directions; the vertically adjacent modules are fixed at a phase difference of 180 degrees; the diagonal modules are gradiently distributed with magnetic pole strength; High-precision Hall sensor arrays (density: 4 / cm², sampling rate 1kHz) are embedded in the module joint surface to dynamically capture the magnetic field superposition strength, phase shift and edge decay rate.

[0060] Real-time calibration of positioning deviation by laser interferometer, detection of displacement deviation > 3 μm, tracing to specific module; reverse magnetic pole unit activation in magnetic field oversaturation area, 0.02-0.05 mm piezoelectric displacement compensation combined with thermal expansion data in deformation sensitive area; refresh magnetic pole parameters every 5 ms, until error < ±0.013 mm.

[0061] According to the above patent method, the application also provides a modular magnetic attraction calibration jig, as shown in Figure 2 The calibration jig comprises: A combination management module for establishing a unique electronic file for each module, storing physical parameters, material properties and historical operation data, and calculating the fitness of the new module combination based on the module combination library; A magnetic pole control module for performing magnetic field simulation to generate a thermal map, analyzing magnetic field distribution characteristics through a learning model, predicting a magnetic pole activation scheme and dynamically adjusting the magnetic pole current intensity and direction; A temperature monitoring module, which prepositions a distributed temperature sensor array at the module contact surface, collects temperature data of the thermal deformation sensitive area in real time and generates a temperature distribution dataset; A vibration isolation execution module, which contains a periodically arranged silicon and aluminum composite phononic crystal vibration isolation layer, for dynamically adjusting the lattice spacing to block the vibration transmission across the modules according to the vibration spectrum; A stress prediction module, which simulates electromagnetic field distribution and structural stress based on the temperature distribution dataset and thermal expansion difference rate, and predicts safe operation parameters; A cooperative control module for compensating deformation error based on thermal expansion difference rate, generating control strategy based on the corrected magnetic pole activation scheme, and triggering safety warning when the structural stress exceeds the threshold.

[0062] The technical solutions in the above embodiments of the application have at least the following technical effects or advantages: The application adopts a three-dimensional magnetic pole optimization strategy to solve the problem of positioning accuracy decline caused by magnetic field line overlap in high-density module combination: horizontal adjacent modules are forced to arrange reverse poles to form a closed magnetic circuit to reduce 70% magnetic leakage; vertically stacked modules are fixed with 180° phase offset to suppress more than 15% eddy current loss; diagonal area implements magnetic pole strength gradient distribution to solve the problem of magnetic field edge attenuation. Through the real-time monitoring of magnetic field superposition, phase shift and edge attenuation by the Hall sensor array, and the deep cooperation of thermal deformation data and vibration signals, micron-level reliable support is provided for cutting-edge scenarios.

[0063] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0064] Those skilled in the art will appreciate that embodiments of the present application can be devised for a variety of applications. It is intended that the present application be limited only by the scope of the appended claims, and it is intended that various modifications and alterations made by those skilled in the art be considered as within the scope of the present application. The embodiments of the present application will be described with reference to the attached drawings, wherein:

[0065] The present application is described in reference to the drawings using a flowchart and / or a block diagram of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagram, and combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0066] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0068] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to cover all such modifications and variations as fall within the scope of the present application.

[0069] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A modular magnetic calibration method, characterized in that, include: S1: Establish a module combination library. When selecting a new module combination, perform feature matching with the module combination library and obtain the fit degree based on the feature matching results. When the fit degree is lower than the matching threshold, mark it as an irregular module combination, extract key features and save it to the module combination library. S2: Using key features as input parameters, perform magnetic field line simulation and generate a heat map. By learning the model, analyze the distribution characteristics of the heat map and predict the magnetic pole activation scheme. S3: An isolation layer is embedded in the contact surface of the irregular module combination, and a pre-set temperature sensor array is used to monitor the temperature of the thermal deformation sensitive area in real time to generate a temperature distribution dataset of the contact surface; the thermal expansion difference rate is calculated based on the module combination library, and the deformation error of the combination is compensated according to the thermal expansion difference rate; the magnetic pole activation scheme is corrected according to the compensation result. S4: Based on the temperature distribution dataset, simulate the electromagnetic field distribution and structural stress of the new module combination, predict safe operating parameters, and generate control strategies by coordinating the corrected magnetic pole activation scheme.

2. The modular magnetic calibration method according to claim 1, characterized in that, The module combination library establishes a unique electronic file for each module, including physical parameters, material properties and functional identifiers, and records historical operating data and system monitoring data; a combination evaluation matrix is ​​established based on the module combination library; and the module combination fit is calculated based on the combination evaluation matrix.

3. The modular magnetic calibration method according to claim 1, characterized in that, The fit is a quantitative index obtained by matching and calculating the multi-dimensional features stored in the module combination library. Based on the quantitative index, the module combination is divided into highly fit module combination, optimizable module combination and irregular module combination. The matching threshold is a dynamically calculated hierarchical threshold. When the fit metric of a module combination is less than the matching threshold, the module combination is marked as an irregular module combination.

4. The modular magnetic calibration method according to claim 1, characterized in that, The heat map step includes: establishing a magnetic circuit topology model based on the geometric configuration of the irregular module combination and the electromagnet parameters; calculating the magnetic flux density value of each coordinate point on the module contact surface through the magnetic circuit topology model; forming magnetic flux density distribution data of the contact surface based on the magnetic flux density value of each coordinate point; generating a heat map based on the magnetic flux density distribution data and marking the magnetic field anomaly characteristics.

5. The modular magnetic calibration method according to claim 1, characterized in that, The heat deformation sensitive area includes: the contact interface area between modules, the end area of ​​the cantilever structure, the circumferential area of ​​the positioning pin hole, and the heat dissipation dead angle area of ​​the electromagnet. The temperature distribution dataset includes: the real-time coordinate position of each temperature sensor on the module contact surface and its corresponding temperature value, the timestamp and temperature change curve of each temperature sampling point, the material thermal expansion coefficient and thermal conductivity stored in the associated module combination library, and high-risk type data marked in the sensor coverage area.

6. The modular magnetic calibration method according to claim 1, characterized in that, The thermal expansion difference rate is the relative deformation rate between adjacent modules caused by differences in the coefficients of thermal expansion of materials and uneven temperature distribution. The calculation formula is as follows: ， in, The coefficient of thermal expansion is N, where N is the total number of sampling points. The sum of the temperatures at all sampling points i on the contact surface. Let A be the coefficient of thermal expansion of module A. Let be the coefficient of thermal expansion of module B, Ti be the current temperature of the i-th sampling point on the contact surface, Tr be the reference temperature of the module combination, K be the magnetic field distribution influence factor (taken as 1.5 when the magnetic flux gradient in the thermal map is >0.3T / mm, 0.5 when it is <0.1T / mm, otherwise 1.0), and B be the normalized magnetic flux density at the center coordinate point i of the thermal map. This is the reference length of the module contact surface. is the characteristic length of the cell containing the i-th point after the contact surface is meshed.

7. The modular magnetic calibration method according to claim 1, characterized in that, The electromagnetic field distribution is an anti-deformation magnetic field generated by dynamically adjusting the magnetic pole activation direction based on the real-time temperature distribution dataset of the module combination and the thermal expansion difference rate. The structural stress is the deformation stress caused by the difference in thermal expansion coefficients between modules, calculated from the temperature distribution dataset of the contact surface collected by the temperature sensor.

8. The modular magnetic calibration method according to claim 1, characterized in that, The control strategy includes: adjusting the magnetic pole activation direction based on the electromagnetic field distribution; adjusting the physical structure of the vibration isolation layer according to the temperature distribution dataset; and triggering a safety warning action when the predicted structural stress exceeds the safety threshold.

9. The modular magnetic calibration method according to claim 1, characterized in that, The vibration isolation layer includes a phononic crystal vibration isolation layer, which is composed of periodically arranged silicon and aluminum composite units; the spacing between the units is dynamically adjusted by the phononic crystal vibration isolation layer to block the transmission of vibration across modules; The magnetic pole activation scheme further includes: setting the magnetic pole activation directions of adjacent modules to be opposite to suppress magnetic field interaction; setting the magnetic pole activation directions of horizontally adjacent modules to be opposite to each other; and maintaining a 180-degree staggered arrangement of the magnetic pole activation phases of vertically adjacent modules.

10. A modular magnetic calibration fixture, applied to a modular magnetic calibration method as described in any one of claims 1 to 9, characterized in that, Include: The module for combination management is used to create a unique electronic file for each module, store physical parameters, material properties and historical operating data, and calculate the compatibility of new module combinations based on the module combination library. The magnetic pole control module is used to perform magnetic field line simulation to generate heat maps, analyze the magnetic field distribution characteristics through learning models, predict magnetic pole activation schemes, and dynamically adjust the magnetic pole current intensity and direction. The temperature monitoring module has a pre-installed distributed temperature sensor array on the module contact surface to collect temperature data of the heat deformation sensitive area in real time and generate a temperature distribution dataset. The vibration isolation execution module includes a periodically arranged silicon and aluminum composite phononic crystal vibration isolation layer, which is used to dynamically adjust the lattice spacing according to the vibration spectrum to block the transmission of vibration across the module; The stress prediction module, based on temperature distribution datasets and thermal expansion difference rates, simulates electromagnetic field distribution and structural stress to predict safe operating parameters; The collaborative control module is used to compensate for deformation errors based on the thermal expansion difference rate, generate control strategies based on the collaboratively corrected magnetic pole activation scheme, and trigger safety warnings when the structural stress exceeds the threshold.

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