A multi-physical field test method and system based on synchronous acquisition

Abstract

The application provides a multi-physical field test method and system based on synchronous acquisition, relates to the technical field of multi-physical field test, and a main sensor generates a test trigger signal during multi-physical field test, synchronously collects initial test data of a to-be-tested object under multiple physical fields through each slave sensor, generates spatially-aligned multi-physical field data based on a preset synchronous background grid according to all the initial test data, guides the physical field component perpendicular to the test plane in the spatially-aligned multi-physical field data to the test plane through a test direction conversion condition, and performs mapping and synchronization on the converted physical field component based on the historical physical field component in the test plane, the main sensor checks and corresponds the synchronous test data according to the synchronous node identification of each slave sensor, and outputs a multi-physical field test result. The application can realize equivalent conversion of the physical field component during multi-physical field test, so as to improve the precision of multi-physical field synchronous test under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of multiphysics testing technology, and more specifically, to a multiphysics testing method and system based on synchronous acquisition. Background Technology

[0002] Multiphysics testing (MPT) is a testing method that simultaneously collects, measures, and analyzes the characteristics of multiple types of physical field parameters under actual or simulated operating conditions for an engineering test object. It is a practical means of verifying the performance and studying the structural characteristics of engineering equipment. The physical fields involved in MPT typically include stress fields, temperature fields, vibration fields, pressure fields, electromagnetic fields, and acoustic fields. It can completely reproduce the true response characteristics of the test object under the coupling effect of multiple physical fields during service, and is widely used in aerospace, engineering machinery, new energy equipment, and electronic devices.

[0003] However, traditional multiphysics testing lacks mechanisms for end-to-end timing synchronization control of master-slave architecture and synchronous calibration of reference data mapping. This leads to a disconnect between the testing requirements for timing synchronization accuracy and field information integrity, resulting in timing misalignment of multiphysics data and loss of field information. Consequently, it is difficult to guarantee the testing accuracy of multiphysics coupling characteristic analysis of the object under test during synchronous acquisition. Therefore, how to achieve equivalent transformation of physical field components during multiphysics testing to improve the accuracy of synchronous multiphysics testing under complex conditions is a problem facing the industry. Summary of the Invention

[0004] This application provides a multi-physics field testing method and system based on synchronous acquisition, which can realize the equivalent transformation of physical field components during multi-physics field testing, thereby improving the accuracy of synchronous multi-physics field testing under complex working conditions.

[0005] In a first aspect, this application provides a multiphysics field testing method based on synchronous acquisition, the testing method comprising the following steps:

[0006] The master sensor generates a test trigger signal for multiphysics field testing and synchronously configures the test trigger signal to multiple slave sensors.

[0007] By synchronously acquiring initial test data of the object under test from each sensor under multiple physical fields, spatially aligned multiphysics data is generated based on all the initial test data and a preset synchronous background grid.

[0008] The physical field components perpendicular to the test plane in the spatially aligned multiphysics data are guided into the test plane through test direction conversion conditions, and the converted physical field components are mapped and synchronized based on the historical physical field components in the test plane to generate synchronized test data in the test plane.

[0009] The master sensor verifies the synchronization test data according to the synchronization node identifiers of each slave sensor and outputs the multiphysics test results.

[0010] In this embodiment, synchronizing the test trigger signal to multiple slave sensors specifically includes:

[0011] Determine the synchronization registration matrix when the test trigger signal is synchronized;

[0012] The phase offset of the test trigger signal is mapped step by step to the sampling trigger register of each slave sensor through the synchronization registration matrix to obtain a parallel trigger sequence;

[0013] The deviations configured synchronously on multiple slave sensors are dynamically calibrated based on the cross-correlation parameters of the responses fed back by each slave sensor according to the parallel trigger sequence.

[0014] In this embodiment, the initial test data of the object under test under multiple physical fields synchronously collected from each sensor specifically includes:

[0015] A reference mapping matrix covering multiple physical fields is determined based on the phase difference sequence between the local clock source of each sensor and the master reference clock.

[0016] The test data corresponding to the test object in multiple physical fields are synchronized and aligned using the reference mapping matrix to obtain the test data vector. The multiple physical fields include pressure field, temperature field and vibration field.

[0017] The initial test data of each slave sensor under multiple physical fields are determined based on the data vector to be tested and the preset synchronous acquisition quality threshold.

[0018] In this embodiment, generating spatially aligned multiphysics data based on a preset synchronous background grid according to all initial test data specifically includes:

[0019] Based on the spatial coordinates of all initial test data at each sensor node and the node relationship of the preset synchronization background grid, the mapping radial matrix corresponding to multiple synchronization background grid nodes is determined.

[0020] The discrete state vector at each synchronous background grid node is determined based on the mapped radial matrix and the initial test data collected from the sensors.

[0021] The discrete state vectors are reconstructed into a continuous field along the topology of the synchronous background grid to generate a continuous field model in the spatial domain of the object under test.

[0022] Spatially aligned multiphysics data are determined based on the gradient rate of change at the boundary nodes of the synchronous background grid according to the continuous field model.

[0023] In this embodiment, reconstructing the discrete state vector along the topology of the synchronous background grid to generate a continuous field model in the spatial domain of the object under test specifically includes:

[0024] The interpolation basis function system in the multiphysics field is determined based on the shape function of the discrete state vector in the synchronous background grid.

[0025] The field value continuity constraint conditions at the synchronous background grid nodes are determined based on the interpolation basis function system and the discrete state vector.

[0026] By fusing the boundary field distribution of the synchronous background grid with the continuous field value constraint, a continuous field model is generated in the spatial domain of the object under test.

[0027] In this embodiment, guiding the physical field components perpendicular to the test plane in the spatially aligned multiphysics data to the test plane through test direction conversion conditions specifically includes:

[0028] The physical field components perpendicular to the test plane are determined by the dot product of the field vector directions of each grid node in the spatially aligned multiphysics data and the test plane normal vector.

[0029] Vector projection is performed using the physical field components perpendicular to the test plane and the preset transformation direction cosine matrix within the test plane to generate multiple mapping relationships between physical field components and coordinate axes within the test plane.

[0030] Based on the mapping relationship and the shape function reconstruction conditions in the synchronous background grid, the planar distribution field of the equivalent physical field components at each grid node in the test plane is determined.

[0031] In this embodiment, the process of mapping and synchronizing the transformed physical field components based on the historical physical field components within the test plane to generate synchronized test data within the test plane specifically includes:

[0032] The timing synchronization rules, indexed by grid nodes, are determined based on the historical and transformed physical field components within the test plane.

[0033] The synchronization mapping coefficients of the converted physical field components are determined based on the time-series synchronization rules and the gradient of the changes of historical physical field components within a preset time window.

[0034] The state iteration of the historical physical field components and the transformed physical field components is performed by the synchronization mapping coefficient to generate preliminary synchronization test data on each grid node in the test plane.

[0035] Error correction is performed based on the deviation between the preliminary synchronous test data and the boundary constraints of the test plane to obtain synchronous test data within the test plane.

[0036] In this embodiment, the process of generating preliminary synchronization test data for each grid node in the test plane by iterating the state of the historical physical field components and the transformed physical field components using the synchronization mapping coefficients specifically includes:

[0037] Based on the state covariance matrix of the historical physical field components and the transformed physical field components on the time axis, the initial values ​​for iterative updates of the state vector at each grid node are constructed based on the synchronization mapping coefficient.

[0038] The state residuals of the historical physical field components and the transformed physical field components are iteratively fused using the synchronization mapping coefficients to obtain the minimum variance estimate of the state vector at each grid node.

[0039] The preliminary synchronization test data on each grid node in the test plane is determined based on the minimum variance estimate and the iterative convergence criterion.

[0040] In this embodiment, the master sensor verifies the synchronization test data according to the synchronization node identifiers of each slave sensor, and outputs multiphysics test results, specifically including:

[0041] The node attribution verification vector of each slave sensor data is obtained by comparing the slave sensor synchronization node identifier carried in the synchronization test data with the node topology mapping table stored locally by the master sensor.

[0042] The synchronization test data is verified node by node using the node affiliation verification vector to generate a synchronization integrity identifier for each sensor data.

[0043] Based on the synchronization integrity identifier and the preset multiphysics data fusion rules, the data that has passed the verification of each node are aggregated and output to obtain the multiphysics test results containing node traceability information.

[0044] Secondly, this application provides a multiphysics testing system based on synchronous acquisition, used to execute a multiphysics testing method based on synchronous acquisition, the testing system comprising:

[0045] The configuration module is used to generate a test trigger signal during multiphysics field testing by the main sensor and to synchronously configure the test trigger signal to multiple slave sensors.

[0046] The acquisition module is used to synchronously acquire initial test data of the object under test under multiple physical fields through each sensor, and generate spatially aligned multiphysics data based on a preset synchronous background grid according to all the initial test data;

[0047] The mapping module is used to guide the physical field components perpendicular to the test plane in the spatially aligned multiphysics data to the test plane through the test direction conversion condition, and to map and synchronize the converted physical field components based on the historical physical field components in the test plane to generate synchronized test data in the test plane.

[0048] The verification module is used by the master sensor to verify the synchronization test data according to the synchronization node identifier of each slave sensor, and output the multiphysics test results.

[0049] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0050] The master sensor generates a test trigger signal for multiphysics testing and synchronously configures the test trigger signal to multiple slave sensors. Each slave sensor synchronously collects initial test data of the object under test under multiple physical fields. Based on all the initial test data and a preset synchronous background grid, spatially aligned multiphysics data is generated. The physical field components perpendicular to the test plane in the spatially aligned multiphysics data are guided into the test plane through test direction conversion conditions. The converted physical field components are mapped and synchronized based on historical physical field components within the test plane to generate synchronous test data within the test plane. The master sensor verifies the synchronous test data according to the synchronization node identifiers of each slave sensor and outputs the multiphysics test results.

[0051] Therefore, in this application, the master sensor verifies the synchronous test data according to the synchronization node identifiers of each slave sensor and outputs multiphysics test results. Specifically, by determining the spatially aligned multiphysics data covering the entire domain of the object under test, a standardized global dataset containing complete multiphysics parameters corresponding to each grid node, constructed based on a unified global spatial coordinate reference, is obtained. This solves the problems of spatial non-overlapping of acquisition nodes from multiple source slave sensors and spatial misalignment of different physical field data, eliminates component conversion system errors caused by spatial matching deviations, and ensures the consistency of the reference for physical field component decomposition and conversion. The test plane is determined. By using the synchronous test data within the test plane, a standardized dataset of equivalent multi-physics components that is continuous and consistent across the entire plane after equivalent transformation of out-of-plane components, synchronous calibration of historical reference mapping, and correction of boundary errors can be obtained. This fully preserves the effective physical information of the original out-of-plane physical field components, eliminates systematic errors and random jitter introduced during component transformation, ensures the temporal synchronization, amplitude consistency, and field distribution continuity of all physical field components within the test plane, avoids interference from component transformation distortion and synchronization deviation on the test results, and achieves accurate synchronous matching of multi-physics data from the component dimension, thus comprehensively improving the overall accuracy of multi-physics synchronous testing under complex working conditions.

[0052] In summary, the technical solution adopted in this application can realize the equivalent transformation of physical field components during multi-physics field testing, thereby improving the accuracy of synchronous testing of multi-physics fields under complex working conditions. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is an exemplary flowchart of a multiphysics testing method based on synchronous acquisition provided in this application;

[0055] Figure 2 This is a flowchart illustrating the process of generating multiphysics data according to the present application;

[0056] Figure 3 This is a flowchart illustrating the process of generating synchronous test data provided in this application;

[0057] Figure 4 This is a schematic diagram of the test plane synchronous background grid and multiphysics sampling node distribution provided in this application;

[0058] Figure 5 This is a module structure diagram of a multiphysics field testing system based on synchronous acquisition provided in this application. Detailed Implementation

[0059] 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 of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0060] This application provides a multiphysics field testing method and system based on synchronous acquisition. The core of the method involves a master sensor generating a test trigger signal for multiphysics field testing, and synchronously configuring this signal to multiple slave sensors. Each slave sensor synchronously acquires initial test data of the object under test under multiple physical fields. Based on all the initial test data, spatially aligned multiphysics field data is generated using a preset synchronous background grid. Physical field components perpendicular to the test plane in the spatially aligned multiphysics field data are guided into the test plane using test direction conversion conditions. The converted physical field components are then mapped and synchronized based on historical physical field components within the test plane to generate synchronous test data within the test plane. The master sensor verifies the synchronous test data according to the synchronization node identifiers of each slave sensor and outputs the multiphysics field test results.

[0061] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a multiphysics testing method based on synchronous acquisition according to this embodiment of the present application. The testing method includes the following steps:

[0062] In step S1, the master sensor generates a test trigger signal for multiphysics testing and synchronously configures the test trigger signal to multiple slave sensors.

[0063] In specific implementation, the test trigger signal generated by the main sensor during multi-physics field testing can be implemented in the following way: First, before the test starts, the main sensor completes initialization. Based on the structural properties of the object under test, the test conditions, and the type of physical field under test, the core test rules such as the total sampling duration, sampling frequency, trigger mode, and acquisition channel parameters are preset. The main sensor uses the 10MHz high-stability reference clock output by the internal constant temperature crystal oscillator as the only time source to generate a test trigger signal containing the synchronous clock reference edge and the acquisition execution command. The synchronous edge adopts a unified rising edge trigger mode to avoid timing deviations caused by transmission jitter. The main sensor sends signals through a star coaxial hardware trigger bus and EtherCAT industrial Ethernet dual link. Before sending, three pre-synchronization checks are completed. After confirming that all slave sensors have completed timing lock, the formal test trigger signal is output.

[0064] It should be noted that in this application, the test trigger signal is the reference control command signal for defining multi-physics field tests; the main sensor refers to the core control unit that undertakes test process management, trigger signal generation, synchronous configuration distribution and result verification output in multi-physics field tests.

[0065] In this embodiment, the test trigger signal can be synchronously configured to multiple slave sensors using the following steps:

[0066] Determine the synchronization registration matrix when the test trigger signal is synchronized;

[0067] The phase offset of the test trigger signal is mapped step by step to the sampling trigger register of each slave sensor through the synchronization registration matrix to obtain a parallel trigger sequence;

[0068] The deviations configured synchronously on multiple slave sensors are dynamically calibrated based on the cross-correlation parameters of the responses fed back by each slave sensor according to the parallel trigger sequence.

[0069] In practice, firstly, before the test starts, the master sensor uses its own temperature-controlled crystal oscillator output clock as the sole reference system and adopts the pulse response calibration method to independently calibrate each master-slave trigger transmission link. The master sensor sends a calibration pulse of fixed pulse width to each slave sensor in sequence, records the pulse transmission time and the reception time of the corresponding slave sensor's response, calculates the single-link transmission delay, phase mapping coefficient, clock division ratio and trigger latch threshold, and constructs a synchronization registration matrix with the number of rows equal to the number of slave sensors and the number of columns corresponding to the above calibration parameters. Each row of the matrix corresponds to the synchronization configuration parameters of a unique slave sensor. Then, the master sensor first uses its own reference clock as a reference to calculate the reference phase of the test trigger signal. Combining this with the link transmission delay corresponding to each slave sensor in the synchronization registration matrix, it calculates the phase offset specific to each slave sensor. According to the phase mapping coefficients in the synchronization registration matrix, the phase offset is converted into latching parameters recognizable by the corresponding slave sensor's sampling trigger register. Latching is then distributed level by level according to the slave sensor topology. After all slave sensors have completed parameter latching, a parallel trigger sequence, perfectly aligned with the reference clock and capable of triggering all slave sensors in parallel, is generated. Finally, after the master sensor distributes the parallel trigger sequence, it receives the trigger response signals returned by each slave sensor in real time. Using the reference timing of the parallel trigger sequence as a reference signal and the response signals of each slave sensor as comparison signals, a cross-correlation algorithm is used to calculate the cross-correlation function of the two sets of signals. The delay difference corresponding to the peak value of the cross-correlation function is taken as the response cross-correlation parameter. This response cross-correlation parameter is compared with a preset threshold in the synchronization registration matrix. If it exceeds the threshold range, the link delay parameter of the corresponding slave sensor in the matrix is ​​updated, and the corrected parallel trigger sequence is re-distributed, completing the dynamic calibration of the synchronization deviation.

[0070] It should be noted that, in this application, the synchronization registration matrix refers to the reference matrix of the delay and phase mapping relationship of the trigger link between the master and slave sensors; the phase offset refers to the timing phase deviation generated by the master-slave transmission of the trigger signal; the sampling trigger register refers to the hardware execution unit deployed inside the slave sensor for latching trigger parameters and controlling the sampling start timing; the parallel trigger sequence refers to the standardized trigger instruction sequence that is synchronously issued to all slave sensors to ensure that the sampling actions of multiple slave sensors start synchronously; and the response cross-correlation parameter is a parameter that characterizes the timing matching degree between the slave sensor response and the reference trigger signal.

[0071] In step S2, initial test data of the object under test under multiple physical fields are collected synchronously from each sensor, and spatially aligned multiphysics data is generated based on a preset synchronous background grid according to all the initial test data.

[0072] In this embodiment, the initial test data of the object under test under multiple physical fields can be synchronously acquired from each sensor using the following steps:

[0073] A reference mapping matrix covering multiple physical fields is determined based on the phase difference sequence between the local clock source of each sensor and the master reference clock.

[0074] The test data corresponding to the test object in multiple physical fields are synchronized and aligned using the reference mapping matrix to obtain the test data vector. The multiple physical fields include pressure field, temperature field and vibration field.

[0075] The initial test data of each slave sensor under multiple physical fields are determined based on the data vector to be tested and the preset synchronous acquisition quality threshold.

[0076] In practice, firstly, the master sensor sends its master control reference clock to each slave sensor at fixed intervals. Each slave sensor collects the rising edge time difference between its local clock source and the received master control reference clock in real time, generating a continuous phase difference sequence and transmitting it back to the master sensor. The master sensor uses the least squares method to fit the clock frequency offset and phase offset parameters of each slave sensor. Based on the physical field channel attributes collected by each slave sensor, a reference mapping matrix is ​​constructed with the number of rows equal to the number of slave sensors and the number of columns corresponding to the clock calibration parameters and physical field mapping parameters. Then, each slave sensor collects the test data corresponding to the pressure field, temperature field, and vibration field of the object under test in real time through its corresponding physical field acquisition channel. Each sampling point is marked with a corresponding local timestamp and transmitted back to the master sensor. The master sensor uses the reference mapping matrix to convert the local time of each sampling point into the global master control reference time. Linear interpolation matching is performed on the multi-physical field test data under the same global time, and the multi-physical field data at the same spatial location and the same global time are arranged in a preset order to generate a test data vector. Finally, before testing, a synchronous acquisition quality threshold is pre-set, including three categories: timing synchronization deviation threshold, data amplitude fluctuation threshold, and channel consistency threshold. For each data vector to be tested, the main sensor calculates the timing deviation between the corresponding global time and the reference time, the amplitude fluctuation coefficient of each physical field data, and the consistency coefficient between multiple physical field channels. The three types of parameters are compared with the corresponding thresholds one by one, and invalid vectors that exceed the thresholds are eliminated. The valid vectors that pass the verification are sorted and organized according to the sensor number and physical field type to obtain the initial test data.

[0077] It should be noted that, in this application, the local clock source refers to an independent clock generation unit deployed inside a single slave sensor, which provides a local timing reference for the sampling action of that slave sensor; the master control reference clock is a highly stable standard clock signal output by the master sensor, providing a unique global timing reference for multi-physics field testing; the phase difference sequence refers to the variation law of timing deviation between the slave sensor's local clock source and the master control reference clock; the reference mapping matrix refers to the reference matrix for aligning the synchronization time of multi-physics field data; the test data refers to the original sampled data of the physical field corresponding to the test object obtained by the corresponding physical field acquisition channel of the slave sensor, without timing alignment processing; synchronization time alignment is the timing calibration process of uniformly mapping the sampled data of different slave sensors and different physical fields to the global master control timing reference at the same time. The data vector to be measured refers to a standardized data vector composed of multi-physics field sampling data at the same global time and spatial location after synchronization and alignment processing; the pressure field refers to the scalar physical field representing the magnitude, spatial distribution, and temporal variation of the pressure load at various spatial locations inside and on the surface of the object under test; the temperature field refers to the scalar physical field representing the temperature values, spatial distribution, and temporal variation at various spatial locations inside and on the surface of the object under test; the vibration field refers to the vector physical field representing the spatial distribution and temporal variation of the vibration displacement, velocity, and acceleration at various spatial locations inside and on the surface of the object under test after excitation; the synchronous acquisition quality threshold refers to a pre-set multi-dimensional quantitative judgment benchmark for determining the validity of synchronously acquired data; the initial test data refers to the multi-physics field sampling dataset that has passed the validity verification.

[0078] Preferably, in this embodiment, spatially aligned multiphysics data is generated based on a preset synchronous background grid according to all the initial test data, with reference to... Figure 2 As shown in the figure, this is a schematic diagram of the process for generating multiphysics data in some embodiments of this application. In this embodiment, the generation of multiphysics data can be achieved by the following steps:

[0079] In step S21, based on the spatial coordinates of all initial test data at each sensor node and the node relationship of the preset synchronization background grid, the mapping radial matrix corresponding to multiple synchronization background grid nodes is determined;

[0080] In step S22, the discrete state vector at each synchronous background grid node is determined based on the mapped radial matrix and the initial test data collected from the sensors;

[0081] In step S23, the discrete state vector is reconstructed into a continuous field along the topology of the synchronous background grid to generate a continuous field model in the spatial domain of the object under test.

[0082] In step S24, spatially aligned multiphysics data are determined based on the gradient change rate of the continuous field model at the boundary nodes of the synchronous background grid.

[0083] In specific implementation, firstly, the preset synchronous background grid is a tetrahedral discrete grid generated using the Delaunay triangulation method based on the three-dimensional geometric model of the object under test. Each node has a unique global three-dimensional spatial coordinate. The main sensor first extracts the measured spatial coordinates of each slave sensor node. For each synchronous background grid node, it searches for all slave sensor nodes within its preset search radius, calculates the Euclidean distance from each slave sensor node to the grid node, calculates weight coefficients based on the Gaussian radial basis function, and constructs a mapping radial matrix with the number of rows equal to the number of synchronous background grid nodes and the number of columns equal to the number of slave sensor nodes. Next, the main sensor first organizes the initial test data collected by each slave sensor into a global measured data column vector according to the unique number of the corresponding slave sensor node. It then performs a standardized matrix multiplication operation on this column vector and the mapping radial matrix to obtain the multiphysics parameter interpolation results corresponding to each synchronous background grid node. Finally, the measured physical field parameters, including pressure field, temperature field, and vibration field, at the same synchronous background grid node are arranged in a preset fixed order to generate a one-dimensional discrete state vector corresponding to that synchronous background grid node. Then, the main sensor uses the node topology of the synchronous background grid as the spatial reference and the discrete state vectors corresponding to each node of the synchronous background grid as the node constraints. It employs the finite element linear shape function interpolation method to continuously interpolate the multiphysics parameters within each grid cell. The interpolation results of all grid cells are then globally stitched together and globally smoothed according to the geometric boundary constraints of the object under test, generating a continuous multiphysics model covering the entire spatial domain of the object under test, where all physical parameters are continuously differentiable. Finally, the main sensor first extracts the global coordinates of all boundary nodes of the synchronous background grid. Based on the continuous field model, it uses the central difference method to calculate the gradient rate of change of each physical parameter along the normal and tangential directions at each boundary node. The calculated gradient rate of change is compared one by one with a preset field gradient threshold. Boundary node data exceeding the threshold are locally smoothed and corrected. Based on the corrected continuous field model, the multiphysics parameters at all nodes of the synchronous background grid are extracted, organized by node number and physical field type, and spatially aligned multiphysics data is generated.

[0084] It should be noted that, in this application, the synchronous background grid refers to the unified global spatial coordinate reference required for multiphysics test data; the node relationship refers to the reference correspondence between the spatial association attributes of the sensor's measured sampling nodes and the synchronous background grid reference nodes; the synchronous background grid node refers to the smallest discrete reference unit constituting the synchronous background grid, with a unique global three-dimensional spatial coordinate; the mapping radial matrix refers to the weight matrix of the spatial mapping relationship between the sensor nodes and the synchronous background grid nodes; the discrete state vector refers to the standardized discrete data vector of all measured physical field parameters at a single node of the synchronous background grid; and the topology refers to the connection relationship between the nodes of the synchronous background grid. With spatial arrangement rules; continuous field reconstruction refers to the standardized processing of converting multiphysics data of discrete grid nodes into a continuous field distribution model covering the entire domain of the object under test; continuous field model refers to a continuous multiphysics distribution mathematical model covering the entire domain of the object under test, which is reconstructed based on discrete node data; boundary node refers to a grid node located at the position where the synchronous background grid coincides with the geometric boundary of the object under test; gradient change rate refers to the rate of change of physical field parameters along spatial coordinates at the boundary node of the continuous field model; spatially aligned multiphysics data refers to a standardized dataset that is uniformly mapped to the global coordinate reference of the synchronous background grid, and the same spatial position corresponds to complete multiphysics parameters.

[0085] In addition, in this embodiment, the continuous field reconstruction of the discrete state vector along the topology of the synchronous background grid to generate a continuous field model in the spatial domain of the object under test can be achieved by the following steps:

[0086] The interpolation basis function system in the multiphysics field is determined based on the shape function of the discrete state vector in the synchronous background grid.

[0087] The field value continuity constraint conditions at the synchronous background grid nodes are determined based on the interpolation basis function system and the discrete state vector.

[0088] By fusing the boundary field distribution of the synchronous background grid with the continuous field value constraint, a continuous field model is generated in the spatial domain of the object under test.

[0089] In specific implementation, firstly, the synchronous background mesh used is a tetrahedral element discrete mesh generated based on the geometric model of the object under test. For each mesh element, using the synchronous background mesh nodes corresponding to the four vertices of the element as the reference, a first-order polynomial shape function is generated within the element using the linear shape function construction rules of the finite element method. The shape function satisfies the standardized rule that the value is 1 at the node and 0 at the other nodes. Corresponding element shape functions are constructed for the pressure field, temperature field, and vibration field respectively. All element shape functions are spliced ​​together globally according to the mesh topology to generate an interpolation basis function system covering the entire domain of multiple physics fields. Then, the main sensor uses the interpolation basis function system as the mathematical basis and the discrete state vector corresponding to each synchronous background mesh node as the node field value reference. For any two adjacent mesh elements in the synchronous background mesh, the common node at the common boundary of the two elements is extracted. Based on the interpolation basis function system, the field value expression and normal partial derivative expression of the two elements at the common boundary are calculated respectively. The field value and normal partial derivative of the two elements at the common boundary are made completely equal to construct the field value continuity constraint conditions of adjacent elements. All the constraint conditions of adjacent elements are summarized to form the global field value continuity constraint conditions. Finally, the main sensor first extracts the synchronous background grid boundary nodes corresponding to the geometric boundary of the object under test, and uses the measured multiphysics field parameters at the boundary as the boundary field distribution, setting it as the first type of boundary condition for field reconstruction. With the discrete state vector as the node field value reference and the interpolation basis function system as the interpolation carrier, combined with the global field value continuity constraint and the boundary field distribution constraint, a system of linear equations for the global field distribution is constructed. The Gaussian elimination method is used to solve the system of equations to obtain the global multiphysics continuous distribution expression, and generate a continuous field model in the spatial domain of the object under test.

[0090] It should be noted that, in this application, the shape function refers to the standardized polynomial function that maps the field value at any position within a cell to the field value at a node; the interpolation basis function system refers to the set of standardized interpolation functions that are generated by the global extension of the shape functions of each cell in the synchronous background grid and cover the entire spatial domain of the object under test; the field value continuity constraint condition refers to the quantitative constraint rule that ensures the continuity and consistency of the field value and field gradient at the common boundary of adjacent cells in the synchronous background grid; and the boundary field distribution refers to the boundary constraint data of the distribution characteristics of multi-physics field parameters at the geometric boundary of the object under test.

[0091] In step S3, the physical field components perpendicular to the test plane in the spatially aligned multiphysics data are guided into the test plane through the test direction conversion condition, and the converted physical field components are mapped and synchronized based on the historical physical field components in the test plane to generate synchronized test data in the test plane.

[0092] In this embodiment, guiding the physical field components perpendicular to the test plane in the spatially aligned multiphysics data into the test plane through test direction conversion conditions can be achieved using the following steps:

[0093] The physical field components perpendicular to the test plane are determined by the dot product of the field vector directions of each grid node in the spatially aligned multiphysics data and the test plane normal vector.

[0094] Vector projection is performed using the physical field components perpendicular to the test plane and the preset transformation direction cosine matrix within the test plane to generate multiple mapping relationships between physical field components and coordinate axes within the test plane.

[0095] Based on the mapping relationship and the shape function reconstruction conditions in the synchronous background grid, the planar distribution field of the equivalent physical field components at each grid node in the test plane is determined.

[0096] In practice, firstly, before testing, the main sensor pre-determines the unit normal vector of the test plane. This unit normal vector is a standardized unit vector used to calibrate the spatial attitude of the test plane. For each synchronous background grid node in the spatially aligned multiphysics data, the main sensor extracts the complete field vectors corresponding to the pressure field, temperature field, and vibration field at that node. The field vectors are then multiplied by the test plane normal vector, and the result is multiplied by the unit normal vector of the test plane to obtain the physical field components perpendicular to the test plane at that node. Next, the main sensor establishes a two-dimensional orthogonal local coordinate system within the test plane, determines the spatial orientation of the coordinate axes within the test plane, and generates a third-order orthogonal transformation direction cosine matrix based on the angles between the global and local coordinate systems and the direction cosine construction rule. The physical field components perpendicular to the test plane at each node are then multiplied by this matrix using a standardized matrix multiplication operation to complete the projection of the vectors onto the coordinate axes within the test plane. The transformation coefficients between each physical field component and the coordinate axes within the plane are calculated, generating a standardized mapping relationship. Finally, the main sensor first calculates the equivalent physical field components at each synchronous background grid node in the test plane based on the generated mapping relationship. The equivalent physical field components are used as the reference for the node field values. The shape functions of the units in the test plane in the synchronous background grid are used as the interpolation basis. According to the constraint rules of continuous common boundary field values ​​and continuous normal partial derivatives of adjacent units in the shape function reconstruction conditions, the element linear interpolation method is used to continuously interpolate and stitch the entire domain of all units in the test plane to generate the equivalent physical field component planar distribution field covering the entire domain of the test plane.

[0097] It should be noted that, in this application, the test plane refers to the designated two-dimensional reference plane of the object under test for multiphysics characteristic analysis; the test direction conversion condition refers to the preset conversion rule for standardizing and converting the out-of-plane physical field components perpendicular to the test plane into equivalent components within the test plane; the grid node refers to the smallest discrete reference unit constituting the synchronous background grid; the field vector direction refers to the spatial pointing attribute of the multiphysics vector at the grid node in the global coordinate system; the test plane normal vector refers to the unit vector perpendicular to the test plane and pointing in a preset positive direction; the physical field component refers to the out-of-plane component in the multiphysics vector whose direction is parallel to the test plane normal vector; the conversion direction cosine matrix refers to the standardized orthogonal matrix of the direction mapping relationship between the global coordinate system and the local coordinate system of the test plane; the vector projection refers to the standardized vector transformation operation for converting the out-of-plane physical field components perpendicular to the test plane into equivalent components of the coordinate axis directions within the test plane; the shape function reconstruction condition refers to the interpolation constraint rule for maintaining the continuous and smooth distribution of the equivalent physical field components within the plane; and the planar distribution field refers to the continuous field model of the spatial distribution characteristics of the equivalent physical field components.

[0098] Preferably, in this embodiment, the converted physical field components are mapped and synchronized based on the historical physical field components within the test plane to generate synchronized test data within the test plane, with reference to... Figure 3 As shown in the figure, this is a schematic diagram of the process for generating synchronous test data in some embodiments of this application. In this embodiment, the generation of synchronous test data can be achieved by the following steps:

[0099] In step S31, a timing synchronization rule indexed by grid nodes is determined based on the historical physics components and the transformed physics components in the test plane.

[0100] In step S32, the synchronization mapping coefficients of the converted physical field components are determined based on the time synchronization rules and the gradient of the changes of historical physical field components within a preset time window.

[0101] In step S33, the historical physical field components and the transformed physical field components are iterated through the synchronization mapping coefficient to generate preliminary synchronization test data on each grid node in the test plane.

[0102] In step S34, error correction is performed based on the deviation between the preliminary synchronous test data and the boundary constraints of the test plane to obtain synchronous test data within the test plane.

[0103] In practice, firstly, the main sensor uses the unique number of each grid node in the test plane as an index to extract the complete time series of the historical physics field components and the transformed physics field components of the corresponding node. Using the global time series benchmark of the historical physics field components as a reference, a cross-correlation algorithm is employed to calculate the time difference corresponding to the correlation peak of the two time series, determining the time series offset compensation amount for each grid node. The compensation rules and time series matching logic of all nodes are then summarized to generate a time series synchronization rule indexed by the grid nodes. Next, the main sensor first performs time series offset pre-compensation for the transformed physics field components according to the time series synchronization rule. Then, it extracts the complete time series of the historical physics field components within a preset time window, uses the central difference method to calculate the amplitude change gradient at each moment within the complete time series, and uses the amplitude change gradient as a dynamic weight to construct a least-squares objective function with the historical components as the benchmark and the transformed components as the calibration object. The minimum value of the objective function is then solved to obtain the synchronization mapping coefficients of each grid node. Then, the main sensor uses the synchronization mapping coefficients corresponding to each grid node as the correction benchmark, historical physical field components as state reference values, and the transformed physical field components as the state values ​​to be calibrated. A linear Kalman filter iterative algorithm is employed, with the state transition matrix set as the identity matrix and the observation matrix as the synchronization mapping coefficients. Time-by-time state prediction and update iterations are performed to eliminate residual amplitude deviations and phase jitter between components. After iterative convergence, preliminary synchronization test data for each grid node is generated. Finally, the main sensor first extracts the preliminary synchronization test data for all grid nodes at the test plane boundary, calculates the deviation between this data and the preset test plane boundary constraints, uses the deviation value as the boundary correction amount, and employs a boundary weighted smoothing correction method to correct the boundary node data. Then, using the corrected boundary data as constraints, global smoothing interpolation is performed on the node data inside the test plane to eliminate boundary distortions in the global field distribution, ultimately generating synchronization test data within the test plane.

[0104] It should be noted that, in this application, historical physics components refer to reference data that has been pre-acquired and calibrated under the same test plane and operating conditions; mapping synchronization refers to the process of matching and calibrating the timing, amplitude, and phase of the converted physics components in the plane to achieve full-domain synchronization and consistency of multiple physics components; the converted physics components are the equivalent physics vector components in the test plane obtained after direction conversion; the timing synchronization rule refers to the standardized matching rule that uses grid nodes as unique indexes to calibrate the timing correspondence between the converted physics components and historical physics components; the change gradient refers to the quantization parameter of the amplitude and timing change rate of the historical physics components within a preset time window; and the synchronization mapping coefficient refers to the converted physics components... The standardized correction coefficient matrix is ​​synchronized with the amplitude and phase of the historical physics components; state iteration refers to the standardized processing process of iteratively matching and calibrating the historical physics components and the transformed physics components based on the synchronization mapping coefficients at each time step; preliminary synchronization test data refers to the multiphysics dataset of each grid node in the test plane with preliminary synchronization matching of time sequence and amplitude; test plane boundary constraints refer to the legal variation range of physics parameters and field continuity constraint rules at the pre-calibrated test plane boundary; error correction refers to the standardized processing process of calibrating the boundary distortion and global residual deviation of the preliminary synchronization test data; synchronized test data refers to the standardized multiphysics dataset in the test plane with complete synchronization matching of time sequence, amplitude, and spatial distribution.

[0105] In addition, in this embodiment, the preliminary synchronization test data on each grid node in the test plane can be generated by iterating the state of the historical physical field components and the transformed physical field components through the synchronization mapping coefficients using the following steps:

[0106] Based on the state covariance matrix of the historical physical field components and the transformed physical field components on the time axis, the initial values ​​for iterative updates of the state vector at each grid node are constructed based on the synchronization mapping coefficient.

[0107] The state residuals of the historical physical field components and the transformed physical field components are iteratively fused using the synchronization mapping coefficients to obtain the minimum variance estimate of the state vector at each grid node.

[0108] The preliminary synchronization test data on each grid node in the test plane is determined based on the minimum variance estimate and the iterative convergence criterion.

[0109] In specific implementation, firstly, the main sensor treats a single grid node in the test plane as an independent processing unit, extracting the complete time series sequences of the historical physics components and the transformed physics components after time-series alignment on the time axis of that node. Using a covariance calculation method, the state covariance matrix of the two sequences is solved. This state covariance matrix characterizes the fluctuation characteristics and statistical correlation of the components. Using the synchronization mapping coefficient as a weighting benchmark, a state vector containing component amplitude and phase information is constructed, and initial values ​​for iterative updates are set. Then, the main sensor uses the initial values ​​for iterative updates as the starting point for computation. It first calculates the predicted state value of the historical physics components in the current iteration step, and subtracts it from the measured state value of the transformed physics components to obtain the state residual for the corresponding iteration step. Using the synchronization mapping coefficient as the observation weighting coefficient, the minimum variance unbiased estimation method is used to perform weighted iterative fusion on the state residual, successively updating the state vector and covariance matrix, and solving for the minimum variance estimate of the state vector. Finally, before testing, the iterative convergence criteria are pre-set, including two types: the relative difference threshold between adjacent iteration steps of the state vector and the covariance matrix convergence threshold. After each iteration, the main sensor calculates the relative deviation by subtracting the current minimum variance estimate from the previous iteration result, and calculates the convergence degree of the covariance matrix. It compares the results with the corresponding thresholds one by one. If the convergence criteria are met, the iteration is terminated. The converged estimates are then organized according to the grid node number to generate preliminary synchronous test data.

[0110] It should be noted that, in this application, the state covariance matrix refers to the quantification matrix of the state fluctuation characteristics and statistical correlation between the historical physics components and the transformed physics components on the time axis; the synchronization mapping coefficient refers to the standardized weighted correction coefficient for the synchronous matching calibration of the amplitude, phase, and timing of the transformed physics components in the test plane; the iterative update initial value refers to the initial calibration value of the state vector set when the state iteration operation starts; the state residual refers to the quantization deviation between the measured state value of the transformed physics components and the predicted state value of the historical physics components; the iterative fusion refers to the iterative operation process of achieving synchronous matching of the states of the historical physics components and the transformed physics components; the state vector is a standardized vector that integrates the amplitude, phase, and timing states of the corresponding historical and transformed physics components, with a single grid node as the index; the minimum variance estimate refers to the optimal unbiased estimate of the physics component with the smallest state estimation error variance; and the iterative convergence criterion refers to the two-dimensional quantization judgment rule for determining whether the state iteration operation has reached a stable synchronous state.

[0111] In step S4, the master sensor verifies the synchronization test data according to the synchronization node identifier of each slave sensor and outputs the multiphysics test results.

[0112] In this embodiment, the master sensor verifies the synchronization test data according to the synchronization node identifiers of each slave sensor, and outputs the multiphysics test results by the following steps:

[0113] The node attribution verification vector of each slave sensor data is obtained by comparing the slave sensor synchronization node identifier carried in the synchronization test data with the node topology mapping table stored locally by the master sensor.

[0114] The synchronization test data is verified node by node using the node affiliation verification vector to generate a synchronization integrity identifier for each sensor data.

[0115] Based on the synchronization integrity identifier and the preset multiphysics data fusion rules, the data that has passed the verification of each node are aggregated and output to obtain the multiphysics test results containing node traceability information.

[0116] In practice, firstly, before the test starts, the main sensor pre-builds and stores a node topology mapping table locally. This table records the unique binding relationships between all legitimate synchronization node identifiers and their corresponding slave sensor numbers, grid node numbers, and physical field types. The main sensor extracts the slave sensor synchronization node identifiers carried in each set of synchronization test data frames and compares them one by one with the legitimate identifiers in the node topology mapping table, generating a one-dimensional node affiliation verification vector containing identifier legitimacy and affiliation matching degree. Each element in this node affiliation verification vector corresponds to the verification result of a single set of data. Then, using the unique number of each grid node in the test plane as an index, the main sensor performs node-by-node verification of the corresponding node's synchronization test data based on the judgment result of the node affiliation verification vector. It checks the temporal synchronization, data frame integrity, and identifier consistency of all physical field data corresponding to that node. A multi-dimensional threshold verification method is used to compare the verification results with preset integrity thresholds one by one, generating a unique synchronization integrity identifier for each slave sensor's corresponding data. Finally, the main sensor first filters out all valid synchronization test data based on the synchronization integrity identifier, and then, according to the preset multi-physics data fusion rules, uses the grid node number as the spatial index and the global time series as the time reference to standardize and aggregate all physical field data at the same node and at the same time. Each group of data is bound to the corresponding synchronization node identifier as traceability information, and finally the multi-physics test results are generated and output.

[0117] It should be noted that, in this application, the synchronization node identifier refers to the unique identification identifier pre-assigned to each slave sensor; verification correspondence refers to the process by which the master sensor verifies and matches the acquisition nodes of the synchronization test data based on the synchronization node identifiers of the slave sensors; multiphysics test results refer to the bound node traceability information and a standardized multiphysics dataset covering the entire domain of the object under test; the node topology mapping table refers to a standardized data table pre-stored locally by the master sensor, recording the unique binding relationship between legitimate synchronization node identifiers and their corresponding slave sensors, grid nodes, and physical field types; and the node affiliation verification vector refers to the synchronization node carried in the synchronization test data. The standardized one-dimensional verification vector is the result of matching the point identifier with the node topology mapping table; node-by-node verification refers to the process of verifying the synchronous test data of the corresponding node based on the node attribution verification vector, with a single grid node in the test plane as the smallest processing unit; synchronization integrity identifier refers to the quantitative status identifier of the attribution legality, temporal synchronization and data integrity of a single set of synchronous test data after verification; multi-physics data fusion rule refers to the rule of standardizing and aggregating the multi-physics data of each node that has passed verification according to spatial topology and temporal reference; node traceability information refers to the identity attribute information of the traceable data acquisition source link bound to the final test data.

[0118] In this embodiment, reference Figure 4 As shown in the figure, this diagram illustrates the distribution of the synchronous background grid and multiphysics sampling nodes on the test plane. In this diagram, XYZ represents the global spatial rectangular coordinate system. The XY plane is the pre-defined test plane of this scheme, serving as the core reference plane for equivalent transformation, synchronous calibration, and characteristic analysis of multiphysics components. The Z-axis represents the direction of the unit normal vector of the test plane. The regular grid structure within the test plane is the synchronous background grid, serving as the unified spatial reference for spatial alignment, interpolation reconstruction, and component transformation of multiphysics data. Each intersection of the grid is a synchronous background grid node, providing a unified global spatial anchor point for data acquisition from multiple sources from sensors. Nodes 1 to 4, marked with solid black dots, are synchronous sampling nodes from sensors deployed on the test plane. Each node corresponds to a unique synchronous node identifier and is the actual acquisition unit for multiphysics data such as pressure, temperature, and vibration fields, corresponding to the installation and sampling positions of the sensors in this scheme. The electric, magnetic, and temperature fields marked in the figure represent the types of multiphysics fields to be measured, illustrating the multiphysics distribution characteristics of the object under test within the entire test plane and its corresponding spatial domain.

[0119] Therefore, in this application, the master sensor verifies the synchronous test data according to the synchronization node identifiers of each slave sensor and outputs multiphysics test results. Specifically, by determining the spatially aligned multiphysics data covering the entire domain of the object under test, a standardized global dataset containing complete multiphysics parameters corresponding to each grid node, constructed based on a unified global spatial coordinate reference, is obtained. This solves the problems of spatial non-overlapping of acquisition nodes from multiple source slave sensors and spatial misalignment of different physical field data, eliminates component conversion system errors caused by spatial matching deviations, and ensures the consistency of the reference for physical field component decomposition and conversion. The test plane is determined. By using the synchronous test data within the test plane, a standardized dataset of equivalent multi-physics components that is continuous and consistent across the entire plane after equivalent transformation of out-of-plane components, synchronous calibration of historical reference mapping, and correction of boundary errors can be obtained. This fully preserves the effective physical information of the original out-of-plane physical field components, eliminates systematic errors and random jitter introduced during component transformation, ensures the temporal synchronization, amplitude consistency, and field distribution continuity of all physical field components within the test plane, avoids interference from component transformation distortion and synchronization deviation on the test results, and achieves accurate synchronous matching of multi-physics data from the component dimension, thus comprehensively improving the overall accuracy of multi-physics synchronous testing under complex working conditions.

[0120] In summary, the technical solution adopted in this application can realize the equivalent transformation of physical field components during multi-physics field testing, thereby improving the accuracy of synchronous testing of multi-physics fields under complex working conditions.

[0121] Example 2: This application provides a multiphysics testing system based on synchronous data acquisition, referencing... Figure 5 As shown in the figure, this is a modular structure diagram of a multiphysics testing system based on synchronous acquisition according to this embodiment of the present application. The testing system includes:

[0122] The configuration module 100 is used to generate a test trigger signal during multi-physics field testing by the main sensor and to synchronously configure the test trigger signal to multiple slave sensors.

[0123] The acquisition module 200 is used to synchronously acquire initial test data of the object under test under multiple physical fields through each sensor, and generate spatially aligned multiphysics data based on a preset synchronous background grid according to all the initial test data;

[0124] The mapping module 300 is used to guide the physical field components perpendicular to the test plane in the spatially aligned multi-physics data to the test plane through the test direction conversion condition, and to map and synchronize the converted physical field components based on the historical physical field components in the test plane to generate synchronized test data in the test plane.

[0125] The verification module 400 is used by the main sensor to verify the synchronization test data according to the synchronization node identifier of each slave sensor and output the multiphysics test results.

[0126] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, 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 processor, 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, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0128] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A multiphysics testing method based on synchronous acquisition, characterized in that, The testing method includes the following steps: The master sensor generates a test trigger signal for multiphysics field testing and determines a synchronization registration matrix for the synchronous configuration of the test trigger signal. The phase offset of the test trigger signal is mapped step by step to the sampling trigger register of each slave sensor through the synchronization registration matrix to obtain a parallel trigger sequence. The deviation of the synchronous configuration on multiple slave sensors is dynamically calibrated according to the cross-correlation parameters of the responses fed back by each slave sensor and the parallel trigger sequence. A reference mapping matrix covering multiple physical fields is determined based on the phase difference sequence between the local clock source of each slave sensor and the master reference clock. The test data corresponding to the object under test in the multiple physical fields are synchronized using the reference mapping matrix to obtain a test data vector. The multiple physical fields include pressure, temperature, and vibration fields. Initial test data for each slave sensor under the multiple physical fields is determined based on the test data vector and a preset synchronous acquisition quality threshold. A mapping radial matrix corresponding to multiple synchronous background grid nodes is determined based on the spatial coordinates of all initial test data at each slave sensor node and the node relationship of a preset synchronous background grid. A discrete state vector at each synchronous background grid node is determined based on the mapping radial matrix and the initial test data acquired by each slave sensor. The discrete state vector is reconstructed along the topology of the synchronous background grid to generate a continuous field model in the spatial domain of the object under test. Spatially aligned multiphysics data is determined based on the gradient rate of change of the continuous field model at the boundary nodes of the synchronous background grid. The physical field components perpendicular to the test plane are determined by the dot product of the field vector directions of each grid node in the spatially aligned multiphysics data and the test plane normal vector. Vector projection is performed using the physical field components perpendicular to the test plane and a preset transformation direction cosine matrix within the test plane to generate multiple mapping relationships between physical field components and coordinate axes within the test plane. Based on these mapping relationships and the shape function reconstruction conditions in the synchronization background grid, the planar distribution field of the equivalent physical field components at each grid node within the test plane is determined. Temporal synchronization rules indexed by grid nodes are determined based on the historical and transformed physical field components within the test plane. Synchronization mapping coefficients for the transformed physical field components are determined based on these temporal synchronization rules and the gradient of historical physical field component changes within a preset time window. The state of the historical and transformed physical field components is iterated using these synchronization mapping coefficients to generate preliminary synchronization test data for each grid node within the test plane. Error correction is performed based on the deviation between the preliminary synchronous test data and the boundary constraints of the test plane to obtain synchronous test data within the test plane; The master sensor verifies the synchronization test data according to the synchronization node identifiers of each slave sensor and outputs the multiphysics test results.

2. The multiphysics testing method based on synchronous acquisition as described in claim 1, characterized in that, The process of reconstructing a continuous field model in the spatial domain of the object under test by reconstructing the discrete state vector along the topological structure of the synchronous background grid specifically includes: The interpolation basis function system in the multiphysics field is determined based on the shape function of the discrete state vector in the synchronous background grid. The field value continuity constraint conditions at the synchronous background grid nodes are determined based on the interpolation basis function system and the discrete state vector. By fusing the boundary field distribution of the synchronous background grid with the continuous field value constraint, a continuous field model is generated in the spatial domain of the object under test.

3. The multiphysics testing method based on synchronous acquisition as described in claim 1, characterized in that, The process of iterating the state of the historical and transformed physical field components using the synchronization mapping coefficients to generate preliminary synchronization test data for each grid node in the test plane specifically includes: Based on the state covariance matrix of the historical physical field components and the transformed physical field components on the time axis, the initial values ​​for iterative updates of the state vector at each grid node are constructed based on the synchronization mapping coefficient. The state residuals of the historical physical field components and the transformed physical field components are iteratively fused using the synchronization mapping coefficients to obtain the minimum variance estimate of the state vector at each grid node. The preliminary synchronization test data on each grid node in the test plane is determined based on the minimum variance estimate and the iterative convergence criterion.

4. The multiphysics testing method based on synchronous acquisition as described in claim 1, characterized in that, The master sensor verifies the synchronization test data according to the synchronization node identifiers of each slave sensor, and outputs multiphysics test results, specifically including: The node attribution verification vector of each slave sensor data is obtained by comparing the slave sensor synchronization node identifier carried in the synchronization test data with the node topology mapping table stored locally by the master sensor. The synchronization test data is verified node by node using the node affiliation verification vector to generate a synchronization integrity identifier for each sensor data. Based on the synchronization integrity identifier and the preset multiphysics data fusion rules, the data that has passed the verification of each node are aggregated and output to obtain the multiphysics test results containing node traceability information.

5. A multiphysics testing system based on synchronous acquisition, used to execute a multiphysics testing method based on synchronous acquisition as described in any one of claims 1 to 4, characterized in that, The testing system includes: The configuration module is used to generate a test trigger signal during multiphysics field testing by the main sensor and to synchronously configure the test trigger signal to multiple slave sensors. The acquisition module is used to synchronously acquire initial test data of the object under test under multiple physical fields through each sensor, and generate spatially aligned multiphysics data based on a preset synchronous background grid according to all the initial test data; The mapping module is used to guide the physical field components perpendicular to the test plane in the spatially aligned multiphysics data to the test plane through the test direction conversion condition, and to map and synchronize the converted physical field components based on the historical physical field components in the test plane to generate synchronized test data in the test plane. The verification module is used by the master sensor to verify the synchronization test data according to the synchronization node identifier of each slave sensor, and output the multiphysics test results.

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