A contactless detection method and system for contacts based on x-ray spectral signatures

Abstract

A contactless detection method and system for contact based on X-ray spectrum characteristics, comprising: step 1, obtaining contact surface point cloud data, and establishing a contact surface defect feature vector; step 2, based on the feature vector in step 1, constructing a field emission model; step 3, based on the model in step 2, solving the electron motion trajectory by using the multi-physical field coupling method; step 4, based on the electron motion trajectory in step 3, modeling the X-ray radiation characteristics by using the Monte Carlo simulation method, and obtaining the X-ray spectrum distribution; step 5, based on the X-ray spectrum distribution obtained in step 4, extracting feature parameters, and constructing a spectrum feature vector; step 6, based on the spectrum feature vector in step 5, carrying out contact surface state inversion and quantitative evaluation, and realizing the non-contact quantitative detection of contact defects. The present application accurately reflects the influence of the contact surface on the local electric field enhancement and the field emission behavior, and improves the accuracy and physical interpretability of the detection results.

Description

Technical Field

[0001] This invention relates to the field of power equipment testing, and in particular to a non-contact contact testing method and system based on X-ray energy spectrum characteristics. Background Technology

[0002] With the continuous improvement of power system voltage levels, vacuum circuit breakers are increasingly widely used in high-voltage and ultra-high-voltage power transmission and transformation equipment. As a key component of the vacuum interrupter, the surface condition of the contacts directly affects the electric field distribution characteristics and arc-extinguishing performance. During long-term operation, under the influence of arc erosion, mechanical wear, and material migration, the surface of the contacts will form varying degrees of local depressions and sharp structures, leading to enhanced local electric field distortion, which in turn can trigger abnormal field emission phenomena. In severe cases, it may even induce insulation breakdown or equipment failure.

[0003] Currently, the main methods for detecting the surface condition of contacts include optical inspection after disassembly, electron microscopy observation, and indirect electrical parameter analysis. Among these, direct detection methods can obtain relatively accurate surface morphology information, but they usually require disassembly of the vacuum interrupter, which has disadvantages such as complex operation and high cost. Indirect methods based on electrical parameters, such as current characteristics and voltage characteristics, are difficult to accurately reflect the microscopic defect structure of the contact surface, resulting in lower detection accuracy.

[0004] Current research indicates that the microstructure of the contact surface significantly affects the behavior of field-induced emission electrons, and further influences the trajectory of electrons in the electric field and the X-ray radiation characteristics generated by their interaction with the anode material. Existing technology 1 discloses a method for evaluating the discharge characteristics of SF6 in GIS under high-energy X-rays (CN117169662A), including building a GIS physical model and an equivalent electric field ionization test platform; constructing a geometric model M1 matching the GIS physical model using commercial finite element software; using commercial finite element software to sample the actual X-ray source characteristics in the GIS physical model to obtain an ionization electric field geometric model M3; recording the transport process information Q of electrons generated on the inner wall of the GIS entering the SF6 gas under ionization and electric field conditions; and finally evaluating the current ionization electric field geometry based on the information data Q and the critical conditions for streamer discharge. Whether the SF6 gas discharge in model M3 will produce a streamer discharge event is still under investigation. However, the shortcomings of this prior art 1 are that although it involves the simulation analysis of electric field distortion and electron avalanche caused by defects, its core purpose is to assess whether SF6 gas in GIS will produce a streamer discharge. Its output results are only for insulation risk assessment. It does not establish a quantitative correlation between the micro-morphological parameters of the contact surface in the vacuum interrupter and the X-ray energy spectrum characteristics, nor does it involve the inversion calculation of contact surface defects based on energy spectrum characteristics. Its description of "burrs" or "particles" is only used to analyze the local electric field distortion and electron avalanche process, and does not perform parameterized modeling of surface defects.

[0005] Prior art 2 discloses a vacuum degree detection device and method for a vacuum interrupter (CN121409505A), including: setting a field emission cathode on a metal end cap of one of the vacuum interrupters; setting an anode on a metal shield inside the vacuum interrupter near the field emission cathode; connecting the field emission cathode and the anode with a high-voltage DC power supply, the high-voltage DC power supply being used to apply voltage to the electrodes to drive the field emission cathode to emit an electron beam that strikes the anode; and an X-ray energy spectrum detector facing the anode, the X-ray energy spectrum detector being used to measure the bremsstrahlung X-ray energy spectrum generated when the electron beam strikes the anode. By actively setting an electron emission source inside the interrupter, the distribution of the X-ray energy spectrum generated is measured. However, the shortcoming of prior art 2 is that although it uses X-ray energy spectrum for detection, its detection object is the gas pressure inside the vacuum interrupter. Its technical principle is based on the electron energy attenuation caused by residual gas collisions, and the vacuum degree is inverted through the energy spectrum change. It also does not involve parameterized modeling of micro-defects on the contact surface, field enhancement behavior analysis, and surface state inversion.

[0006] Therefore, there is an urgent need for a detection method and system for quantitative inversion of the microstructure of the contact surface based on X-ray energy spectrum characteristics. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a non-contact contact detection method and system based on X-ray energy spectrum characteristics. This solves the problem that existing technologies rely on complex disassembly for contact surface condition detection and lack the ability to assess microscopic defects, thereby achieving non-contact quantitative detection of contact surface defects.

[0008] The present invention adopts the following technical solution.

[0009] The first aspect of this invention provides a non-contact contact detection method based on X-ray energy spectrum characteristics, comprising: Step 1: Obtain point cloud data of the contact surface and establish feature vector of contact surface defects; Step 2: Based on the feature vectors from Step 1, construct the field-induced emission model; Step 3: Based on the model in Step 2, the electron trajectory is solved using a multiphysics coupling method; Step 4: Based on the electron trajectory in Step 3, the X-ray radiation characteristics are modeled using the Monte Carlo simulation method to obtain the X-ray energy spectrum distribution; Step 5: Based on the X-ray energy spectrum distribution obtained in Step 4, feature parameters are extracted to construct an energy spectrum feature vector; Step 6: Based on the energy spectrum feature vector from Step 5, perform contact surface state inversion and quantitative evaluation to obtain the contact surface defect feature vector after inversion, thereby realizing non-contact quantitative detection of contact defects.

[0010] Preferably, in step 1, the feature vector of the contact surface defect is:

[0011] Where S is the feature vector of the contact surface defect; The surface arithmetic mean height; The root mean square height; For contact surface deflection; Kubularity of the contact surface; This refers to the local defect density per unit area. The autocorrelation length; The average feature depth; The mean radius of curvature; It is the minimum radius of curvature.

[0012] Preferably, the autocorrelation length is:

[0013] in, The autocorrelation length; This represents the distance the surface moves laterally. This represents the distance the surface moves longitudinally. This is a height function.

[0014] Preferably, in step 2, the field emission model is established based on the Murphy-Good theory: in, These are dimensionless field-induced emission parameters; Let be the initial field-induced emission current density at the k-th grid cell; Considering thermal correction for the field emission current density at the kth grid cell; Let be the normal electric field intensity of the k-th grid cell; It is the equivalent field strength enhancement factor; It is a work function; T This refers to the surface temperature of the cathode emission region in the vacuum interrupter. For thermal field-induced emission temperature correction parameters; This is the barrier height correction function. This is the correction function for the barrier transmission effect.

[0015] Preferably, the equivalent field strength enhancement factor is:

[0016] in, It is the equivalent field strength enhancement factor; The weighting coefficients are and satisfy the following conditions: ; Let be the local field enhancement factor for the i-th local defect; This refers to the number of local defects within the detection area.

[0017] Preferably, in step 3, finite element simulation software is used to perform multi-physics coupled electric field and particle motion equations to calculate the motion trajectory:

[0018] in, m For electronic quality; v The electron velocity vector is denoted by 'e'; 'e' is the absolute value of the electron charge. Let be the local electric field intensity vector at the k-th spatial grid cell.

[0019] Preferably, multiphysics coupling is performed on the local grid of emitted electrons to calculate the number of emitted electrons in the local grid:

[0020] in, The number of electrons emitted by local grid k; For local field emission area; t The time is the instantaneous steady-state field.

[0021] Preferably, in step 5, the X-ray energy spectrum eigenvector is:

[0022] in, This represents the eigenvectors of the X-ray energy spectrum. This represents the total number of response events; The energy center of gravity of the energy spectrum; The proportion of high-energy regions; The energy spectrum distribution dispersion is denoted as .

[0023] Preferably, the total number of response events is:

[0024] in, X-ray energy spectrum distribution, E This represents the energy spectrum of different events.

[0025] Preferably, the contact surface state inversion model is as follows:

[0026] Where S is the feature vector of the contact surface defect; F For contact surface state inversion model; This represents the eigenvectors of the X-ray energy spectrum.

[0027] A second aspect of the present invention provides a non-contact contact detection system utilizing the above-described method, comprising: The module includes a contact data acquisition module, a field emission model construction module, an electron trajectory solving module, an X-ray energy spectrum distribution module, an energy spectrum eigenvector construction module, and an inversion module. Among them, the contact data acquisition module is used to acquire point cloud data of the contact surface and establish a feature vector of contact surface defects; The field emission model building module is used to build field emission models; The electron trajectory solving module is used to solve the electron trajectory using a multiphysics coupling method; The X-ray energy spectrum distribution module is used to model the X-ray radiation characteristics using the Monte Carlo simulation method to obtain the X-ray energy spectrum distribution. The energy spectrum feature vector construction module is used to extract feature parameters and construct energy spectrum feature vectors; The inversion module is used to perform inversion and quantitative evaluation of the contact surface condition, enabling non-contact quantitative detection of contact defects.

[0028] A third aspect of the present invention provides a terminal, including a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0029] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0030] Compared with the prior art, the present invention has at least the following beneficial effects: This invention addresses the problems of existing technologies, such as reliance on disassembly for vacuum contact surface condition detection, difficulty in quantitative assessment of microscopic defects, and lack of non-contact detection methods. It proposes a non-contact contact detection method and system based on X-ray energy spectrum characteristics. Unlike existing technologies that primarily focus on vacuum level detection, gas discharge assessment, or X-ray imaging, this invention, for the first time, takes the microscopic morphology of the contact surface as the core detection object. It establishes a coupling relationship between "microscopic defects on the contact surface—local field enhancement—field emission behavior—electron transport process—X-ray energy spectrum characteristics," realizing a technical path for inverting and analyzing the contact surface condition using measurable X-ray energy spectra.

[0031] This invention employs a non-contact detection method, enabling the detection of the contact surface condition without disassembling the vacuum interrupter. This avoids the problems of complex operation, high cost, and potential damage to the original operating state of the equipment associated with traditional disassembly and detection methods. It is suitable for on-site detection and has the potential to be further applied to online condition monitoring, demonstrating promising engineering application prospects.

[0032] This invention employs a modeling approach that combines surface statistical parameters with local geometric parameters to provide a unified parametric characterization of the macroscopic roughness and local sharp defects on the contact surface. It considers both the overall surface undulations and microstructural features such as local radius of curvature, feature depth, and defect density. This allows for a more accurate reflection of the contact surface's influence on local electric field enhancement and field emission behavior, improving the accuracy, sensitivity, and physical interpretability of the detection results.

[0033] Furthermore, this invention does not merely perform simple X-ray signal measurements, but establishes a quantitative mapping relationship between energy spectrum characteristic parameters and contact surface defect parameters through multiphysics coupling modeling. By extracting characteristic parameters such as the total number of response events, energy centroid, high-energy region proportion, and energy spectrum dispersion, a quantitative assessment of the degree of contact surface defects is achieved, rather than simply a qualitative judgment as in traditional methods. Compared to existing technologies that can only be used for discharge risk analysis, vacuum degree detection, or structural imaging, this invention can further achieve non-contact inversion of the microscopic defect state of the contact surface, improving detection depth and state assessment capabilities.

[0034] Furthermore, the detection method based on X-ray energy spectrum characteristics established in this invention has good scalability and can be adaptively modeled according to different contact structures, voltage levels and surface defect types, providing a new technical approach for contact condition detection and life assessment of high-voltage vacuum circuit breakers.

[0035] Currently, there is no technical solution for quantitative inversion of the microstructure of contact surfaces using X-ray energy dispersive spectroscopy (EDS). This invention establishes a coupling relationship between "contact surface microstructure—local field enhancement—field emission behavior—electron transport process—X-ray energy dispersive spectroscopy (EDS) characteristics," achieving non-contact quantitative inversion of contact surface defect parameters by extracting EDS characteristics. The detection object, technical path, and implementation goals of this application differ from existing technologies, demonstrating significant inventiveness and technological advancement. Attached Figure Description

[0036] Figure 1 This invention relates to a non-contact contact detection method and system based on X-ray energy spectrum characteristics.

[0037] Figure 2 This invention proposes a method for calculating the trajectory characteristics of field-induced electrons in a vacuum arc-extinguishing chamber. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0040] Example 1 like Figure 1-2 As shown, Embodiment 1 of the present invention provides a non-contact contact detection method based on X-ray energy spectrum characteristics, comprising: (1) Obtain point cloud data of the contact surface by three-dimensional surface topography measurement, obtain the surface height function by equivalent modeling method, and extract the overall surface undulation parameters, height distribution morphology parameters, spatial organization parameters and local geometric parameters to establish the contact surface state feature vector; (2) Based on the surface defect characteristics, establish an electric field distribution model, calculate the local electric field intensity on the contact surface, define the local field enhancement factor, and construct an equivalent field enhancement factor to characterize the overall field emission capability; (3) Based on the electric field distribution and field emission model, calculate the electron emission current density and initial energy distribution, and solve the trajectory and energy evolution process of electrons in the vacuum interrupter by multi-physics coupling method; (4) Based on the Monte Carlo method, the interaction process between electrons and the internal structure of the arc-extinguishing chamber was simulated to obtain the X-ray energy spectrum distribution under a given voltage condition; (5) Extract feature parameters from the X-ray energy spectrum, including but not limited to the total number of response events, energy centroid, high energy region proportion and energy spectrum dispersion, and construct an energy spectrum feature vector; (6) Establish the mapping relationship between the energy spectrum feature vector and the surface defect features of the contact to realize the inversion and quantitative evaluation of the surface state of the contact; (7) In actual testing, energy spectrum data is collected by an external X-ray detector and combined with the mapping model to achieve non-contact detection of contact defects.

[0041] Example 2 Embodiment 2 of the present invention provides a non-contact contact detection method based on X-ray energy spectrum characteristics, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain point cloud data of the contact surface and establish feature vector of contact surface defects; Point cloud data of contact surfaces with different defect levels were acquired using a 3D surface topography measurement device, and the height function for stereo vision reconstruction was obtained through grayscale mapping. A set of surface defect characteristic parameters for characterizing the unevenness of the contact surface is established. These surface defect characteristic parameters include, but are not limited to, overall surface undulation parameters, height distribution morphology parameters, spatial organization parameters, and local defect parameters, specifically defined as follows: Overall surface undulation parameters include the surface arithmetic mean height. and surface root mean square height Surface arithmetic mean height The calculation formula is:

[0042] in, The surface arithmetic mean height represents the average deviation of the surface height from the mean surface; A is the area of ​​the sampling region. This is a height function.

[0043] Root mean square height The calculation formula is:

[0044] in, , where is the root mean square height, an index characterizing the surface roughness of the contact; A is the area of ​​the sampling region; This is a height function.

[0045] The height distribution morphological parameters include skewness and kurtosis Deflection of the contact surface The calculation formula is:

[0046] in, The contact surface skewness describes the direction of skewness in the surface height distribution. When it is less than 0, it indicates that the depression is dominant, and when it is greater than 0, it indicates that the protrusion is dominant. The root mean square height is A; A is the area of ​​the sampling region. This is a height function.

[0047] Contact surface kurtosis The calculation formula is:

[0048] in, The kurtosis of the contact surface describes the sharpness of the height distribution. When it is greater than 3, there is a sharp extreme value. When it is approximately equal to 3, it is close to a normal distribution. It can be used to determine whether there are strong field emission sensitive points. The root mean square height is A; A is the area of ​​the sampling region. This is a height function.

[0049] Spatial organization parameters include the density of local defects per unit area. and autocorrelation length Local area within a unit area Defect density The calculation formula is:

[0050] in, This represents the local defect density per unit area, used to describe the spatial distribution characteristics of surface defects. A represents the number of local defects within the detection area; A is the area of ​​the sampling area.

[0051] Autocorrelation length The calculation formula is:

[0052] in, The autocorrelation length is used to characterize the spatial correlation scale of surface texture; when the surface height increases or decreases to a threshold interval, This represents the distance the surface moves laterally. The threshold interval represents the distance the surface moves longitudinally. The size of the threshold interval depends on the level of detail in the contact surface description; higher detail results in a lower threshold. For example, when the 3D surface sampling interval is 1... The time threshold interval can be 10. ; This is a height function.

[0053] For the i-th local defect, extract its geometric feature parameters:

[0054] in, Let be the geometric feature vector of the i-th local defect; Let be the feature depth of the i-th local defect. Let be the feature width of the i-th local defect. Let be the minimum radius of curvature of the edge or tip of the i-th local defect. Let be the local slope angle of the i-th local defect.

[0055] Further defining the statistic, the average characteristic depth of the contact surface. for:

[0056] Among them, the average feature depth Used to describe the overall level of damage; To determine the number of local defects within the detection area; Let be the feature depth of the i-th local defect.

[0057] Average radius of curvature of defects on the contact surface for:

[0058] Among them, the average radius of curvature Describe the overall "sharpness" of the surface; To determine the number of local defects within the detection area; Let be the minimum radius of curvature of the edge or tip of the i-th local defect.

[0059] Minimum radius of curvature for:

[0060] Minimum radius of curvature It determines the maximum field enhancement factor; Let be the minimum radius of curvature of the edge or tip of the i-th local defect.

[0061] Based on the above parameters, a feature vector S of contact surface defects is constructed:

[0062] Where S is the feature vector of the contact surface defect; The surface arithmetic mean height; The root mean square height; For contact surface deflection; Kubularity of the contact surface; This refers to the local defect density per unit area. The autocorrelation length; The average feature depth; The mean radius of curvature; It is the minimum radius of curvature.

[0063] The feature vector of the contact surface defect characterizes both the overall unevenness of the contact surface and the characteristics of local sharp defects, thus reflecting both the macroscopic surface roughness and the microscopic field emission sensitive structure.

[0064] Step 2: Based on the feature vector of the contact surface defect in Step 1, construct the electric field distribution and field emission model; Define the nominal average electric field between contacts for:

[0065] in, This represents the nominal average electric field between the contacts; U Apply voltage across the moving and stationary contacts; d This refers to the contact gap.

[0066] Based on the contact surface defect feature vector S obtained in step 1, the local electric field intensity at each location on the contact cathode surface is obtained through finite element simulation. E Define the local field enhancement factor for the i-th local defect. for:

[0067] in, Let be the local field enhancement factor for the i-th local defect; This represents the maximum local electric field intensity in the local defect region. This represents the nominal average electric field between the contacts.

[0068] To characterize the overall field-induced emission capability of the contact, this application introduces an equivalent field enhancement factor. Existing technologies typically analyze single defect regions using local maximum field enhancement factors, which only reflect the degree of electric field distortion near sharp local structures and are insufficient to effectively describe the overall field emission behavior of complex contact surfaces under the combined influence of multiple defects. Especially for contact surfaces in actual operation, defects are often randomly distributed, multi-scale coexistence, and exhibit significant differences in local field enhancement levels. Using only local maximum field strength is insufficient to establish a stable correspondence between surface states and X-ray energy spectra. Therefore, this application introduces an equivalent field enhancement factor by weighting and equivalently processing the field enhancement factors of different local defect regions to comprehensively characterize the overall field emission capability of the contact. This parameter considers not only local maximum field strength but also the defect area ratio, local emission contribution, and spatial distribution characteristics, thus more accurately reflecting the overall impact of complex surface structures on electron emission behavior. By introducing the equivalent field enhancement factor, this application achieves a unified description from local microscopic defects to overall field emission behavior, improving the stability and interpretability of the mapping relationship between contact surface states and X-ray energy spectrum characteristics, and providing a foundation for subsequent quantitative inversion of contact surface defects based on X-ray energy spectrum characteristics.

[0069]

[0070] in, It is the equivalent field strength enhancement factor; The weighting coefficients are and satisfy the following conditions: It includes, but is not limited to, determination based on defect area ratio, local current contribution, or geometric significance; This refers to the number of local defects within the detection area.

[0071] Based on the microscopic protrusions, depressions, and local electric field enhancement effects caused by the protrusions on the contact surface, the electric field strength is obtained through an electric field solution model, and then a field emission current model is established based on Murphy-Good theory: in, The electric field strength in the normal direction of the k-th grid cell is expressed in V / cm. The initial field-induced emission current density at the k-th grid cell is expressed in A / cm². 2 ; The current density coefficient is given under the condition that the electric field strength is normalized to V / cm and the work function is normalized to eV. The field emission current density at the k-th grid cell considering thermal correction is expressed in A / cm². 2 ; The work function is expressed in eV. These are dimensionless field-induced emission parameters. and This is a field emission correction function that takes into account the mirror barrier effect. It is a correction term in the existing Murphy-Good field emission theory and is used to correct the error caused by the triangular barrier approximation in the ideal Fowler-Nordheim model. This is the barrier height correction function. This is the barrier transmission effect correction function, the specific form of which can be obtained by using the approximate expression in the existing Murphy-Good field emission theory or by numerical calculation. T The surface temperature of the cathode emission region in the vacuum interrupter is expressed in K. Under low field emission current conditions, the surface temperature of the cathode emission region can be approximated as the ambient temperature. When the field emission current increases and local heat accumulation becomes non-negligible, the local temperature rise can be further corrected by combining the Joule heating effect and the electron emission heating effect. The thermal field-induced emission temperature correction parameter is dimensionless. From this, the electron emission current density and the initial electron energy distribution are obtained.

[0072] Step 3: Based on the model in Step 2, the electron trajectory is solved using a multiphysics coupling method; A simulation model of the vacuum interrupter under non-closing conditions with external voltage applied across the moving and stationary contacts is established. The calculation method for the field-induced emission electron trajectory characteristics within the vacuum interrupter is as follows: Figure 2 As shown.

[0073] Calculate the local field emission area S(k) and the instantaneous steady-state field time t, where the local field emission area is related to the mesh partitioning, and the instantaneous steady-state field is related to the waveform of the voltage U applied across the moving and stationary contacts. Calculate the number of electrons emitted by the local mesh k. :

[0074] in, The number of electrons emitted by local grid k; The local field emission area is expressed in cm². 2 ; t The time of the instantaneous steady-state field is expressed in seconds. e This is the absolute value of the electron charge, expressed in carbon (C). If the calculated... If the value is less than or equal to 0, then the grid cell is determined to be unable to emit electrons.

[0075] The electron trajectory was calculated by using finite element method (FEM) simulation software to perform multiphysics coupled electric field and particle motion equations.

[0076] in, m For electronic quality; v The electron velocity vector is denoted by 'e'; 'e' is the absolute value of the electron charge. Let be the local electric field intensity vector at the k-th spatial grid cell. Update the instantaneous steady-state field according to the change of the applied voltage. The particle emission characteristics are calculated iteratively until the instantaneous steady-state field change ends. This yields information such as the spatial distribution characteristics of electrons, electron velocity vectors, and electron energy.

[0077] Step 4: Based on the electron motion trajectory, the Monte Carlo simulation method is used to model the X-ray radiation characteristics and obtain the X-ray energy spectrum distribution; Based on the transport simulation of X-ray radiation and its propagation behavior generated after collisions between electrons and key components within the arc-extinguishing chamber, such as the anode, shield, and conductive rod, the Monte Carlo method is used for simulation. This simulation includes, but is not limited to, the process of electrons bombarding a metal target to generate braking radiation, the generation and propagation of X-ray photons, and the energy deposition process of the X-ray detector. The X-ray energy spectrum distribution under the applied voltage U across the moving and stationary contacts and the characteristic vector S of the contact surface defects is obtained by statistically analyzing the emitted electron groups. , This represents the number of response events within the corresponding energy range. E The energy spectrum represents the energy of different events, U is the voltage applied across the moving and stationary contacts, and S is the characteristic vector of the defects on the contact surface.

[0078] Step 5: Based on the X-ray energy spectrum distribution obtained in Step 4, feature parameters are extracted to construct an energy spectrum feature vector; The X-ray energy spectrum distribution was obtained from the aforementioned Monte Carlo simulation. Extract feature parameters from it, including but not limited to the total number of response events. Energy spectral center of gravity High-energy zone proportion and energy spectrum distribution dispersion : Calculate the total number of response events :

[0079] in, The total number of response events characterizes the overall radiation intensity under this voltage condition; This represents the X-ray energy spectrum distribution.

[0080] Calculate the energy barycenter of the X-ray energy spectrum :

[0081] in, The energy centroid of the X-ray energy spectrum characterizes the electron energy distribution and braking radiation intensity structure, with units of eV; E This represents the energy spectrum of different events.

[0082] Calculate the proportion of high-energy regions :

[0083] in, The proportion of high-energy regions; It represents the lower limit of the energy range in the high-energy region, characterizing the tail features of the high-energy region.

[0084] Calculate the energy spectrum distribution dispersion :

[0085] in, The energy spectrum dispersion characterizes the width of the electron energy distribution, with units of eV.

[0086] Finally, the X-ray energy spectrum eigenvectors are constructed. :

[0087] in, This represents the eigenvectors of the X-ray energy spectrum. This represents the total number of response events; The energy center of gravity of the energy spectrum; The proportion of high-energy regions; The energy spectrum distribution dispersion is denoted as .

[0088] Step 6: Based on the energy spectrum feature vector from Step 5, perform contact surface state inversion and quantitative evaluation; realize non-contact quantitative detection of contact defects.

[0089] First, by measuring the surface morphology of contact samples with different defect levels, the corresponding contact surface defect feature vector S was obtained. Simultaneously, under the same applied voltage conditions, corresponding X-ray energy dispersive spectroscopy data were acquired, and the energy dispersive spectral feature vector was extracted. Based on sample data, establish the mapping relationship between the energy spectrum feature vector and the feature vector of contact surface defects:

[0090] in, F This is a contact surface state inversion model used to map X-ray energy spectrum feature vectors to contact surface defect parameters. The inversion model can employ methods including, but not limited to, multivariate nonlinear regression, support vector machines, neural networks, and physically constrained machine learning models. The non-contact detection process for contact defects mainly includes: An X-ray detector is placed outside the arc-extinguishing chamber; A voltage excitation is applied across the moving and stationary contacts; Collect the X-ray energy spectrum generated by the vacuum interrupter; In the actual testing process, an X-ray detector located outside the arc-extinguishing chamber collects the X-ray energy spectrum generated by the contact under test under applied voltage. Characteristic parameters such as the total number of response events, the energy centroid of the X-ray energy spectrum, the proportion of high-energy regions, and the dispersion of the energy spectrum distribution are extracted to construct a feature vector X' of the measured energy spectrum. Subsequently, the feature vector X' is input into the contact surface state inversion model. F The corresponding inverted contact surface defect feature vector S' is obtained:

[0091] The inverted contact surface defect feature vector S' includes, but is not limited to, parameters used to characterize the contact surface state, such as overall surface undulation parameters, height distribution morphology parameters, spatial organization parameters, and local defect parameters.

[0092] Based on the inversion results, non-contact quantitative detection and evaluation of the surface defects of the contact can be achieved.

[0093] Example 3 Embodiment 3 of the present invention provides a non-contact contact detection system employing the method of Embodiment 1 or 2, comprising: The module includes a contact data acquisition module, a field emission model construction module, an electron trajectory solving module, an X-ray energy spectrum distribution module, an energy spectrum eigenvector construction module, and an inversion module. Among them, the contact data acquisition module is used to acquire point cloud data of the contact surface and establish a feature vector of contact surface defects; The field emission model building module is used to build field emission models; The electron trajectory solving module is used to solve the electron trajectory using a multiphysics coupling method; The X-ray energy spectrum distribution module is used to model the X-ray radiation characteristics using the Monte Carlo simulation method to obtain the X-ray energy spectrum distribution. The energy spectrum feature vector construction module is used to extract feature parameters and construct energy spectrum feature vectors; The inversion module is used to perform inversion and quantitative evaluation of the contact surface condition, enabling non-contact quantitative detection of contact defects.

[0094] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0095] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0096] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0097] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for non-contact detection of a contact based on X-ray spectral signature, characterized in that, include: Step 1: Obtain point cloud data of the contact surface and establish feature vector of contact surface defects; Step 2: Based on the feature vectors from Step 1, construct the field-induced emission model; Step 3: Based on the model in Step 2, the electron trajectory is solved using a multiphysics coupling method; Step 4: Based on the electron trajectory in Step 3, the X-ray radiation characteristics are modeled using the Monte Carlo simulation method to obtain the X-ray energy spectrum distribution; Step 5: Based on the X-ray energy spectrum distribution obtained in Step 4, feature parameters are extracted to construct an energy spectrum feature vector; Step 6: Based on the energy spectrum feature vector from Step 5, perform contact surface state inversion and quantitative evaluation to obtain the contact surface defect feature vector after inversion, thereby realizing non-contact quantitative detection of contact defects.

2. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 1, characterized in that: In step 1, the feature vector of the contact surface defect is: Where S is the feature vector of the contact surface defect; The surface arithmetic mean height; The root mean square height; For contact surface deflection; Kubularity of the contact surface; This refers to the local defect density per unit area. The autocorrelation length; The average feature depth; The mean radius of curvature; It is the minimum radius of curvature.

3. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 2, characterized in that: The autocorrelation length is: in, The autocorrelation length; This represents the distance the surface moves laterally. This represents the distance the surface moves longitudinally. This is a height function.

4. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 1, characterized in that: In step 2, the field emission model is established based on the Murphy-Good theory: in, These are dimensionless field-induced emission parameters; Let be the initial field-induced emission current density at the k-th grid cell; Considering thermal correction for the field emission current density at the kth grid cell; Let be the normal electric field intensity of the k-th grid cell; It is the equivalent field strength enhancement factor; It is a work function; T This refers to the surface temperature of the cathode emission region in the vacuum interrupter. For thermal field-induced emission temperature correction parameters; This is the barrier height correction function. This is the correction function for the barrier transmission effect.

5. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 4, characterized in that: The equivalent field strength enhancement factor is: in, It is the equivalent field strength enhancement factor; The weighting coefficients are and satisfy the following conditions: ; Let be the local field enhancement factor for the i-th local defect; This refers to the number of local defects within the detection area.

6. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 4, characterized in that: In step 3, finite element simulation software is used to perform multiphysics coupled electric field and particle motion equations to calculate the motion trajectory: in, m For electronic quality; v The electron velocity vector is denoted by 'e'; 'e' is the absolute value of the electron charge. Let be the local electric field intensity vector at the k-th spatial grid cell.

7. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 6, characterized in that: Multiphysics coupling is applied to the local grid of emitted electrons to calculate the number of emitted electrons in that local grid: in, The number of electrons emitted by local grid k; For local field emission area; t The time is the instantaneous steady-state field.

8. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 1, characterized in that: In step 5, the X-ray energy spectrum eigenvector is: in, This represents the eigenvectors of the X-ray energy spectrum. This represents the total number of response events; The energy center of gravity of the energy spectrum; The proportion of high-energy regions; The energy spectrum distribution dispersion is denoted as .

9. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 8, characterized in that: The total number of response events is: in, X-ray energy spectrum distribution, E This represents the energy spectrum of different events.

10. The non-contact contact detection method based on X-ray energy spectrum characteristics according to claim 1, characterized in that: The contact surface state inversion model is as follows: Where S is the feature vector of the contact surface defect; F For contact surface state inversion model; This represents the eigenvectors of the X-ray energy spectrum.

11. A non-contact contact detection system utilizing the method according to any one of claims 1-10, characterized in that, include: The module includes a contact data acquisition module, a field emission model construction module, an electron trajectory solving module, an X-ray energy spectrum distribution module, an energy spectrum eigenvector construction module, and an inversion module. Among them, the contact data acquisition module is used to acquire point cloud data of the contact surface and establish a feature vector of contact surface defects; The field emission model building module is used to build field emission models; The electron trajectory solving module is used to solve the electron trajectory using a multiphysics coupling method; The X-ray energy spectrum distribution module is used to model the X-ray radiation characteristics using the Monte Carlo simulation method to obtain the X-ray energy spectrum distribution. The energy spectrum feature vector construction module is used to extract feature parameters and construct energy spectrum feature vectors; The inversion module is used to perform inversion and quantitative evaluation of the contact surface condition, enabling non-contact quantitative detection of contact defects.

12. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-10.

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