Multispectral fusion dynamic defect visual detection method and system

Abstract

The invention relates to the field of visual detection, discloses a multispectral fusion dynamic defect visual detection method and system, and solves the defects in the prior art through quantization spectrum fusion, curvature manifold embedding, soliton packet generation and topological mark extraction. Comprising the following steps: converting multispectral data into a Compton wavelength ratio tensor, eliminating physical characteristics of a sensor through Planck scale normalization, establishing a defect quantum tunneling probability model based on a Fermi golden rule, quantifying an electron transition behavior of a defect tip, embedding defect probability distribution into a Riemannian manifold, and establishing a defect quantum tunneling probability model based on the Riemannian manifold. And a curvature scalar invariant field is extracted by using conformal mapping, topological feature continuous tracking of defect evolution is realized, and a defect life cycle is analyzed and predicted based on a quantum state density matrix and a de-coherence process. The method has the characteristics of high sensitivity, strong anti-interference capability and accurate prediction, and can be widely applied to the fields of high-end equipment manufacturing, material science, industrial nondestructive testing and the like.

Description

Technical Field

[0001] The present invention relates to the field of visual inspection, and in particular to a multi-spectral fusion dynamic defect visual inspection method and system. Background Art

[0002] In the fields of industrial manufacturing and materials science, defect detection is a core link in ensuring product quality, extending equipment life, and preventing catastrophic failures. As high-end equipment develops towards high precision and high complexity, the dynamic evolution characteristics of material defects are becoming increasingly prominent, and the demand for their detection has shifted from static morphological characterization to dynamic spatiotemporal evolution tracking. However, traditional defect detection technology faces multiple challenges:

[0003] Existing methods mostly rely on a single spectrum or simple multi-spectral superposition, but the response mechanisms of different bands to defects vary significantly. Differences in the physical properties of sensors also lead to scale mismatches and semantic gaps when directly fusing data.

[0004] The spatiotemporal evolution of defects involves non-classical physical processes such as quantum tunneling and curvature-driven geometric phase transitions. However, traditional methods based on continuum mechanics or empirical threshold segmentation have difficulty capturing the quantum-classical coupling characteristics of defect evolution.

[0005] The dynamic evolution of defects is accompanied by sudden changes in topological invariants. However, existing technologies only focus on the geometric parameters of defects and ignore the spatiotemporal continuity of their topological signatures, resulting in insufficient recognition of key stages such as early defect initiation and bifurcation evolution.

[0006] Traditional methods predict defect expansion through stress intensity factor (SIF) or Paris formula, but do not consider the quantum decoherence effect of defect evolution and the change of quantum state density due to material fatigue, resulting in significant deviation between the predicted results and the actual lifespan, especially in extreme environments such as high temperature and irradiation.

[0007] Therefore, we proposed a multi-spectral fusion dynamic defect visual detection method and system to solve the above problems. Summary of the Invention

[0008] The present invention provides a multi-spectral fusion dynamic defect visual detection method and system, which solves the shortcomings of the existing technology through quantized spectrum fusion, curvature manifold embedding, soliton wave packet generation and topological marker extraction.

[0009] The first aspect of the present application provides a multispectral fusion dynamic defect visual inspection method, which comprises the following steps: converting photon energy of each waveband in a multispectral dynamic sequence into a Compton wavelength ratio to generate a spectral tensor; constructing a quantum tunneling probability model of a defect region, calculating an energy barrier penetration coefficient of the quantized spectral tensor, and outputting a defect probability cloud map; embedding the defect probability cloud map according to a Riemann manifold, calculating scalar invariants of a multispectral curvature tensor by conformal mapping, and generating a curvature fusion feature field; constructing a soliton wave generator driven by a nonlinear Schrodinger equation, taking the curvature fusion feature field as a potential well boundary condition, and generating a defect soliton wave packet; extracting a Berry phase singularity of the defect soliton wave packet, and outputting a defect spacetime topology marker map according to the law of conservation of vortex charge.

[0010] Optionally, in the first implementation manner of the first aspect of the present application, the method comprises the following steps: acquiring original photon energy values of each waveband in a multispectral dynamic sequence, converting the photon energy values into corresponding Compton wavelength ratios to generate a wavelength ratio tensor, wherein the Compton wavelength ratio is defined as the ratio of an electron static Compton wavelength to a photon wavelength; performing scale normalization on the wavelength ratio tensor by taking a Planck length as a reference unit, eliminating quantum efficiency differences of different sensors through Boltzmann entropy balance, and outputting a calibrated entropy balance tensor; quantizing the entropy balance tensor by an integer multiple of a Planck constant, assigning quantum state labels to each group of quantized units, and generating a quantized spectral tensor.

[0011] Optionally, in the second implementation manner of the first aspect of the present application, the method comprises the following steps: mapping the quantized spectral tensor to a three-dimensional potential energy field according to a material dielectric constant distribution, generating a sudden energy barrier potential function in a defect boundary region; initializing an initial state and a final state wave function based on Fermi's golden rule, calculating transition matrix elements of the energy barrier region, and outputting a quantum tunneling probability amplitude; performing a Born rule probability conversion on the quantum tunneling probability amplitude, and integrating along the spacetime dimension to generate a defect probability cloud map.

[0012] Optionally, in the third implementation manner of the first aspect of the present application, the method comprises the following steps: interpreting the defect probability cloud map as a matter density distribution of a Riemann manifold, generating a curvature basis tensor field according to a mass-curvature relationship of general relativity; defining a conformal transformation group on the multispectral curvature basis tensor field, eliminating perspective distortion through an isogonal mapping, and outputting a spectrum-aligned curvature invariant field; extracting a Ricci curvature scalar invariant of each spectrum channel, fusing the invariant field in terms of topological connectivity, and generating a curvature fusion feature field.

[0013] Optionally, in the fourth implementation form of the first aspect of the present application, the method further comprises: mapping the curvature fusion feature field into a three-dimensional potential well parameter of the nonlinear Schrödinger equation, and generating a potential well constraint matrix according to the curvature gradient; solving an eigenvalue problem of the nonlinear Schrödinger equation with the potential well constraint matrix as a boundary condition, extracting a coherent superposition state of a ground state wave function and a first excited state wave function of the defect eigenmode, and outputting the defect eigenmode; and applying a spatiotemporal modulated phase vortex to the defect eigenmode, and synthesizing a defect soliton wave packet through Heisenberg uncertainty constraint.

[0014] Optionally, in the fifth implementation form of the first aspect of the present application, the method further comprises: holographic interference demodulating the defect soliton wave packet, extracting a phase component of a complex amplitude distribution, generating a defect phase vortex field, constructing a closed loop for the defect phase vortex field in a parameter space, calculating a loop integral to extract a phase singularity coordinate, and outputting a Berry phase singularity map; and verifying spatiotemporal continuity according to a singularity topological charge conservation law, and fusing multiple spectral singularities to generate a defect spatiotemporal topological marker map.

[0015] Optionally, in the sixth implementation form of the first aspect of the present application, the method further comprises defect motion phase space reconstruction: taking a vortex charge value of the defect spatiotemporal topological marker map as a momentum coordinate and a spatiotemporal gradient as a position coordinate to construct a phase space of a dynamic defect, intercepting a motion trajectory intersection sequence through a Poincare section, and generating a defect evolution phase diagram; constructing a defect quantum state density matrix based on a trajectory divergence of the defect evolution phase diagram, calculating a decoherence time constant through a Lindblad operator, and outputting a defect stability tensor; converting the defect stability tensor into a material fatigue life percentage, combining a stress intensity factor threshold to generate a defect life cycle prediction map.

[0016] The second aspect of the present application provides a multispectral fusion dynamic defect visual detection system, which comprises: a fusion module configured to convert photon energies of each waveband in a multispectral dynamic sequence into Compton wavelength ratios to generate a spectral tensor; a modeling module configured to construct a quantum tunneling probability model of a defect region, calculate an energy barrier penetration coefficient for the quantized spectral tensor, and output a defect probability cloud map; an analysis module configured to embed a Riemann manifold according to the defect probability cloud map, calculate scalar invariants of a multispectral curvature tensor through conformal mapping, and generate a curvature fusion feature field; an evolution module configured to construct a soliton wave generator driven by a nonlinear Schrödinger equation, generate a defect soliton wave packet with the curvature fusion feature field as a potential well boundary condition; and a marking module configured to extract a Berry phase singularity of the defect soliton wave packet, and output a defect spatiotemporal topological marker map according to a vortex charge conservation law.

[0017] The mechanism of the application is as follows: a cross-scale physical synergistic mechanism of quantum tunneling probability detection, curvature manifold fusion, soliton wave energy focusing, and topological vortex conservation is constructed, and quantum perception of nanoscale defects, relativistic multispectral fusion, and topologically invariant dynamic tracking are realized in a non-machine learning framework.

[0018] Beneficial effects: The multispectral data is converted into a Compton wavelength ratio tensor, and the sensor physical differences are eliminated by normalization through the Planck scale, realizing quantum state unified representation of cross-modal data, solving the data fusion distortion problem caused by sensor quantum efficiency and response wavelength differences in traditional methods, and providing a physically consistent fusion basis for multispectral detection.

[0019] Based on the Fermi golden rule, an energy barrier penetration coefficient model of the defect region is constructed, the quantum tunneling effect is introduced into defect detection, the electron transition probability of the defect tip is quantified, the detection limit of micro / nano scale defects in continuous medium mechanics is broken through, and the sensitivity to early microcracks and subsurface defects is significantly improved.

[0020] The defect probability cloud chart is embedded in the Riemann manifold, the curvature scalar invariant field is calculated through conformal mapping, and the Beri phase singularity and vortex charge conservation marker are extracted, the curvature-mass relationship of general relativity is introduced into defect detection, the geometric phase change topological tracking of defect evolution is realized, and the low recognition rate of traditional methods for defect bifurcation and merging is solved.

[0021] A soliton wave generator driven by a nonlinear Schrödinger equation is constructed, the curvature fusion characteristic field is used as the potential well boundary condition, the defect soliton wave packet is generated, the stability and phase vortex characteristics of the soliton wave are used to realize the spatiotemporal continuity modeling of defect evolution, and a new theoretical tool is provided for dynamic defect tracking.

[0022] Based on the phase space reconstruction and quantum state density matrix analysis of defect motion, the dephasing time constant is calculated by combining the Lindblad operator, the defect stability tensor and life prediction chart are generated, the dependence on static parameters of the traditional Paris formula is broken through, the quantum dephasing effect of defect evolution is quantified for the first time, and the accuracy of life prediction under extreme environments such as high temperature and irradiation is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 An embodiment of the multispectral fusion dynamic defect visual detection method in the embodiment of the application is shown in the figure;

[0024] Figure 2 An embodiment of the multispectral fusion dynamic defect visual detection system in the embodiment of the application is shown in the figure;

[0025] Figure 3 An embodiment of the multispectral fusion dynamic defect visual detection device in the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0026] Embodiments of the present invention provide a multi-spectral fusion dynamic defect visual inspection method and system, which addresses the shortcomings of the prior art through quantized spectral fusion, curvature manifold embedding, soliton wave packet generation, and topological marker extraction. The terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units expressly listed, but may include other steps or units not expressly listed or inherent to such process, method, product, or apparatus.

[0027] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 An embodiment of the multi-spectral fusion dynamic defect visual detection method in the embodiment of the present invention includes:

[0028] 101. Quantized spectral energy mapping: Converts the photon energy of each band in a multi-spectral dynamic sequence into Compton wavelength ratios to generate a spectral tensor; eliminates sensor physical differences through Planck scale normalization;

[0029] It is understandable that the execution subject of the present invention can be a multi-spectral fusion dynamic defect visual detection system, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example.

[0030] Specifically, photon energy-matter wave conversion is performed to obtain the original photon energy value of each band in the multi-spectral dynamic sequence; the photon energy value is converted into the corresponding Compton wavelength ratio to generate a wavelength ratio tensor; wherein the Compton wavelength ratio is defined as the ratio of the electron's rest Compton wavelength to the photon wavelength;

[0031] Planck scale calibration, using the Planck length as a reference unit, normalizing the wavelength ratio tensor; eliminating quantum efficiency differences between different sensors through Boltzmann entropy balance, and outputting a calibrated entropy balance tensor;

[0032] The quantized spectrum is constructed by quantizing and grouping the entropy balance tensor according to integer multiples of the Planck constant; and assigning a quantum state label to each group of quantized units to generate a quantized spectrum tensor.

[0033] 102、Defect field quantum tunneling modeling: Constructing quantum tunneling probability model of defect region based on Fermi's golden rule; Calculating energy barrier penetration coefficient of the quantized spectral tensor, outputting defect probability cloud map;

[0034] Specifically, defect energy barrier potential function construction, mapping the quantized spectral tensor to a three-dimensional potential field according to the material dielectric constant distribution; Generating a sudden energy barrier potential function in the defect boundary region;

[0035] Tunneling transition matrix calculation, initializing initial and final state wave functions based on Fermi's golden rule; Calculate the transition matrix element in the energy barrier region, output the quantum tunneling probability amplitude;

[0036] Probability cloud map generation, performing Born rule probability conversion on the quantum tunneling probability amplitude; Integrate along the spacetime dimension to generate a defect probability cloud map.

[0037] 103、Space-time curvature feature fusion: Embedding the defect probability cloud map into a Riemannian manifold; Calculate the scalar invariant of the multi-spectral curvature tensor by conformal mapping, generate the curvature fusion feature field;

[0038] Specifically, probability mass field construction, interpreting the defect probability cloud map as a matter density distribution of the Riemannian manifold; Generating a curvature basis tensor field according to the mass-curvature relationship of general relativity;

[0039] Conformal invariant mapping, define the conformal transformation group on the multi-spectral curvature basis tensor field; Eliminate the perspective distortion by conformal mapping, output the spectral-aligned curvature invariant field;

[0040] Scalar invariant fusion, extract the Ricci curvature scalar invariant of each spectral channel; Perform topological connectivity fusion on the invariant field to generate a curvature fusion feature field.

[0041] 104、Soliton wave resonance enhancement: Constructing a soliton wave generator driven by a nonlinear Schrödinger equation; Generate a defect soliton wave packet with the curvature fusion feature field as the potential well boundary condition;

[0042] Specifically, curvature potential well constraint matrix construction, mapping the curvature fusion feature field to the three-dimensional potential well parameters of the nonlinear Schrödinger equation; Generating a potential well constraint matrix according to the curvature gradient;

[0043] Soliton wave eigenmode solving, solving the eigenvalue problem of the nonlinear Schrödinger equation with the potential well constraint matrix as the boundary condition; Extracting the coherent superposition state of the ground state wave function and the first excited state wave function, outputting the defect eigenmode;

[0044] Dynamic soliton wave packet synthesis, applying a spatiotemporal modulation phase vortex to the defect eigenmode; Synthesize the defect soliton wave packet by Heisenberg uncertainty constraint.

[0045] 105、Topological vortex detection: extract the Bary phase singularity of the defect soliton wave packet; output the defect spacetime topological marker graph according to the vortex charge conservation law.

[0046] Specifically, the complex phase field reconstruction, the defect soliton wave packet is holographic interference demodulation, extracts the phase component of the complex amplitude distribution; generate defect phase vortex field;

[0047] Bary phase singularity detection, construct a closed loop in the parameter space for the defect phase vortex field; calculate the loop integral to extract the phase singularity coordinates, and output the Bary phase singularity graph;

[0048] Vortex charge conservation verification, verify the spacetime continuity according to the singularity topological charge conservation law; fusion multispectral singularity generates defect spacetime topological marker graph.

[0049] 106, also includes defect motion phase space reconstruction:

[0050] With the vortex charge value of the defect spacetime topological marker graph as the momentum coordinate, and the spacetime gradient as the position coordinate, the phase space of the dynamic defect is constructed; the Poincare section is used to intercept the motion trajectory intersection sequence, and the defect evolution phase diagram is generated;

[0051] Based on the trajectory divergence of the defect evolution phase diagram, the defect quantum state density matrix is constructed; the dephasing time constant is calculated through the Lindblad operator, and the defect stability tensor is output;

[0052] Convert the defect stability tensor into the percentage of material fatigue life; combined with the stress intensity factor threshold to generate the defect life cycle prediction graph.

[0053] In the embodiment of the present application, the quantum concept is introduced, the photon energy and spectral information are quantized, the defect characteristics are excavated from the microscopic quantum level, the micro defects that are difficult to detect by traditional methods can be detected, and the sensitivity and precision of detection are greatly improved. Through the normalization of Planck scale and the balance of Boltzmann entropy, the physical differences and quantum efficiency differences between different sensors are effectively eliminated, so that the data obtained by different sensors can be processed and analyzed under the unified standard, and the universality and compatibility of the detection system are enhanced.

[0054] The quantum tunneling probability model of the defect region is constructed based on Fermi's golden rule, which can accurately describe the quantum tunneling phenomenon of the defect region, and the defect probability cloud diagram generated can intuitively show the position and probability distribution of the defect, providing a strong basis for accurate positioning and quantitative characterization of the defect. The defect probability cloud diagram is embedded in the Riemannian manifold and the spatiotemporal curvature features are fused, which fully utilizes the features of multispectral information in different spatiotemporal dimensions, eliminates the perspective distortion through conformal mapping and scalar invariant fusion technology, extracts more representative and robust curvature fusion feature fields, and effectively improves the accuracy and reliability of defect detection.

[0055] A soliton wave generator driven by a nonlinear Schrödinger equation is constructed, and a defect soliton wave packet is generated with the curvature fusion feature field as the potential well boundary condition. The stability and resonance characteristics of the soliton wave are utilized to enhance the characteristics of the defect signal, making the defect more prominent in a complex background and facilitating detection and identification. The Berrypoint singularity of the defect soliton wave packet is extracted, and a defect spatiotemporal topology marker map is output according to the conservation law of vortex charge, which explores the intrinsic characteristics and spatiotemporal evolution law of the defect from the perspective of topology, providing new ideas and methods for defect classification, identification, and prediction.

[0056] By constructing a phase space of dynamic defects and generating a defect evolution phase diagram, the motion trajectory and evolution process of the defects can be monitored in real time, and the dynamic changes of the defects can be intuitively displayed, providing an important means for timely discovering the development trend and potential risks of the defects. Based on the defect evolution phase diagram, a defect quantum state density matrix is constructed, the decoherence time constant is calculated, the defect stability tensor is output and converted into the material fatigue life percentage, the defect life cycle prediction diagram is generated combined with the stress intensity factor threshold, and the accurate prediction of the defect life cycle is realized, which provides a scientific basis for the maintenance and replacement of materials, helps to take measures in advance to avoid equipment failure and safety accidents caused by defects, and improves the reliability and safety of equipment operation.

[0057] The multispectral fusion dynamic defect visual detection method in the embodiments of the present application is described above, and the multispectral fusion dynamic defect visual detection system in the embodiments of the present application is described below. Please refer to Figure 2In an embodiment of the present application, one embodiment of the multispectral fusion dynamic defect visual detection system comprises: a fusion module 201, configured to convert photon energy of each waveband in a multispectral dynamic sequence into a Compton wavelength ratio to generate a spectral tensor; a modeling module 202, configured to construct a quantum tunneling probability model of a defect area, calculate an energy barrier penetration coefficient for the quantized spectral tensor, and output a defect probability cloud picture; an analysis module 203, configured to embed the defect probability cloud picture in a Riemann manifold, calculate scalar invariants of a multispectral curvature tensor by conformal mapping, and generate a curvature fusion feature field; an evolution module 204, configured to construct a soliton wave generator driven by a nonlinear Schrodinger equation, take the curvature fusion feature field as a potential well boundary condition, and generate a defect soliton wave packet; and a marking module 205, configured to extract a Berry phase singularity of the defect soliton wave packet, and output a defect spacetime topology marking map according to the law of conservation of vortex charge.

[0058] In an embodiment of the present application, multispectral detection is combined with quantum tunneling, Compton wavelength and other quantum theories to break through the limitations of traditional detection techniques, provide a new perspective and method for defect detection, greatly enrich the detection theoretical system; by constructing a quantum tunneling probability model and calculating an energy barrier penetration coefficient, a defect probability cloud picture is generated, which can more accurately quantify the possibility of the existence of defects, and compared with traditional methods, the positioning and degree judgment of defects are more accurate, and the misjudgment and omission rates are reduced; the defect probability cloud picture is embedded in a Riemann manifold to calculate a curvature fusion feature field, deep defect feature information that is difficult to obtain by traditional methods is mined, richer basis is provided for comprehensive analysis of defects, and it is helpful to more deeply understand the nature of defects; a soliton wave generator driven by a nonlinear Schrodinger equation is constructed, a defect soliton wave packet is generated by taking the curvature fusion feature field as a potential well boundary condition, dynamic capture and efficient expression of defect features are realized, the defect features are more prominent in a complex background, and subsequent analysis and processing are facilitated; a Berry phase singularity of the defect soliton wave packet is extracted, a defect spacetime topology marking map is output according to the law of conservation of vortex charge, and the distribution and change of defects in the spacetime dimension are presented in an intuitive graphical manner, which provides strong support for real-time monitoring, trend prediction and maintenance decision of defects, and improves production efficiency and product quality.

[0059] The above Figure 2 The multispectral fusion dynamic defect visual detection system in an embodiment of the present application is described in detail from the perspective of modular functional entities, and the multispectral fusion dynamic defect visual detection device in an embodiment of the present application is described in detail from the perspective of hardware processing.

[0060] Figure 3Fig. 1 is a structural schematic diagram of a multispectral fusion dynamic defect visual inspection device according to an embodiment of the present application. The multispectral fusion dynamic defect visual inspection device 300 can have a great difference due to different configurations or performances, and can include one or more central processing units (CPUs) 310 (for example, one or more processors) and a memory 320, one or more storage media 330 (for example, one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and the storage media 330 can be temporary storage or persistent storage. The programs stored in the storage media 330 can include one or more modules (not shown in the figure), and each module can include a series of instruction operations in the multispectral fusion dynamic defect visual inspection device 300. Further, the processor 310 can be configured to communicate with the storage media 330 and execute the series of instruction operations in the storage media 330 on the multispectral fusion dynamic defect visual inspection device 300.

[0061] The multispectral fusion dynamic defect visual inspection device 300 can further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that the multispectral fusion dynamic defect visual inspection device 300 can include more or fewer components than those shown in the figure, or some components can be combined, or different components can be arranged. Figure 3 The multispectral fusion dynamic defect visual inspection device structure shown does not constitute a limitation on the multispectral fusion dynamic defect visual inspection device, and can include more or fewer components than those shown in the figure, or some components can be combined, or different components can be arranged.

[0062] The present application also provides a multispectral fusion dynamic defect visual inspection device including a memory and a processor, the memory storing computer readable instructions, and the computer readable instructions being executed by the processor to cause the processor to perform the steps of the multispectral fusion dynamic defect visual inspection method in each of the embodiments.

[0063] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium, the computer readable storage medium storing instructions, and the instructions being executed on a computer to cause the computer to perform the steps of the multispectral fusion dynamic defect visual inspection method.

[0064] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described here again.

[0065] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0066] The above-described and the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-spectral fusion dynamic defect visual detection method, characterized in that: The multi-spectral fusion dynamic defect visual detection method includes: The photon energy of each band in the multi-spectral dynamic sequence is converted into the Compton wavelength ratio to generate a spectral tensor; Constructing a quantum tunneling probability model for the defect region, calculating the energy barrier penetration coefficient for the quantized spectral tensor, and outputting a defect probability cloud map; Embedding the defect probability cloud map into a Riemannian manifold, calculating the scalar invariant of the multispectral curvature tensor through conformal mapping, and generating a curvature fusion feature field; Constructing a soliton wave generator driven by the nonlinear Schrödinger equation, using the curvature fusion characteristic field as a potential well boundary condition to generate defect soliton wave packets; The Berry phase singularity of the defect soliton wave packet is extracted, and a defect space-time topological marker map is output according to the law of conservation of vortex charge.

2. The multi-spectral fusion dynamic defect visual detection method according to claim 1 is characterized in that: include: Obtaining the original photon energy value of each band in the multi-spectral dynamic sequence, converting the photon energy value into the corresponding Compton wavelength ratio, and generating a wavelength ratio tensor, wherein the Compton wavelength ratio is defined as the ratio of the electron's rest Compton wavelength to the photon wavelength; The wavelength ratio tensor is scale-normalized using the Planck length as a reference unit, and the quantum efficiency difference between different sensors is eliminated through Boltzmann entropy balance, and a calibrated entropy balance tensor is output; The entropy balance tensor is quantized and grouped according to integer multiples of the Planck constant, and a quantum state label is assigned to each group of quantized units to generate a quantized spectrum tensor.

3. The multi-spectral fusion dynamic defect visual detection method according to claim 2 is characterized in that: include: The quantized spectral tensor is mapped into a three-dimensional potential energy field according to the dielectric constant distribution of the material, and a sudden energy barrier potential function is generated in the defect boundary region; Initialize the initial and final state wave functions based on Fermi's golden rule, calculate the transition matrix elements in the energy barrier region, and output the quantum tunneling probability amplitude; The quantum tunneling probability amplitude is converted into a probability by Born rule, and the defect probability cloud map is generated by integrating along the space-time dimension.

4. The multi-spectral fusion dynamic defect visual detection method according to claim 3 is characterized in that: include: The defect probability cloud map is interpreted as the material density distribution of the Riemannian manifold, and the curvature basis tensor field is generated according to the mass-curvature relationship of general relativity; A conformal transformation group is defined on the multispectral curvature basis tensor field, and the perspective distortion is eliminated by conformal mapping, and a spectrally aligned curvature invariant field is output. The Ricci curvature scalar invariant of each spectral channel is extracted, and the invariant field is fused by topological connectivity to generate the curvature fusion feature field.

5. The multi-spectral fusion dynamic defect visual detection method according to claim 4 is characterized in that: include: The curvature fusion characteristic field is mapped into the three-dimensional potential well parameters of the nonlinear Schrödinger equation, and the potential well constraint matrix is ​​generated according to the curvature gradient; Using the potential well constraint matrix as a boundary condition, solving the eigenvalue problem of the nonlinear Schrödinger equation, extracting the coherent superposition state of the ground state wave function and the first excited state wave function, and outputting the defect eigenmode; A spatiotemporally modulated phase vortex is imposed on the defect eigenmode, and the defect soliton wave packet is synthesized through Heisenberg uncertainty constraints.

6. The multi-spectral fusion dynamic defect visual detection method according to claim 5, characterized in that: include: Holographic interferometry demodulation is performed on the defect soliton wave packet to extract the phase component of the complex amplitude distribution and generate the defect phase vortex field; A closed loop is constructed for the defect phase vortex field in the parameter space, the loop integral is calculated to extract the phase singularity coordinates, and the Berry phase singularity diagram is output; The space-time continuity is verified according to the law of conservation of singularity topological charge, and the defect space-time topological marker map is generated by fusing multi-spectral singularities.

7. The multi-spectral fusion dynamic defect visual detection method according to claim 6, characterized in that: Also includes defect motion phase space reconstruction: The vortex charge value of the defect space-time topological marker is used as the momentum coordinate and the space-time gradient is used as the position coordinate to construct the phase space of the dynamic defect, and the intersection sequence of the motion trajectory is intercepted by the Poincare section to generate the defect evolution phase diagram; Constructing a defect quantum state density matrix based on the trajectory divergence of the defect evolution phase diagram, calculating the decoherence time constant through the Lindblad operator, and outputting a defect stability tensor; The defect stability tensor is converted into a percentage of material fatigue life and combined with the stress intensity factor threshold to generate a defect life cycle prediction graph.

8. A multi-spectral fusion dynamic defect visual detection system, characterized in that: The multi-spectral fusion dynamic defect visual detection system includes: A fusion module is used to convert the photon energy of each band in the multi-spectral dynamic sequence into the Compton wavelength ratio to generate a spectral tensor; A modeling module is used to construct a quantum tunneling probability model of the defect area, calculate the energy barrier penetration coefficient of the quantized spectral tensor, and output a defect probability cloud map; An analysis module is used to embed the defect probability cloud map into a Riemannian manifold, calculate the scalar invariant of the multispectral curvature tensor through conformal mapping, and generate a curvature fusion feature field; An evolution module is used to construct a soliton wave generator driven by the nonlinear Schrödinger equation, using the curvature fusion characteristic field as a potential well boundary condition to generate defect soliton wave packets; The marking module is used to extract the Berry phase singularity of the defect soliton wave packet and output a defect space-time topology marking map according to the vortex charge conservation law.

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