Detection device and method for power plant metallographic authenticity discrimination
By using multimodal detection devices and methods, combining vibration, electrochemical, and optical characteristics, the problem of misjudgment of forged photographs in metallographic testing of power plants has been solved, achieving high-precision identification of authenticity and reliable testing under complex environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN THERMAL POWER RES INST CO LTD
- Filing Date
- 2026-01-05
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies are susceptible to misjudgment by forged photographs in metallographic inspection of power plants, lack proactive verification mechanisms, and cannot meet the high security requirements of complex environments.
A multimodal detection device is employed, including a resonant cavity module, an electrochemical microprobe module, and an optical polarization modulation module. Through the coordinated operation of vibration, electrochemical, and optical signatures, multidimensional matching analysis is performed in conjunction with a signal processor.
It improves the accuracy of identifying forged photos, breaks through database dependence, enables direct identification of genuine samples, and enhances anti-counterfeiting capabilities and detection reliability.
Smart Images
Figure CN122017012A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of materials testing technology, specifically relating to a testing device and method for metallographic authenticity identification in power plants. Background Technology
[0002] In power plant materials testing, metallographic analysis is a crucial method for evaluating the microstructure of metallic materials, used to determine their performance and quality. However, forged metallographic photographs can lead to incorrect material performance assessments, thereby posing potential equipment safety hazards. Current technologies typically rely on metallographic photograph databases, using image comparison algorithms to distinguish genuine from forged photographs. The new photograph is compared pixel-level or region-level with existing photographs in the database; if the overlap exceeds a certain proportion, the photograph is considered forged. This method is simple to operate and suitable for rapid screening.
[0003] However, the comparison results of existing technologies are highly dependent on the completeness of the database. When the database does not cover new forged photos or real samples, the detection may fail. Furthermore, the algorithm has low sensitivity to minor edits of photos, easily leading to false positives or false negatives. Moreover, this method is based solely on static visual features, ignoring the dynamic characteristics of the physical samples corresponding to the metallographic photos, and cannot effectively deal with forgeries based on real samples but digitally edited. Finally, existing technologies lack an active verification mechanism, making it difficult to adapt to the high security requirements of the complex environment of power plants. These limitations make it difficult for existing methods to meet the high reliability requirements of power plants for material testing. Therefore, we propose a detection device and method for metallographic authenticity identification in power plants. Summary of the Invention
[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, and to provide a detection device and method for metallographic authenticity identification in power plants.
[0005] One aspect of the present invention provides a detection device for metallographic authenticity identification in power plants, comprising: A sample positioning platform, the upper surface of which integrates multiple sets of micro-clamps; The resonant cavity module is fixed above the sample positioning platform by a bracket and includes an array of multiple surface acoustic wave sensors for generating a vibration signature by low-frequency acoustic excitation during operation. An electrochemical microprobe module is embedded in the upper surface of the sample positioning platform and includes multiple micron-sized probes for applying potential gradients and capturing electrochemical responses during operation. An optical polarization modulation module, fixed to the side of the bracket, includes a laser diode and a photodetector for applying polarized light and recording changes in polarization scattering during operation; and The signal processor is electrically connected to the resonant cavity module, the electrochemical microprobe module, and the optical polarization modulation module.
[0006] Furthermore, the sample positioning platform is a stainless steel platform, and an anti-slip pad is provided at the lower end.
[0007] Specifically, the surface acoustic wave sensor array includes multiple surface acoustic wave sensor units with piezoelectric ceramic elements as the sensitive substrate.
[0008] Specifically, the micron-sized probe is a titanium alloy probe.
[0009] Preferably, the signal processor is an embedded computer based on the ARM architecture.
[0010] Specifically, the incident angle of the laser beam of the laser diode is in the range of 30° to 60°, and the incident angle is the angle between the propagation direction of the laser beam at the incident point of the sample and the tangent plane of the sample surface to be tested.
[0011] Another aspect of the present invention provides a detection method for metallographic authenticity identification in power plants. The method is implemented using the aforementioned detection device for metallographic authenticity identification in power plants and includes the following steps: S1: Place the physical sample corresponding to the metallographic photograph on the sample positioning platform, input the metallographic photograph into the signal processor, and extract the micro-texture features of the metallographic photograph to form visual feature data. S2: Activate the resonant cavity module, the electrochemical microprobe module and the optical polarization modulation module. The resonant cavity module applies low-frequency acoustic wave excitation and records the vibration response of the sample to obtain vibration signature data reflecting the elastic properties of the material. S3: The electrochemical microprobe module applies a potential gradient, records the electrochemical response data on the sample surface, and combines the electrochemical response signal with the vibration signature data to form composite data; S4: The optical polarization modulation module applies a polarized beam to scan the sample surface, records the polarization change data of light scattering, and combines it with the vibration signature data to form joint data; S5: The signal processor performs multidimensional matching analysis on the visual feature data, composite data, and combined data, determines the authenticity of the metallographic photograph based on the matching analysis results, and outputs the detection results.
[0012] Furthermore, the signal processor receives the input metallographic photograph image to extract the microscopic texture features of the metallographic photograph image and generate visual feature data.
[0013] Furthermore, the vibration signature data includes vibration frequency and amplitude characteristics, the electrochemical response data includes current intensity and phase characteristics, and the polarization change data includes scattering angle and polarization component characteristics.
[0014] Specifically, the detection results include visualization charts and matching analysis reports of the data from the resonant cavity module, the electrochemical microprobe module, and the optical polarization modulation module.
[0015] The beneficial effects of this invention are as follows: The device is equipped with a resonant cavity module, an electrochemical microprobe module, and an optical polarization modulation module. Through multimodal collaboration, it overcomes the limitations of single visual comparison and actively generates vibrational, electrochemical, and optical signatures, enhancing its anti-counterfeiting capabilities. The device does not rely on a large database and is adaptable to the complex environment of power plants. The modules work together to form a closed-loop verification mechanism. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of a metallographic authenticity identification device for power plants, according to a specific embodiment of the present invention. Figure 2 A flowchart illustrating the steps of a metallographic authenticity identification device for power plants, according to a specific embodiment of the present invention. Figure 3 This is a flowchart illustrating the detection process of a metallographic authenticity identification device for power plants, according to a specific embodiment of the present invention.
[0017] The components include: 1. Sample positioning platform; 2. Optical polarization modulation module; 3. Micro clamp; and 4. Electrochemical microprobe module. Detailed Implementation
[0018] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0019] like Figure 1 As shown in the figure, a specific embodiment of the present invention provides a detection device for metallographic authenticity identification in power plants, comprising: The sample positioning platform 1 integrates multiple sets of micro-clamps 3 on its upper surface; a resonant cavity module, which is fixed above the sample positioning platform 1 by a bracket and includes multiple surface acoustic wave sensor arrays for generating vibration signatures through low-frequency acoustic wave excitation during operation; an electrochemical microprobe module 4, which is embedded in the upper surface of the sample positioning platform 1 and includes multiple micron-sized probes for applying potential gradients and capturing electrochemical responses during operation; an optical polarization modulation module 2, which is fixed to the side of the bracket and includes a laser diode and a photodetector for applying polarized light and recording changes in polarization scattering during operation; and a signal processor electrically connected to the resonant cavity module, the electrochemical microprobe module 4, and the optical polarization modulation module 2.
[0020] Specifically, the sample positioning platform 1 is a stainless steel platform, and the lower end of the sample positioning platform 1 is equipped with an anti-slip pad to ensure stability.
[0021] Furthermore, the surface acoustic wave sensor array includes multiple surface acoustic wave sensor units with piezoelectric ceramic elements as the sensitive substrate.
[0022] Furthermore, using piezoelectric ceramic elements as the sensing substrate can significantly improve the electromechanical coupling efficiency and detection sensitivity of the surface acoustic wave sensor array, making the energy conversion of surface acoustic waves during propagation more complete, thereby obtaining a higher signal-to-noise ratio and response accuracy. At the same time, piezoelectric ceramic materials have excellent thermal stability and mechanical strength, which can ensure the long-term stable operation of the sensor array in complex environments. In addition, piezoelectric ceramics are easy to fabricate in arrays, which is beneficial for realizing multi-point parallel detection and high-resolution signal acquisition.
[0023] Based on the above basic implementation method, the micron-sized probe is a titanium alloy probe.
[0024] Specifically, using titanium alloy as a micron-scale probe material can significantly improve the mechanical strength and corrosion resistance of the probe, enabling it to maintain stable morphology and conductivity during microscale contact and electrochemical detection. Simultaneously, titanium alloy possesses excellent thermal stability and fatigue resistance, maintaining reliable output under repeated cyclic excitation. Furthermore, titanium alloy exhibits good chemical inertness and biocompatibility, effectively preventing sample surface contamination or corrosion, thereby improving the accuracy of detection data and the system's lifespan.
[0025] In one specific implementation, the signal processor is an embedded computer based on the ARM architecture.
[0026] In this embodiment, the signal processor is electrically connected to each module via a high-speed interface.
[0027] Furthermore, using an embedded computer based on the ARM architecture as the signal processor can significantly reduce system power consumption and improve energy efficiency, enabling the detection device to operate stably for extended periods under low power conditions. At the same time, the high integration and real-time processing capabilities of the ARM architecture facilitate rapid synchronous analysis of multimodal sensing signals, improving system response speed and detection accuracy. In addition, this architecture has good scalability and compatibility, making it easy to connect with various sensing modules and communication interfaces, thereby enhancing the system's versatility and reliability.
[0028] Furthermore, the incident angle of the laser beam of the wave laser diode is in the range of 30° to 60° to ensure that the scanning range of the laser beam covers the entire area of the sample during operation. The incident angle is the angle between the propagation direction of the laser beam at the incident point of the sample and the tangent plane of the sample surface to be tested.
[0029] In another embodiment, the present invention also provides a detection method for metallographic authenticity identification in power plants. The method is implemented using the aforementioned detection device for metallographic authenticity identification in power plants and includes the following steps: S1: Place the physical sample corresponding to the metallographic photograph on the sample positioning platform 1, input the metallographic photograph into the signal processor, and extract the micro-texture features of the metallographic photograph to form visual feature data. S2: Activate the resonant cavity module, electrochemical microprobe module 4 and optical polarization modulation module 2. The resonant cavity module applies low-frequency acoustic wave excitation and records the vibration response of the sample to obtain vibration signature data reflecting the elastic properties of the material. S3: Electrochemical microprobe module 4 applies a potential gradient, records the electrochemical response data on the sample surface, and combines the electrochemical response signal with the vibration signature data to form composite data; S4: Optical polarization modulation module 2 applies a polarized beam to scan the sample surface, records the polarization change data of light scattering, and combines it with vibration signature data to form joint data; S5: The signal processor performs multidimensional matching analysis on the visual feature data, composite data, and combined data, determines the authenticity of the metallographic photograph based on the matching analysis results, and outputs the detection results.
[0030] In this embodiment, the signal processor receives the input metallographic image to extract the microscopic texture features of the metallographic image and generate visual feature data; the detection results include visualization charts and matching analysis reports of the data from the resonant cavity module, the electrochemical microprobe module 4, and the optical polarization modulation module 2.
[0031] Specifically, vibration signature data includes vibration frequency and amplitude characteristics, electrochemical response data includes current intensity and phase characteristics, and polarization change data includes scattering angle and polarization component characteristics.
[0032] In another specific embodiment, the detection method is performed according to the following steps: Sample preparation and visual feature extraction: The metallographic sample is placed on sample positioning platform 1 to complete standard metallographic preparation, including cutting, polishing, and etching; the metallographic image is input to the signal processor via USB interface, and the image is divided into 256-pixel × 256-pixel sub-regions using an edge detection algorithm, and texture features, such as grain boundary distribution and phase structure morphology, are extracted to form visual feature data; Vibration response generation involves activating the resonant cavity module, applying acoustic excitation, and having the sensor array record the vibration response of the sample under excitation. This generates vibration signature data reflecting the elastic properties of the material, including vibration frequency and amplitude characteristics. Electrochemical response and cross-calibration: The electrochemical microprobe module 4 is activated, and a 50 mV potential gradient is applied simultaneously with acoustic excitation. The electrochemical response current on the sample surface is recorded, generating electrochemical response data, including current intensity and phase characteristics. This electrochemical response data is combined with vibration signature data, and data processing is used to form composite data reflecting the coupling of mechanical and chemical properties. Optical polarization modulation: Activate optical polarization modulation module 2, polarized beam, incident angle 45 degrees, scan sample surface, record the polarization change of light scattering, generate polarization data, including scattering angle and polarization component characteristics, combine polarization data with vibration signature data to form joint data, reflecting the coupling between crystal orientation and vibration characteristics; Multidimensional matching analysis involves the signal processor performing principal component analysis on visual feature data, composite data, and joint data to generate fused data. The degree of matching is determined by calculating the difference between the fused data and the visual feature data. If the degree of matching meets a preset standard, the photo is considered genuine; otherwise, it is considered fake. The output includes a matching analysis report, vibration signature data, composite data, and visualization charts of joint data, stored as a PDF log file for user viewing.
[0033] In another specific embodiment, the detection method steps are as follows: The metallographic sample is placed on sample positioning platform 1 to complete standard metallographic preparation. The metallographic image is input to the signal processor via USB interface. An edge detection algorithm is used to divide the sample into 256-pixel × 256 pixel sub-regions, and texture features are extracted. ,in, Calculation based on gray-level co-occurrence matrix This is a visual feature vector, representing the mathematical representation of the microscopic texture features extracted from the metallographic photograph, used for subsequent matching analysis with the physical response. , where i = 1, 2, ..., n is a single texture descriptor, representing the microstructural features of a sub-region in a metallographic photograph, such as the distribution of grain boundaries or the contrast and correlation of phase structures; The resonant cavity module is activated, and acoustic excitation with a frequency range of 20 Hz to 2 kHz is applied. The sensor array records the response signal s(t). The vibration signature is calculated using Fast Fourier Transform (FFT). ,in, The frequency domain representation of the vibration response of a sample under low-frequency acoustic excitation reflects the material's elastic modulus and damping characteristics, such as the peak position of its natural vibration frequency. Angular frequency, measured in radians per second, represents the frequency component of the acoustic excitation and is used to analyze the vibration behavior of a sample at different frequencies. s(t) is the time-domain vibration response signal, representing the microscopic vibration displacement or acceleration of the sample under acoustic excitation over time. It is acquired by the sensor array of the resonant cavity module and outputs a spectrum. For subsequent analysis; Activate electrochemical microprobe module 4, apply a potential gradient to record current i(t), and generate an electrochemical response: ,in, This indicates the coherence characteristics of ion migration currents on the sample surface under the influence of a potential gradient, reflects the chemical activity of the material surface, and is used in conjunction with vibrational signatures. This represents the phase distribution of the electrochemical response current. Let be the current component of the k-th probe, representing the local current intensity measured by a single probe in the electrochemical microprobe array. This represents the phase shift of the corresponding current component, reflecting the temporal characteristics of the electrochemical response. A composite spectrum is then generated through convolution operations. ,in, The frequency components representing the vibration response, The phase distribution representing the electrochemical response, This is a time lag variable, representing the time offset in the convolution operation. Vibration signature at frequency shift The components at that point are used for convolution calculations. Electrochemical response in phase shift The components at that point are used for convolution calculations. This spectrum captures the mechatronic coupling effect.
[0034] Furthermore, the optical polarization modulation module 2 is activated, a polarized beam is applied, and the polarization change of the scattering angle θ is recorded. Generate the Jones matrix:
[0035] in, This represents the light scattering polarization characteristics of the sample surface under a polarized beam scan, based on the Jones matrix representation, reflecting the anisotropy of the material's crystal orientation. This represents the polarization intensity of the scattered light in the horizontal direction, calculated based on Malus's law. , representing the polarization intensity of the scattered light in the vertical direction, calculated based on Malus's law; By fusing vibration signatures, a joint vector is generated:
[0036] in, This represents the fusion result of vibrational signature and polarized light scattering characteristics, reflecting the coupling between material crystal orientation and vibrational properties; Signal processor Principal component analysis is performed to reduce dimensionality and generate a fusion vector. Calculate the matching degree: ,in, This represents the difference between visual feature data and physical response data, used to determine the authenticity of metallographic photographs. Describing the texture features of metallographic photographs, This indicates the combined characteristics of vibrational and electrochemical responses. This represents the combined characteristics of vibration and polarization.
[0037] The final output includes a matching report and a spectrogram. Composite spectrum Joint vector The visualization is stored as a PDF log file.
[0038] Furthermore, the device integrates an adaptive calibration unit, using standard samples to calibrate the excitation parameters before operation. The calibration process dynamically adjusts the acoustic excitation frequency and potential gradient through a feedback loop to adapt to changes in ambient temperature. After calibration, the device enters detection mode to ensure synchronization of signals from all modes.
[0039] To aid in a better understanding of the present invention, a more comprehensive and specific embodiment is described. In this embodiment, the present invention provides a detection device for metallographic authenticity identification in power plants, comprising: a sample positioning platform 1, the upper surface of which integrates multiple sets of micro-clamps 3; a resonant cavity module, which is fixed above the sample positioning platform 1 by a bracket and includes multiple surface acoustic wave sensor arrays for generating vibration signatures through low-frequency acoustic wave excitation during operation; an electrochemical microprobe module 4, which is embedded in the upper surface of the sample positioning platform 1 and includes multiple micron-sized probes for applying potential gradients and capturing electrochemical responses during operation; an optical polarization modulation module 2, which is fixed to the side of the bracket and includes a laser diode and a photodetector for applying polarized light and recording changes in polarization scattering during operation; and a signal processor electrically connected to the resonant cavity module, the electrochemical microprobe module 4, and the optical polarization modulation module 2.
[0040] In this embodiment, the sample positioning platform 1 is a stainless steel platform, and an anti-slip pad is provided at the lower end of the sample positioning platform 1; the surface acoustic wave sensor array includes multiple surface acoustic wave sensor units with piezoelectric ceramic elements as the sensitive substrate; the micron-level probe is a titanium alloy probe; the signal processor is an embedded computer based on the ARM architecture; the incident angle of the laser beam of the laser diode is in the range of 30° to 60° to ensure that the scanning range of the laser beam covers the entire area of the sample during operation, and the incident angle is the angle between the propagation direction of the laser beam at the incident point of the sample and the tangential plane of the sample surface to be tested.
[0041] Furthermore, another aspect of the present invention provides a detection method for metallographic authenticity identification in power plants. The method is implemented using the aforementioned detection device for metallographic authenticity identification in power plants and includes the following steps: S1: Place the physical sample corresponding to the metallographic photograph on the sample positioning platform 1, input the metallographic photograph into the signal processor, and extract the micro-texture features of the metallographic photograph to form visual feature data. S2: Activate the resonant cavity module, electrochemical microprobe module 4 and optical polarization modulation module 2. The resonant cavity module applies low-frequency acoustic wave excitation and records the vibration response of the sample to obtain vibration signature data reflecting the elastic properties of the material. S3: Electrochemical microprobe module 4 applies a potential gradient, records the electrochemical response data on the sample surface, and combines the electrochemical response signal with the vibration signature data to form composite data; S4: The optical polarization modulation module 2 applies a polarized beam to scan the sample surface, records the polarization change data of light scattering, and combines it with the vibration signature data to form joint data; S5: The signal processor performs multidimensional matching analysis on visual feature data, composite data, and joint data. Based on the matching analysis results, it determines the authenticity of the metallographic photograph and outputs the detection results.
[0042] Furthermore, the signal processor receives the input metallographic image to extract the microscopic texture features of the metallographic image and generate visual feature data; vibration signature data includes vibration frequency and amplitude features, electrochemical response data includes current intensity and phase features, polarization change data includes scattering angle and polarization component features; detection results include visualization charts and matching analysis reports of data from the resonant cavity module, data from the electrochemical microprobe module, and data from the optical polarization modulation module.
[0043] In summary, the embodiments disclosed herein have at least the following technical effects: Multimodal active detection improves the accuracy of authenticity identification. This invention, through the coordinated work of the resonant cavity module, the electrochemical microprobe module 4 and the optical polarization modulation module 2, introduces three dynamic information in addition to static visual features: the vibration response, electrochemical response and optical polarization characteristics of the material, forming multimodal composite data, thereby significantly improving the accuracy of identifying counterfeit samples and the ability to resist interference. By integrating dynamic response information, this invention breaks through database dependence; it directly acts on physical samples during the detection process to extract their physical response characteristics, and can determine authenticity without relying on existing database templates, effectively avoiding the problem of misjudgment or omission caused by incomplete databases in traditional algorithms. It achieves multi-dimensional matching analysis based on vibration, electrochemical and optical characteristics; through signal processor, it performs multi-dimensional fusion and intelligent matching analysis on visual feature data, vibration signature data, electrochemical response data and optical polarization change data, realizes cross-modal information association judgment, and can effectively identify metallographic photographs based on real samples but digitally forged or edited. The piezoelectric ceramic sensing substrate is used to improve acoustic response sensitivity; the surface acoustic wave sensor array uses piezoelectric ceramic elements as the sensing substrate, which has a high electromechanical coupling coefficient, excellent frequency stability and good temperature resistance, and can realize high-sensitivity low-frequency sound wave detection and high signal-to-noise ratio vibration data acquisition. The use of titanium alloy microprobes enhances the stability and durability of electrochemical measurements. The electrochemical microprobe module uses titanium alloy probes, which have excellent conductivity, corrosion resistance and mechanical strength, and can maintain measurement accuracy under acoustic excitation and repeated contact operations, ensuring long-term stable operation. An embedded signal processor based on the ARM architecture is used to achieve efficient edge computing. The signal processor is an embedded computer based on the ARM architecture, which has low power consumption, high computing efficiency and good portability. It can realize fast synchronous processing and intelligent analysis of multi-modal signals and is suitable for real-time detection and judgment in the complex field environment of power plants. It has the ability to actively verify and visualize output, which improves the reliability and practicality of the system. The system can not only automatically generate matching analysis reports, but also output visualized charts of vibration, electrochemical and optical data, providing operation and maintenance personnel with intuitive basis for authenticity judgment, and significantly improving the reliability, safety and traceability of metallographic testing in power plants.
[0044] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.
Claims
1. A detection device for metallographic authenticity identification in power plants, characterized in that, include: A sample positioning platform, the upper surface of which integrates multiple sets of micro-clamps; The resonant cavity module is fixed above the sample positioning platform by a bracket and includes an array of multiple surface acoustic wave sensors for generating a vibration signature by low-frequency acoustic excitation during operation. An electrochemical microprobe module is embedded in the upper surface of the sample positioning platform and includes multiple micron-sized probes for applying potential gradients and capturing electrochemical responses during operation. An optical polarization modulation module, which is fixed to the side of the bracket, includes a wave laser diode and a photodetector for applying polarized light and recording changes in polarization scattering during operation; as well as The signal processor is electrically connected to the resonant cavity module, the electrochemical microprobe module, and the optical polarization modulation module.
2. The detection device for metallographic authenticity identification in power plants according to claim 1, characterized in that, The sample positioning platform is a stainless steel platform with an anti-slip pad at the bottom.
3. The detection device for metallographic authenticity identification in power plants according to claim 1, characterized in that, The surface acoustic wave sensor array includes multiple surface acoustic wave sensor units with piezoelectric ceramic elements as the sensitive substrate.
4. The detection device for metallographic authenticity identification in power plants according to claim 1, characterized in that, The micron-sized probe is a titanium alloy probe.
5. The detection device for metallographic authenticity identification in power plants according to claim 1, characterized in that, The signal processor is an embedded computer based on the ARM architecture.
6. The detection device for metallographic authenticity identification in power plants according to any one of claims 1 to 5, characterized in that, The incident angle of the laser beam of the laser diode is in the range of 30° to 60°, and the incident angle is the angle between the propagation direction of the laser beam at the incident point of the sample and the tangent plane of the sample surface to be tested.
7. A detection method for metallographic authenticity identification in power plants, characterized in that, The method is implemented using the detection device for metallographic authenticity identification in power plants according to any one of claims 1 to 6, characterized by comprising the following steps: S1: Place the physical sample corresponding to the metallographic photograph on the sample positioning platform, input the metallographic photograph into the signal processor, and extract the micro-texture features of the metallographic photograph to form visual feature data. S2: Activate the resonant cavity module, the electrochemical microprobe module and the optical polarization modulation module. The resonant cavity module applies low-frequency acoustic wave excitation and records the vibration response of the sample to obtain vibration signature data reflecting the elastic properties of the material. S3: The electrochemical microprobe module applies a potential gradient, records the electrochemical response data on the sample surface, and combines the electrochemical response signal with the vibration signature data to form composite data; S4: The optical polarization modulation module applies a polarized beam to scan the sample surface, records the polarization change data of light scattering, and combines it with the vibration signature data to form joint data; S5: The signal processor performs multidimensional matching analysis on the visual feature data, composite data, and combined data, determines the authenticity of the metallographic photograph based on the matching analysis results, and outputs the detection results.
8. The detection method for metallographic authenticity identification in power plants according to claim 7, characterized in that, The signal processor receives the input metallographic photograph image to extract the microscopic texture features of the metallographic photograph image and generate visual feature data.
9. The detection method for metallographic authenticity identification in power plants according to claim 7, characterized in that, The vibration signature data includes vibration frequency and amplitude characteristics, the electrochemical response data includes current intensity and phase characteristics, and the polarization change data includes scattering angle and polarization component characteristics.
10. The detection method for metallographic authenticity identification in power plants according to claim 7, characterized in that, The detection results include visualization charts and matching analysis reports of the data from the resonant cavity module, the electrochemical microprobe module, and the optical polarization modulation module.