Power grid material stress corrosion prediction method and device, computer equipment, storage medium and computer program product

Abstract

The invention relates to a power grid material stress corrosion prediction method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a current holographic image and a historical holographic image of a to-be-detected power grid material; acquiring first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image; extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information; carrying out subtraction on the first phase information and the second phase information to obtain a current phase difference; determining a solution refractive index change value of a solution in which the to-be-detected power grid material is located according to the current phase difference; and determining a stress corrosion prediction result of the to-be-detected power grid material according to the solution refractive index change value and the current value and the voltage value of the to-be-detected power grid material. By adopting the method, the prediction efficiency of the stress corrosion of the power grid material can be improved.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for predicting stress corrosion of power grid materials. Background Art

[0002] In the field of power grids, predicting stress corrosion of power grid materials is crucial to ensure the normal operation of the power system.

[0003] In traditional technology, when predicting stress corrosion of power grid materials, artificial experience prediction is generally used; however, this method is cumbersome and requires a lot of time and manpower, resulting in low prediction efficiency of stress corrosion of power grid materials. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for predicting stress corrosion of power grid materials, which can improve the prediction efficiency of stress corrosion of power grid materials, in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for predicting stress corrosion of power grid materials, comprising:

[0006] Obtaining current holographic images and historical holographic images of the power grid material to be inspected;

[0007] Acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image;

[0008] Extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information;

[0009] Performing difference processing on the first phase information and the second phase information to obtain a current phase difference;

[0010] Determining a change value of the refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference;

[0011] The stress corrosion prediction result of the power grid material to be detected is determined according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected.

[0012] In one embodiment, the acquiring the first complex amplitude distribution information of the current holographic image and the second complex amplitude distribution information of the historical holographic image includes:

[0013] Performing Fourier transform processing on the current holographic image and the historical holographic image respectively to obtain first spectrum information of the current holographic image and second spectrum information of the historical holographic image;

[0014] Performing filtering processing on the first spectrum information and the second spectrum information respectively to obtain first processed spectrum information and second processed spectrum information;

[0015] The first processed spectrum information and the second processed spectrum information are respectively subjected to inverse Fourier transform processing to obtain first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image.

[0016] In one embodiment, determining the change value of the solution refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference includes:

[0017] Acquire the wavelength value of the laser associated with the to-be-detected grid material, and the path length value of the laser passing through the solution in which the to-be-detected grid material is located;

[0018] The refractive index change value of the solution in which the to-be-detected electric grid material is located is determined according to the current phase difference, the wavelength value, and the path length value.

[0019] In one embodiment, determining the stress corrosion prediction result of the power grid material to be detected based on the change value of the solution refractive index, the current value and the voltage value of the power grid material to be detected includes:

[0020] Determining the current resistance value of the power grid material to be detected according to the current value and voltage value of the power grid material to be detected;

[0021] Acquire the historical resistance value of the power grid material to be detected, and determine the resistance change value of the power grid material to be detected according to the historical resistance value and the current resistance value;

[0022] The stress corrosion prediction result of the power grid material to be tested is determined according to the change value of the solution refractive index and the change value of the resistance.

[0023] In one embodiment, determining the stress corrosion prediction result of the power grid material to be tested according to the change value of the solution refractive index and the change value of the resistance includes:

[0024] Inputting the solution refractive index change value and the resistance change value into a pre-trained stress corrosion prediction model to obtain a plurality of preset stress corrosion prediction results of the power grid material to be tested, and a prediction probability corresponding to each preset stress corrosion prediction result;

[0025] From each of the preset stress corrosion prediction results, a preset stress corrosion prediction result with the largest corresponding prediction probability is screened out as the stress corrosion prediction result of the power grid material to be detected.

[0026] In one embodiment, the step of obtaining the current holographic image and the historical holographic image of the power grid material to be inspected includes:

[0027] Obtain holographic video of the power grid material to be tested;

[0028] Performing denoising processing on the holographic video to obtain a processed holographic video;

[0029] Performing frame processing on the processed holographic video to obtain an image sequence corresponding to the processed holographic video;

[0030] The current holographic image and the historical holographic image of the power grid material to be detected are extracted from the image sequence.

[0031] In a second aspect, the present application also provides a device for predicting stress corrosion of power grid materials, comprising:

[0032] An image acquisition module, used to acquire current holographic images and historical holographic images of the power grid material to be detected;

[0033] An information acquisition module, used to acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image;

[0034] an information extraction module, configured to extract first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and to extract second phase information corresponding to the historical holographic image from the second complex amplitude distribution information;

[0035] An information processing module, used for performing difference processing on the first phase information and the second phase information to obtain a current phase difference;

[0036] An information determination module, used to determine a change value of the solution refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference;

[0037] The result determination module is used to determine the stress corrosion prediction result of the power grid material to be detected according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected.

[0038] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0039] Obtaining current holographic images and historical holographic images of the power grid material to be inspected;

[0040] Acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image;

[0041] Extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information;

[0042] Performing difference processing on the first phase information and the second phase information to obtain a current phase difference;

[0043] Determining a change value of the refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference;

[0044] The stress corrosion prediction result of the power grid material to be detected is determined according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0046] Obtaining current holographic images and historical holographic images of the power grid material to be inspected;

[0047] Acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image;

[0048] Extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information;

[0049] Performing difference processing on the first phase information and the second phase information to obtain a current phase difference;

[0050] Determining a change value of the refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference;

[0051] The stress corrosion prediction result of the power grid material to be detected is determined according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected.

[0052] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0053] Obtaining current holographic images and historical holographic images of the power grid material to be inspected;

[0054] Acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image;

[0055] Extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information;

[0056] Performing difference processing on the first phase information and the second phase information to obtain a current phase difference;

[0057] Determining a change value of the refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference;

[0058] The stress corrosion prediction result of the power grid material to be detected is determined according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected.

[0059] The above-mentioned method, device, computer equipment, storage medium and computer program product for predicting stress corrosion of power grid materials first obtain the current holographic image and the historical holographic image of the power grid material to be detected, and obtain the first complex amplitude distribution information of the current holographic image and the second complex amplitude distribution information of the historical holographic image, then extract the first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extract the second phase information corresponding to the historical holographic image from the second complex amplitude distribution information, then perform difference processing on the first phase information and the second phase information to obtain the current phase difference, then determine the solution refractive index change value of the solution in which the power grid material to be detected is located based on the current phase difference, and finally determine the stress corrosion prediction result of the power grid material to be detected based on the solution refractive index change value, the current value and the voltage value of the power grid material to be detected. In this way, when predicting the stress corrosion of the power grid material, a series of processing is performed on the current holographic image and the historical holographic image of the power grid material to be detected to obtain the current phase difference, and then the solution refractive index change value of the solution in which the power grid material to be detected is directly determined according to the current phase difference, and combined with the current value and voltage value of the power grid material to be detected, the stress corrosion prediction result of the power grid material to be detected is directly determined, which is conducive to improving the prediction efficiency of the stress corrosion of the power grid material; moreover, no manual intervention is required throughout, avoiding the defect of low prediction efficiency of stress corrosion of power grid materials due to the cumbersome method of using manual experience prediction, which requires a lot of time and manpower, and further improves the prediction efficiency of stress corrosion of power grid materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic diagram of a process flow of a method for predicting stress corrosion of power grid materials in one embodiment;

[0062] Figure 2 is a schematic diagram of a holographic recording system device in one embodiment;

[0063] Figure 3 is a schematic flow chart of a phase difference measurement process in one embodiment;

[0064] Figure 4 A schematic flow chart of the steps of determining the stress corrosion prediction result of the power grid material to be tested in one embodiment;

[0065] Figure 5 A schematic diagram of a flow chart of a method for predicting stress corrosion of power grid materials in another embodiment;

[0066] Figure 6 is a structural block diagram of a device for predicting stress corrosion of power grid materials in one embodiment;

[0067] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0070] At present, in the power grid system, the service environment of power grid materials is complex and changeable, and even extremely harsh. In the presence of tensile stress and corrosive ions (such as chloride ions), power grid materials are prone to intergranular corrosion (IGC, Intergranular Corrosion), pitting, uniform corrosion and stress corrosion cracking (SCC, Stress Corrosion Cracking). Among them, stress corrosion cracking can cause damage to materials, mechanical parts or components. This damage has a hysteresis, that is, under the long-term action of stress, the material gradually breaks down, and eventually leads to sudden fracture without obvious signs, which not only causes significant economic losses, but also has potential safety hazards. Therefore, predicting the stress corrosion of power grid materials is crucial to ensure the normal operation of the power system. In traditional technology, when predicting the stress corrosion of power grid materials, artificial experience prediction is generally used; however, this method is relatively cumbersome, requires a lot of time and manpower, and results in low prediction efficiency of stress corrosion of power grid materials. Therefore, the present application provides a method for predicting stress corrosion of power grid materials, which can solve the problem of low prediction efficiency of stress corrosion of power grid materials and achieve the effect of improving the prediction efficiency of stress corrosion of power grid materials.

[0071] In an exemplary embodiment, Figure 1 As shown, a method for predicting stress corrosion of power grid materials is provided. This embodiment uses the method applied to a server as an example for illustration; it can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptops, smart phones and tablets; the server can be implemented with an independent server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0072] Step S101, obtaining the current holographic image and the historical holographic image of the power grid material to be detected.

[0073] The power grid material to be tested refers to the power grid material that needs to be predicted for stress corrosion. In actual scenarios, the power grid material to be tested is a metal material.

[0074] The current holographic image refers to the holographic image of the power grid material to be detected at the current time.

[0075] The holographic image is obtained through a digital holographic recording system. Specifically, Figure 2As shown, in the digital holographic system, the laser light emitted by the helium-neon laser passes through the reflector and is divided into an object beam and a reference beam by the first beam splitter. The object beam directly passes through the grid material to be detected, interacts with the surface of the grid material to be detected, and forms a changed object beam. The reference beam directly enters the interference system. The changed object beam and the reference beam interfere at the second beam splitter to form an interference image, which is focused onto a CCD (Charge-coupled Device) camera through an objective lens, and the interference image is converted into a holographic image by the CCD camera.

[0076] The historical holographic image refers to the holographic image of the power grid material to be tested at the initial time.

[0077] Exemplarily, the server obtains a holographic image of the power grid material to be detected at the current time as the current holographic image through a CCD camera in the digital holographic recording system, and obtains a holographic image of the power grid material to be detected at an initial time as a historical holographic image.

[0078] Step S102, obtaining first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image.

[0079] The first complex amplitude distribution information refers to the complex amplitude distribution information of the current holographic image.

[0080] The complex amplitude distribution information is used to characterize the amplitude information and phase information of the holographic image.

[0081] The second complex amplitude distribution information refers to the complex amplitude distribution information of the historical holographic image.

[0082] Exemplarily, the server processes the current holographic image through a data processing system to obtain complex amplitude distribution information of the current holographic image as the first complex amplitude distribution information; then, the server processes the historical holographic image through the data processing system to obtain complex amplitude distribution information of the historical holographic image as the second complex amplitude distribution information.

[0083] Step S103: extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information.

[0084] The first phase information refers to the phase information corresponding to the current holographic image.

[0085] Among them, the phase information is used to characterize the phase distribution of the light wave field in the holographic image.

[0086] The second phase information refers to the phase information corresponding to the historical holographic image.

[0087] Exemplarily, the server inputs the first complex amplitude distribution information into the phase information prediction model, obtains the phase information corresponding to the first complex amplitude distribution information, as the first phase information corresponding to the current holographic image; then, the server inputs the second complex amplitude distribution information into the phase information prediction model, obtains the phase information corresponding to the second complex amplitude distribution information, as the second phase information corresponding to the historical holographic image.

[0088] Step S104: performing difference processing on the first phase information and the second phase information to obtain a current phase difference.

[0089] The current phase difference is used to represent a phase value obtained by performing a difference process on the first phase information and the second phase information.

[0090] Exemplarily, the server performs difference processing on the first phase information and the second phase information to obtain a difference value; then, the server performs absolute value processing on the difference value to obtain a current phase difference.

[0091] Step S105, determining the change value of the refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference.

[0092] The change value of the solution refractive index is used to characterize the change of the refractive index of the solution in which the power grid material to be detected is located.

[0093] Exemplarily, the server queries the correspondence between the phase difference and the solution refractive index change value according to the current phase difference, obtains the solution refractive index change value corresponding to the current phase difference, and uses it as the solution refractive index change value of the solution in which the power grid material to be detected is located.

[0094] Step S106, determining the stress corrosion prediction result of the power grid material to be detected according to the change value of the solution refractive index, the current value and the voltage value of the power grid material to be detected.

[0095] The current value and voltage value of the power grid material to be detected are obtained by collecting the power grid material to be detected through a current and voltage collection system (such as a multimeter).

[0096] Among them, the stress corrosion prediction results are used to characterize the possibility of stress corrosion occurring in the power grid material to be tested.

[0097] Exemplarily, the server obtains the current value and voltage value of the power grid material to be tested; then, the server inputs the solution refractive index change value, the current value and voltage value of the power grid material to be tested into a pre-trained stress corrosion prediction model to obtain the stress corrosion prediction result of the power grid material to be tested.

[0098] In the above-mentioned method for predicting stress corrosion of power grid materials, the current holographic image and the historical holographic image of the power grid material to be detected are first obtained, and the first complex amplitude distribution information of the current holographic image and the second complex amplitude distribution information of the historical holographic image are obtained. Then, the first phase information corresponding to the current holographic image is extracted from the first complex amplitude distribution information, and the second phase information corresponding to the historical holographic image is extracted from the second complex amplitude distribution information. Then, the first phase information and the second phase information are differenced to obtain the current phase difference. Then, according to the current phase difference, the solution refractive index change value of the solution in which the power grid material to be detected is determined. Finally, according to the solution refractive index change value, the current value and the voltage value of the power grid material to be detected, the stress corrosion prediction result of the power grid material to be detected is determined. In this way, when predicting the stress corrosion of the power grid material, a series of processing is performed on the current holographic image and the historical holographic image of the power grid material to be detected to obtain the current phase difference, and then the solution refractive index change value of the solution in which the power grid material to be detected is directly determined according to the current phase difference, and combined with the current value and voltage value of the power grid material to be detected, the stress corrosion prediction result of the power grid material to be detected is directly determined, which is conducive to improving the prediction efficiency of the stress corrosion of the power grid material; moreover, no manual intervention is required throughout, avoiding the defect of low prediction efficiency of stress corrosion of power grid materials due to the cumbersome method of using manual experience prediction, which requires a lot of time and manpower, and further improves the prediction efficiency of stress corrosion of power grid materials.

[0099] In an exemplary embodiment, the above step S102, obtaining the first complex amplitude distribution information of the current holographic image, and the second complex amplitude distribution information of the historical holographic image, specifically includes the following contents: performing Fourier transform processing on the current holographic image and the historical holographic image, respectively, to obtain the first spectrum information of the current holographic image, and the second spectrum information of the historical holographic image; performing filtering processing on the first spectrum information and the second spectrum information, respectively, to obtain the first processed spectrum information and the second processed spectrum information; performing inverse Fourier transform processing on the first processed spectrum information and the second processed spectrum information, respectively, to obtain the first complex amplitude distribution information of the current holographic image, and the second complex amplitude distribution information of the historical holographic image.

[0100] Among them, Fourier transform processing refers to the processing process of converting the current holographic image and the historical holographic image from the spatial domain to the corresponding frequency domain.

[0101] The first spectrum information refers to the spectrum information of the current holographic image.

[0102] Among them, the spectrum information is used to characterize the distribution of different spatial frequencies in the holographic image.

[0103] The second spectrum information refers to the spectrum information of the historical holographic image.

[0104] Among them, filtering processing refers to the processing process corresponding to removing irrelevant noise or interference components in the spectrum information.

[0105] The first processed spectrum information refers to spectrum information obtained by filtering the first spectrum information.

[0106] The second processed spectrum information refers to spectrum information obtained by filtering the second spectrum information.

[0107] Among them, the inverse Fourier transform processing refers to the processing process of converting the current holographic image and the historical holographic image from the frequency domain to the corresponding spatial domain.

[0108] Exemplarily, the server performs Fourier transform processing on the current holographic image to obtain the spectrum information of the current holographic image as the first spectrum information, and performs Fourier transform processing on the historical holographic image to obtain the spectrum information of the historical holographic image as the second spectrum information; then, the server performs filtering processing on the first spectrum information and the second spectrum information respectively to obtain the first processed spectrum information and the second processed spectrum information; for example, the server identifies the noise in the first spectrum information and the second spectrum information, and removes the noise in the first spectrum information and the second spectrum information to obtain the first processed spectrum information and the second processed spectrum information; then, the server performs inverse Fourier transform processing on the first processed spectrum information to obtain the complex amplitude distribution information corresponding to the first processed spectrum information as the first complex amplitude distribution information of the current holographic image, and performs inverse Fourier transform processing on the second processed spectrum information to obtain the complex amplitude distribution information corresponding to the second processed spectrum information as the second complex amplitude distribution information of the historical holographic image.

[0109] For example, see Figure 3 , the server performs two-dimensional fast Fourier transform on the hologram at time t and the initial hologram through the data processing system, respectively, to obtain the spectrum information corresponding to the hologram at time t and the spectrum information corresponding to the initial hologram; then, the spectrum information corresponding to the hologram at time t and the spectrum information corresponding to the initial hologram are filtered through a bandpass filter to obtain the processed spectrum information corresponding to the hologram at time t and the processed spectrum information corresponding to the initial hologram; then, the server performs two-dimensional inverse Fourier transform on the processed spectrum information corresponding to the hologram at time t and the processed spectrum information corresponding to the initial hologram, to obtain the complex amplitude distribution information corresponding to the hologram at time t and the complex amplitude distribution information corresponding to the initial hologram, which are respectively used as the first complex amplitude distribution information of the current holographic image and the second complex amplitude distribution information of the historical holographic image.

[0110] In this embodiment, by performing Fourier transform processing, filtering processing and inverse Fourier transform processing on the current holographic image and the historical holographic image, the complex amplitude distribution information corresponding to the current holographic image and the historical holographic image can be quickly obtained; moreover, the entire process does not require human intervention, avoiding the defect that manual processing is easy to consume a lot of time and leads to low efficiency in determining the complex amplitude distribution information.

[0111] In an exemplary embodiment, the above step S105 determines the change value of the solution refractive index of the solution in which the grid material to be detected is located based on the current phase difference, and specifically includes the following contents: obtaining the wavelength value of the laser associated with the grid material to be detected, and the path length value of the laser passing through the solution in which the grid material to be detected is located; determining the change value of the solution refractive index of the solution in which the grid material to be detected is located based on the current phase difference, wavelength value, and path length value.

[0112] The laser associated with the power grid material to be detected refers to the laser acting on the surface of the power grid material to be detected.

[0113] The wavelength value refers to the wavelength of the laser.

[0114] The path length value refers to the effective distance that the laser passes through the solution where the grid material to be detected is located.

[0115] Exemplarily, the server obtains the wavelength value of the laser associated with the power grid material to be detected, and the path length value of the laser through the solution in which the power grid material to be detected is located; then, the server queries the phase difference, wavelength value, path length value and solution refractive index change value based on the current phase difference, wavelength value and path length value, and obtains the solution refractive index change value corresponding to the current phase difference, wavelength value and path length value as the solution refractive index change value of the solution in which the power grid material to be detected is located.

[0116] For example, the server can determine the change value of the solution refractive index of the solution in which the power grid material to be detected is located by the following formula:

[0117] ΔΦ=(2πd / λ)Δn, formula (1)

[0118] Among them, ΔΦ refers to the current phase difference, λ refers to the wavelength value of the laser associated with the grid material to be detected, d refers to the path length value of the laser through the solution in which the grid material to be detected is located, and Δn refers to the change value of the refractive index of the solution in which the grid material to be detected is located.

[0119] In this embodiment, by integrating various different element information such as the current phase difference, wavelength value and path length value, the change value of the solution refractive index can be accurately determined, and then high-precision measurement of the changes in the solution environment of the power grid material can be achieved, providing a data basis for the subsequent stress corrosion prediction results of the power grid material to be tested.

[0120] In an exemplary embodiment, Figure 4 As shown, the above step S106, according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected, determines the stress corrosion prediction result of the power grid material to be detected, and specifically includes the following steps:

[0121] Step S401, determining the current resistance value of the power grid material to be detected according to the current value and voltage value of the power grid material to be detected.

[0122] Step S402, obtaining the historical resistance value of the power grid material to be detected, and determining the resistance change value of the power grid material to be detected according to the historical resistance value and the current resistance value.

[0123] Step S403, determining the stress corrosion prediction result of the power grid material to be tested according to the change value of the solution refractive index and the change value of the resistance.

[0124] The current resistance value refers to the resistance of the power grid material to be detected at the current time.

[0125] The historical resistance value refers to the resistance of the power grid material to be detected at the initial time.

[0126] The resistance change value is used to represent the difference between the current resistance value and the historical resistance value.

[0127] Exemplarily, the server determines the current resistance value of the grid material to be detected based on the current value and voltage value of the grid material to be detected; for example, the server divides the voltage value of the grid material to be detected by the current value of the grid material to be detected to obtain the current resistance value of the grid material to be detected; then, the server obtains the historical resistance value of the grid material to be detected from the database, and performs difference processing on the historical resistance value and the current resistance value to obtain the resistance change value of the grid material to be detected; then, the server determines the stress corrosion prediction result of the grid material to be detected based on the solution refractive index change value and the resistance change value; for example, the server determines a first preset threshold corresponding to the solution refractive index change value and a second preset threshold corresponding to the resistance change value, and determines the stress corrosion prediction result corresponding to the first preset threshold and the second preset threshold based on the first preset threshold and the second preset threshold as the stress corrosion prediction result of the grid material to be detected.

[0128] In this embodiment, by combining two different angles, namely, the change in solution refractive index and the change in resistance, a comprehensive evaluation is performed on the stress corrosion prediction results of the power grid material to be tested, thereby being able to more comprehensively reflect the actual state of the power grid material to be tested and avoiding the limitations of a single parameter evaluation.

[0129] In an exemplary embodiment, the above-mentioned step S403 determines the stress corrosion prediction result of the power grid material to be detected based on the solution refractive index change value and the resistance change value, and specifically includes the following contents: the solution refractive index change value and the resistance change value are input into a pre-trained stress corrosion prediction model to obtain multiple preset stress corrosion prediction results of the power grid material to be detected, and the prediction probability corresponding to each preset stress corrosion prediction result; from each preset stress corrosion prediction result, the preset stress corrosion prediction result with the largest corresponding prediction probability is screened out as the stress corrosion prediction result of the power grid material to be detected.

[0130] Among them, the stress corrosion prediction model refers to a network model that can use the solution refractive index change value and resistance change value to obtain multiple preset stress corrosion prediction results of the power grid material to be tested, as well as the prediction probability corresponding to each preset stress corrosion prediction result.

[0131] Among them, the preset stress corrosion prediction results refer to the pre-set stress corrosion prediction results, including no stress corrosion risk, low stress corrosion risk, medium stress corrosion risk, high stress corrosion risk, etc.

[0132] Among them, the prediction probability is used to characterize the possibility that the stress corrosion prediction model determines that the preset stress corrosion prediction result is correct.

[0133] Exemplarily, the server inputs the solution refractive index change value and resistance change value of the power grid material to be detected into a pre-trained stress corrosion prediction model, and obtains multiple preset stress corrosion prediction results of the power grid material to be detected and the prediction probability corresponding to each preset stress corrosion prediction result through the pre-trained stress corrosion prediction model; then, the server selects the preset stress corrosion prediction result with the largest corresponding prediction probability from each preset stress corrosion prediction result as the stress corrosion prediction result of the power grid material to be detected.

[0134] For example, the server inputs the solution refractive index change value and resistance change value of the power grid material to be detected into a pre-trained stress corrosion prediction model. Through the pre-trained stress corrosion prediction model, the prediction probability corresponding to the power grid material to be detected having no stress corrosion risk is 20%, the prediction probability corresponding to the power grid material to be detected having a low stress corrosion risk is 40%, the prediction probability corresponding to the power grid material to be detected having a medium stress corrosion risk is 30%, and the prediction probability corresponding to the power grid material to be detected having a high stress corrosion risk is 10%. Then, the server selects the preset stress corrosion prediction result with the largest corresponding prediction probability as the low stress corrosion risk from each preset stress corrosion prediction result as the stress corrosion prediction result for the power grid material to be detected.

[0135] In this embodiment, each preset stress corrosion prediction result is evaluated and compared through a pre-trained stress corrosion prediction model, and the preset stress corrosion prediction result with the highest prediction probability is selected as the final stress corrosion prediction result, thereby increasing the reliability and credibility of the stress corrosion prediction result, which is beneficial to improving the accuracy of determining the stress corrosion prediction result of the power grid material to be tested.

[0136] In an exemplary embodiment, the above step S101, obtaining the current holographic image and the historical holographic images of the power grid material to be detected, specifically includes the following contents: obtaining a holographic video of the power grid material to be detected; denoising the holographic video to obtain a processed holographic video; frame-processing the processed holographic video to obtain an image sequence corresponding to the processed holographic video; extracting the current holographic image and the historical holographic images of the power grid material to be detected from the image sequence.

[0137] Among them, holographic video refers to the video obtained by shooting the power grid material to be tested using holographic imaging technology.

[0138] The processed holographic video refers to the holographic video after denoising.

[0139] Among them, frame processing refers to the process of dividing the processed holographic video into a series of frames.

[0140] The image sequence refers to a set of images obtained by performing frame processing on the processed holographic video.

[0141] Exemplarily, the server obtains a holographic video of the power grid material to be detected from a database; then, the server denoises the holographic video to obtain a denoised holographic video as a processed holographic video; then, the server performs frame processing on the processed holographic video to obtain multiple images corresponding to the processed holographic video as an image sequence corresponding to the processed holographic video; then, the server extracts a holographic image of the power grid material to be detected at the current time from the image sequence as the current holographic image, and extracts a holographic image of the power grid material to be detected at an initial time from the image sequence as a historical holographic image.

[0142] In this embodiment, by denoising the holographic video of the power grid material to be tested, the quality of the holographic video can be improved to facilitate subsequent processing; moreover, by comparing the holographic images at different time points, the changes in the power grid material to be tested can be detected, providing an important basis for the stress corrosion prediction of the power grid material to be tested.

[0143] In an exemplary embodiment, Figure 5 As shown, another method for predicting stress corrosion of power grid materials is provided, which is described by taking the application of the method to a server as an example, and includes the following steps:

[0144] Step S501, obtaining a holographic video of the power grid material to be detected; performing denoising processing on the holographic video to obtain a processed holographic video; performing frame processing on the processed holographic video to obtain an image sequence corresponding to the processed holographic video; and extracting a current holographic image and a historical holographic image of the power grid material to be detected from the image sequence.

[0145] Step S502, Fourier transform processing is performed on the current holographic image and the historical holographic image respectively to obtain first spectrum information of the current holographic image and second spectrum information of the historical holographic image.

[0146] Step S503: filter the first spectrum information and the second spectrum information respectively to obtain first processed spectrum information and second processed spectrum information.

[0147] Step S504, performing inverse Fourier transform processing on the first processed spectrum information and the second processed spectrum information respectively, to obtain first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image.

[0148] Step S505: extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information.

[0149] Step S506: perform difference processing on the first phase information and the second phase information to obtain a current phase difference.

[0150] Step S507, obtaining the wavelength value of the laser associated with the to-be-detected power grid material and the path length value of the laser passing through the solution in which the to-be-detected power grid material is located.

[0151] Step S508, determining the change value of the solution refractive index of the solution in which the to-be-detected power grid material is located according to the current phase difference, wavelength value, and path length value.

[0152] Step S509, determining the current resistance value of the power grid material to be detected according to the current value and voltage value of the power grid material to be detected; obtaining the historical resistance value of the power grid material to be detected, and determining the resistance change value of the power grid material to be detected according to the historical resistance value and the current resistance value.

[0153] Step S510, inputting the solution refractive index change value and resistance change value into a pre-trained stress corrosion prediction model to obtain a plurality of preset stress corrosion prediction results of the power grid material to be tested, and the prediction probability corresponding to each preset stress corrosion prediction result.

[0154] Step S511, from each preset stress corrosion prediction result, select the preset stress corrosion prediction result with the largest corresponding prediction probability as the stress corrosion prediction result of the power grid material to be detected.

[0155] In the above-mentioned method for predicting stress corrosion of power grid materials, when predicting stress corrosion of power grid materials, a series of processing is performed on the current holographic image and the historical holographic image of the power grid material to be detected to obtain the current phase difference, and then the solution refractive index change value of the solution in which the power grid material to be detected is directly determined according to the current phase difference, and combined with the current value and voltage value of the power grid material to be detected, the stress corrosion prediction result of the power grid material to be detected is directly determined, which is conducive to improving the prediction efficiency of stress corrosion of power grid materials; moreover, no manual intervention is required throughout, avoiding the defect of low prediction efficiency of stress corrosion of power grid materials caused by the method of using manual experience prediction, which is cumbersome and requires a lot of time and manpower, and further improves the prediction efficiency of stress corrosion of power grid materials.

[0156] In an exemplary embodiment, in order to more clearly illustrate the method for predicting stress corrosion of power grid materials provided in the embodiment of the present application, the method for predicting stress corrosion of power grid materials is specifically described below with a specific embodiment. In one embodiment, the present application also provides a method for in-situ monitoring stress corrosion of power grid materials. When predicting stress corrosion of power grid materials, the current holographic image and the historical holographic image of the power grid material to be detected are first obtained, and the first complex amplitude distribution information of the current holographic image and the second complex amplitude distribution information of the historical holographic image are obtained. Then, the first phase information corresponding to the current holographic image is extracted from the first complex amplitude distribution information, and the second phase information corresponding to the historical holographic image is extracted from the second complex amplitude distribution information. Then, the first phase information and the second phase information are subjected to difference processing to obtain the current phase difference, and then, according to the current phase difference, the solution refractive index change value of the solution in which the power grid material to be detected is determined. Finally, according to the solution refractive index change value, the current value and voltage value of the power grid material to be detected, the stress corrosion prediction result of the power grid material to be detected is determined. Specifically including the following contents:

[0157] 1. Digital holographic recording system. The digital holographic system includes a helium-neon laser with a wavelength of 632.8nm and an output power of 2mW, as well as optical components including a mirror (M), a beam splitter (BS), a spatial filter (SF), a collimating lens (L1 and L3), an objective lens (L2), and a CCD camera as a recording medium, which is connected to a computer for real-time recording and processing of holograms.

[0158] The digital holographic recording system uses a helium-neon laser with a wavelength of 632.8nm and a power of 2mW. The laser should be placed on a stable platform to prevent vibration from affecting the quality of the beam. The laser beam outlet should be aligned with the spatial filter to ensure that the beam can smoothly enter the subsequent optical elements.

[0159] Among them, the spatial filter is used to remove high-frequency noise and irregular parts in the laser beam, thereby improving the quality of the beam. The spatial filter is installed at the exit of the laser beam, and its position and angle are adjusted so that the laser beam can pass through the pinhole of the filter and form a clean circular spot.

[0160] Among them, collimating lenses (L1 and L3) are used to collimate the light beam after passing through the spatial filter. The collimating lens should be placed after the spatial filter so that the light beam becomes a parallel beam after passing through the lens. Adjust the position and focal length of the lens to ensure that the light beam forms a uniform parallel beam after the lens for subsequent beam separation and interference imaging.

[0161] The beam splitter is used to split the collimated laser beam into an object beam and a reference beam. The beam splitter is placed in the optical path behind the collimating lens. The angle of the beam splitter is adjusted so that the beam can be separated along a predetermined path. One part of the beam continues to move forward as the object beam, and the other part of the beam is deflected as the reference beam.

[0162] After the object beam passes through the beam splitter, it continues to move forward and interacts with the metal surface to be detected. In order to ensure that the object beam can be correctly irradiated, a reflector (M) should be installed on the object beam path to adjust and guide the beam direction. Install the objective lens (L2) to focus the object beam on the metal surface to be detected. The beam focused by the objective lens can interact with the metal surface to be detected to form an interference image.

[0163] Among them, after passing through the beam splitter, the reference beam moves along a predetermined path and directly enters the interference system. No other optical elements need to be installed on the reference beam path to ensure that it can merge with the object beam in the interference system. The reference beam and the object beam interfere at the second beam splitter to form an interference image. The position and angle of the second beam splitter are adjusted so that the object beam and the reference beam can accurately merge to form a clear interference image.

[0164] The CCD camera is installed at the recording position of the interference image to ensure that it can accurately capture the interference image. The CCD camera should be aligned with the second beam splitter, and the position and angle of the camera should be adjusted to enable it to obtain the best interference image. The CCD camera is connected to a computer, and the interference image collected by the CCD camera is received and recorded in real time by the computer.

[0165] Specifically, the light emitted by the laser is collimated by a spatial filter and a collimating lens, and then separated into an object beam and a reference beam by a beam splitter. The object beam passes directly through the metal to be detected, interacts with the metal surface, and forms an interference image. The generated interference image is recorded in real time by a CCD camera. The reference beam directly enters the interference system. The two beams of light interfere at the second beam splitter to form an interference image, which is focused on the CCD camera through the objective lens, recorded, and transmitted to the computer.

[0166] Among them, the digital holographic recording system can save and reconstruct the phase image obtained by monitoring in the form of AVI (Audio Video Interleave) files through video recording and processing, so as to observe the real-time changes of the material surface and solution interface; moreover, the digital holographic recording system can capture tiny concentration gradients and refractive index changes through accurate calculation and reconstruction of phase differences, and has high sensitivity.

[0167] 2. Current and voltage acquisition system: Use a multimeter to record the current and voltage changes on the surface of the metal in the service environment.

[0168] 3. Data processing system. The CDD camera is connected to a computer to receive the signal from the CCD camera and perform hologram processing and phase image reconstruction through Matlab (Matrix Laboratory) software, including the following:

[0169] (1) Fourier transform. Perform a two-dimensional fast Fourier transform on the recorded hologram to convert the hologram from the spatial domain to the frequency domain. The information in the frequency domain contains the interference terms between the object light and the reference light, which carry the phase information of the object. In the frequency domain, different components in the hologram are separated, such as the zero-order diffraction light and the first-order diffraction light. The first-order diffraction light is selected and filtered to remove unnecessary background noise.

[0170] (2) Spectral filtering. The Fourier transformed spectrum is filtered in the frequency domain to retain the useful frequency components related to the phase. This step is to enhance the signal of phase information and remove irrelevant noise or interference components.

[0171] (3) Inverse Fourier transform. Perform a two-dimensional inverse Fourier transform on the filtered spectrum and convert it back to the spatial domain to obtain a complex amplitude distribution. The complex amplitude distribution contains the phase and amplitude information of the object.

[0172] (4) Phase map extraction. Phase information is extracted from the complex amplitude distribution to form a phase map. The phase map shows the phase distribution on the sample surface or interface, reflecting the morphological changes on the surface of the object or the concentration gradient changes during the electrochemical reaction. The final phase map is used to analyze the dynamic changes of the sample during the electrochemical reaction, such as corrosion behavior at the interface, crack formation and expansion, etc. These phase maps can intuitively show the changes in local concentration and the influence of stress on the material surface.

[0173] Specifically, numerical methods were used to process the holograms corresponding to different states of the material interface. The hologram and current were recorded simultaneously in real time. Continuous holograms in AVI format at up to 25 frames per second were stored in a personal computer to study the dynamic process of surface reactions. After the computer received the signal from the CCD camera, data processing was performed using Matlab software, including Fourier transform and inverse Fourier transform, to reconstruct the phase map to observe the concentration changes of ions on the electrode surface and in the electrolyte. The relationship between the phase difference (ΔΦ) and the change in the refractive index of the solution (Δn) is:

[0174] ΔΦ=(2πd / λ)Δn, formula (1)

[0175] Where λ is the wavelength of the laser light (632.8nm) and d is the geometric path length where the refractive index changes. Phase changes in the light beam are very important because they can help capture otherwise inconspicuous information in the specimen for real-time display and analysis of the dynamic changes of ions on and near the electrode surface. Matlab software is used to perform Fourier transform and inverse Fourier transform on the holographic image recorded by the CCD camera to reconstruct the phase map. Based on the relationship between phase difference and refractive index change, the change in refractive index is calculated, and then the dynamic changes in the concentration of ions on and near the metal surface to be detected are analyzed.

[0176] In the above embodiment, when predicting the stress corrosion of the power grid material, a series of processing is performed on the current holographic image and the historical holographic image of the power grid material to be detected to obtain the current phase difference, and then the solution refractive index change value of the solution in which the power grid material to be detected is directly determined according to the current phase difference, and the stress corrosion prediction result of the power grid material to be detected is directly determined in combination with the current value and voltage value of the power grid material to be detected, which is conducive to improving the prediction efficiency of the stress corrosion of the power grid material; moreover, no manual intervention is required, avoiding the defect of using the method of artificial experience prediction, which is cumbersome, requires a lot of time and manpower, and leads to low prediction efficiency of the stress corrosion of the power grid material, and further improves the prediction efficiency of the stress corrosion of the power grid material. At the same time, the digital holography used provides a high-resolution phase map, which can observe the dynamic changes of the electrode surface and the electrolyte in real time and non-destructively, which is of great significance for studying the microscopic mechanism and dynamic behavior in the electrochemical process; compared with the traditional electrochemical analysis method, digital holography can capture more detailed information, improve the accuracy and reliability of the experimental results; through digital holography, the three-dimensional movement and distribution of ions in the electrolyte can be intuitively observed. This helps to understand and explain the effect of magnetohydrodynamics (MHD) flow on electrochemical processes and reveal the mechanism of the effect of external magnetic field on anodic dissolution. This three-dimensional observation capability enables researchers to have a more comprehensive understanding of mass transfer and electrode processes in electrochemical reactions, which helps to optimize the design and operation of electrochemical systems. As a non-destructive testing technology, digital holography does not cause any physical damage to electrodes and electrolytes, which allows experiments to be repeated in the same system, ensuring the repeatability and consistency of experimental results. The characteristics of non-destructive testing also make the system suitable for electrochemical research in various complex environments and extreme conditions, and has broad application prospects. The system can adapt to different types of electrode materials and electrolytes and is suitable for a variety of electrochemical research fields. It is not only suitable for the study of the anodic dissolution process of copper electrodes, but can also be extended to the corrosion and protection research of other metal materials. The modular design of the system allows researchers to flexibly adjust and configure it according to specific experimental needs, improving the applicability and research efficiency of the system. The system has important application value in power systems and can be used to monitor and maintain metal corrosion in transmission lines and substation equipment to ensure the safe and stable operation of power systems. In the fields of marine engineering, battery technology and electrochemical sensors, the system also has broad application prospects, which will help improve the service life and reliability of equipment and promote the development of related technologies.

[0177] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0178] Based on the same inventive concept, the embodiment of the present application also provides a power grid material stress corrosion prediction device for implementing the power grid material stress corrosion prediction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more power grid material stress corrosion prediction device embodiments provided below can refer to the limitations of the power grid material stress corrosion prediction method above, and will not be repeated here.

[0179] In an exemplary embodiment, Figure 6 As shown, a device for predicting stress corrosion of power grid materials is provided, comprising: an image acquisition module 601, an information acquisition module 602, an information extraction module 603, an information processing module 604, an information determination module 605 and a result determination module 606, wherein:

[0180] The image acquisition module 601 is used to acquire the current holographic image and the historical holographic image of the power grid material to be detected.

[0181] The information acquisition module 602 is used to acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image.

[0182] The information extraction module 603 is used to extract the first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and to extract the second phase information corresponding to the historical holographic image from the second complex amplitude distribution information.

[0183] The information processing module 604 is used to perform difference processing on the first phase information and the second phase information to obtain a current phase difference.

[0184] The information determination module 605 is used to determine the change value of the solution refractive index of the solution in which the grid material to be detected is located according to the current phase difference.

[0185] The result determination module 606 is used to determine the stress corrosion prediction result of the power grid material to be detected according to the change value of the solution refractive index, the current value and the voltage value of the power grid material to be detected.

[0186] In an exemplary embodiment, the information acquisition module 602 is further used to perform Fourier transform processing on the current holographic image and the historical holographic image, respectively, to obtain first spectrum information of the current holographic image, and second spectrum information of the historical holographic image; to perform filtering processing on the first spectrum information and the second spectrum information, respectively, to obtain first processed spectrum information and second processed spectrum information; to perform inverse Fourier transform processing on the first processed spectrum information and the second processed spectrum information, respectively, to obtain first complex amplitude distribution information of the current holographic image, and second complex amplitude distribution information of the historical holographic image.

[0187] In an exemplary embodiment, the information determination module 605 is also used to obtain the wavelength value of the laser associated with the grid material to be detected, and the path length value of the laser passing through the solution in which the grid material to be detected is located; based on the current phase difference, wavelength value, and path length value, the solution refractive index change value of the solution in which the grid material to be detected is located is determined.

[0188] In an exemplary embodiment, the result determination module 606 is also used to determine the current resistance value of the power grid material to be detected based on the current value and voltage value of the power grid material to be detected; obtain the historical resistance value of the power grid material to be detected, and determine the resistance change value of the power grid material to be detected based on the historical resistance value and the current resistance value; determine the stress corrosion prediction result of the power grid material to be detected based on the solution refractive index change value and the resistance change value.

[0189] In an exemplary embodiment, the result determination module 606 is also used to input the solution refractive index change value and the resistance change value into a pre-trained stress corrosion prediction model to obtain multiple preset stress corrosion prediction results for the power grid material to be tested, and the prediction probability corresponding to each preset stress corrosion prediction result; from each preset stress corrosion prediction result, the preset stress corrosion prediction result with the largest corresponding prediction probability is screened out as the stress corrosion prediction result for the power grid material to be tested.

[0190] In an exemplary embodiment, the image acquisition module 601 is also used to acquire a holographic video of the power grid material to be detected; perform denoising on the holographic video to obtain a processed holographic video; perform frame processing on the processed holographic video to obtain an image sequence corresponding to the processed holographic video; and extract the current holographic image and historical holographic images of the power grid material to be detected from the image sequence.

[0191] Each module in the above-mentioned power grid material stress corrosion prediction device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0192] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as current holographic images and historical holographic images. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting stress corrosion of power grid materials is implemented.

[0193] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0194] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0195] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0196] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0197] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0198] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0199] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting stress corrosion of power grid materials, characterized in that: The method comprises: Obtaining current holographic images and historical holographic images of the power grid material to be inspected; Acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image; Extracting first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and extracting second phase information corresponding to the historical holographic image from the second complex amplitude distribution information; Performing difference processing on the first phase information and the second phase information to obtain a current phase difference; Determining a change value of the refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference; The stress corrosion prediction result of the power grid material to be detected is determined according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected.

2. The method according to claim 1, characterized in that The acquiring the first complex amplitude distribution information of the current holographic image and the second complex amplitude distribution information of the historical holographic image comprises: Performing Fourier transform processing on the current holographic image and the historical holographic image respectively to obtain first spectrum information of the current holographic image and second spectrum information of the historical holographic image; Performing filtering processing on the first spectrum information and the second spectrum information respectively to obtain first processed spectrum information and second processed spectrum information; The first processed spectrum information and the second processed spectrum information are respectively subjected to inverse Fourier transform processing to obtain first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image.

3. The method according to claim 1, characterized in that: Determining the change value of the solution refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference includes: Acquire the wavelength value of the laser associated with the to-be-detected grid material, and the path length value of the laser through the solution in which the to-be-detected grid material is located; The refractive index change value of the solution in which the to-be-detected electric grid material is located is determined according to the current phase difference, the wavelength value, and the path length value.

4. The method according to claim 1, characterized in that: Determining the stress corrosion prediction result of the power grid material to be detected according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected includes: Determining the current resistance value of the power grid material to be detected according to the current value and voltage value of the power grid material to be detected; Acquire the historical resistance value of the power grid material to be detected, and determine the resistance change value of the power grid material to be detected according to the historical resistance value and the current resistance value; The stress corrosion prediction result of the power grid material to be tested is determined according to the change value of the solution refractive index and the change value of the resistance.

5. The method according to claim 4, characterized in that Determining the stress corrosion prediction result of the power grid material to be detected according to the change value of the solution refractive index and the change value of the resistance includes: Inputting the solution refractive index change value and the resistance change value into a pre-trained stress corrosion prediction model to obtain a plurality of preset stress corrosion prediction results of the power grid material to be tested, and a prediction probability corresponding to each preset stress corrosion prediction result; From each of the preset stress corrosion prediction results, a preset stress corrosion prediction result with the largest corresponding prediction probability is screened out as the stress corrosion prediction result of the power grid material to be detected.

6. The method according to any one of claims 1 to 5, characterized in that The step of obtaining the current holographic image and the historical holographic image of the power grid material to be detected includes: Obtain holographic video of the power grid material to be tested; Performing denoising processing on the holographic video to obtain a processed holographic video; Performing frame processing on the processed holographic video to obtain an image sequence corresponding to the processed holographic video; The current holographic image and the historical holographic image of the power grid material to be detected are extracted from the image sequence.

7. A device for predicting stress corrosion of power grid materials, characterized in that: The device comprises: An image acquisition module, used to acquire current holographic images and historical holographic images of the power grid material to be detected; An information acquisition module, used to acquire first complex amplitude distribution information of the current holographic image and second complex amplitude distribution information of the historical holographic image; an information extraction module, configured to extract first phase information corresponding to the current holographic image from the first complex amplitude distribution information, and to extract second phase information corresponding to the historical holographic image from the second complex amplitude distribution information; An information processing module, used for performing difference processing on the first phase information and the second phase information to obtain a current phase difference; An information determination module, used to determine a change value of the solution refractive index of the solution in which the to-be-detected grid material is located according to the current phase difference; The result determination module is used to determine the stress corrosion prediction result of the power grid material to be detected according to the change value of the refractive index of the solution, the current value and the voltage value of the power grid material to be detected.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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