Method and system for predicting ultra-fast diffusion of copper ions on copper-aluminum solid-liquid interface

Abstract

The invention belongs to the technical field of copper-aluminum bimetal composite plate manufacturing. The method comprises the following steps: determining the mean square displacement of the copper ions at the current moment according to the temperature, the diffusion coefficient, the activation energy, the molar gas constant and the time dependence constant at the current moment; determining a copper ion mean square displacement curve at different moments according to the copper ion mean square displacement at different moments, and further obtaining a change rule of the copper ion mean square displacement; according to the method, the mean square displacement change curve of the fast diffusion of the copper ions is quantified, and the accurate prediction of the change rule of the mean square displacement of the copper ions is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing copper-aluminum bimetallic composite plates, and specifically relates to a method for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper and aluminum, a system for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper and aluminum, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Bimetallic composite materials are new materials composed of two metals with different physical, chemical, or mechanical properties. Compared with single-metal materials, this material has better properties, which is attributed to its combination of the advantages of two metals and the improvement of the defects of single materials. For example, in the copper-aluminum composite structure, copper has characteristics such as high electrical conductivity, high thermal conductivity, and low contact resistance, while aluminum has excellent properties such as low cost, low density, high electrical conductivity, and high corrosion resistance. Due to its excellent properties, it is widely used in the fields of electricity, aerospace, and automobiles. Solid-liquid composite casting can obtain bimetallic composite materials by directly contacting the molten metal with the solid metal to form a solid-liquid diffusion reaction zone, and the atoms of the solid-liquid metal can diffuse through the interface. The key to the preparation technology is the formation mechanism of the material interface. Sufficient element diffusion can achieve good metallurgical bonding, while other situations will lead to defects near the interface and deterioration of performance. During the formation of the material interface, the mean square displacement of element diffusion exhibits the characteristics of ultra-fast diffusion.

[0004] At present, the structural derivative diffusion model has been successfully applied to describe the ultra-fast diffusion phenomenon in which the mean square displacement increases exponentially with time. However, in the existing ultra-fast diffusion models, the diffusion coefficient is generally a fitting constant and does not consider its influencing factors. However, at the copper-aluminum solid-liquid interface, its diffusion process is affected by temperature, which in turn causes the particle diffusion rate to change. That is to say, the diffusion coefficient is not a fixed constant but a non-linear function that changes with temperature. Therefore, the existing models cannot accurately describe the ultra-fast diffusion process under the action of temperature. Summary of the Invention

[0005] To solve the deficiencies of the prior art, the present invention provides a method and system for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper and aluminum, quantifies the mean square displacement change curve of the ultra-fast diffusion of copper ions, and realizes the accurate prediction of the change law of the mean square displacement of copper ions.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper and aluminum.

[0008] A method for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper-aluminum, comprising the following processes:

[0009] Obtain the temperature at the current moment;

[0010] Determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant and time-dependent constant at the current moment;

[0011] Determine the mean square displacement curves of copper ions at different moments according to the mean square displacements of copper ions at different moments, and then obtain the change law of the mean square displacement of copper ions.

[0012] As a further limitation of the first aspect of the present invention, the mean square displacement of copper ions at the current moment t is: wherein, T is the temperature at the current moment, D0 is the diffusion coefficient, Q is the activation energy, R is the molar gas constant, α is the time-dependent constant, and f(x,t) is the concentration of copper ions at depth x and time t.

[0013] As a further limitation of the first aspect of the present invention, according to the calculation formula of the mean square displacement of copper ions at the current moment t, fit the experimental data of the mean square displacement of copper ions changing with time at different temperatures to obtain the curve of the mean square displacement of copper ions changing with time.

[0014] As a further limitation of the first aspect of the present invention, the experimental data is: the change characteristics that the mean square displacement of copper ions in the copper-aluminum composite plate increases with the increase of temperature and time.

[0015] As a further limitation of the first aspect of the present invention, compare the predicted change law of the mean square displacement of copper ions with time with the experimental data under the new temperature scale and the curve of the predicted change law of the mean square displacement of copper ions with time under the fractal derivative diffusion model, and determine the prediction accuracy according to the comparison result.

[0016] As a further limitation of the first aspect of the present invention, the diffusion coefficient, activation energy and time-dependent constant are obtained by fitting using the non-linear least squares method.

[0017] In a second aspect, the present invention provides a system for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper-aluminum.

[0018] A system for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper-aluminum, comprising:

[0019] A temperature data acquisition unit configured to: obtain the temperature at the current moment;

[0020] A mean square displacement prediction unit configured to: determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant and time-dependent constant at the current moment;

[0021] A mean square displacement variation law generation unit, configured to: determine a mean square displacement curve of copper ions at different times according to the mean square displacement of copper ions at different times, and further obtain the variation law of the mean square displacement of copper ions.

[0022] As a further limitation of the second aspect of the present invention, in the mean square displacement prediction unit, the mean square displacement of copper ions at the current time t is: where T is the temperature at the current time, D0 is the diffusion coefficient, Q is the activation energy, R is the molar gas constant, α is the time-dependent constant, and f(x,t) is the concentration of copper ions at depth x and time t.

[0023] In a third aspect, the present invention provides a computer device, including: a processor and a computer-readable storage medium;

[0024] The processor is adapted to execute a computer program;

[0025] The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method for measuring the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as described in the first aspect of the present invention.

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, and the computer program is adapted to be loaded and executed by a processor to implement the method for measuring the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as described in the first aspect of the present invention.

[0027] In a fifth aspect, the present invention provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the method for measuring the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as described in the first aspect of the present invention.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] The present invention innovatively proposes a method for predicting the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface. According to the temperature, diffusion coefficient, activation energy, molar gas constant, and time-dependent constant at the current time, the mean square displacement of copper ions at the current time is determined. According to the mean square displacement of copper ions at different times, the mean square displacement curve of copper ions at different times is determined, quantifying the variation law of the mean square displacement of copper ions, and realizing the accurate prediction of the variation law of the mean square displacement of copper ions.

[0030] The advantages of the additional aspects of the present invention will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present invention. Description of the Drawings

[0031] The accompanying drawings of the specification, which form a part of the present invention, are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0032] Figure 1 It is a schematic diagram of the method for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper-aluminum provided in Embodiment 1 of the present invention;

[0033] Figure 2 It is a growth diagram of the mean square displacement of copper ions at 950K provided in Embodiment 1 of the present invention;

[0034] Figure 3 It is a growth diagram of the mean square displacement of copper ions at 900K provided in Embodiment 1 of the present invention;

[0035] Figure 4 It is a growth diagram of the mean square displacement of copper ions at 850K provided in Embodiment 1 of the present invention;

[0036] Figure 5 It is a schematic diagram of a system for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper-aluminum provided in Embodiment 2 of the present invention;

[0037] Figure 6 It is a schematic diagram of a computer device provided in Embodiment 3 of the present invention. Detailed Embodiments

[0038] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0039] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0040] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0041] Embodiment 1:

[0042] Compared with the structural derivative, the fractal derivative and the fractional derivative, the former's kernel function, also known as the structure function, can be any function, while the kernel functions of the fractal derivative and the fractional derivative are both power-law functions. The structural derivative can reduce the parameters of the model and lower the computational cost. A large number of experiments and models show that the diffusion process of copper ions at the copper-aluminum solid-liquid interface generally exhibits the correlation characteristics of time scales, and the existing ultra-fast diffusion models can be used to describe this process. However, the diffusion coefficients of the models are mostly fitting constants rather than non-linear functions that vary with temperature. To overcome the limitations of the existing methods for describing ultra-fast diffusion, the present invention proposes a variable diffusion coefficient structural derivative diffusion model, derives the analytical solution and the mean square displacement expression of the model, and predicts the mean square displacement law of copper ions in the ultra-fast diffusion at the copper-aluminum solid-liquid interface at different temperatures.

[0043] More specifically, it includes the following processes:

[0044] S1: Select the diffusion process of copper ions during the casting process of a specific copper-aluminum composite plate as the research object, conduct casting experiments on liquid aluminum or semi-solid aluminum and metal copper blocks, and obtain the experimental data of the mean square displacement of copper ions varying with time at different temperatures. Specifically, it shows the characteristic that the mean square displacement of copper ions at the copper-aluminum solid-liquid interface increases exponentially with time.

[0045] S2: Establish a variable diffusion coefficient structural derivative ultra-fast diffusion model, derive the analytical solution and the mean square displacement expression of the model. The variable diffusion coefficient structural derivative diffusion model is:

[0046]

[0047] where f(x, t) is the concentration of copper ions at depth x and time t, α is the time-dependent constant,

[0048] is the diffusion coefficient that depends exponentially on temperature, D0 is the diffusion coefficient, Q is the activation energy, R is the molar gas constant. Through the scale transformation process, the analytical solution of the variable diffusion coefficient structural derivative diffusion model is derived as:

[0049]

[0050] Furthermore, based on the above formula, the mean square displacement that can describe the ultra-fast diffusion kinetics under different thermal conditions is derived (the mean square displacement is the average of the squares of the deviations of a group of particles from their initial positions during continuous movement):

[0051]

[0052] S3: Combine the experimental data of the mean square displacement of copper ions varying with time at different temperatures in step S1, fit to obtain the parameters of the variable diffusion coefficient structural derivative diffusion model in step S2, and use the analytical solution of the model to quantify the experimental data in step S1.

[0053] Specifically: Using the nonlinear least squares method and the lsqcurvefit command, obtain the most suitable matching parameters for the mean square displacement in the variable diffusion coefficient structural derivative diffusion model, which minimizes the mean square error between the variable diffusion coefficient structural derivative diffusion equation and the casting experimental data; the parameters include: the diffusion coefficient D0, the time dependence constant α, and the activation energy Q.

[0054] In the present invention, (<x 2 (t), T, t) is a set of experimental observables and satisfies <x 2 (t)> = exp(t, T, D0, Q, α) of the theoretical function, find the optimal estimated values of the parameters D0, α, Q of <x 2 (t)> = exp(t, T, D0, Q, α), for the given n sets of observed data <x 2 (t)>, solve the minimum value of the following objective function corresponding to the parameters D 0i , α i , Q i :

[0055]

[0056] Where i represents the i-th set of experimental observables.

[0057] In this implementation, use the mean square displacement expression of the variable diffusion coefficient structural derivative diffusion model to fit the experimental data of the mean square displacement of copper ions varying with time at different temperatures obtained in step S1, and obtain the curve of the mean square displacement of copper ions varying with time of the experimental data.

[0058] S4. According to the values of the parameters of the variable diffusion coefficient structural derivative diffusion model obtained by fitting in step S3, substitute them into the mean square displacement expression in step S2, and only change the temperature parameter to obtain the copper ion growth curve under the new temperature scale. This curve is the image of the law of change of the mean square displacement of copper ions with time predicted by the variable diffusion coefficient structural derivative diffusion model, and is compared with the experimental data under the new temperature scale and the curve of the law of change of the mean square displacement of copper ions with time predicted by the common fractal derivative diffusion model to further guide the actual engineering application.

[0059] As Figure 2 , Figure 3 and Figure 4 shown, this implementation gives the following example. Specifically, it includes the following process:

[0060] Step (1): Conduct a casting test on molten aluminum (or semi-solid aluminum) and metal copper blocks at a temperature of 950K to obtain data on the mean square displacement of copper ions over time at 950K.

[0061] Step (2): Fit the experimental data in Step (1) using the mean square displacement expression of the variable diffusion coefficient structural derivative diffusion model, where the time t in Step (1) ranges from 0 to 2 ns, as shown in Figure 2 , and through the non-linear least squares method, the calculated model parameters and mean square error are shown in Table 1. It can be seen from Table 1 that the feasibility of this prediction method.

[0062] Table 1: Parameters and mean square error for fitting the mean square displacement of copper ions at 950K.

[0063]

[0064] Step (3): According to the parameters in Step (2) and the analytical solution of the variable diffusion coefficient structural derivative diffusion model, substitute the parameters fitted by the model at 950K into the mean square displacement expression, and use the variable diffusion coefficient structural derivative diffusion model and the known fractal derivative diffusion model to predict the mean square displacement of copper ions at 900K and 850K respectively, demonstrating the obvious advantage of the variable diffusion coefficient structural derivative diffusion model in prediction, as shown in Table 2, Table 3, Figure 3 and Figure 4 , and the prediction accuracy gap between the two different models is obvious, thus demonstrating the effectiveness of the prediction method of the present invention.

[0065] Table 2: Mean square error of the mean square displacement of copper ions at 900K for different models.

[0066]

[0067] Table 3: Mean square error of the mean square displacement of copper ions at 900K for different models.

[0068]

[0069] In summary, the present invention takes the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as the research object, obtains experimental data on the mean square displacement of copper ions over time at different temperatures, then establishes a variable diffusion coefficient structural derivative diffusion model, derives the analytical solution and mean square displacement expression of the variable diffusion coefficient structural derivative diffusion model, combines the experimental data to determine the parameters of the variable diffusion coefficient structural derivative diffusion model, quantifies the mean square displacement change curve of ultra-fast diffusion of copper ions, and predicts the change law of the mean square displacement of copper ions. In the method of the present invention, a diffusion coefficient dependent on temperature index is adopted, with a simple mathematical form, easy calculation, high prediction accuracy, and has a wide range of engineering application backgrounds, and can provide an effective guiding role for the optimization and design of bimetallic composite materials.

[0070] Example 2:

[0071] As Figure 5 shown, this implementation provides a system for predicting the ultra-fast diffusion of copper ions at the solid-liquid interface of copper-aluminum, including:

[0072] A temperature data acquisition unit, configured to: acquire the temperature at the current moment;

[0073] A mean square displacement prediction unit, configured to: determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant, and time-dependent constant at the current moment;

[0074] A variation law generation unit, configured to: determine the mean square displacement curve of copper ions at different moments according to the mean square displacements of copper ions at different moments, and further obtain the variation law of the mean square displacement of copper ions.

[0075] The specific working processes of the above units are described in Example 1 and will not be elaborated here.

[0076] It can be understood that the above units can be respectively or all combined into one or several other units to form, or some of them can be further split into multiple smaller units with functional division to form, which can achieve the same operation without affecting the implementation of the technical effects of the embodiments of the present application. The above units are divided based on logical functions. In practical applications, the function of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In practical applications, these functions can also be assisted by other units and can be achieved by the cooperation of multiple units.

[0077] According to another embodiment of the present application, the system described in this embodiment can be constructed by running a computer program (including program code) capable of executing the respective steps involved in the corresponding method described in Example 1 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), a random access memory (RAM), and a read-only memory (ROM), and the method of Example 1 of the present application can be implemented. The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the above computing device through the computer-readable recording medium, and run therein.

[0078] Example 3:

[0079] As Figure 6As shown in the figure, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. Among them, the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected through a bus or other means.

[0080] Among them, the communication interface 1002 is used to receive and send data. The computer-readable storage medium 1003 can be stored in the memory of the electronic device. The computer-readable storage medium 1003 is used to store computer programs. The computer programs include program instructions. The processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0081] The processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function.

[0082] The processor 1001 is configured to execute the following process:

[0083] Obtain the temperature at the current moment;

[0084] Determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant, and time-dependent constant at the current moment;

[0085] Determine the mean square displacement curve of copper ions at different moments according to the mean square displacement of copper ions at different moments, and then obtain the change law of the mean square displacement of copper ions.

[0086] For the specific working process, please refer to the introduction in Embodiment 1, which will not be elaborated here.

[0087] Embodiment 4:

[0088] This implementation provides a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in the electronic device, which is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The computer-readable storage medium provides a storage space, and this storage space stores the processing system of the electronic device.

[0089] Moreover, one or more instructions suitable for being loaded and executed by a processor are stored in this storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory; optionally, it can also be at least one computer-readable storage medium located far from the aforementioned processor.

[0090] In one embodiment, one or more instructions are stored in the computer-readable storage medium; one or more instructions stored in the computer-readable storage medium are loaded and executed by a processor to implement the following process:

[0091] Obtain the temperature at the current moment;

[0092] Determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant, and time-dependent constant at the current moment;

[0093] Determine the mean square displacement curve of copper ions at different moments according to the mean square displacement of copper ions at different moments, and then obtain the variation law of the mean square displacement of copper ions.

[0094] For the specific working process, see the introduction in Embodiment 1 and will not be elaborated here.

[0095] Embodiment 5:

[0096] This implementation provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the electronic device to perform the following process:

[0097] Obtain the temperature at the current moment;

[0098] Determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant, and time-dependent constant at the current moment;

[0099] Determine the mean square displacement curve of copper ions at different moments according to the mean square displacement of copper ions at different moments, and then obtain the variation law of the mean square displacement of copper ions.

[0100] For the specific working process, see the introduction in Embodiment 1 and will not be elaborated here.

[0101] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0102] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of this application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data processing device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0103] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for predicting the ultra-fast diffusion of copper ions at a copper-aluminum solid-liquid interface, characterized in that: The process includes: Get the current temperature; Determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant and time-dependent constant at the current moment; According to the copper ion mean square displacement at different times, the copper ion mean square displacement curve at different times is determined, and then the variation law of the copper ion mean square displacement is obtained.

2. The method for predicting the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface according to claim 1, characterized in that: The mean square displacement of copper ions at the current time t is: Where T is the current temperature, D0 is the diffusion coefficient, Q is the activation energy, R is the molar gas constant, α is the time-dependent constant, and f(x,t) is the concentration of copper ions at depth x and time t.

3. The method for predicting the ultra-fast diffusion of copper ions at a copper-aluminum solid-liquid interface as claimed in claim 1 or 2, characterized in that: According to the calculation formula of the mean square displacement of copper ions at the current time t, the experimental data of the mean square displacement of copper ions at different temperatures changing with time are fitted to obtain the curve of the mean square displacement of copper ions changing with time.

4. The method for predicting the ultra-fast diffusion of copper ions at a copper-aluminum solid-liquid interface as claimed in claim 1 or 2, characterized in that: The test data are: the variation characteristics of the mean square displacement of copper ions in the copper-aluminum composite plate as the temperature and time increase; The predicted temporal variation of the mean square displacement of copper ions was compared with the experimental data under the new temperature scale and the temporal variation curve of the mean square displacement of copper ions predicted by the fractal derivative diffusion model, and the prediction accuracy was determined based on the comparison results.

5. The method for predicting the ultra-fast diffusion of copper ions at a copper-aluminum solid-liquid interface as claimed in claim 1 or 2, characterized in that: The diffusion coefficient, activation energy and time-dependent constant were obtained by nonlinear least squares fitting.

6. A system for predicting the ultra-fast diffusion of copper ions at a copper-aluminum solid-liquid interface, characterized in that: include: The temperature data acquisition unit is configured to: acquire the temperature at the current moment; The mean square displacement prediction unit is configured to: determine the mean square displacement of copper ions at the current moment according to the temperature, diffusion coefficient, activation energy, molar gas constant and time-dependent constant at the current moment; The change rule generating unit is configured to: determine the copper ion mean square displacement curve at different times according to the copper ion mean square displacement at different times, and then obtain the change rule of the copper ion mean square displacement.

7. The system for measuring the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as claimed in claim 6, characterized in that: In the mean square displacement prediction unit, the mean square displacement of copper ions at the current time t is: Where T is the current temperature, D0 is the diffusion coefficient, Q is the activation energy, R is the molar gas constant, α is the time-dependent constant, and f(x,t) is the concentration of copper ions at depth x and time t.

8. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by the processor, implements the method for measuring the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the method for measuring the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as described in any one of claims 1 to 5.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the method for measuring the ultra-fast diffusion of copper ions at the copper-aluminum solid-liquid interface as described in any one of claims 1 to 5.

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