Metal melt resistivity testing method for metal liquid structure identification

Abstract

The invention discloses a metal melt resistivity test method for metal liquid structure identification, and relates to the technical field of resistance measurement, and the method comprises the steps: obtaining a conversion function of a conventional melt partition and a characteristic melt partition; partitioning the feature melt to form a neural network model; the hidden layer is composed of a convection simulation model and a temperature correlation simulation model; training the convection simulation model; training the temperature correlation simulation model; forming a superposition algorithm mechanism for the convection simulation model and the temperature correlation simulation model; the temperatures of the characteristic melt partitions are obtained through measurement, and the temperatures of the melt partitions are obtained through a superposition algorithm mechanism; and a relation model of the temperature and the resistivity is established, the resistivity of the melt partition is obtained based on the relation model of the temperature and the resistivity, the resistance of the melt partition is averaged, and the resistivity of the metal melt is obtained. By forming a neural network model, the total resistivity of the metal melt is comprehensively obtained according to the relation between the temperature and the resistivity.

Description

Technical Field

[0001] The present invention relates to the technical field of resistance measurement, and specifically relates to a method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal. Background Art

[0002] The research methods for the liquid metal structure mainly include direct testing, theoretical calculation, and physical property testing. The direct testing mainly includes melt X-ray diffraction, electron and neutron diffraction, and synchrotron radiation technology. Some structure-sensitive physical properties of liquid metals are closely related to the melt structure, and the changes in macroscopic physical properties are the external manifestations of the changes in the microscopic structure of the melt. The structure-sensitive physical properties mainly include melt viscosity, density, resistivity, and thermoelectric potential testing, etc. Resistivity, as the electrical property of an alloy melt, can reflect the microscopic structure of the alloy melt, the interaction potential energy between electrons and atoms, and the electron state, etc.

[0003] During the measurement, due to the complex fluid motion, there is not only the situation of temperature loss, but also the situation of fluid motion, and the internal melt is blocked by the external melt, resulting in difficulty in accurately measuring the temperature. Since the resistivity of the molten metal is closely related to the temperature, this leads to inaccurate measurement of the resistivity. Summary of the Invention

[0004] To solve the above technical problems, a method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal is provided, and this technical solution solves the problems raised in the above background art.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal, comprising:

[0007] Establish a three-dimensional coordinate system for the environment where the molten metal is located;

[0008] Divide the molten metal into blocks to obtain at least one melt partition, take the melt partition at the frontmost side of the upper left corner as the characteristic melt partition, take the remaining melt partitions as the conventional melt partitions, and obtain the conversion function between the conventional melt partitions and the characteristic melt partition;

[0009] Form a neural network model for the characteristic melt partition. The neural network model is composed of an input layer, a hidden layer, and an output layer. The input layer inputs the surface temperature of the characteristic melt partition and the coordinates of the temperature prediction position, and the output layer outputs the temperature at the temperature prediction position;

[0010] The hidden layer is composed of a convection simulation model and a temperature correlation simulation model;

[0011] Train the convection simulation model until the prediction error of the flow simulation model for the convective motion of the molten metal is less than the preset distance;

[0012] Train the temperature correlation simulation model until the prediction error of the temperature correlation simulation model for the heat conduction of the molten metal is less than the preset temperature;

[0013] Form a superposition algorithm mechanism for the convection simulation model and the temperature correlation simulation model. The superposition algorithm mechanism converts the surface temperature of the characteristic melt partition and the coordinates of the temperature prediction position into the temperature at the temperature prediction position;

[0014] Measure the temperature of the characteristic melt partition, and obtain the temperature of the melt partition through the superposition algorithm mechanism;

[0015] Establish a relationship model between temperature and resistivity. Based on the relationship model between temperature and resistivity, obtain the resistivity of the melt partition, take the average value of the resistance of the melt partition, and obtain the resistivity of the molten metal.

[0016] Preferably, the step of dividing the molten metal into blocks to obtain at least one melt partition includes the following steps:

[0017] Test to obtain a preset volume, satisfying that the fluctuation range of the resistivity within the preset volume is less than the allowable error of the test;

[0018] Uniformly divide the molten metal into blocks to obtain at least one melt partition, and the volume of the melt partition is equal to the preset volume.

[0019] Preferably, the step of obtaining the conversion function between the conventional melt partition and the characteristic melt partition includes the following steps:

[0020] Take the melt partition located at the center of the molten metal as the target melt partition;

[0021] Obtain the central coordinates of the characteristic melt partition as the characteristic coordinates, and obtain the central coordinates of the conventional melt partition as the conventional coordinates;

[0022] Calculate the distance between the characteristic coordinates and the target melt partition to obtain the characteristic distance, calculate the distance between the conventional coordinates and the target melt partition to obtain the conventional distance, and divide the conventional distance by the characteristic distance to obtain the conversion coefficient;

[0023] Pair and fit the conventional coordinates with the conversion coefficient to obtain the conversion function, where the conventional coordinates are the independent variables and the conversion coefficient is the dependent variable.

[0024] Preferably, the step of training the convection simulation model includes the following steps:

[0025] Obtain the first displacement range of the molecular motion in the molten metal, and equally spaced divide the first displacement range of the molecular motion to obtain at least one first displacement point;

[0026] In a non-viscous environment, obtain the second displacement range of the single-molecule motion of the molten metal, equally divide the second displacement range of the single-molecule motion, and obtain at least one second displacement point. The number of the second displacement points is the same as that of the first displacement points. Both the second displacement points and the first displacement points are vectors, and the magnitude of the second displacement points and the first displacement points is their modulus length;

[0027] Number the first displacement points from smallest to largest, number the second displacement points from smallest to largest, and after subtracting the values of the second displacement points with the same number from the first displacement points, obtain the viscous resistance value;

[0028] Pair and fit the second displacement points with the viscous resistance values to obtain a viscous resistance function, and the viscous resistance function is a vector;

[0029] In a non-viscous environment, obtain the ambient temperature at which the single-molecule motion displacement of the molten metal is equal to the second displacement point;

[0030] Pair and fit the ambient temperature with the motion displacement to obtain a thermal motion function, and the thermal motion function is a vector;

[0031] Form a convection simulation model, and the convection simulation model includes aP(Q(k)) + bQ(k), where a is the first training coefficient, k is the temperature, b is the second training coefficient, Q(k) is the thermal motion function, and P(Q(k)) is the viscous resistance function;

[0032] Obtain the first data set trained by the user, and the first data set is composed of the temperature and the convection displacement caused by the temperature;

[0033] Input the temperature in the first data set into the convection simulation model. When the difference between the displacement output by the convection simulation model and the convection displacement caused by the temperature in the first data set is not less than the preset distance, then adjust a and b until the difference between the displacement output by the convection simulation model and the convection displacement caused by the temperature in the first data set is less than the preset distance. The preset distance is set based on the allowable error of the test.

[0034] Preferably, the training of the temperature correlation simulation model includes the following steps:

[0035] Obtain the temperature change range of the characteristic melt partition, equally divide the temperature change range, and obtain at least one test point;

[0036] Obtain the distance range from the melt partition to the characteristic melt partition, equally divide the distance range, and obtain at least one sample point;

[0037] Randomly combine the test points and the sample points to obtain at least one test group;

[0038] Under the conditions of the test group, obtain the temperature value at the melt partition;

[0039] Pair and fit the test points, sample points with the temperature value at the melt partition to obtain a temperature correlation function, where the test points and sample points are independent variables, and the temperature value at the melt partition is the dependent variable;

[0040] Form a temperature correlation simulation model, the temperature correlation simulation model is cL(x, y), c is the training balance coefficient, L(x, y) is the temperature correlation function, x is the surface temperature of the characteristic melt partition, and y is the distance from the temperature prediction position to the characteristic melt partition;

[0041] Obtain a second data set, which consists of the surface temperature of the characteristic melt partition, the distance from the temperature prediction position to the characteristic melt partition, and the sample temperature at the temperature prediction position;

[0042] Input the surface temperature of the characteristic melt partition and the distance from the temperature prediction position to the characteristic melt partition in the second data set into the temperature correlation simulation model. When the difference between the test temperature output by the temperature correlation simulation model and the corresponding sample temperature is not less than the preset temperature, adjust c until the difference between the test temperature output by the temperature correlation simulation model and the corresponding sample temperature is less than the preset temperature. The preset temperature is the allowable error of the test.

[0043] Preferably, the formation of the superposition algorithm mechanism for the convection simulation model and the temperature correlation simulation model includes the following steps:

[0044] Input the surface temperature of the characteristic melt partition and the distance from the temperature prediction position to the characteristic melt partition into the temperature correlation simulation model to obtain the first temperature at the temperature prediction position;

[0045] Solve for the compensation distance w from the equation w + aP(Q(cL(x, w))) + bQ(cL(x, w)) = y;

[0046] Input the surface temperature of the characteristic melt partition and the compensation distance into the temperature correlation simulation model to obtain the second temperature at the coordinate position;

[0047] Input the coordinates of the temperature prediction position into the conversion function to obtain the actual coefficient, multiply the average value of the first temperature and the second temperature by the actual coefficient to obtain the temperature at the temperature prediction position.

[0048] Preferably, the establishment of the relationship model between temperature and resistivity includes the following steps:

[0049] Obtain a sample block, and the volume of the sample block is equal to the preset volume;

[0050] Heat the sample block, and obtain the resistivity of the sample block under the condition that the temperature of the sample block is equal to the value at the test point;

[0051] Pair the values at the recognition points with the resistivity of the sample blocks and fit them to obtain a resistivity prediction function.

[0052] Preferably, obtaining the resistivity of the melt partition based on the relationship model between temperature and resistivity includes the following steps:

[0053] Substitute the temperature of the melt partition into the resistivity prediction function to obtain the resistivity of the melt partition.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] A method for testing the resistivity of a metal melt for identifying the liquid structure of a metal according to the present invention forms a neural network model. In the neural network model, not only the diffusion of temperature is predicted, but also the influence of fluid viscosity on fluid convection is predicted. Furthermore, based on the temperature measurement of the characteristic melt partition, the temperature at different positions in the metal melt can be predicted more accurately. Then, through the relationship between temperature and resistivity, the overall resistivity of the metal melt can be obtained comprehensively. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic flow chart of the method for testing the resistivity of a metal melt for identifying the liquid structure of a metal according to the present invention;

[0057] Figure 2 It is a schematic flow chart of dividing the metal melt into blocks to obtain at least one melt partition according to the present invention;

[0058] Figure 3 It is a schematic flow chart of obtaining the conversion function between the conventional melt partition and the characteristic melt partition according to the present invention;

[0059] Figure 4 It is a schematic flow chart of training the convection simulation model according to the present invention;

[0060] Figure 5 It is a schematic flow chart of training the temperature correlation simulation model according to the present invention;

[0061] Figure 6 It is a schematic flow chart of forming a superposition algorithm mechanism for the convection simulation model and the temperature correlation simulation model according to the present invention;

[0062] Figure 7 It is a schematic flow chart of establishing the relationship model between temperature and resistivity according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0063] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations.

[0064] Reference Figure 1 As shown, a metal melt resistivity testing method for identifying metal liquid structure includes:

[0065] Establish a three-dimensional coordinate system for the environment where the metal melt is located;

[0066] Divide the metal melt into blocks to obtain at least one melt partition, use the melt partition at the front of the upper left corner as a characteristic melt partition, use the remaining melt partitions as regular melt partitions, and obtain a conversion function between the regular melt partition and the characteristic melt partition;

[0067] A neural network model is formed for the characteristic melt partition. The neural network model consists of an input layer, a hidden layer, and an output layer. The input layer inputs the surface temperature of the characteristic melt partition and the coordinates of the temperature prediction position, and the output layer outputs the temperature of the temperature prediction position.

[0068] The hidden layer consists of a convection simulation model and a temperature correlation simulation model;

[0069] The convection simulation model is trained until the prediction error of the convection motion of the molten metal by the flow simulation model is less than a preset distance;

[0070] The temperature correlation simulation model is trained until the prediction error of the temperature correlation simulation model for the heat conduction of the metal melt is less than a preset temperature;

[0071] A superposition algorithm mechanism is formed for the convection simulation model and the temperature correlation simulation model, and the superposition algorithm mechanism converts the surface temperature of the characteristic melt partition and the coordinates of the temperature prediction position into the temperature of the temperature prediction position;

[0072] The temperature of the characteristic melt partition is measured and the temperature of the melt partition is obtained through the superposition algorithm mechanism;

[0073] A temperature-resistivity relationship model is established. Based on the temperature-resistivity relationship model, the resistivity of the melt partition is obtained. The resistance of the melt partition is averaged to obtain the resistivity of the metal melt.

[0074] When measuring, the metal melt is the medium for conducting the temperature of the metal melt. Therefore, the temperature at different positions in the metal melt is different, and the internal metal melt is difficult to measure directly due to obstruction. Since the metal melt is fluid, it is difficult to expose the internal metal melt by peeling. In the peeling process, convection will occur inside the metal melt, which will also cause the internal temperature to be exchanged with the outside world, thereby also causing changes in the overall temperature. These changes are caused by traditional measurements. Therefore, these problems need to be avoided. In this solution, the mentioned problems are solved in a targeted manner.

[0075] Reference Figure 2 As shown, the steps for dividing a molten metal into blocks to obtain at least one melt partition are as follows:

[0076] Test to obtain a preset volume such that the fluctuation range of the resistivity within the preset volume is less than the allowable error of the test;

[0077] Uniformly divide the molten metal into blocks to obtain at least one melt partition, and the volume of the melt partition is equal to the preset volume.

[0078] A melt partition is a region in the molten metal where different temperature predictions can be made. Therefore, by making different predictions for the melt partitions and based on the superposition effect of different conditions, the temperature at different positions of the molten metal can be predicted more accurately.

[0079] Reference Figure 3 As shown, the steps for obtaining the conversion function between a conventional melt partition and a characteristic melt partition are as follows:

[0080] Take the melt partition at the center of the molten metal as the target melt partition;

[0081] Obtain the central coordinates of the characteristic melt partition as the characteristic coordinates, and obtain the central coordinates of the conventional melt partition as the conventional coordinates;

[0082] Calculate the distance between the characteristic coordinates and the target melt partition to obtain the characteristic distance, calculate the distance between the conventional coordinates and the target melt partition to obtain the conventional distance, and divide the conventional distance by the characteristic distance to obtain the conversion coefficient;

[0083] Pair and fit the conventional coordinates and the conversion coefficient to obtain the conversion function, where the conventional coordinates are the independent variables and the conversion coefficient is the dependent variable.

[0084] The conversion coefficient is mainly for predicting the temperature loss situation, which mainly occurs between the molten metal and the environment. Therefore, the loss situation is related to the distance to the center of the molten metal. The closer the distance, the less the loss. Thus, the conversion function is established.

[0085] Reference Figure 4 As shown, the steps for training a convection simulation model are as follows:

[0086] Obtain the first displacement range of the molecular motion in the molten metal, and equally divide the first displacement range of the molecular motion to obtain at least one first displacement point;

[0087] In a non-viscous environment, obtain the second displacement range of the single molecular motion of the molten metal, and equally divide the second displacement range of the single molecular motion to obtain at least one second displacement point. The number of the second displacement points is the same as that of the first displacement points. Both the second displacement points and the first displacement points are vectors, and the magnitude of the second displacement points and the first displacement points is their modulus length;

[0088] Number the first displacement points from smallest to largest, number the second displacement points from smallest to largest, and after subtracting the value of the second displacement point with the same number from the first displacement point, obtain the viscous resistance value;

[0089] Pair the second displacement points with the viscous resistance values and fit them to obtain the viscous resistance function, and the viscous resistance function is a vector;

[0090] In a non-viscous environment, obtain the ambient temperature at which the movement displacement of a single molecule of the molten metal is equal to the second displacement point;

[0091] Pair the ambient temperature with the movement displacement and fit them to obtain the thermal motion function, and the thermal motion function is a vector;

[0092] Form a convection simulation model, and the convection simulation model includes aP(Q(k)) + bQ(k), where a is the first training coefficient, k is the temperature, b is the second training coefficient, Q(k) is the thermal motion function, and P(Q(k)) is the viscous resistance function;

[0093] Obtain the first data set trained by the user, and the first data set consists of the temperature and the convection displacement caused by the temperature;

[0094] Input the temperature in the first data set into the convection simulation model. When the displacement output by the convection simulation model and the convection displacement caused by the temperature in the first data set have a difference not less than the preset distance, then adjust a and b until the difference between the displacement output by the convection simulation model and the convection displacement caused by the temperature in the first data set is less than the preset distance. The preset distance is set based on the allowable error of the test.

[0095] The displacement vector output by the convection simulation model can be represented by coordinates and can thus be regarded as coordinates. During the convection process, the main factor at work is the thermal diffusion of molecules caused by temperature. However, since the molten metal itself may have viscosity, this viscosity will hinder the movement of molecules. Therefore, it is necessary to comprehensively consider these two situations. Therefore, the convection simulation model includes aP(Q(k)) + bQ(k). At the same time, since the initially established model may have insufficient simulation effects, it is necessary to perform data training and adjust a and b according to the training results. The ranges of a and b can be delimited, and within the delimited ranges, take a sufficient number of combinations of a and b. Thus, select the combination of a and b with the best simulation effect, and then obtain the final convection simulation model. A similar process is applied to the temperature correlation simulation model.

[0096] Refer to Figure 5 As shown, the training of the temperature correlation simulation model includes the following steps:

[0097] Obtain the temperature change range of the characteristic melt zone, equally divide the temperature change range at equal intervals to obtain at least one test point;

[0098] Obtain the distance range from the melt zone to the characteristic melt zone, equally divide the distance range at equal intervals to obtain at least one sample point;

[0099] Randomly combine the test points and sample points to obtain at least one test group;

[0100] Under the conditions of the test group, obtain the temperature value at the melt zone;

[0101] Pair and fit the test points, sample points and the temperature value at the melt zone to obtain a temperature correlation function, where the test points and sample points are independent variables, and the temperature value at the melt zone is the dependent variable;

[0102] Form a temperature correlation simulation model, the temperature correlation simulation model is cL(x, y), c is the training balance coefficient, L(x, y) is the temperature correlation function, x is the surface temperature of the characteristic melt zone, and y is the distance from the temperature prediction position to the characteristic melt zone;

[0103] Obtain a second data set, which consists of the surface temperature of the characteristic melt zone, the distance from the temperature prediction position to the characteristic melt zone, and the sample temperature at the temperature prediction position;

[0104] Input the surface temperature of the characteristic melt zone and the distance from the temperature prediction position to the characteristic melt zone in the second data set into the temperature correlation simulation model. When the difference between the test temperature output by the temperature correlation simulation model and the corresponding sample temperature is not less than the preset temperature, adjust c until the difference between the test temperature output by the temperature correlation simulation model and the corresponding sample temperature is less than the preset temperature. The preset temperature is the allowable error of the test.

[0105] In the same metal melt, the temperatures of different zones are related due to heat conduction. Therefore, in the absence of external influence, there is a certain connection between them. Thus, through this connection, a relationship function between the temperature of the characteristic melt zone and the temperatures of the other melt zones is established. Therefore, by accurately measuring the characteristic melt zone, the temperatures of the other melt zones can be predicted. Here, since the characteristic melt zone is almost all exposed to the outside and its volume is very small, its measurement error is very small and it can be accurately measured.

[0106] Refer to Figure 6 As shown, the formation of a superimposed algorithm mechanism for the convection simulation model and the temperature correlation simulation model includes the following steps:

[0107] Input the surface temperature of the characteristic melt zone and the distance from the temperature prediction position to the characteristic melt zone into the temperature correlation simulation model to obtain the first temperature at the temperature prediction position;

[0108] Solve for the compensation distance w from the equation w + aP(Q(cL(x, w))) + bQ(cL(x, w)) = y;

[0109] In the characteristic melt zone surface temperature and compensation distance input temperature correlation simulation model, obtain the second temperature at the coordinate position;

[0110] Input the coordinates of the temperature prediction position into the conversion function to obtain the actual coefficient, multiply the average of the first temperature and the second temperature by the actual coefficient to obtain the temperature at the temperature prediction position.

[0111] The temperature at the coordinates of the temperature prediction position mainly comes from two aspects. One is directly caused by heat exchange, namely the temperature loss to the environment and the internal temperature conduction, which can be obtained from the conversion function and the temperature correlation simulation model. The other is the temperature of the molecules that move to this position due to convection. For this, it is necessary to determine the original distance from the molecules that convect to this position to the characteristic melt zone, that is, the compensation distance. Therefore, by solving the equation to obtain the compensation distance, the temperature of the molecules that convect to this position can be approximated using the second temperature, and then the temperature at the temperature prediction position can be obtained by the superposition method. Thus, the prediction can be completed, and the neural network model can predict the temperature at the specified position through the established function and mechanism.

[0112] Refer to Figure 7 As shown, establishing the relationship model between temperature and resistivity includes the following steps:

[0113] Obtain a sample block, the volume of the sample block is equal to the preset volume;

[0114] Heat the sample block, and obtain the resistivity of the sample block under the condition that the temperature of the sample block is equal to the value at the test point;

[0115] Pair and fit the value at the identification point with the resistivity of the sample block to obtain the resistivity prediction function.

[0116] Since the resistivity of the metal melt is mainly determined by temperature when the material is determined, and the material of the characteristic melt zone is similar to that of the other melt zones, the temperature of the characteristic melt zone contains the material factor. Therefore, only the influence of temperature on resistivity needs to be considered, and thus establish the relationship model between temperature and resistivity to predict the resistivity of the melt zone.

[0117] Based on the relationship model between temperature and resistivity, obtaining the resistivity of the melt zone includes the following steps:

[0118] Substitute the temperature of the melt zone into the resistivity prediction function to obtain the resistivity of the melt zone.

[0119] Furthermore, the present solution also provides a storage medium, on which a computer-readable program is stored. When the computer-readable program is called, it executes the above-mentioned method for measuring the resistivity of a molten metal for identifying the liquid structure of the metal.

[0120] It can be understood that the storage medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0121] In summary, the advantages of the present invention are as follows: By forming a neural network model, in the neural network model, not only the diffusion of temperature is predicted, but also the influence of fluid viscosity on fluid convection is predicted. Furthermore, based on the temperature measurement of the characteristic melt zone, the temperature at different positions in the molten metal can be predicted more accurately. Then, through the relationship between temperature and resistivity, the overall resistivity of the molten metal can be obtained comprehensively.

[0122] The above has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements will occur to the present invention, and all these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing the resistivity of a molten metal for identifying the liquid structure of a metal, characterized in that, Including: Establish a three-dimensional coordinate system for the environment where the molten metal is located; Divide the molten metal into blocks to obtain at least one molten metal partition. Take the molten metal partition at the frontmost position in the upper left corner as the characteristic molten metal partition, and the remaining molten metal partitions as the regular molten metal partitions. Obtain the conversion function between the regular molten metal partitions and the characteristic molten metal partition; Form a neural network model for the characteristic molten metal partition. The neural network model consists of an input layer, a hidden layer, and an output layer. The input layer inputs the surface temperature of the characteristic molten metal partition and the coordinates of the temperature prediction position, and the output layer outputs the temperature at the temperature prediction position; The hidden layer consists of a convection simulation model and a temperature correlation simulation model; Train the convection simulation model until the prediction error of the convection movement of the molten metal by the convection simulation model is less than a preset distance; Train the temperature correlation simulation model until the prediction error of the heat conduction of the molten metal by the temperature correlation simulation model is less than a preset temperature; Form a superposition algorithm mechanism for the convection simulation model and the temperature correlation simulation model. The superposition algorithm mechanism converts the surface temperature of the characteristic molten metal partition and the coordinates of the temperature prediction position into the temperature at the temperature prediction position; Measure the temperature of the characteristic molten metal partition, and obtain the temperature of the molten metal partition through the superposition algorithm mechanism; Establish a relationship model between temperature and resistivity. Based on the relationship model between temperature and resistivity, obtain the resistivity of the molten metal partition. Take the average value of the resistance of the molten metal partition to obtain the resistivity of the molten metal.

2. The method for testing the resistivity of a molten metal for identifying the liquid structure of a metal according to claim 1, wherein The step of dividing the molten metal into blocks to obtain at least one molten metal partition includes the following steps: Test and obtain a preset volume that satisfies that the fluctuation range of the resistivity within the preset volume is less than the allowable error of the test; Uniformly divide the molten metal into blocks to obtain at least one molten metal partition, and the volume of the molten metal partition is equal to the preset volume.

3. A method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal according to claim 2, characterized in that The step of obtaining the conversion function between the regular molten metal partition and the characteristic molten metal partition includes the following steps: Take the molten metal partition located at the center of the molten metal as the target molten metal partition; Obtain the center coordinates of the characteristic molten metal partition as the characteristic coordinates, and obtain the center coordinates of the regular molten metal partition as the regular coordinates; Calculate the distance between the characteristic coordinates and the target molten metal partition to obtain the characteristic distance. Calculate the distance between the regular coordinates and the target molten metal partition to obtain the regular distance. Divide the regular distance by the characteristic distance to obtain the conversion coefficient; Pair and fit the regular coordinates with the conversion coefficient to obtain the conversion function, where the regular coordinates are the independent variables and the conversion coefficient is the dependent variable.

4. A method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal according to claim 3, characterized in that The step of training the convection simulation model includes the following steps: Obtain the first displacement range of the molecular motion in the molten metal, and equally divide the first displacement range of the molecular motion to obtain at least one first displacement point; In a non-viscous environment, obtain the second displacement range of the single molecular motion of the molten metal, and equally divide the second displacement range of the single molecular motion to obtain at least one second displacement point. The number of the second displacement points is the same as that of the first displacement points. The second displacement points and the first displacement points are both vectors, and the magnitude of the second displacement points and the first displacement points is their modulus length; Number the first displacement points from small to large, number the second displacement points from small to large, and after subtracting the values of the second displacement points with the same number from the first displacement points, obtain the viscous resistance value; Pair and fit the second displacement point with the viscous drag value to obtain a viscous drag function, where the viscous drag function is a vector; In a non-viscous environment, obtain the ambient temperature at which the displacement of a single molecule of the molten metal is equal to the second displacement point; Pair and fit the ambient temperature with the motion displacement to obtain a thermal motion function, where the thermal motion function is a vector; Form a convection simulation model, where the convection simulation model includes aP(Q(k)) + bQ(k); where a is the first training coefficient, k is the temperature, b is the second training coefficient, Q(k) is the thermal motion function, and P(Q(k)) is the viscous drag function; Obtain the first dataset trained by the user, where the first dataset consists of temperature and the convection displacement caused by the temperature; Input the temperature in the first dataset into the convection simulation model. When the displacement output by the convection simulation model and the convection displacement caused by the temperature in the first dataset have a difference not less than the preset distance, then adjust a and b until the difference between the displacement output by the convection simulation model and the convection displacement caused by the temperature in the first dataset is less than the preset distance, and the preset distance is set based on the allowable error of the test.

5. A method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal according to claim 4, characterized in that, The training of the temperature correlation simulation model includes the following steps: Obtain the temperature change range of the characteristic melt zone, equally spaced divide the temperature change range to obtain at least one test point; Obtain the distance range from the melt zone to the characteristic melt zone, equally spaced divide the distance range to obtain at least one sample point; Randomly combine the test points and the sample points to obtain at least one test group; Under the conditions of the test group, obtain the temperature value at the melt zone; Pair and fit the test points, sample points with the temperature value at the melt zone to obtain a temperature correlation function, where the test points and sample points are independent variables and the temperature value at the melt zone is the dependent variable; Form a temperature correlation simulation model, where the temperature correlation simulation model is cL(x, y), c is the training balance coefficient, L(x, y) is the temperature correlation function, x is the surface temperature of the characteristic melt zone, and y is the distance from the temperature prediction position to the characteristic melt zone; Obtain the second dataset, where the second dataset consists of the surface temperature of the characteristic melt zone, the distance from the temperature prediction position to the characteristic melt zone, and the sample temperature at the temperature prediction position; Input the surface temperature of the characteristic melt zone and the distance from the temperature prediction position to the characteristic melt zone in the second dataset into the temperature correlation simulation model. When the difference between the test temperature output by the temperature correlation simulation model and the corresponding sample temperature is not less than the preset temperature, then adjust c until the difference between the test temperature output by the temperature correlation simulation model and the corresponding sample temperature is less than the preset temperature, and the preset temperature is the allowable error of the test.

6. A method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal according to claim 5, characterized in that The formation of a superposition algorithm mechanism for the convection simulation model and the temperature correlation simulation model includes the following steps: Input the surface temperature of the characteristic melt zone and the distance from the temperature prediction position to the characteristic melt zone into the temperature correlation simulation model to obtain the first temperature at the temperature prediction position; Solve the compensation distance w from the equation w + aP(Q(cL(x, w))) + bQ(cL(x, w)) = y; In the correlation simulation model of the surface temperature of the characteristic melt partition and the input temperature of the compensation distance, the second temperature at the coordinate position is obtained; The coordinates of the temperature prediction position are input into the conversion function to obtain the actual coefficient. The average value of the first temperature and the second temperature is multiplied by the actual coefficient to obtain the temperature at the temperature prediction position.

7. A method for testing the resistivity of molten metal for identifying the liquid structure of metal according to claim 6, characterized in that, The establishment of the relationship model between temperature and resistivity includes the following steps: Obtain a sample block, and the volume of the sample block is equal to the preset volume; Heat the sample block, and obtain the resistivity of the sample block under the condition that the temperature of the sample block is equal to the value at the test point; Pair and fit the value at the identification point with the resistivity of the sample block to obtain the resistivity prediction function.

8. A method for measuring the resistivity of a molten metal for identifying the liquid structure of a metal according to claim 7, characterized in that Based on the relationship model between temperature and resistivity, obtaining the resistivity of the melt partition includes the following steps: Substitute the temperature of the melt partition into the resistivity prediction function to obtain the resistivity of the melt partition.

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