Electrolysis optimization method and system based on physical property analysis of rare earth oxides

Abstract

The present invention relates to the technical field of oxide electrolysis, and is an electrolysis optimization method and system based on the physical property analysis of rare earth oxides, comprising: constructing a target physical property set, based on the target physical property set, measuring the physical properties of each block oxide in the block oxide group to obtain an oxide property group set, constructing a standard electrolysis data set, based on the oxide property group set, querying the optimal electrolysis parameter group corresponding to the target oxide in the standard electrolysis data set, electrolyzing the local oxide, recording the actual electrolysis efficiency factor, determining the target electrolysis parameter group according to the actual electrolysis efficiency factor and the standard electrolysis efficiency factor, summarizing the target electrolysis parameter group, and obtaining the target electrolysis parameter group set. The present invention can improve the efficiency of rare earth oxide electrolysis and reduce the cost of electrolysis.

Description

Technical Field

[0001] The present invention relates to the technical field of oxide electrolysis, and in particular to an electrolysis optimization method and system based on physical property analysis of rare earth oxides. Background Art

[0002] Rare earth elements are a group of chemical elements, including lanthanides, scandium, yttrium, etc. They play a vital role in modern science and industry, especially in the fields of magnetic materials, luminescent materials, catalysts and electronic devices. Electrolysis is an effective method to extract rare earth elements from rare earth oxides. By electrolyzing rare earth element salt solutions, rare earth metals can be reduced at the cathode and then used to manufacture various high-performance materials and devices.

[0003] Currently, the main method for electrolyzing rare earth oxides is through molten salt electrolysis, but the high energy consumption and high equipment requirements of this method lead to increased production costs. At the same time, side reactions and material losses during the electrolysis process also reduce the overall efficiency. Summary of the invention

[0004] The present invention provides an electrolysis optimization method and system based on the analysis of the physical properties of rare earth oxides, the main purpose of which is to improve the efficiency of rare earth oxide electrolysis and reduce the electrolysis cost.

[0005] To achieve the above object, the present invention provides an electrolysis optimization method based on physical property analysis of rare earth oxides, comprising:

[0006] receiving an electrolysis optimization instruction, obtaining an oxide to be electrolyzed based on the electrolysis optimization instruction, and crushing the oxide to be electrolyzed to obtain a block oxide group;

[0007] Constructing a target physical property set, and based on the target physical property set, measuring the physical properties of each bulk oxide in the bulk oxide group to obtain an oxide property group set, wherein the oxide properties in the oxide property group correspond one-to-one to the target physical properties in the target physical property set;

[0008] Constructing a standard electrolysis data set, and extracting target oxides in the bulk oxide group in sequence, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group;

[0009] Based on the oxide property set, querying the optimal electrolysis parameter set corresponding to the target oxide in the standard electrolysis data set;

[0010] Extracting local oxides from the bulk oxides, electrolyzing the local oxides based on an optimal electrolysis parameter set, and recording an actual electrolysis efficiency factor in the step of electrolyzing the local oxides;

[0011] If the actual electrolysis efficiency factor is less than the preset standard electrolysis efficiency factor, then determining the optimal electrolysis data corresponding to the optimal electrolysis parameter group, and removing the optimal electrolysis data from the standard electrolysis data set to obtain a removed electrolysis data set;

[0012] The step of using the eliminated electrolysis data set as a standard electrolysis data set, returning the oxide property set, and searching the standard electrolysis data set for the optimal electrolysis parameter set corresponding to the target oxide;

[0013] If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter group is recorded as the target electrolysis parameter group;

[0014] The target electrolysis parameter groups are summarized to obtain a target electrolysis parameter set, and electrolysis optimization based on the analysis of the physical properties of rare earth oxides is completed based on the target electrolysis parameter set.

[0015] Optionally, the step of crushing the oxide to be electrolyzed to obtain a block oxide group comprises:

[0016] Mechanically crushing the oxide to be electrolyzed according to a preset crushing time to obtain the oxide to be measured;

[0017] Using a pre-constructed screening system to screen the oxide to be measured, to obtain a screened oxide group, wherein the screening system comprises a plurality of sieves with different mesh sizes, and each sieve is stacked together, and the number of screened oxides in the screened oxide group is the same as the number of sieves in the screening system;

[0018] Performing particle size analysis on the screened oxide group to obtain the original oxide particle size;

[0019] Determining whether the original oxide particle size is greater than a preset standard oxide particle size;

[0020] If the original oxide particle size is larger than the standard oxide particle size, each sieved oxide in the sieved oxide group is combined to obtain a secondary crushed oxide;

[0021] Using the secondary crushed oxide as the oxide to be electrolyzed, and returning to the step of mechanically crushing the oxide to be electrolyzed according to the preset crushing time;

[0022] If the original oxide particle size is not larger than the standard oxide particle size, the sieved oxide group is recorded as a massive oxide group.

[0023] Optionally, the step of performing particle size analysis on the screened oxide group to obtain the original oxide particle size comprises:

[0024] Obtaining the screening mass of each screening oxide in the screening oxide group to obtain a screening mass group, and recording the screening aperture of each sieve in the screening system to obtain a screening aperture group, wherein the screening mass in the screening mass group corresponds one to one to the screening aperture in the screening aperture group;

[0025] According to the screening quality group and the screening aperture group, the original oxide particle size is calculated, wherein the original oxide particle size is expressed as:

[0026] ;

[0027] in, represents the original oxide particle size, n Indicates the number of sieving apertures in a sieving aperture group or the number of sieving masses in a sieving mass group, Indicates the first i Screening aperture, Indicates the first i Screening quality.

[0028] Optionally, constructing a target physical property set includes:

[0029] Obtaining a control oxide set, and determining a control property group of each control oxide in the control oxide set according to a preset original physical property set, to obtain a control property group set, wherein the number of control properties in the control property group is the same as the number of original physical properties in the original physical property set, and the mass of each control oxide in the control oxide set is the same;

[0030] Extract reference oxides in sequence from the reference oxide set, conduct electrolysis tests on the reference oxides, and obtain electrolysis efficiency factors;

[0031] Summarizing the electrolysis efficiency factors of each reference oxide to obtain an electrolysis efficiency factor set;

[0032] According to the electrolysis efficiency factor set and the control property group set, an importance analysis is performed on the original physical property set to obtain a target physical property set.

[0033] Optionally, the electrolysis test is performed on the control oxide to obtain the electrolysis efficiency factor, including:

[0034] selecting a control electrolyte based on the control oxide, and adding the control oxide to the control electrolyte to obtain a control ionic solution;

[0035] detecting a first ion concentration in a reference ion solution, and calculating a theoretical electrolysis energy required for complete electrolysis of the reference ion solution;

[0036] Determine an electrolytic anode and an electrolytic cathode, insert the electrolytic anode and the electrolytic cathode into a reference ion solution, and respectively connect the electrolytic anode and the electrolytic cathode to a pre-acquired direct current power supply to obtain an electrolytic test device, wherein the electrolytic anode and the electrolytic cathode are both graphite electrodes, and the electrolytic anode and the electrolytic cathode are respectively connected to the positive electrode and the negative electrode of the direct current power supply;

[0037] Setting an electrolysis parameter group, wherein the electrolysis parameter group includes: electrolysis time, electrolysis current and electrolysis voltage;

[0038] An electrolysis efficiency test is performed based on the electrolysis parameter set and the electrolysis test device, and an electrolysis efficiency factor in the step of performing the electrolysis efficiency test is calculated according to the first ion concentration.

[0039] Optionally, the calculating the electrolysis efficiency factor in the step of performing the electrolysis efficiency test comprises:

[0040] Based on the electrolysis parameter set, the electrolysis test device is electrolyzed, and after the electrolysis is completed, the second ion concentration in the electrolysis test device is detected, and based on the electrolysis parameter set, the actual electrolysis energy consumed by electrolyzing the control ion solution is calculated, wherein the actual electrolysis energy is expressed as:

[0041] ;

[0042] in, represents the actual electrolysis energy, represents the electrolysis current, represents the electrolysis voltage, Indicates the duration of electrolysis;

[0043] The electrolysis efficiency factor is calculated according to the first ion concentration, the second ion concentration, the theoretical electrolysis energy and the actual electrolysis energy, wherein the electrolysis efficiency factor is expressed as:

[0044] ;

[0045] in, represents the electrolysis efficiency factor, represents the first ion concentration, represents the second ion concentration, Represents the theoretical electrolysis energy.

[0046] Optionally, the importance analysis of the original physical property set is performed according to the electrolysis efficiency factor set and the control property group set to obtain the target physical property set, including:

[0047] Extracting original physical properties in the original physical property set in sequence, removing the original physical properties from the original physical property set, and obtaining an intermediate physical property set;

[0048] Based on the intermediate physical property set, an intermediate property group set is determined in the control property group set, and the electrolysis efficiency factor in the electrolysis efficiency factor set is paired with the corresponding intermediate property group in the intermediate property group set to obtain a basic electrolysis data set;

[0049] According to the basic electrolysis data set, the pre-acquired deep learning model is used to perform data prediction to obtain the prediction deviation value;

[0050] The predicted deviation values ​​of each original physical property are aggregated to obtain a predicted deviation value set, a target deviation value set is identified in the predicted deviation value set based on a preset number of target properties, and a target physical property set corresponding to the target deviation value set is identified in the original physical property set, wherein the number of target deviation values ​​in the target deviation value set is the same as the number of target properties.

[0051] Optionally, performing data prediction on the pre-acquired deep learning model to obtain a prediction deviation value includes:

[0052] The basic electrolysis data set is divided into a training electrolysis data set and a verification electrolysis data set, and the deep learning model is trained using the training electrolysis data set to obtain an electrolysis efficiency prediction model;

[0053] Determine a verification efficiency factor set and a verification property group set in a verification electrolysis data set;

[0054] The verification property groups in the verification property group set are sequentially input into the electrolysis efficiency prediction model to obtain a prediction efficiency factor set. Based on the prediction efficiency factor set and the verification efficiency factor set, the prediction deviation value is calculated using the following formula:

[0055] ;

[0056] in, represents the prediction deviation value, represents the number of prediction efficiency factors in the prediction efficiency factor set or the number of verification efficiency factors in the verification efficiency factor set, represents the jth prediction efficiency factor in the prediction efficiency factor set, Represents the j-th verification efficiency factor in the verification efficiency factor set.

[0057] Optionally, querying the optimal electrolysis parameter group corresponding to the target oxide in the standard electrolysis data set includes:

[0058] extracting standard electrolysis data in the standard electrolysis data set in sequence, and identifying standard physical property groups in the standard electrolysis data;

[0059] Identifying a target oxide property group corresponding to the target oxide in a set of oxide property groups;

[0060] Based on the target oxide property group and the standard physical property group, the property matching degree is calculated, wherein the property matching degree is expressed as:

[0061] ;

[0062] in, Indicates the property matching degree, represents the number of target oxide properties in the target oxide property group or the number of standard physical properties in the standard physical property group, Indicates the first k The target oxide properties, Indicates the first in the standard physical properties group k Standard physical properties;

[0063] The property matching degrees are summarized to obtain a property matching degree set, the optimal matching degree in the property matching degree set is identified, and the optimal electrolysis data corresponding to the optimal matching degree is identified in the standard electrolysis data set, and the optimal electrolysis parameter group in the optimal electrolysis data is determined.

[0064] To achieve the above object, the present invention also provides an electrolysis optimization system based on the analysis of physical properties of rare earth oxides, comprising:

[0065] a physical property determination module, configured to receive an electrolysis optimization instruction, obtain an oxide to be electrolyzed based on the electrolysis optimization instruction, crush the oxide to be electrolyzed to obtain a block oxide group, construct a target physical property set, and measure the physical properties of each block oxide in the block oxide group based on the target physical property set to obtain an oxide property group set, wherein the oxide properties in the oxide property group correspond one-to-one to the target physical properties in the target physical property set;

[0066] The electrolysis parameter analysis module is used to construct a standard electrolysis data set and sequentially extract target oxides from the bulk oxide group, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group. Based on the oxide property group set, the optimal electrolysis parameter group corresponding to the target oxide is queried in the standard electrolysis data set;

[0067] an electrolysis efficiency calculation module, for extracting local oxides from the bulk oxides, and electrolyzing the local oxides based on an optimal electrolysis parameter group, and recording an actual electrolysis efficiency factor in the step of electrolyzing the local oxides; if the actual electrolysis efficiency factor is less than a preset standard electrolysis efficiency factor, determining optimal electrolysis data corresponding to the optimal electrolysis parameter group, and eliminating the optimal electrolysis data from a standard electrolysis data set to obtain an eliminated electrolysis data set;

[0068] The target parameter identification module is used to remove the electrolysis data set as the standard electrolysis data set, and return the oxide property group set, and query the optimal electrolysis parameter group corresponding to the target oxide in the standard electrolysis data set. If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter group is recorded as the target electrolysis parameter group, and the target electrolysis parameter group is summarized to obtain a target electrolysis parameter group set.

[0069] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0070] A memory storing at least one instruction;

[0071] A processor executes instructions stored in the memory to implement the electrolysis optimization method based on the analysis of physical properties of rare earth oxides described above.

[0072] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned electrolysis optimization method based on rare earth oxide physical property analysis.

[0073] In order to solve the problems described in the background technology, the present invention firstly crushes the oxide to be electrolyzed to obtain a block oxide group. This step can make the subsequent physical property determination and electrolysis test more accurate and controllable by crushing the oxide to be electrolyzed. At the same time, the crushing process also increases the electrolysis reaction area of ​​the oxide and improves the efficiency of electrolysis. Then, a target physical property set is constructed, and based on the target physical property set, the physical properties of each block oxide in the block oxide group are measured to obtain an oxide property group set. This step can establish detailed physical property data for each block oxide by measuring the physical properties of the oxide, which is helpful for subsequent electrolysis parameter optimization and electrolysis efficiency improvement. At the same time, parameters such as electrolysis efficiency factor and predicted deviation value are introduced in the construction of the target physical property set, so that the target physical property set has a strong influence on the efficiency of oxide electrolysis, thereby improving the subsequent The efficiency of electrolysis is determined, and then based on the oxide property group set, the optimal electrolysis parameter group corresponding to the target oxide is queried in the standard electrolysis data set. This step can quickly determine the possible optimal electrolysis parameter group by matching the physical properties of the oxide with the existing standard data. Further, in the electrolysis test, the actual electrolysis efficiency factor is detected, and the actual electrolysis efficiency factor is compared with the standard electrolysis efficiency factor to determine whether the optimal electrolysis parameter group is the target electrolysis parameter group, further improving the matching degree of the electrolysis parameter group, thereby improving the electrolysis efficiency during subsequent electrolysis. Finally, by summarizing and analyzing all the target electrolysis parameter groups, a comprehensive target electrolysis parameter group can be obtained, which provides comprehensive data support for the optimization of the entire electrolysis process, helps to improve the electrolysis efficiency, and at the same time, the optimal electrolysis parameter group enables electrolysis to consume less energy, thereby reducing the cost during electrolysis. Therefore, the present invention can improve the efficiency of rare earth oxide electrolysis and reduce the cost of electrolysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic flow chart of an electrolysis optimization method based on physical property analysis of rare earth oxides provided in one embodiment of the present invention;

[0075] Figure 2 A functional module diagram of an electrolysis optimization system based on rare earth oxide physical property analysis provided by one embodiment of the present invention;

[0076] Figure 3 A schematic diagram of the structure of an electronic device for implementing the electrolysis optimization method based on the analysis of physical properties of rare earth oxides provided in one embodiment of the present invention.

[0077] Description of reference numerals:

[0078] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0079] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0080] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0081] The embodiment of the present application provides an electrolysis optimization method based on the analysis of the physical properties of rare earth oxides. The execution subject of the electrolysis optimization method based on the analysis of the physical properties of rare earth oxides includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the electrolysis optimization method based on the analysis of the physical properties of rare earth oxides can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0082] Reference Figure 1 FIG. 1 is a flow chart of an electrolysis optimization method based on analysis of physical properties of rare earth oxides provided in one embodiment of the present invention. In this embodiment, the electrolysis optimization method based on analysis of physical properties of rare earth oxides includes:

[0083] S1. Receive an electrolysis optimization instruction, obtain the oxide to be electrolyzed based on the electrolysis optimization instruction, and crush the oxide to be electrolyzed to obtain a block oxide group.

[0084] It can be understood that the electrolysis optimization instruction refers to an instruction initiated manually to electrolyze rare earth oxides, and the oxide to be electrolyzed refers to the rare earth oxide that needs to be electrolyzed in the electrolysis optimization instruction. Since there are significant differences in the rare earth element content in different parts of the oxide to be electrolyzed, in order to improve the electrolysis efficiency, these oxides need to be crushed to obtain block oxide groups.

[0085] Furthermore, the method for obtaining the oxide to be electrolyzed can be: purchasing the required rare earth oxide from a professional rare earth material supplier, extracting it from mineral resources containing rare earth elements, such as rare earth ores and rare earth ore sands, etc. The method for crushing the oxide to be electrolyzed can be: crushing it using a jaw crusher.

[0086] For example, Xiao Zhang is an operator of a materials company. One day, Xiao Zhang receives an electrolysis optimization instruction, requiring him to electrolyze a rare earth oxide. Then, Xiao Zhang obtains the corresponding oxide to be electrolyzed according to the electrolysis optimization instruction, and crushes the oxide to be electrolyzed to obtain a block oxide group.

[0087] In detail, the oxide to be electrolyzed is crushed to obtain a block oxide group, comprising:

[0088] Mechanically crushing the oxide to be electrolyzed according to a preset crushing time to obtain the oxide to be measured;

[0089] Using a pre-constructed screening system to screen the oxide to be measured, to obtain a screened oxide group, wherein the screening system comprises a plurality of sieves with different mesh sizes, and each sieve is stacked together, and the number of screened oxides in the screened oxide group is the same as the number of sieves in the screening system;

[0090] Performing particle size analysis on the screened oxide group to obtain the original oxide particle size;

[0091] Determining whether the original oxide particle size is greater than a preset standard oxide particle size;

[0092] If the original oxide particle size is larger than the standard oxide particle size, each sieved oxide in the sieved oxide group is combined to obtain a secondary crushed oxide;

[0093] Using the secondary crushed oxide as the oxide to be electrolyzed, and returning to the step of mechanically crushing the oxide to be electrolyzed according to the preset crushing time;

[0094] If the original oxide particle size is not larger than the standard oxide particle size, the sieved oxide group is recorded as a massive oxide group.

[0095] It can be understood that the crushing time refers to the artificially set time for mechanical crushing, the mechanical crushing refers to crushing using a jaw crusher, the oxide to be measured refers to the oxide to be electrolyzed after crushing, the original oxide particle size refers to a numerical value used to represent the particle size of the oxide to be measured, the standard oxide particle size refers to an artificially set particle size constant, which represents the maximum oxide particle size that can be accepted in subsequent electrolysis, and the secondary crushed oxide refers to the oxide obtained after merging and screening the oxide groups.

[0096] It can be understood that the screening system refers to a device for screening the oxide to be measured, which is composed of multiple sieves, wherein the number of sieves in the screening system is set manually, and the sieve hole diameter of each sieve is different. In this screening system, different sieves will be stacked together, and each sieve will be stacked from top to bottom in the order of the sieve hole diameter from large to small.

[0097] Furthermore, the sieved oxide group refers to a combination of oxides to be tested that remain on different sieves after sieving, and the detailed steps of the sieving are: pour the oxide to be tested evenly on the top sieve in the sieving system, and then vibrate the sieve by means of vibration, shaking or mechanical transmission, so that the oxide particles move on the sieve. Due to the different sizes of the sieve holes, particles with a diameter smaller than the sieve holes will pass through the sieve holes and fall onto the next sieve, while particles with a diameter larger than the sieve holes will be retained on the current sieve. After waiting for a preset sieving time, stop vibrating the sieving system, and measure the mass of the oxide particles in each sieve respectively to obtain a sieving mass group, and the sieve hole diameter corresponding to each sieving mass in the sieving mass group constitutes a sieving aperture group.

[0098] In detail, the particle size analysis of the screened oxide group to obtain the original oxide particle size includes:

[0099] Obtaining the screening mass of each screening oxide in the screening oxide group to obtain a screening mass group, and recording the screening aperture of each sieve in the screening system to obtain a screening aperture group, wherein the screening mass in the screening mass group corresponds one to one to the screening aperture in the screening aperture group;

[0100] According to the screening quality group and the screening aperture group, the original oxide particle size is calculated, wherein the original oxide particle size is expressed as:

[0101] ;

[0102] in, represents the original oxide particle size, n Indicates the number of sieving apertures in a sieving aperture group or the number of sieving masses in a sieving mass group, Indicates the first i Screening aperture, Indicates the first i Screening quality.

[0103] It should be explained that the screening mass refers to the mass of the screened oxide. The calculation process of the screening mass is: first, obtain the original mass of the sieve where the screened oxide is located, wherein the original mass is the mass of the sieve without any load; then measure the actual mass of the sieve after screening, wherein the actual mass is the sum of the mass of the sieve itself and the mass of the screened oxide it carries; then, calculate the difference between the actual mass and the original mass to obtain the screening mass.

[0104] It can be understood that the screening aperture group refers to a combination of aperture diameters of each screen in the screening system, wherein, in the same screen, the apertures of all screens are the same.

[0105] S2. Construct a target physical property set, and based on the target physical property set, measure the physical properties of each bulk oxide in the bulk oxide group to obtain an oxide property group set, wherein the oxide properties in the oxide property group correspond one-to-one to the target physical properties in the target physical property set.

[0106] It should be explained that the target physical property set refers to a set of physical properties that have a strong influence on the electrolysis process. The target physical property set needs to be selected from the preset original physical property set, and the detailed steps of the selection will be given later.

[0107] It can be understood that the oxide property group refers to a combination of various physical properties of bulk oxides, wherein the number of oxide properties in the oxide property group is artificially set, and the oxide property group includes: density, hardness, specific surface area, thermal conductivity, electrical conductivity, etc. The methods for measuring these oxide properties are all existing technologies, for example: the method for measuring density is the pycnometer method or the buoyancy method: the bulk oxide is placed in a known volume of water, and the change in water level is measured to determine the volume of the bulk oxide, and then the mass of the bulk oxide is measured using a balance, and the density is calculated by the mass and volume. The method for measuring hardness is to use a Mohs hardness tester or a microhardness tester for measurement, and the method for measuring specific surface area is to use the BET method to calculate the specific surface area by the amount of adsorbed gas such as nitrogen on the surface of the bulk oxide.

[0108] Furthermore, since bulk oxides have many physical properties, if all physical properties are taken into consideration in the subsequent acquisition of electrolysis parameters, it will lead to a large amount of calculations and low electrolysis efficiency. Therefore, it is necessary to determine multiple target physical properties among the preset multiple physical properties, and use these multiple target physical properties as the physical properties to be considered when subsequently acquiring the electrolysis parameters.

[0109] In detail, the construction of the target physical property set includes:

[0110] Obtaining a control oxide set, and determining a control property group of each control oxide in the control oxide set according to a preset original physical property set, to obtain a control property group set, wherein the number of control properties in the control property group is the same as the number of original physical properties in the original physical property set, and the mass of each control oxide in the control oxide set is the same;

[0111] Extract reference oxides in sequence from the reference oxide set, conduct electrolysis tests on the reference oxides, and obtain electrolysis efficiency factors;

[0112] Summarizing the electrolysis efficiency factors of each reference oxide to obtain an electrolysis efficiency factor set;

[0113] According to the electrolysis efficiency factor set and the control property group set, an importance analysis is performed on the original physical property set to obtain a target physical property set.

[0114] It can be understood that the control oxide set refers to an artificially obtained set of oxides with detailed physical property records. In order to ensure that the variables of the subsequent electrolysis test are controllable, it is necessary to make the mass of each control oxide in the control oxide set the same. The original physical property set refers to an artificially set set of physical properties, such as density, hardness, specific surface area, thermal conductivity, electrical conductivity, etc. The detailed steps for conducting the electrolysis test will be given later. The electrolysis efficiency factor refers to a numerical value used to represent the electrolysis efficiency of the control oxide. The control property group refers to a combination of physical properties of the control oxide, and the types of control properties in the control property group appear in the original physical property set.

[0115] It should be explained that the steps for obtaining the reference oxide can be: since some companies and laboratories specialize in producing and selling standard substances with precise physical and chemical properties, reference oxides can be purchased from these companies and laboratories. Some online databases provide physical property data of materials, and the physical property data of the reference oxide can be obtained from these databases, and corresponding control samples can be prepared or purchased accordingly.

[0116] In detail, the electrolysis test is performed on the control oxide to obtain the electrolysis efficiency factor, including:

[0117] selecting a control electrolyte based on the control oxide, and adding the control oxide to the control electrolyte to obtain a control ionic solution;

[0118] detecting a first ion concentration in a reference ion solution, and calculating a theoretical electrolysis energy required for complete electrolysis of the reference ion solution;

[0119] Determine an electrolytic anode and an electrolytic cathode, insert the electrolytic anode and the electrolytic cathode into a reference ion solution, and respectively connect the electrolytic anode and the electrolytic cathode to a pre-acquired direct current power supply to obtain an electrolytic test device, wherein the electrolytic anode and the electrolytic cathode are both graphite electrodes, and the electrolytic anode and the electrolytic cathode are respectively connected to the positive electrode and the negative electrode of the direct current power supply;

[0120] Setting an electrolysis parameter group, wherein the electrolysis parameter group includes: electrolysis time, electrolysis current and electrolysis voltage;

[0121] An electrolysis efficiency test is performed based on the electrolysis parameter set and the electrolysis test device, and an electrolysis efficiency factor in the step of performing the electrolysis efficiency test is calculated according to the first ion concentration.

[0122] It can be understood that the control electrolyte refers to an electrolyte used together with a control oxide in an electrolysis test, wherein the control electrolyte is selected artificially, for example, when the control oxide is copper oxide, since copper oxide is insoluble in water, it needs to be dissolved in an acidic solution, so dilute sulfuric acid can be selected as the control electrolyte, the first ion concentration refers to the solute concentration of the control ion solution, and the theoretical electrolysis energy refers to the energy required to completely electrolyze the control ion solution.

[0123] Furthermore, the calculation formula of the theoretical electrolysis energy is: ,in, represents the theoretical electrolysis energy, represents the number of moles of electrons transferred during the electrolysis process, is the Faraday constant, Indicates the mass of the reference oxide.

[0124] In detail, the step of calculating the electrolysis efficiency factor in the step of performing the electrolysis efficiency test comprises:

[0125] Based on the electrolysis parameter set, the electrolysis test device is electrolyzed, and after the electrolysis is completed, the second ion concentration in the electrolysis test device is detected, and based on the electrolysis parameter set, the actual electrolysis energy consumed by electrolyzing the control ion solution is calculated, wherein the actual electrolysis energy is expressed as:

[0126] ;

[0127] in, represents the actual electrolysis energy, represents the electrolysis current, represents the electrolysis voltage, Indicates the duration of electrolysis;

[0128] The electrolysis efficiency factor is calculated according to the first ion concentration, the second ion concentration, the theoretical electrolysis energy and the actual electrolysis energy, wherein the electrolysis efficiency factor is expressed as:

[0129] ;

[0130] in, represents the electrolysis efficiency factor, represents the first ion concentration, represents the second ion concentration, Represents the theoretical electrolysis energy.

[0131] It can be understood that the second ion concentration refers to the concentration of oxide remaining in the electrolysis test device after the electrolysis test is completed. This concentration can be compared with the first ion concentration and can be used to evaluate the consumption of oxide during the electrolysis process. The actual electrolysis energy refers to the total energy consumed in this electrolysis process.

[0132] In detail, the importance analysis of the original physical property set is performed according to the electrolysis efficiency factor set and the control property group set to obtain the target physical property set, including:

[0133] Extracting original physical properties in the original physical property set in sequence, removing the original physical properties from the original physical property set, and obtaining an intermediate physical property set;

[0134] Based on the intermediate physical property set, an intermediate property group set is determined in the control property group set, and the electrolysis efficiency factor in the electrolysis efficiency factor set is paired with the corresponding intermediate property group in the intermediate property group set to obtain a basic electrolysis data set;

[0135] According to the basic electrolysis data set, the pre-acquired deep learning model is used to perform data prediction to obtain the prediction deviation value;

[0136] The predicted deviation values ​​of each original physical property are aggregated to obtain a predicted deviation value set, a target deviation value set is identified in the predicted deviation value set based on a preset number of target properties, and a target physical property set corresponding to the target deviation value set is identified in the original physical property set, wherein the number of target deviation values ​​in the target deviation value set is the same as the number of target properties.

[0137] Specifically, the pairing refers to combining the electrolysis efficiency factor and the intermediate property group. For example, if the electrolysis efficiency factor is A and the intermediate property group is B, then after pairing them, the basic electrolysis data obtained is: (A, B).

[0138] It can be understood that the intermediate physical property set refers to the original physical property set after the original physical properties are eliminated, the intermediate property group set refers to the control property group set that only contains all the intermediate physical properties in the intermediate physical property set, the basic electrolysis data refers to the combination of the electrolysis efficiency factor and the intermediate property group, the target property quantity refers to an artificially set constant, which is used to determine the number of target physical properties in the final target physical property set, and the target deviation value set refers to the set of the first a predicted deviation values ​​with the smallest values ​​in the predicted deviation value set, where a represents the target property quantity.

[0139] In detail, the data prediction of the pre-acquired deep learning model is performed to obtain the prediction deviation value, including:

[0140] The basic electrolysis data set is divided into a training electrolysis data set and a verification electrolysis data set, and the deep learning model is trained using the training electrolysis data set to obtain an electrolysis efficiency prediction model;

[0141] Determine a verification efficiency factor set and a verification property group set in a verification electrolysis data set;

[0142] The verification property groups in the verification property group set are sequentially input into the electrolysis efficiency prediction model to obtain a prediction efficiency factor set. Based on the prediction efficiency factor set and the verification efficiency factor set, the prediction deviation value is calculated using the following formula:

[0143] ;

[0144] in, represents the prediction deviation value, represents the number of prediction efficiency factors in the prediction efficiency factor set or the number of verification efficiency factors in the verification efficiency factor set, represents the jth prediction efficiency factor in the prediction efficiency factor set, Represents the j-th verification efficiency factor in the verification efficiency factor set.

[0145] It can be understood that the training electrolysis data set refers to a data set used to train a deep learning model in subsequent machine learning, the verification electrolysis data set refers to a data set used to verify the trained model in subsequent machine learning, and the detailed steps of data division are: dividing the basic electrolysis data set according to a preset division ratio, the electrolysis efficiency prediction model refers to a trained deep learning model, wherein the deep learning model can be: a convolutional neural network, a random forest network, etc., and the verification efficiency factor and the verification property group respectively refer to the electrolysis efficiency factor and the intermediate property group in the verification electrolysis data.

[0146] It should be explained that the predicted efficiency factor refers to the efficiency factor output by the electrolysis efficiency prediction model after the verification property group is input, and the predicted deviation value refers to a numerical value used to represent the degree of prediction deviation when the electrolysis efficiency prediction model predicts the efficiency factor.

[0147] S3. Construct a standard electrolysis data set, and extract target oxides in the bulk oxide group in sequence, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group.

[0148] It can be understood that the standard electrolysis data set refers to a data set containing different standard physical property groups and the optimal electrolysis parameter group under the standard physical property group, and the standard electrolysis parameter group refers to the electrolysis parameter combination that can achieve maximum electrolysis efficiency under the standard physical property group, wherein the standard electrolysis parameter group includes: standard electrolysis voltage, standard electrolysis current and standard electrolysis time.

[0149] It should be explained that the standard electrolysis data set can be obtained by relevant personnel from online databases such as some scientific databases: TechReports, NIST, MatWeb, etc., or from relevant literature on electrolysis.

[0150] Furthermore, the specific standard of the optimal electrolysis parameter group is determined by the literature from which the standard electrolysis data set is obtained.

[0151] For example, in a certain experiment on hydrogen production by electrolysis of water, a document records as follows: when the electrolysis voltage is 1.23V, the electrolysis current is 200A, and the electrolysis time is 20s, the electrolysis process can achieve the highest electrolysis efficiency. Therefore, when relevant personnel inquire about this record, they can use the electrolysis voltage, electrolysis current and electrolysis time at this time as the optimal electrolysis parameter group.

[0152] It is clear that, since the optimal electrolysis parameter groups recorded in different documents are in different forms, the specific optimal electrolysis parameter group needs to be manually identified by relevant personnel who query the documents.

[0153] For example, Xiao Zhang found the changing curves of electrolysis parameters and electrolysis efficiency in a certain document on water electrolysis, where these changing curves are the changing curves of electrolysis voltage and electrolysis efficiency, the changing curves of electrolysis current and electrolysis efficiency, and the changing curves of electrolysis time and electrolysis efficiency. Then, Xiao Zhang identified the electrolysis voltage, electrolysis current and electrolysis time corresponding to the maximum electrolysis efficiency from these curves, and used them as the optimal electrolysis parameter group.

[0154] S4. Based on the oxide property set, query the optimal electrolysis parameter set corresponding to the target oxide in the standard electrolysis data set.

[0155] It can be understood that the optimal electrolysis parameter group means the best combination of electrolysis parameters when electrolyzing the target oxide.

[0156] In detail, the step of searching the standard electrolysis data set for the optimal electrolysis parameter group corresponding to the target oxide includes:

[0157] extracting standard electrolysis data in the standard electrolysis data set in sequence, and identifying standard physical property groups in the standard electrolysis data;

[0158] Identifying a target oxide property group corresponding to the target oxide in a set of oxide property groups;

[0159] Based on the target oxide property group and the standard physical property group, the property matching degree is calculated, wherein the property matching degree is expressed as:

[0160] ;

[0161] in, Indicates the property matching degree, represents the number of target oxide properties in the target oxide property group or the number of standard physical properties in the standard physical property group, Indicates the first k The target oxide properties, Indicates the first in the standard physical properties group k Standard physical properties;

[0162] The property matching degrees are summarized to obtain a property matching degree set, the optimal matching degree in the property matching degree set is identified, and the optimal electrolysis data corresponding to the optimal matching degree is identified in the standard electrolysis data set, and the optimal electrolysis parameter group in the optimal electrolysis data is determined.

[0163] It should be explained that the property matching degree refers to a numerical value used to represent the numerical similarity between the target oxide property group and the standard physical property group, the optimal matching degree refers to the property matching degree with the largest numerical value in the property matching degree set, the optimal electrolysis data refers to the standard electrolysis data corresponding to the optimal matching degree in the standard electrolysis data set, and the optimal electrolysis parameter group is the standard electrolysis parameter group in the optimal electrolysis data in numerical form.

[0164] S5. Extracting local oxides from the bulk oxides, electrolyzing the local oxides based on an optimal electrolysis parameter set, and recording actual electrolysis efficiency factors in the step of electrolyzing the local oxides.

[0165] It can be understood that the local oxide refers to part of the oxide extracted from the bulk oxide, wherein the detailed steps of the extraction are: manually extracting a small part of the oxide from the bulk oxide, this small part of the oxide is the local oxide, and the ratio of the mass of the extracted small part of the oxide to the total mass of the bulk oxide can be preset or set by the relevant operator. The detailed method of electrolysis has been given in the above steps. The actual electrolysis efficiency factor refers to the numerical value used to represent the electrolysis efficiency in the step of electrolyzing the local oxide. The calculation process of the actual electrolysis efficiency factor is the same as the calculation process of the above-mentioned electrolysis efficiency factor, which will not be repeated here.

[0166] S6. If the actual electrolysis efficiency factor is less than the preset standard electrolysis efficiency factor, the optimal electrolysis data corresponding to the optimal electrolysis parameter group is determined, and the optimal electrolysis data is eliminated from the standard electrolysis data set to obtain an eliminated electrolysis data set.

[0167] It should be explained that the standard electrolysis efficiency factor refers to the artificially set minimum electrolysis efficiency, and the eliminated electrolysis data set refers to the standard electrolysis data set after eliminating the optimal electrolysis parameter group.

[0168] S7, taking the eliminated electrolysis data set as the standard electrolysis data set, returning to the oxide property set, and searching the standard electrolysis data set for the optimal electrolysis parameter set corresponding to the target oxide.

[0169] It is clear that when the actual electrolysis efficiency factor is less than the standard electrolysis efficiency factor, it means that the optimal electrolysis parameter group obtained at this time does not meet the actual electrolysis requirements, and it is necessary to return to the step of querying the optimal electrolysis parameter group corresponding to the target oxide.

[0170] S8. If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter set is recorded as the target electrolysis parameter set.

[0171] It is understandable that when the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, it means that the optimal electrolysis parameter group obtained at this time meets the actual electrolysis requirements, and the optimal electrolysis parameter group at this time can be recorded as the target electrolysis parameter group.

[0172] S9. Summarize the target electrolysis parameter groups to obtain a target electrolysis parameter set, and complete the electrolysis optimization based on the physical property analysis of the rare earth oxides based on the target electrolysis parameter set.

[0173] Exemplarily, after Xiao Zhang obtains the target electrolysis parameter set, he sequentially electrolyzes the bulk oxides in the bulk oxide group according to the target electrolysis parameter set in the target electrolysis parameter set, thereby completing the electrolysis of the oxide to be electrolyzed.

[0174] In order to solve the problems described in the background technology, the present invention firstly crushes the oxide to be electrolyzed to obtain a block oxide group. This step can make the subsequent physical property determination and electrolysis test more accurate and controllable by crushing the oxide to be electrolyzed. At the same time, the crushing process also increases the electrolysis reaction area of ​​the oxide and improves the efficiency of electrolysis. Then, a target physical property set is constructed, and based on the target physical property set, the physical properties of each block oxide in the block oxide group are measured to obtain an oxide property group set. This step can establish detailed physical property data for each block oxide by measuring the physical properties of the oxide, which is helpful for subsequent electrolysis parameter optimization and electrolysis efficiency improvement. At the same time, parameters such as electrolysis efficiency factor and predicted deviation value are introduced in the construction of the target physical property set, so that the target physical property set has a strong influence on the efficiency of oxide electrolysis, thereby improving the subsequent The efficiency of electrolysis is determined, and then based on the oxide property group set, the optimal electrolysis parameter group corresponding to the target oxide is queried in the standard electrolysis data set. This step can quickly determine the possible optimal electrolysis parameter group by matching the physical properties of the oxide with the existing standard data. Further, in the electrolysis test, the actual electrolysis efficiency factor is detected, and the actual electrolysis efficiency factor is compared with the standard electrolysis efficiency factor to determine whether the optimal electrolysis parameter group is the target electrolysis parameter group, further improving the matching degree of the electrolysis parameter group, thereby improving the electrolysis efficiency during subsequent electrolysis. Finally, by summarizing and analyzing all the target electrolysis parameter groups, a comprehensive target electrolysis parameter group can be obtained, which provides comprehensive data support for the optimization of the entire electrolysis process, helps to improve the electrolysis efficiency, and at the same time, the optimal electrolysis parameter group enables electrolysis to consume less energy, thereby reducing the cost during electrolysis. Therefore, the present invention can improve the efficiency of rare earth oxide electrolysis and reduce the cost of electrolysis.

[0175] like Figure 2 1 is a functional module diagram of an electrolysis optimization system based on rare earth oxide physical property analysis provided by one embodiment of the present invention.

[0176] The electrolysis optimization system 100 based on the analysis of the physical properties of rare earth oxides of the present invention can be installed in an electronic device. According to the functions to be implemented, the electrolysis optimization system 100 based on the analysis of the physical properties of rare earth oxides can include a physical property determination module 101, an electrolysis parameter analysis module 102, an electrolysis efficiency calculation module 103 and a target parameter identification module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0177] The physical property determination module 101 is used to receive an electrolysis optimization instruction, obtain an oxide to be electrolyzed based on the electrolysis optimization instruction, crush the oxide to be electrolyzed to obtain a block oxide group, construct a target physical property set, and measure the physical properties of each block oxide in the block oxide group based on the target physical property set to obtain an oxide property group set, wherein the oxide properties in the oxide property group correspond one-to-one to the target physical properties in the target physical property set;

[0178] The electrolysis parameter analysis module 102 is used to construct a standard electrolysis data set and sequentially extract target oxides from the bulk oxide group, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group, and based on the oxide property group set, the optimal electrolysis parameter group corresponding to the target oxide is queried in the standard electrolysis data set;

[0179] The electrolysis efficiency calculation module 103 is used to extract local oxides from the bulk oxides, and electrolyze the local oxides based on the optimal electrolysis parameter group, and record the actual electrolysis efficiency factor in the step of electrolyzing the local oxides. If the actual electrolysis efficiency factor is less than the preset standard electrolysis efficiency factor, the optimal electrolysis data corresponding to the optimal electrolysis parameter group is determined, and the optimal electrolysis data is eliminated from the standard electrolysis data set to obtain an eliminated electrolysis data set;

[0180] The target parameter identification module 104 is used to remove the electrolysis data set as the standard electrolysis data set, and return the oxide property group set, and query the optimal electrolysis parameter group corresponding to the target oxide in the standard electrolysis data set. If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter group is recorded as the target electrolysis parameter group, and the target electrolysis parameter groups are summarized to obtain the target electrolysis parameter group set.

[0181] In detail, each module in the electrolysis optimization system 100 based on the analysis of the physical properties of rare earth oxides in the embodiment of the present invention is used in the same manner as described above. Figure 1 The electrolysis optimization method based on the physical property analysis of rare earth oxides described in the present invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0182] like Figure 3 1 is a schematic diagram of the structure of an electronic device for implementing an electrolysis optimization method based on analysis of physical properties of rare earth oxides provided by an embodiment of the present invention.

[0183] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an electrolysis optimization method program based on physical property analysis of rare earth oxides.

[0184] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the electrolysis optimization method program based on the physical property analysis of rare earth oxides, but also can be used to temporarily store data that has been output or is to be output.

[0185] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as electrolysis optimization method programs based on physical property analysis of rare earth oxides, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0186] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0187] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0188] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0189] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0190] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0191] The electrolysis optimization method program based on the analysis of the physical properties of rare earth oxides stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0192] receiving an electrolysis optimization instruction, obtaining an oxide to be electrolyzed based on the electrolysis optimization instruction, and crushing the oxide to be electrolyzed to obtain a block oxide group;

[0193] Constructing a target physical property set, and based on the target physical property set, measuring the physical properties of each bulk oxide in the bulk oxide group to obtain an oxide property group set, wherein the oxide properties in the oxide property group correspond one-to-one to the target physical properties in the target physical property set;

[0194] Constructing a standard electrolysis data set, and extracting target oxides in the bulk oxide group in sequence, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group;

[0195] Based on the oxide property set, querying the optimal electrolysis parameter set corresponding to the target oxide in the standard electrolysis data set;

[0196] Extracting local oxides from the bulk oxides, electrolyzing the local oxides based on an optimal electrolysis parameter set, and recording an actual electrolysis efficiency factor in the step of electrolyzing the local oxides;

[0197] If the actual electrolysis efficiency factor is less than the preset standard electrolysis efficiency factor, then determining the optimal electrolysis data corresponding to the optimal electrolysis parameter group, and removing the optimal electrolysis data from the standard electrolysis data set to obtain a removed electrolysis data set;

[0198] The step of using the eliminated electrolysis data set as a standard electrolysis data set, returning the oxide property set, and searching the standard electrolysis data set for the optimal electrolysis parameter set corresponding to the target oxide;

[0199] If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter group is recorded as the target electrolysis parameter group;

[0200] The target electrolysis parameter groups are summarized to obtain a target electrolysis parameter set, and electrolysis optimization based on the analysis of the physical properties of rare earth oxides is completed based on the target electrolysis parameter set.

[0201] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0202] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0203] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0204] receiving an electrolysis optimization instruction, obtaining an oxide to be electrolyzed based on the electrolysis optimization instruction, and crushing the oxide to be electrolyzed to obtain a block oxide group;

[0205] Constructing a target physical property set, and based on the target physical property set, measuring the physical properties of each bulk oxide in the bulk oxide group to obtain an oxide property group set, wherein the oxide properties in the oxide property group correspond one-to-one to the target physical properties in the target physical property set;

[0206] Constructing a standard electrolysis data set, and extracting target oxides in the bulk oxide group in sequence, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group;

[0207] Based on the oxide property set, querying the optimal electrolysis parameter set corresponding to the target oxide in the standard electrolysis data set;

[0208] Extracting local oxides from the bulk oxides, electrolyzing the local oxides based on an optimal electrolysis parameter set, and recording an actual electrolysis efficiency factor in the step of electrolyzing the local oxides;

[0209] If the actual electrolysis efficiency factor is less than the preset standard electrolysis efficiency factor, then determining the optimal electrolysis data corresponding to the optimal electrolysis parameter group, and removing the optimal electrolysis data from the standard electrolysis data set to obtain a removed electrolysis data set;

[0210] The step of using the eliminated electrolysis data set as a standard electrolysis data set, returning the oxide property set, and searching the standard electrolysis data set for the optimal electrolysis parameter set corresponding to the target oxide;

[0211] If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter group is recorded as the target electrolysis parameter group;

[0212] The target electrolysis parameter groups are summarized to obtain a target electrolysis parameter set, and electrolysis optimization based on the analysis of the physical properties of rare earth oxides is completed based on the target electrolysis parameter set.

[0213] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0214] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0215] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0216] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An electrolysis optimization method based on physical property analysis of rare earth oxides, characterized in that: The method comprises: receiving an electrolysis optimization instruction, obtaining an oxide to be electrolyzed based on the electrolysis optimization instruction, and crushing the oxide to be electrolyzed to obtain a block oxide group; Constructing a target physical property set, and based on the target physical property set, measuring the physical properties of each bulk oxide in the bulk oxide group to obtain an oxide property group set, wherein the oxide property group set includes a plurality of oxide property groups, and the oxide properties in the oxide property groups correspond one-to-one to the target physical properties in the target physical property set; Constructing a standard electrolysis data set, and extracting target oxides in the bulk oxide group in sequence, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group; Based on the oxide property set, querying the optimal electrolysis parameter set corresponding to the target oxide in the standard electrolysis data set; Extracting local oxides from the bulk oxides, electrolyzing the local oxides based on an optimal electrolysis parameter set, and recording an actual electrolysis efficiency factor in the step of electrolyzing the local oxides, wherein the actual electrolysis efficiency factor is an indicator calculated by the ion concentration of the ion solution before and after the electrolysis step, the actual electrolysis energy consumed in the electrolysis step, and the theoretical electrolysis energy that should be consumed in the electrolysis step; If the actual electrolysis efficiency factor is less than the preset standard electrolysis efficiency factor, then determining the optimal electrolysis data corresponding to the optimal electrolysis parameter group, and removing the optimal electrolysis data from the standard electrolysis data set to obtain a removed electrolysis data set; The step of using the eliminated electrolysis data set as a standard electrolysis data set, returning the oxide property set, and searching the standard electrolysis data set for the optimal electrolysis parameter set corresponding to the target oxide; If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter group is recorded as the target electrolysis parameter group; The target electrolysis parameter groups are summarized to obtain a target electrolysis parameter set, and electrolysis optimization based on the analysis of the physical properties of rare earth oxides is completed based on the target electrolysis parameter set.

2. The electrolysis optimization method based on the physical property analysis of rare earth oxides according to claim 1, characterized in that: The method of crushing the oxide to be electrolyzed to obtain a block oxide group comprises: Mechanically crushing the oxide to be electrolyzed according to a preset crushing time to obtain the oxide to be measured; Using a pre-constructed screening system to screen the oxide to be measured, to obtain a screened oxide group, wherein the screening system comprises a plurality of sieves with different mesh sizes, and each sieve is stacked together, and the number of screened oxides in the screened oxide group is the same as the number of sieves in the screening system; Performing particle size analysis on the screened oxide group to obtain the original oxide particle size; Determining whether the original oxide particle size is greater than a preset standard oxide particle size; If the original oxide particle size is larger than the standard oxide particle size, each sieved oxide in the sieved oxide group is combined to obtain a secondary crushed oxide; Using the secondary crushed oxide as the oxide to be electrolyzed, and returning to the step of mechanically crushing the oxide to be electrolyzed according to the preset crushing time; If the original oxide particle size is not larger than the standard oxide particle size, the sieved oxide group is recorded as a massive oxide group.

3. The electrolysis optimization method based on the physical property analysis of rare earth oxides according to claim 2, characterized in that: The particle size analysis of the sieved oxide group to obtain the original oxide particle size includes: Obtaining the screening mass of each screening oxide in the screening oxide group to obtain a screening mass group, and recording the screening aperture of each sieve in the screening system to obtain a screening aperture group, wherein the screening mass in the screening mass group corresponds one to one to the screening aperture in the screening aperture group; According to the screening quality group and the screening aperture group, the original oxide particle size is calculated, wherein the original oxide particle size is expressed as: ； in, represents the original oxide particle size, n Indicates the number of sieving apertures in a sieving aperture group or the number of sieving masses in a sieving mass group, Indicates the first i Screening aperture, Indicates the first i Screening quality.

4. The electrolysis optimization method based on the physical property analysis of rare earth oxides according to claim 3, characterized in that: The constructing of the target physical property set includes: Obtaining a control oxide set, and determining a control property group of each control oxide in the control oxide set according to a preset original physical property set, to obtain a control property group set, wherein the number of control properties in the control property group is the same as the number of original physical properties in the original physical property set, and the mass of each control oxide in the control oxide set is the same; Extract reference oxides in sequence from the reference oxide set, conduct electrolysis tests on the reference oxides, and obtain electrolysis efficiency factors; Summarizing the electrolysis efficiency factors of each reference oxide to obtain an electrolysis efficiency factor set; According to the electrolysis efficiency factor set and the control property group set, an importance analysis is performed on the original physical property set to obtain a target physical property set.

5. The electrolysis optimization method based on the physical property analysis of rare earth oxides according to claim 4, characterized in that: The electrolysis test is performed on the control oxide to obtain the electrolysis efficiency factor, including: selecting a control electrolyte based on the control oxide, and adding the control oxide to the control electrolyte to obtain a control ionic solution; detecting a first ion concentration in a reference ion solution, and calculating a theoretical electrolysis energy required for complete electrolysis of the reference ion solution; Determine an electrolytic anode and an electrolytic cathode, insert the electrolytic anode and the electrolytic cathode into a reference ion solution, and respectively connect the electrolytic anode and the electrolytic cathode to a pre-acquired direct current power supply to obtain an electrolytic test device, wherein the electrolytic anode and the electrolytic cathode are both graphite electrodes, and the electrolytic anode and the electrolytic cathode are respectively connected to the positive electrode and the negative electrode of the direct current power supply; Setting an electrolysis parameter group, wherein the electrolysis parameter group includes: electrolysis time, electrolysis current and electrolysis voltage; An electrolysis efficiency test is performed based on the electrolysis parameter set and the electrolysis test device, and an electrolysis efficiency factor in the step of performing the electrolysis efficiency test is calculated according to the first ion concentration.

6. The electrolysis optimization method based on the physical property analysis of rare earth oxides according to claim 5, characterized in that: The step of calculating the electrolysis efficiency factor in the step of performing the electrolysis efficiency test comprises: Based on the electrolysis parameter set, the electrolysis test device is electrolyzed, and after the electrolysis is completed, the second ion concentration in the electrolysis test device is detected, and based on the electrolysis parameter set, the actual electrolysis energy consumed by electrolyzing the control ion solution is calculated, wherein the actual electrolysis energy is expressed as: ； in, represents the actual electrolysis energy, represents the electrolysis current, represents the electrolysis voltage, Indicates the duration of electrolysis; The electrolysis efficiency factor is calculated according to the first ion concentration, the second ion concentration, the theoretical electrolysis energy and the actual electrolysis energy, wherein the electrolysis efficiency factor is expressed as: ； in, represents the electrolysis efficiency factor, represents the first ion concentration, represents the second ion concentration, Represents the theoretical electrolysis energy.

7. The electrolysis optimization method based on the physical property analysis of rare earth oxides according to claim 6, characterized in that: The importance analysis of the original physical property set is performed according to the electrolysis efficiency factor set and the control property group set to obtain the target physical property set, including: Extracting original physical properties in the original physical property set in sequence, removing the original physical properties from the original physical property set, and obtaining an intermediate physical property set; Based on the intermediate physical property set, an intermediate property group set is determined in the control property group set, and the electrolysis efficiency factor in the electrolysis efficiency factor set is paired with the corresponding intermediate property group in the intermediate property group set to obtain a basic electrolysis data set; According to the basic electrolysis data set, the pre-acquired deep learning model is used to perform data prediction to obtain the prediction deviation value; The predicted deviation values ​​of each original physical property are aggregated to obtain a predicted deviation value set, a target deviation value set is identified in the predicted deviation value set based on a preset number of target properties, and a target physical property set corresponding to the target deviation value set is identified in the original physical property set, wherein the number of target deviation values ​​in the target deviation value set is the same as the number of target properties.

8. The electrolysis optimization method based on the physical property analysis of rare earth oxides according to claim 7, characterized in that: The step of performing data prediction on the pre-acquired deep learning model to obtain a prediction deviation value includes: The basic electrolysis data set is divided into a training electrolysis data set and a verification electrolysis data set, and the deep learning model is trained using the training electrolysis data set to obtain an electrolysis efficiency prediction model; Determine a verification efficiency factor set and a verification property group set in a verification electrolysis data set; The verification property groups in the verification property group set are sequentially input into the electrolysis efficiency prediction model to obtain a prediction efficiency factor set. Based on the prediction efficiency factor set and the verification efficiency factor set, the prediction deviation value is calculated using the following formula: ； in, represents the prediction deviation value, represents the number of prediction efficiency factors in the prediction efficiency factor set or the number of verification efficiency factors in the verification efficiency factor set, represents the jth prediction efficiency factor in the prediction efficiency factor set, Represents the j-th verification efficiency factor in the verification efficiency factor set.

9. The electrolysis optimization method based on rare earth oxide physical property analysis according to claim 8, characterized in that: The step of searching the standard electrolysis data set for the optimal electrolysis parameter group corresponding to the target oxide comprises: extracting standard electrolysis data in the standard electrolysis data set in sequence, and identifying standard physical property groups in the standard electrolysis data; Identifying a target oxide property group corresponding to the target oxide in a set of oxide property groups; Based on the target oxide property group and the standard physical property group, the property matching degree is calculated, wherein the property matching degree is expressed as: ； in, Indicates the property matching degree, represents the number of target oxide properties in the target oxide property group or the number of standard physical properties in the standard physical property group, Indicates the first k The target oxide properties, Indicates the first in the standard physical properties group k Standard physical properties; The property matching degrees are summarized to obtain a property matching degree set, the optimal matching degree in the property matching degree set is identified, and the optimal electrolysis data corresponding to the optimal matching degree is identified in the standard electrolysis data set, and the optimal electrolysis parameter group in the optimal electrolysis data is determined.

10. An electrolysis optimization system based on physical property analysis of rare earth oxides, characterized in that: The system comprises: a physical property determination module, configured to receive an electrolysis optimization instruction, obtain an oxide to be electrolyzed based on the electrolysis optimization instruction, crush the oxide to be electrolyzed to obtain a block oxide group, construct a target physical property set, and measure the physical properties of each block oxide in the block oxide group based on the target physical property set to obtain an oxide property group set, wherein the oxide property group set includes a plurality of oxide property groups, and the oxide properties in the oxide property groups correspond one-to-one to the target physical properties in the target physical property set; The electrolysis parameter analysis module is used to construct a standard electrolysis data set and sequentially extract target oxides from the bulk oxide group, wherein the standard electrolysis data includes: a standard electrolysis parameter group and a standard physical property group. Based on the oxide property group set, the optimal electrolysis parameter group corresponding to the target oxide is queried in the standard electrolysis data set; an electrolysis efficiency calculation module, for extracting local oxides from the bulk oxides, and electrolyzing the local oxides based on an optimal electrolysis parameter group, and recording an actual electrolysis efficiency factor in the step of electrolyzing the local oxides, wherein the actual electrolysis efficiency factor is an indicator calculated by the ion concentration of the ion solution before and after the electrolysis step, the actual electrolysis energy consumed in the electrolysis step, and the theoretical electrolysis energy that should be consumed in the electrolysis step; if the actual electrolysis efficiency factor is less than a preset standard electrolysis efficiency factor, then determining the optimal electrolysis data corresponding to the optimal electrolysis parameter group, and eliminating the optimal electrolysis data from the standard electrolysis data set to obtain an eliminated electrolysis data set; The target parameter identification module is used to remove the electrolysis data set as the standard electrolysis data set, and return the oxide property group set, and query the optimal electrolysis parameter group corresponding to the target oxide in the standard electrolysis data set. If the actual electrolysis efficiency factor is not less than the standard electrolysis efficiency factor, the optimal electrolysis parameter group is recorded as the target electrolysis parameter group, and the target electrolysis parameter group is summarized to obtain a target electrolysis parameter group set.

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