System and device for rapidly separating and extracting rare and precious metals through multi-magnetic field coupling

Abstract

The invention relates to the technical field of rare noble metal extraction, and discloses a multi-magnetic-field coupling rapid separation and extraction system and device for rare noble metals, and the system comprises a magnetic field activation module which is used for starting a liquid level controller to detect the liquid level height in a silicon carbide container; the distribution and intensity of a magnetic field in the molten metal are monitored in real time, and the induction intensity and frequency are dynamically adjusted; the metal droplet extraction simulation module is used for acquiring real-time data, constructing a metal droplet extraction model, and inputting the real-time data into the metal droplet extraction model for simulation to obtain optimal adjustment parameters; the optimal adjustment parameter module is used for adjusting coil current, magnetic induction intensity and frequency data parameters based on the optimal adjustment parameters; and analyzing the volume ratio of the precious metal liquid drops in the metal liquid, and if the volume ratio does not reach the standard, readjusting the metal liquid drop extraction model. Closed-loop control is formed, efficient and accurate separation of rare and precious metal liquid drops is achieved, and the automation level and stability of the whole extraction process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rare and precious metal extraction, and in particular to a system and device for rapidly separating and extracting rare and precious metals through multi-magnetic field coupling. Background Art

[0002] In modern industrial production, precious metals are widely used in aerospace, electronics, and catalysts due to their excellent physical, chemical, and electrical properties, as well as their high catalytic activity. However, in actual industrial production, the extraction of precious metals faces challenges such as poor industrial applicability, severe elemental dispersion, and low recovery rates. With technological advancements, the application of electromagnetic molten metal purification technology in metal separation and extraction is expanding. This novel technique utilizes electromagnetic fields to separate non-metallic inclusions from molten metal. Electromagnetic molten metal purification essentially utilizes the Lorentz force, which can be generated in two ways: by applying an alternating electromagnetic field, inducing a current in the molten metal. This current interacts with the magnetic field to generate the Lorentz force; or by passing a current through the molten metal in a static magnetic field, where the magnetic field interacts with the current to generate the Lorentz force. Because the electromagnetic forces acting on non-metallic inclusions differ from those on the molten metal due to their different electrical conductivities, the non-metallic inclusions migrate relative to the molten metal, achieving separation. The electromagnetic purification technology of molten metal is applied to the separation and extraction of rare and precious metals. By coupling multiple magnetic fields, different frequencies are controlled to extract different rare and precious metals required in the molten metal. At the same time, the movement of the molten metal is used to drive the mass and heat transfer of the molten metal.

[0003] Prior art one, a Chinese patent with patent number 202510338744.2, relates to the field of materials and metallurgy, specifically a method for producing nickel-iron alloy by smelting laterite nickel ore. The method involves pressing and forming a batch of laterite nickel ore into pellets, which are then calcined in a vertical calcining furnace to form metallized pellets. The pellets are then smelted and separated to produce nickel-iron alloy and slag. This method eliminates nodules during the reduction roasting stage, while also reducing air usage and lowering exhaust gas temperatures, thereby improving thermal efficiency. The slag is then ground and sorted into slag particles and slag powder. The slag particles are rich in metal elements, which can be extracted as return ore and reincorporated into the mix, increasing metal recovery. The slag powder can be used as slag fine powder. Although the slag can be remelted to adjust its composition, cement or steel slag phosphate fertilizer can be produced in the nickel-iron production process, replacing the slag fine powder and further reducing resource waste, rare and precious metals are subject to significant elemental dispersion in industrial production.

[0004] Prior art 2, a Chinese patent with the patent number 202510336369.8, discloses a method for simulating fatigue crack initiation in metals by coupling pores with microstructures. First, a microscale model is established by collecting microstructural characteristic data and pore distribution data from the metal material. Combined with mechanical property test results, basic parameters are obtained and micro-orthotropic parameters are inferred and calculated. Then, based on the scanned internal defects of the alloy, a pore defect model with the same characteristic information is established. Finite element simulation is used to extract key pores and establish a geometric model. A stress concentration factor is then introduced, and the shear stress amplitude calculation method in the Tanaka-Mura dislocation theory formula is replaced with a stress amplitude calculation method. This completes the microscale numerical simulation of fatigue crack initiation in metal materials containing pore defects. Although accurate prediction of the fatigue crack initiation life of microscale metals can be achieved by considering factors such as the material's microstructure and internal pores, combined with finite element simulation technology, rare metals have poor industrial applicability in industrial production.

[0005] Prior art three, Chinese patent number: 202510318724.9, provides a method for leaching and extracting cobalt and copper from copper-cobalt sulfide concentrate. The method comprises: mixing the copper-cobalt sulfide concentrate with ferroaluminum slag to obtain a mixed ore; mixing the mixed ore with water to obtain a first slurry; pre-acid leaching the first slurry at an acid-ore ratio of 150 kg / t to 220 kg / t to obtain a second slurry; and oxygen pressure leaching the second slurry, followed by solid-liquid separation to obtain a leachate. Ferroaluminum slag is the waste slag precipitated during the neutralization and removal of iron and aluminum from the copper-cobalt sulfide ore during the cobalt and copper extraction process. Although, by mixing copper-cobalt sulfide concentrate with ferroaluminum slag, not only the leaching rate of Co and Cu valuable metals in the copper-cobalt sulfide concentrate is effectively improved, but also the resource recycling of ferroaluminum slag produced in the process of cobalt and copper extraction from copper-cobalt sulfide ore is realized, thus reducing environmental pollution and lowering production costs; however, the recovery rate of rare and precious metals in industrial production is low.

[0006] At present, the existing technologies 1, 2 and 3 have the problems of poor industrial applicability, serious element dispersion and low recovery rate of rare and precious metals in industrial production. In order to solve the above problems, the present invention provides a multi-magnetic field coupling rapid separation and extraction system and device for rare and precious metals. Summary of the Invention

[0007] The main purpose of the present invention is to provide a multi-magnetic field coupling rapid separation and extraction system and device for rare and precious metals, so as to solve the problems of poor industrial applicability, serious element dispersion and low recovery rate of rare and precious metals in industrial production in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A multi-magnetic field coupling rapid separation and extraction system for rare and precious metals, comprising:

[0010] The magnetic field activation module is used to activate the liquid level controller and detect the liquid level in the silicon carbide container. When the liquid level meets the standard, the coil and variable frequency inductor are powered on to generate the initial magnetic field. The magnetic field distribution and intensity in the molten metal are monitored in real time, and the induction intensity and frequency are dynamically adjusted.

[0011] The metal droplet extraction simulation module is used to obtain real-time data, build a metal droplet extraction model based on historical metal droplet extraction data, input the real-time data into the metal droplet extraction model for simulation, and obtain the optimal adjustment parameters;

[0012] The optimal adjustment parameter module is used to adjust the coil current, magnetic induction intensity and frequency data parameters based on the optimal adjustment parameters; the volume proportion of the precious metal droplets in the metal liquid is analyzed, and if it does not meet the standard, the metal droplet extraction model is readjusted.

[0013] As a further improvement of the present invention, the magnetic field activation module includes:

[0014] The initial magnetic field generation submodule is used to start the liquid level controller and detect the height of the molten metal in the silicon carbide container. If the preset standard liquid level is not reached, the water replenishment pump is turned on to inject molten metal until it reaches the preset value. When the liquid level reaches the standard, the coil and variable frequency inductor are activated to generate magnetic induction intensity.

[0015] The real-time monitoring and feedback submodule is used to set the magnetic field strength threshold according to the properties of the molten metal. The Gaussian device monitors the magnetic field strength and distribution, dynamically adjusts the target magnetic induction intensity based on the concentration of inclusions in the molten metal, and records the magnetic field parameters and correlates them with the separation effect.

[0016] The parameter dynamic adjustment submodule is used to control the current size through PLC to adjust the magnetic induction intensity; and optimize the magnetic field penetration and processing of the variable frequency sensor frequency; rotate the magnetic structure to change the magnetic field gradient, and obtain the impact value of frequency adjustment on separation efficiency.

[0017] As a further improvement of the present invention, the metal droplet extraction simulation module includes:

[0018] A training data set submodule is formed to obtain real-time data such as the magnetic field distribution intensity, liquid level state, and metal liquid composition in the current molten metal; rare metal droplet separation parameters and corresponding effect records in the historical database are retrieved to form a data set;

[0019] The model building submodule is used to build a metal droplet extraction model based on historical metal droplet extraction data. The metal droplet extraction model is trained using a training dataset to learn the effect of magnetic induction intensity on droplet volume force, the relationship between frequency and magnetic field penetration, and droplet size and density physical parameters.

[0020] The metal liquid separation prediction submodule is used to input parameters such as magnetic field distribution, metal liquid viscosity and inclusion concentration collected in real time into the metal droplet extraction model to simulate and predict the separation effect under different adjustment parameters.

[0021] As a further improvement of the present invention, a model submodule is constructed, including:

[0022] The data set division unit is used to extract the separation parameters of rare and precious metal liquids and the corresponding effect records, and combine the real-time collected magnetic field distribution and liquid level status data to form a data set; the data set is divided into a training set, a validation set, and a test set;

[0023] The training model unit is used to build a metal droplet extraction model based on historical metal droplet extraction data, setting the input layer to match the feature dimension and the output layer to the prediction target. The metal droplet extraction model is trained using the training set to learn the effect of magnetic induction intensity on the droplet volume force, the relationship between frequency and magnetic field penetration, and the droplet size and density physical parameters.

[0024] Verify the metal liquid separation unit, which is used to test different hyperparameter combinations using the validation set and select the optimal configuration based on the validation set; monitor the loss curves of the training set and the validation set, and terminate training when the continuous loss reaches the preset rounds and does not decrease; optimize the metal droplet extraction model based on the relationship between the peak magnetic induction intensity and the frequency.

[0025] As a further improvement of the present invention, the training model unit includes:

[0026] The data pre-processing sub-unit is used to receive the real-time collected magnetic field distribution intensity, droplet physical parameters and inclusion concentration at the receiving layer, and perform standardization and unified dimension processing;

[0027] The linear transformation subunit is used to perform nonlinear transformations on input features; fit the effect of magnetic induction on the force on the droplet through training data; learn the relationship between frequency and magnetic field area; and encode the effects of droplet size and density into the metal droplet extraction model;

[0028] The verification parameter subunit is used to verify that the output layer prediction target is the separation effect or droplet volume force, quantify the deviation between the predicted value and the true value; and optimize the weights of the magnetic induction intensity and frequency-sensitive parameters; use the training set for multiple iterations and monitor whether the metal droplet extraction model is overfitting through the verification set.

[0029] As a further improvement of the present invention, the metal liquid separation prediction submodule includes:

[0030] The simulation prediction unit is used to dynamically replace the test parameter combination using the real-time collected magnetic field distribution, metal liquid viscosity and inclusion data as the basic input; the adjusted parameter combination is input into the metal droplet extraction model for simulation prediction;

[0031] The parameter combination unit is used to predict the corresponding droplet motion change trend based on the electromagnetic volume force value inputted by the pattern, and generate different parameter combinations. For each parameter combination, the metal droplet extraction model prediction process is independently run to output indicators such as separation effect and droplet residual amount.

[0032] The optimal parameter combination unit is used to compare the prediction results with the separation effect records in the historical database and calculate the prediction error; analyze the contribution of each parameter to the separation effect and update the parameter weights in real time; generate a parameter effect relationship curve to intuitively display the difference in analysis effects under different parameter combinations; and set thresholds to automatically screen the effective parameter range and output the optimal parameter combination.

[0033] As a further improvement of the present invention, the optimal adjustment parameter module includes:

[0034] The coil adjustment submodule is used to determine the optimal adjustment parameters based on the metal droplet extraction model; adjust the supply current to ensure that the coil current reaches the target value; adjust the frequency parameters in conjunction with the variable frequency inductor; and monitor the magnetic field distribution in real time. If the intensity in a certain area deviates from the target value, the coil current distribution in that area will be adjusted.

[0035] The qualified judgment submodule is used to dynamically adjust the frequency parameters according to the viscosity of the metal liquid and the droplet separation efficiency; the volume ratio of the rare metal droplets in the metal liquid is detected online; if qualified, the current parameters are maintained; if unqualified, the parameter feedback mechanism is triggered to compare the actual ratio data with the predicted one;

[0036] The deviation calculation submodule is used to calculate the parameter deviation, send retraining instructions to the metal drop extraction model, update the weight matrix, and visualize the final adjusted data.

[0037] To achieve the above object, the present invention also provides the following technical solutions:

[0038] A multi-magnetic field coupling rapid separation and extraction device for rare and precious metals is applied to the multi-magnetic field coupling rapid separation and extraction system. The multi-magnetic field coupling rapid separation and extraction device for rare and precious metals comprises a liquid level controller installed inside a silicon carbide container. When the molten metal reaches 75% of the silicon carbide capacity, the liquid level controller operates to stop pouring the molten metal; a liquid-slag separation device separates the inclusion slag from the rare and precious metal droplets; a Gaussian device is a magnetic field measuring instrument used to measure the magnetic field strength in the separation device and analyze the ambient magnetic field; a variable frequency inductor is used to achieve stirring and mixing of the solution through the force of the magnetic field on the metal solution, while controlling the direction and intensity of the stirring; and a rare and precious metal droplet collection device collects the rare and precious metal droplets separated from the metal liquid.

[0039] As a further improvement of the present invention, the liquid-slag separation device includes a coil and an inductor that generates an alternating magnetic field when energized. Under the action of the magnetic field, the metal liquid is subjected to electromagnetic volume force, and the metal droplets move under the force, generating an electromagnetic squeezing force on the inclusion slag particles.

[0040] As a further improvement of the present invention, the liquid-slag separation device also includes inclusions that move upward due to electromagnetic extrusion force, a container for holding molten metal, and high temperature caused by Joule heat generated by the molten metal due to the induction heating effect and the heat generated by the friction between the flow of the molten metal and the agitator; cooling water is passed through the coil to prevent the coil from overheating due to heat accumulation caused by long-term power supply; the inside of the coil is a copper wire, and the surface insulation material is a polyimide-fluororesin composite film to generate a changing magnetic field. When current passes through the coil, eddy currents are induced in the molten metal according to the principle of electromagnetic induction.

[0041] The magnetic field activation module of the present invention ensures that the metal liquid is in an optimal magnetic field environment, and provides stable and controllable electromagnetic field conditions for the extraction process by dynamically adjusting the induction intensity and frequency. The metal droplet extraction simulation module uses historical data and real-time monitoring data to optimize the extraction parameters, improve the accuracy and efficiency of metal droplet separation, and reduce the trial and error costs in experiments or production. The optimal adjustment parameter module dynamically adjusts the magnetic field parameters based on the simulation results to ensure that the extraction effect of the metal droplets meets the expectations, and automatically corrects the model when it does not meet the standards, thereby improving the adaptability and reliability of the system; the three modules work together to form a closed-loop control, achieving efficient and precise separation of rare and precious metal droplets, and improving the automation level and stability of the overall extraction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the functional modules of an embodiment of the multi-magnetic field coupling rapid separation and extraction system for rare and precious metals of the present invention;

[0043] Figure 2 This is a flowchart of one embodiment of the multi-magnetic field coupling rapid separation and extraction system for rare and precious metals of the present invention;

[0044] Figure 3 This is a functional module diagram of a magnetic field activation module in one embodiment of a multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to the present invention;

[0045] Figure 4 This is a functional module diagram of a metal droplet extraction simulation module in one embodiment of a multi-magnetic field coupling system for rapid separation and extraction of rare and precious metals according to the present invention;

[0046] Figure 5 A schematic diagram of the functional modules for constructing a model submodule for an embodiment of the multi-magnetic field coupling rapid separation and extraction of rare and precious metals system of the present invention;

[0047] Figure 6 This is a functional module diagram of a training model unit of an embodiment of a multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to the present invention;

[0048] Figure 7 This is a schematic diagram of the functional modules of the prediction metal liquid separation submodule of one embodiment of the multi-magnetic field coupling rapid separation and extraction system for rare and precious metals of the present invention;

[0049] Figure 8 This is a functional module diagram of an optimal parameter adjustment module of an embodiment of a multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to the present invention;

[0050] Figure 9 This is a schematic structural diagram of an embodiment of a device for rapidly separating and extracting rare and precious metals using multi-magnetic field coupling according to the present invention;

[0051] Figure 10 This is a schematic diagram of the liquid-slag separation principle of an embodiment of the multi-magnetic field coupling rapid separation and extraction device for rare and precious metals of the present invention;

[0052] Figure 11 This is a schematic diagram of the liquid-slag separation structure of an embodiment of the multi-magnetic field coupling rapid separation and extraction device for rare and precious metals of the present invention;

[0053] Figure 12 A schematic structural diagram of an embodiment of a variable frequency inductor device according to the present invention;

[0054] Figure 13 This is a schematic structural diagram of an embodiment of an electronic device of the present invention;

[0055] Figure 14 This is a schematic structural diagram of an embodiment of a storage medium of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] The terms "first", "second" and "third" in the present invention are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present invention (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0058] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] like Figure 1 As shown, this embodiment provides an embodiment of a system for rapidly separating and extracting rare and precious metals by coupling multiple magnetic fields. In this embodiment, the system for rapidly separating and extracting rare and precious metals by coupling multiple magnetic fields comprises:

[0060] The magnetic field activation module 100 is used to activate the liquid level controller and detect the liquid level in the silicon carbide container. When the liquid level meets the standard, the coil and variable frequency inductor are powered on to generate an initial magnetic field. The magnetic field distribution and intensity in the molten metal are monitored in real time, and the induction intensity and frequency are dynamically adjusted.

[0061] The metal droplet extraction simulation module 200 is used to obtain real-time data, build a metal droplet extraction model based on historical metal droplet extraction data, input the real-time data into the metal droplet extraction model for simulation, and obtain the optimal adjustment parameters;

[0062] The optimal adjustment parameter module 300 is used to adjust the coil current, magnetic induction intensity and frequency data parameters based on the optimal adjustment parameters; the volume proportion of the precious metal droplets is analyzed in the metal liquid, and if it does not meet the standard, the metal droplet extraction model is readjusted.

[0063] Preferably, in this embodiment, the liquid level controller is turned on at the beginning of separation, and it is determined whether the liquid level has reached the standard liquid level. If it has not reached the standard liquid level, the water supply pump is turned on to flow the molten metal into the silicon carbide container. After the requirements are met, the power switch is turned on, and the coil and the variable frequency inductor are energized. At the same time, the Gaussian device starts to work, monitors and analyzes the magnetic field in the molten metal of the separation device, and adjusts the magnetic field environment to the separation requirements (generally speaking, the magnetic induction intensity should be above 0.045T so that the molten metal generates volume force. With the increase of inclusions and rare metal droplets, the magnetic induction intensity requirement increases accordingly, sometimes even up to 0.085T. In addition, the penetration and stirring force of the magnetic field are also related to the frequency, and there is an optimal frequency for the best separation effect). After 15-20 minutes of work, the volume fraction of the rare metal liquid in the molten metal flowing out of the outlet is detected. After the separation requirements are met, the above steps are repeated to continue separation. If the separation effect does not meet the expectation, readjust the coil current and the frequency of the variable frequency inductor and test again. When the separation effect meets the requirements, the coil current, magnetic induction intensity and inductor frequency and other parameters in this state are counted to standardize the subsequent separation parameters (for the specific principle, refer to the attached Figure 2 ). The magnetic field activation module 100 ensures that the metal liquid is in the optimal magnetic field environment, and provides stable and controllable electromagnetic field conditions for the extraction process by dynamically adjusting the induction intensity and frequency. The metal droplet extraction simulation module 200 uses historical data and real-time monitoring data to optimize the extraction parameters, improve the accuracy and efficiency of metal droplet separation, and reduce the trial and error costs in experiments or production. The optimal adjustment parameter module 300 dynamically adjusts the magnetic field parameters based on the simulation results to ensure that the extraction effect of the metal droplets meets expectations, and automatically corrects the model when it does not meet the standards, thereby improving the adaptability and reliability of the system; the three modules work together to form a closed-loop control, achieving efficient and precise separation of rare and precious metal droplets, and improving the automation level and stability of the overall extraction process.

[0064] Furthermore, if Figure 3 As shown, the magnetic field activation module 100 includes:

[0065] The initial magnetic field generation submodule 101 is used to start the liquid level controller to detect the height of the molten metal in the silicon carbide container. If the liquid level does not reach the preset standard height, the water supply pump is turned on to inject the molten metal until it reaches the preset value. When the liquid level reaches the standard, the coil and the variable frequency inductor are started to generate magnetic induction intensity.

[0066] The real-time monitoring and feedback submodule 102 is used to set the magnetic field strength threshold according to the properties of the molten metal. The Gaussian device monitors the magnetic field strength and distribution, dynamically adjusts the target magnetic induction intensity based on the concentration of inclusions in the molten metal, and records the magnetic field parameters and associates them with the separation effect.

[0067] The parameter dynamic adjustment submodule 103 is used to control the current size through PLC to adjust the magnetic induction intensity; optimize the magnetic field penetration and processing of the variable frequency sensor frequency; rotate the magnetic structure to change the magnetic field gradient, and obtain the impact value of frequency adjustment on separation efficiency.

[0068] Preferably, this embodiment activates a liquid level controller to detect the height of the molten metal in the silicon carbide container; if the preset standard liquid level is not reached, the water supply pump is activated to inject molten metal until the preset value is reached; when the liquid level reaches the standard, the coil and variable frequency inductor are activated to generate magnetic induction intensity; by precisely controlling the liquid level, the initial conditions of the magnetic separation process are ensured to meet the requirements, thereby improving the effectiveness of the subsequent magnetic field effect. In addition, the magnetic induction intensity generated by the variable frequency inductor provides a stable magnetic field environment for subsequent magnetic separation operations; the magnetic field intensity threshold is set according to the properties of the molten metal; a gauss meter is used to monitor the magnetic field intensity and distribution, and the target magnetic induction intensity is dynamically adjusted in combination with the concentration of inclusions in the molten metal, and the magnetic field parameters are recorded in association with the separation effect; by real-time monitoring and dynamic adjustment of the magnetic field intensity, the magnetic field distribution during the magnetic separation process can be optimized, improving separation efficiency. At the same time, recording magnetic field parameters helps analyze the relationship between separation effect and magnetic field strength, providing data support for process optimization. The PLC controls the current and adjusts the magnetic induction intensity. The frequency of the variable frequency inductor is optimized to enhance magnetic field penetration. Rotating the magnetic structure changes the magnetic field gradient to determine the impact of frequency adjustment on separation efficiency. Dynamically adjusting current and frequency can significantly enhance the penetration and gradient variation of the magnetic field, thereby improving separation efficiency. By precisely controlling the liquid level, dynamically adjusting the magnetic field strength, and optimizing the magnetic field distribution, the efficiency of the magnetic separation process is significantly improved. Real-time monitoring and recording of magnetic field parameters provides a reliable data basis for subsequent process optimization.

[0069] Furthermore, if Figure 4 As shown, the metal droplet extraction simulation module 200 includes:

[0070] The training data set submodule 201 is used to obtain real-time data such as the magnetic field distribution intensity, liquid level state, and metal liquid composition in the current molten metal; and retrieve the rare metal droplet separation parameters and corresponding effect records in the historical database to form a data set;

[0071] A model building submodule 202 is used to build a metal droplet extraction model based on historical metal droplet extraction data, and use the training data set to train the metal droplet extraction model to learn the effect of magnetic induction intensity on droplet volume force, the relationship between frequency and magnetic field penetration, and physical parameters such as droplet size and density;

[0072] The metal liquid separation prediction submodule 203 is used to input the parameters such as magnetic field distribution, metal liquid viscosity and inclusion concentration collected in real time into the metal droplet extraction model to simulate and predict the separation effect under different adjustment parameters.

[0073] Preferably, this embodiment collects data such as the magnetic field distribution intensity, liquid level state, and metal liquid composition in the current molten metal in real time, and retrieves the rare metal droplet separation parameters and effect records in the historical database to form a data set for model training; obtains real-time data through sensors or online monitoring systems to ensure that the model can be trained based on the latest process conditions; combines the rare metal droplet separation parameters and effect records in the historical database to provide a more comprehensive training basis for the model; through the combination of real-time data and historical data, the model can better adapt to changes in current and future process conditions; the introduction of historical data helps to improve the model's generalization ability for complex scenarios and avoid deviations caused by a single data set; constructs a metal droplet extraction model based on historically extracted metal droplet data, and uses a training data set to train the model to learn the influence of magnetic induction intensity on droplet volume force, the relationship between frequency and magnetic field penetration, and physical parameters such as droplet size and density; uses supervised learning to adjust model parameters through the training set to achieve Accurately predict the droplet extraction process; use statistical analysis or tree-based methods to screen out features that have a significant impact on the target variable, reduce redundant data, and improve computational efficiency; use the validation set to evaluate model performance, prevent overfitting, and adjust model parameters based on the results; by learning key physical parameters in the droplet extraction process (such as magnetic induction intensity, frequency, etc.), the model can more accurately predict the separation effect; the model can dynamically adjust parameters based on real-time collected data to adapt to changes in process conditions, thereby ensuring the continued accuracy of the prediction results; input parameters such as real-time collected magnetic field distribution, metal liquid viscosity, and inclusion concentration into the metal droplet extraction model to simulate and predict the separation effect under different adjustment parameters; collect data in real time through the online monitoring system, and input these data into the model for prediction; by simulating and predicting the separation effect under different parameter combinations, the optimal process conditions can be quickly found, thereby improving separation efficiency; use model prediction instead of experimental verification to save time and resources, while reducing dependence on physical experimental equipment.

[0074] Furthermore, if Figure 5 As shown, the model building submodule 202 includes:

[0075] The data set division unit 2021 is used to extract the separation parameters of the precious metal liquid and the corresponding effect records, and combine the real-time collected magnetic field distribution, liquid level state and other data to form a data set; the data set is divided into a training set, a validation set and a test set;

[0076] The training model unit 222 is used to build a metal droplet extraction model based on historical metal droplet extraction data, set the input layer to match the feature dimension, and the output layer to correspond to the prediction target; use the training set to train the metal droplet extraction model to learn the effect of magnetic induction intensity on the droplet volume force, the relationship between frequency and magnetic field penetration, and physical parameters such as droplet size and density;

[0077] The characteristic dimensions include parameters such as magnetic field distribution, droplet size, and inclusion concentration; the prediction targets include separation effect or droplet volume force;

[0078] The metal liquid separation verification unit 2023 is used to test different hyperparameter combinations using a verification set and select the optimal configuration based on the verification set; and monitor the loss curves of the training set and the verification set. When the continuous loss reaches a preset number of rounds and does not decrease, the training is terminated; based on the relationship between the peak value of the magnetic induction intensity and the frequency, the metal droplet extraction model is optimized.

[0079] Preferably, the data set of this embodiment is divided into a training set, a validation set, and a test set, which is a common practice in machine learning and is used to ensure the generalization ability of the model and the reliability of performance evaluation. The training set is used to learn the model parameters, the validation set is used to adjust the hyperparameters and select the optimal model configuration, and the test set is used to finally evaluate the performance of the model on unseen data; through reasonable data set division, overfitting can be effectively avoided, the generalization ability of the model can be improved, and the stability and accuracy of the model in practical applications can be ensured; the input layer matches the feature dimensions (such as magnetic field distribution, droplet size and inclusion concentration, etc.), and the output layer corresponds to the prediction target (such as separation effect or droplet volume force). The model predicts the separation effect of rare metal liquid by learning the influence of magnetic induction intensity on droplet volume force, the relationship between frequency and magnetic field penetration, and the relationship between physical parameters such as droplet size and density; different hyperparameter combinations are tested using the validation set, and the optimal configuration is selected based on the validation set. At the same time, the loss curves of the training set and the validation set are monitored, and the training is terminated when the continuous loss does not decrease to prevent overfitting. Hyperparameter optimization helps improve model performance, reduce unnecessary computing resource consumption, and ensure the model's generalization ability through a validation set. Combining real-time data such as magnetic field distribution and liquid level status to form a data set demonstrates the system's dynamic adaptability, enabling it to adjust model parameters and prediction results based on real-time changes. Finally, a test set is used to evaluate model performance, ensuring good generalization on unseen data. The test results can be used to guide actual rare and precious metal separation operations in production.

[0080] Furthermore, if Figure 6 As shown, the training model unit 2022 includes:

[0081] The data pre-processing sub-unit 20221 is used for receiving the real-time collected magnetic field distribution intensity, droplet physical parameters, and inclusion concentration, etc. at the receiving layer, and performing standardization processing to unify the dimensions;

[0082] The linear transformation subunit 20222 is used to perform nonlinear transformation on the input features; fit the effect of magnetic induction on the force on the droplet through training data; learn the relationship between frequency and magnetic field action area; and encode the effect of droplet size and density into the metal droplet extraction model;

[0083] The verification parameter subunit 20223 is used to verify that the output layer prediction target is the separation effect or the droplet volume force, quantify the deviation between the predicted value and the true value; and optimize the weights of sensitive parameters such as magnetic induction intensity and frequency; use the training set for multiple iterations, and monitor whether the metal droplet extraction model is overfitting through the verification set.

[0084] Preferably, this embodiment receives real-time data such as magnetic field distribution intensity, droplet physical parameters, and inclusion concentration, and performs normalization to unify the dimensions. Normalization converts data of different dimensions to the same scale, eliminating the impact of unit differences on the model and improving the accuracy and efficiency of subsequent model training. Furthermore, dimensionality unification helps enhance data comparability, laying the foundation for subsequent feature extraction and model training. A nonlinear transformation is performed on the input features, fitting the effects of magnetic induction on the droplet force using training data, learning the relationship between frequency and magnetic field area, and encoding the effects of droplet size and density into the metal droplet extraction model. Nonlinear transformation enhances the nonlinear effects of the data, enabling the model to better capture complex relationships. Furthermore, by encoding the effects of droplet size and density, the model can more accurately reflect the behavior of droplets in a magnetic field, thereby improving prediction accuracy and robustness. The output layer predicts the target, quantifies the deviation between the predicted value and the true value, and optimizes the weights of sensitive parameters. Multiple iterations are performed using the training set, and the validation set is used to monitor model overfitting. By validating the parameter subunits, the model's generalization ability can be effectively evaluated to avoid overfitting. At the same time, optimizing the weights of sensitive parameters helps improve the adaptability and accuracy of the model to practical problems.

[0085] Further, if Figure 7 As shown, the metal liquid separation prediction submodule 203 includes:

[0086] The simulation prediction unit 2031 is used to dynamically replace the parameter combination of the test using the real-time collected data such as magnetic field distribution, molten metal viscosity and inclusions as basic input; and input the adjusted parameter combination into the metal droplet extraction model for simulation prediction;

[0087] The parameter combination unit 2032 is used to predict the corresponding droplet motion change trend based on the electromagnetic volume force value inputted by the pattern, and generate different parameter combinations; for each parameter combination, the metal droplet extraction model prediction process is independently run to output indicators such as separation effect and droplet residual amount;

[0088] The optimal parameter combination unit 2033 is used to compare the prediction results with the separation effect records in the historical database and calculate the prediction error; analyze the contribution of each parameter to the separation effect and update the parameter weight in real time; generate a parameter effect relationship curve to intuitively display the difference in analysis effects under different parameter combinations; and set a threshold to automatically filter the effective parameter range and output the optimal parameter combination.

[0089] Preferably, this embodiment dynamically replaces the tested parameter combinations by collecting data such as magnetic field distribution, molten metal viscosity, and inclusions in real time. The adjusted parameter combinations are then input into the metal droplet extraction model for simulation prediction. This real-time dynamic adjustment method allows for rapid optimization of model parameters based on current physical conditions, improving prediction accuracy and efficiency. This method, similar to parameter identification techniques based on experimental data and models, continuously adjusts model parameters to reduce prediction error, thereby improving model reliability and accuracy. The droplet motion trend is predicted based on the input electromagnetic volume force value, and different parameter combinations are generated. The metal droplet extraction model is independently run for each parameter combination, outputting metrics such as separation performance and residual droplet volume. This multi-parameter combination method, similar to the multivariable optimization strategies mentioned in the literature, effectively selects the optimal parameter combination by independently running the models and comparing the results. The predicted results are then compared with the separation performance records in a historical database, prediction errors are calculated, and the contribution of each parameter to the separation performance is analyzed. By updating parameter weights in real time and generating parameter-effect relationship curves, the differences in analytical performance under different parameter combinations are intuitively displayed. Furthermore, this unit automatically sets a threshold to filter the valid parameter range and output the optimal parameter combination. By dynamically adjusting parameters in real time and optimizing multi-parameter combinations, the model's prediction accuracy and stability can be significantly improved. For example, the method based on experimental data and model optimization mentioned in the literature continuously adjusts model parameters to reduce errors, thereby improving the accuracy of prediction results. Through the screening mechanism of the optimal parameter combination unit, the best parameter combination can be found, thereby improving the separation efficiency and reducing the amount of droplet residue during the droplet extraction process. The generated parameter-effect relationship curve can intuitively demonstrate the difference in the effects of different parameter combinations. At the same time, the effective parameter range is screened by threshold value, further simplifying manual intervention and improving work efficiency.

[0090] Furthermore, if Figure 8 As shown, the optimal adjustment parameter module 300 includes:

[0091] The coil adjustment submodule 301 is used to determine the optimal adjustment parameters based on the metal droplet extraction model; adjust the supply current to ensure that the coil current reaches the target value; adjust the frequency parameters in conjunction with the variable frequency inductor; and monitor the magnetic field distribution in real time. If the intensity in a certain area deviates from the target value, the coil current distribution in that area is adjusted.

[0092] The qualified judgment submodule 302 is used to dynamically adjust the frequency parameters according to the viscosity of the molten metal and the droplet separation efficiency; online detect the volume ratio of the rare and precious metal droplets in the molten metal; if qualified, maintain the current parameters; if unqualified, trigger the parameter feedback mechanism and compare the actual ratio data with the predicted one;

[0093] The deviation calculation submodule 303 is used to calculate the parameter deviation, send a retraining instruction to the metal drop extraction model, update the weight matrix, and visualize the final adjusted data.

[0094] Preferably, this embodiment monitors the magnetic field distribution in real time and dynamically adjusts the coil current distribution to ensure that the magnetic field strength reaches the target value. This dynamic adjustment technology can respond quickly according to real-time feedback and optimize the magnetic field distribution, thereby improving the efficiency and accuracy of metal droplet separation. The frequency parameters are dynamically adjusted using the metal liquid viscosity and the droplet separation efficiency, and the volume ratio of rare and precious metal droplets is detected online. If the test result is unqualified, the parameter feedback mechanism is started, the actual data is compared with the predicted value, and the optimization adjustment is triggered. It is responsible for calculating the parameter deviation and sending a retraining instruction to the metal droplet extraction model to update the weight matrix. The entire system adopts a closed-loop control strategy, forming a continuous cycle from parameter acquisition, calculation, judgment to tuning. This closed-loop control method ensures the continuity and effectiveness of parameter adjustment. At the same time, combined with the magnetic field control system principle mentioned in, the current and magnetic field output are optimized through real-time monitoring and intelligent algorithms to improve the overall performance of the system. By dynamically adjusting the magnetic field strength and distribution, the efficiency and accuracy of metal droplet separation are significantly improved; intelligent control reduces unnecessary energy waste and improves energy conversion efficiency; real-time monitoring and feedback mechanisms ensure the stable operation of the system, and further enhance the robustness of the system through deviation calculation and model optimization; finally, the visual output of the adjustment data allows operators to intuitively understand the system status and make further optimizations.

[0095] like Figure 9As shown, this embodiment also provides an embodiment of a multi-magnetic field coupling rapid separation and extraction device for rare precious metals. In this embodiment, the multi-magnetic field coupling rapid separation and extraction device for rare precious metals is applied to a multi-magnetic field coupling rapid separation and extraction system for rare precious metals as in the above-mentioned embodiment. The multi-magnetic field coupling rapid separation and extraction device for rare precious metals includes a liquid level controller 1, which is installed inside a silicon carbide container. When the molten metal reaches 75% of the silicon carbide capacity, the liquid level controller works to stop pouring the molten metal, and controls the inflow of the molten metal when the liquid level height is not reached (the molten metal in the silicon carbide should not exceed or be lower than 75% of the total capacity. This is because inclusions are prone to splashing during separation, posing a safety hazard, and too low a level will result in low separation efficiency); a liquid-slag separation device 2, which is the main device of the present invention, is connected to the silicon carbide container. The device is used to separate the inclusion slag from the rare and precious metal droplets; the Gaussian device 3 is a precise magnetic field measuring instrument, which is mainly used to measure the magnetic field strength in the separation device 2 and to analyze the environmental magnetic field to ensure the equipment performance and the slag-liquid separation quality; the variable frequency inductor 4 is mainly used to achieve the stirring and mixing of the solution through the force of the magnetic field on the metal solution, while controlling the direction and intensity of the stirring to optimize the metal smelting process and improve the product quality. In addition, the variable frequency inductor can limit the current shock caused by the sudden change of grid voltage and the operational overvoltage, smooth the peak pulses in the power supply voltage, and the voltage defects generated when the bridge rectifier circuit is commutated, effectively protecting the inverter and improving the power factor; the rare and precious metal droplet collection device 5 is used to collect the rare and precious metal droplets separated from the metal liquid.

[0096] Furthermore, if Figure 10 As shown, the liquid-slag separation device 2 includes a coil and an inductor. When energized, an alternating magnetic field is generated. Under the action of the magnetic field, the metal liquid is subjected to an electromagnetic body force. The metal droplets are forced to move and generate an electromagnetic squeezing force on the inclusion slag particles. The electromagnetic squeezing force is the reaction force of the electromagnetic body force, causing the inclusions to move relative to the metal droplets, thereby separating the inclusions from the metal liquid.

[0097] Furthermore, if Figure 11As shown, the liquid-slag separation device 2 also includes inclusions 6 that move upward due to electromagnetic squeezing force. The material of the container 7 for holding the molten metal is selected to be silicon carbide, which can well withstand the high temperature caused by the Joule heat generated by the molten metal due to the induction heating effect during the electromagnetic stirring process and the heat generated by the friction between the flow of the molten metal and the stirrer. The cooling water 8 passed through the coil has the main function of preventing the coil from overheating due to the accumulation of heat generated by long-term power supply, thereby protecting the coil from damage and ensuring the stable operation and service life of the electromagnetic equipment. The coil 9 has a copper wire inside and a surface insulation material made of polyimide-fluororesin composite film to ensure the insulation performance of the coil; its main function is to generate a changing magnetic field. When current passes through the coil, according to the principle of electromagnetic induction, eddy currents will be induced in the molten metal. These eddy currents are converted into heat energy due to the resistivity of the metal, and at the same time, stirring force is generated to separate the inclusions and rare metal droplets in the molten metal. The rare metal droplets flow out of the outlet 10. The rare metal droplets 11 separated by electromagnetic force (for the specific principle, please refer to the attached Figure 12 ).

[0098] like Figure 13 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 12 includes a processor 121 and a memory 122 coupled to the processor 121.

[0099] The memory 122 stores program instructions for implementing the multi-magnetic field coupling rapid separation and extraction system for rare metals in any of the above embodiments.

[0100] The processor 121 is used to execute program instructions stored in the memory 122 to implement the layout of a multi-magnetic field coupling rapid separation and extraction system for rare metals.

[0101] The processor 121 may also be referred to as a CPU (Central Processing Unit). The processor 121 may be an integrated circuit chip having signal processing capabilities. The processor 121 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor or any conventional processor.

[0102] Furthermore, Figure 14This is a schematic diagram of the structure of a storage medium in an embodiment of the present application. The storage medium 13 in the embodiment of the present application stores program instructions 131 that can implement all the above methods, wherein the program instructions 131 can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, server, mobile phone, and tablet.

[0103] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0104] In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units. The above is only an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0105] The above detailed description of the specific embodiments of the invention is intended to be illustrative only, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of the present invention. Therefore, equivalent changes, modifications, and improvements made without departing from the spirit and scope of the present invention are also encompassed within the scope of the present invention.

Claims

1. A multi-magnetic field coupling rapid separation and extraction system for rare and precious metals, characterized in that: The multi-magnetic field coupling rapid separation and extraction system for rare and precious metals comprises: The magnetic field activation module is used to activate the liquid level controller and detect the liquid level in the silicon carbide container. When the liquid level meets the standard, the coil and variable frequency inductor are powered on to generate the initial magnetic field. The magnetic field distribution and intensity in the molten metal are monitored in real time, and the induction intensity and frequency are dynamically adjusted. The metal droplet extraction simulation module is used to obtain real-time data, build a metal droplet extraction model based on historical metal droplet extraction data, input the real-time data into the metal droplet extraction model for simulation, and obtain the optimal adjustment parameters; The optimal adjustment parameter module is used to adjust the coil current, magnetic induction intensity and frequency data parameters based on the optimal adjustment parameters; the volume proportion of the precious metal droplets in the metal liquid is analyzed, and if it does not meet the standard, the metal droplet extraction model is readjusted.

2. The multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to claim 1, characterized in that: Magnetic field activation module, including: The initial magnetic field generation submodule is used to start the liquid level controller and detect the height of the molten metal in the silicon carbide container. If the preset standard liquid level is not reached, the water replenishment pump is turned on to inject molten metal until it reaches the preset value. When the liquid level reaches the standard, the coil and variable frequency inductor are activated to generate magnetic induction intensity. The real-time monitoring and feedback submodule is used to set the magnetic field strength threshold according to the properties of the molten metal. The Gaussian device monitors the magnetic field strength and distribution, dynamically adjusts the target magnetic induction intensity based on the concentration of inclusions in the molten metal, and records the magnetic field parameters and correlates them with the separation effect. The parameter dynamic adjustment submodule is used to control the current size through PLC to adjust the magnetic induction intensity; and optimize the magnetic field penetration and processing of the variable frequency sensor frequency; rotate the magnetic structure to change the magnetic field gradient, and obtain the impact value of frequency adjustment on separation efficiency.

3. The multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to claim 1, characterized in that: Metal droplet extraction simulation module, including: A training data set submodule is formed to obtain real-time data on the magnetic field distribution intensity, liquid level status, and composition of the current molten metal; rare metal droplet separation parameters and corresponding effect records in the historical database are retrieved to form a data set; The model building submodule is used to build a metal droplet extraction model based on historical metal droplet extraction data. The metal droplet extraction model is trained using a training dataset to learn the effect of magnetic induction intensity on droplet volume force, the relationship between frequency and magnetic field penetration, and droplet size and density physical parameters. The metal liquid separation prediction submodule is used to input the real-time collected magnetic field distribution, metal liquid viscosity and inclusion concentration parameters into the metal droplet extraction model to simulate and predict the separation effect under different adjustment parameters.

4. The multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to claim 3, characterized in that: Construct model submodules, including: The data set division unit is used to extract the separation parameters of rare and precious metal liquids and the corresponding effect records, and combine the real-time collected magnetic field distribution and liquid level status data to form a data set; the data set is divided into a training set, a validation set, and a test set; The training model unit is used to build a metal droplet extraction model based on historical metal droplet extraction data, setting the input layer to match the feature dimension and the output layer to the prediction target. The metal droplet extraction model is trained using the training set to learn the effect of magnetic induction intensity on the droplet volume force, the relationship between frequency and magnetic field penetration, and the droplet size and density physical parameters. Verify the metal liquid separation unit, which is used to test different hyperparameter combinations using the validation set and select the optimal configuration based on the validation set; monitor the loss curves of the training set and the validation set, and terminate training when the continuous loss reaches the preset rounds and does not decrease; optimize the metal droplet extraction model based on the relationship between the peak magnetic induction intensity and the frequency.

5. The multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to claim 4, characterized in that: Training model unit, including: The pre-processing data sub-unit is used to receive the real-time collected magnetic field distribution intensity, droplet physical parameters and inclusion concentration at the receiving layer, and perform standardization and unified dimension processing; The linear transformation subunit is used to perform nonlinear transformations on input features; fit the effect of magnetic induction on the force on the droplet through training data; learn the relationship between frequency and magnetic field area; and encode the effects of droplet size and density into the metal droplet extraction model; The verification parameter subunit is used to verify that the output layer prediction target is the separation effect or droplet volume force, quantify the deviation between the predicted value and the true value; and optimize the weights of the magnetic induction intensity and frequency-sensitive parameters; use the training set for multiple iterations and monitor whether the metal droplet extraction model is overfitting through the verification set.

6. The multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to claim 3, characterized in that: The metal liquid separation prediction submodule includes: The simulation prediction unit is used to dynamically replace the test parameter combination using the real-time collected magnetic field distribution, metal liquid viscosity and inclusion data as the basic input; the adjusted parameter combination is input into the metal droplet extraction model for simulation prediction; The parameter combination unit is used to predict the corresponding droplet motion change trend based on the electromagnetic volume force value inputted by the pattern, and generate different parameter combinations. For each parameter combination, the metal droplet extraction model prediction process is independently run to output the separation effect and droplet residual amount index. The optimal parameter combination unit is used to compare the prediction results with the separation effect records in the historical database and calculate the prediction error; analyze the contribution of each parameter to the separation effect and update the parameter weights in real time; generate a parameter effect relationship curve to intuitively display the difference in analysis effects under different parameter combinations; and set thresholds to automatically screen the effective parameter range and output the optimal parameter combination.

7. The multi-magnetic field coupling rapid separation and extraction system for rare and precious metals according to claim 1, characterized in that: Optimal adjustment parameter module, including: The coil adjustment submodule is used to determine the optimal adjustment parameters based on the metal droplet extraction model; adjust the supply current to ensure that the coil current reaches the target value; adjust the frequency parameters in conjunction with the variable frequency inductor; and monitor the magnetic field distribution in real time. If the intensity in a certain area deviates from the target value, the coil current distribution in that area will be adjusted. The qualified judgment submodule is used to dynamically adjust the frequency parameters according to the viscosity of the metal liquid and the droplet separation efficiency; the volume ratio of the rare metal droplets in the metal liquid is detected online; if qualified, the current parameters are maintained; if unqualified, the parameter feedback mechanism is triggered to compare the actual ratio data with the predicted one; The deviation calculation submodule is used to calculate the parameter deviation, send retraining instructions to the metal drop extraction model, update the weight matrix, and visualize the final adjusted data.

8. A device for rapid separation and extraction of rare metals by multi-magnetic field coupling, which is applied to the system for rapid separation and extraction of rare metals by multi-magnetic field coupling as claimed in any one of claims 1 to 7, characterized in that: The multi-magnetic field coupling rapid separation and extraction device for rare and precious metals includes a liquid level controller installed inside a silicon carbide container. When the molten metal reaches 75% of the silicon carbide capacity, the liquid level controller operates to stop pouring the molten metal; a liquid-slag separation device separates the inclusion slag from the rare and precious metal droplets; a Gaussian device is a magnetic field measuring instrument used to measure the magnetic field strength in the separation device and analyze the ambient magnetic field; a variable frequency inductor is used to stir and mix the metal solution through the force of the magnetic field on the metal solution, while controlling the direction and intensity of the stirring; and a rare and precious metal droplet collection device collects the rare and precious metal droplets separated from the metal solution.

9. The multi-magnetic field coupling rapid separation and extraction device for rare and precious metals according to claim 8, characterized in that: The liquid-slag separation device includes a coil and an inductor. When energized, an alternating magnetic field is generated. Under the action of the magnetic field, the metal liquid is subjected to electromagnetic volume force, and the metal droplets move under the force, generating an electromagnetic squeezing force on the inclusion slag particles.

10. The multi-magnetic field coupling rapid separation and extraction device for rare and precious metals according to claim 8, characterized in that: The liquid-slag separation device 2 also includes inclusions that move upward due to electromagnetic extrusion force, a container for holding molten metal, and high temperature caused by Joule heat generated by the molten metal due to the induction heating effect and the heat generated by the friction between the flow of the molten metal and the agitator; cooling water is passed through the coil to prevent the coil from overheating due to heat accumulation caused by long-term power supply; the inside of the coil is a copper wire, and the surface insulation material is a polyimide-fluororesin composite film, which generates a changing magnetic field. When current passes through the coil, eddy currents are induced in the molten metal according to the principle of electromagnetic induction.

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