Multi-stage magnetic separation equipment for high-purity iron powder and magnetic separation method of multi-stage magnetic separation equipment

Abstract

The invention provides multi-stage magnetic separation equipment for high-purity iron powder and a magnetic separation method of the multi-stage magnetic separation equipment, and relates to the technical field of magnetic separation. The control parameter mapping module is used for performing multi-stage magnetic separation control parameter mapping to generate N-stage reference magnetic separation control parameters; the magnetic separation treatment module is used for executing the N-stage magnetic separation treatment process of the to-be-purified iron powder, and the magnetic separation treatment module comprises a closed-loop control unit used for executing single-stage magnetic separation parameter dynamic closed-loop control according to the synchronous laser-induced breakdown spectroscopy analysis result; the joint debugging optimization unit is used for executing joint debugging optimization of a cross-level Gaussian setting reference; and an iteration execution unit. The technical problems that the magnetic separation process in the prior art is generally only suitable for a single magnetic separation stage and cannot be effectively optimized in a plurality of magnetic separation stages, so that the treatment condition of each stage cannot be fully optimized in the magnetic separation process, and the final iron powder purity is influenced are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetic separation, and particularly relates to a multi-stage magnetic separation device for high-purity iron powder and a magnetic separation method thereof. Background Art

[0002] High-purity iron powder is widely used in fields such as powder metallurgy, magnetic materials, and high-end electronic devices. With the development of industrial technology, the demand for high-purity iron powder is increasing continuously. Especially in high-end manufacturing and high-tech industries, the purity requirements for iron powder are becoming more and more stringent. Therefore, how to effectively remove impurities in iron powder and improve its purity has become a key technical challenge in the current iron powder production process.

[0003] However, most of the existing technologies rely on fixed parameters such as preset magnetic field intensity and magnetic separation time to process iron powder. Such fixed control parameters make the magnetic separation process unable to flexibly handle iron powder particles with different particle sizes and magnetic intensities, resulting in an unsatisfactory magnetic separation effect. It may cause incomplete removal of impurities or over-separation, affecting the purity of the final product. Moreover, the magnetic separation process of the existing technologies usually only applies to a single magnetic separation stage and cannot effectively optimize in multiple magnetic separation stages, resulting in the inability to fully optimize the processing conditions of each stage in the magnetic separation process and unable to maximize the removal of impurities, thus affecting the purity of the final iron powder. Summary of the Invention

[0004] This application provides a multi-stage magnetic separation device for high-purity iron powder and a magnetic separation method thereof, aiming to solve the technical problem that the magnetic separation process of the existing technologies usually only applies to a single magnetic separation stage and cannot effectively optimize in multiple magnetic separation stages, resulting in the inability to fully optimize the processing conditions of each stage in the magnetic separation process, thereby affecting the purity of the final iron powder.

[0005] In the first aspect disclosed in this application, a multi-stage magnetic separation device for high-purity iron powder is provided. The device includes: a component characteristic calling module for cross-device calling of multi-modal iron powder component characteristics; a control parameter mapping module for performing multi-stage magnetic separation control parameter mapping based on the multi-modal iron powder component characteristics to generate N-level reference magnetic separation control parameters, where each level of reference magnetic separation control parameters consists of a Gaussian setting reference, a Gaussian adjustable range, and an execution time reference; a magnetic separation processing module for the magnetic separation hardware system to perform an N-level magnetic separation processing process of the iron powder to be purified based on N execution time references, and the magnetic separation processing module includes: a closed-loop control unit for performing dynamic closed-loop control of single-level magnetic separation parameters within N Gaussian adjustable ranges according to the synchronous laser-induced breakdown spectroscopy analysis results of the LIBS probe; a joint debugging and optimization unit for performing joint debugging and optimization of cross-level Gaussian setting references according to the impurity distribution heat maps at the ends of N execution times; an iterative execution unit for iteratively executing until the N-level iron powder magnetic separation is completed to produce iron powder with a target purity.

[0006] The second aspect disclosed in the present application provides a multi-stage magnetic separation method for high-purity iron powder, which is implemented by the above-mentioned multi-stage magnetic separation equipment for high-purity iron powder, and the method includes: calling multi-modal iron powder component characteristics across devices; mapping multi-stage magnetic separation control parameters according to the multi-modal iron powder component characteristics, and generating N-level benchmark magnetic separation control parameters, wherein each level of benchmark magnetic separation control parameters is composed of a Gaussian setting benchmark, a Gaussian adjustable interval and an execution time benchmark; the magnetic separation hardware system uses N execution time benchmarks as the magnetic separation time limit, and executes N-level magnetic separation processing of the iron powder to be purified according to the N Gaussian setting benchmarks: step a: according to the synchronous laser induced breakdown spectroscopy analysis results of the LIBS probe, perform single-layer magnetic separation parameter dynamic closed-loop control within the N Gaussian adjustable intervals; step b: according to the impurity distribution heat map at the end of the N execution times, perform cross-level Gaussian setting benchmark joint optimization; iterative execution until the N-level iron powder magnetic separation is completed to produce iron powder of target purity.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: By calling the multimodal iron powder component characteristics across devices, the physical and chemical characteristics of the iron powder can be fully understood, which provides accurate data support for the subsequent magnetic separation process and ensures that the magnetic separation parameters can be adjusted in a targeted manner during the magnetic separation process; according to the multimodal iron powder component characteristics of the iron powder, N-level benchmark magnetic separation control parameters are generated through multi-level magnetic separation control parameter mapping. The control parameters of each level include Gaussian setting benchmark, Gaussian adjustable interval and execution time benchmark, ensuring that the magnetic separation process can accurately process iron powders of different particle sizes and magnetic strengths, so that the iron powder can be effectively graded to improve the purity; by using N execution time benchmarks as the magnetic separation time limit, the processing time of each level of magnetic separation is ensured to be accurately controlled to avoid over-processing or under-processing. Within the time limit of each magnetic separation level, the magnetic field strength and processing time are adjusted according to the results of the previous stage, so as to gradually remove the iron powder. Impurities; During the magnetic separation process, the concentration of impurities on the surface of the iron powder is detected in real time through the LIBS probe synchronous laser induced breakdown spectroscopy analysis, and the magnetic separation parameters can be dynamically adjusted according to the real-time data to form a closed-loop control. This dynamic closed-loop control can respond to changes in impurity concentration in real time to ensure that each magnetic separation level can achieve the best removal effect; at the end of each magnetic separation stage, an impurity distribution heat map is generated. Through the impurity distribution heat map, the control parameters can be optimized in the subsequent magnetic separation stage. Through the joint optimization of Gaussian setting benchmark, the operation of each magnetic separation level is ensured to be coordinated and consistent, further improving the efficiency of impurity removal; through iterative execution of the magnetic separation process, the control strategy is continuously optimized until the required target purity is reached. The results of each stage will affect the control strategy of the subsequent stage, thereby ensuring that the entire magnetic separation process can efficiently remove impurities and ultimately produce high-purity iron powder.

[0008] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0009] Figure 1 It is a schematic structural diagram of a multi-stage magnetic separation device for high-purity iron powder provided by an embodiment of this application.

[0010] Figure 2 It is a schematic flow diagram of a multi-stage magnetic separation method for high-purity iron powder provided by an embodiment of this application.

[0011] Description of the reference numerals in the drawings: Component characteristic call module 10, control parameter mapping module 20, magnetic separation processing module 30, closed-loop control unit 31, joint debugging and optimization unit 32, iterative execution unit 33. Detailed Embodiments

[0012] The embodiment of this application provides a multi-stage magnetic separation device for high-purity iron powder and its magnetic separation method, which solves the technical problem that the magnetic separation process in the prior art usually only applies a single magnetic separation stage and cannot effectively optimize in multiple magnetic separation stages, resulting in the inability to fully optimize the processing conditions of each stage in the magnetic separation process, thereby affecting the final purity of the iron powder.

[0013] After introducing the basic principle of this application, the various non-limiting embodiments of this application will be specifically introduced below in conjunction with the drawings in the specification. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0014] Embodiment 1, as Figure 1 shown, the embodiment of this application provides a multi-stage magnetic separation device for high-purity iron powder, and the device includes: Component characteristic call module 10, which is used to call multi-modal iron powder component characteristics across devices.

[0015] Control parameter mapping module 20, which is used to map multi-stage magnetic separation control parameters according to the multi-modal iron powder component characteristics to generate N-level reference magnetic separation control parameters, where each level of reference magnetic separation control parameters consists of a Gaussian setting reference, a Gaussian adjustable range, and an execution time reference.

[0016] Magnetic separation processing module 30, which is used for the magnetic separation hardware system to use N execution time references as the magnetic separation time limit and execute the N-level magnetic separation processing process of the iron powder to be purified according to N Gaussian setting references. The magnetic separation processing module includes: The closed-loop control unit 31 is configured to perform dynamic closed-loop control of single-level magnetic separation parameters within N Gaussian adjustable intervals according to the results of synchronous laser-induced breakdown spectroscopy analysis of the LIBS probe.

[0017] The joint debugging and optimization unit 32 is configured to perform joint debugging and optimization of the cross-level Gaussian setting benchmark according to the impurity distribution thermal maps at the ends of N execution times.

[0018] The iterative execution unit 33 is configured to iteratively execute until N-level iron powder magnetic separation is completed to produce iron powder with the target purity.

[0019] Furthermore, the joint debugging and optimization unit further includes: The preselection-level magnetic separation processing channel is configured to start the feeder to convey the iron powder to be purified into the magnetic separation hardware system to perform preselection-level magnetic separation processing after running the magnetic separation hardware system with the first Gaussian setting benchmark of the first-level reference magnetic separation control parameters.

[0020] The spectroscopic analysis channel is configured to synchronously run the LIBS probe to perform laser-induced breakdown spectroscopy analysis on the conveyed iron powder during the preselection-level magnetic separation process and output a sequence of real-time surface impurity concentrations.

[0021] The dynamic parameter adjustment and update channel is configured to perform dynamic parameter adjustment and update of the first Gaussian setting benchmark with the first Gaussian adjustable interval as a constraint according to the multi-impurity fluctuation characteristics of the real-time surface impurity concentration sequence until it stops when reaching the first execution time benchmark, and generate a first impurity distribution thermal map.

[0022] Furthermore, the iterative execution unit further includes: The cross-level pre-correction channel is configured to perform cross-level pre-correction on the second Gaussian setting benchmark based on the first impurity distribution thermal map to obtain second Gaussian joint debugging parameters.

[0023] The parameter adjustment and update channel is configured to perform parameter adjustment and update of the second Gaussian joint debugging parameters according to the surface impurity concentration data synchronously detected by the LIBS probe during the secondary magnetic separation process of running the magnetic separation hardware system with the second Gaussian joint debugging parameters until the magnetic separation duration reaches the second execution time benchmark and output a second impurity distribution thermal map.

[0024] The Gaussian benchmark pre-joint debugging channel is configured to perform dynamic adjustment of the magnetic separation parameters during the single-level magnetic separation process based on the surface impurity concentration and perform cross-level Gaussian benchmark pre-joint debugging based on the hierarchical impurity thermal distribution until the N-level reference magnetic separation control parameters are executed to completion to produce the iron powder with the target purity.

[0025] Furthermore, the dynamic parameter adjustment and update channel includes: A Gauss adjustment action library construction node is used to interactively obtain multiple groups of Gauss adjustment actions of various iron powder impurities under multiple groups of impurity fluctuation scenarios and construct a Gauss adjustment action library.

[0026] A parallel matching node is used to input the multivariate impurity fluctuation characteristics as multivariate retrieval conditions into the Gauss adjustment action library to parallelly match the multivariate Gauss adjustment actions of the multivariate impurity fluctuation scenarios.

[0027] A union solution node is used to perform a union solution on the multivariate Gauss adjustment actions to locate the first real-time adjustment action.

[0028] A parameter adjustment and update node is used to adopt the first real-time adjustment action to perform parameter adjustment and update of the first Gauss setting benchmark.

[0029] A data acquisition node is used to drive the LIBS probe to collect instantaneous impurity concentration data and synthesize the first impurity distribution heat map when the dynamic parameter adjustment and update of the first Gauss setting benchmark reaches the end of the first execution time benchmark.

[0030] Furthermore, the cross-level pre-correction channel includes: A thermal feature detection network construction node is used to pre-construct a thermal feature detection network, where the thermal feature detection network includes M thermal map feature recognition channels.

[0031] A feature parallel recognition node is used to load the first impurity distribution heat map into the thermal feature detection network and then perform feature parallel recognition through the M thermal map feature recognition channels to output M real-time thermal map quantization features.

[0032] A cross-level pre-correction node is used to perform pre-correction action matching based on the M real-time thermal map quantization features and then perform cross-level pre-correction on the second Gauss setting benchmark to obtain the second Gauss joint adjustment parameters.

[0033] Furthermore, the cross-level pre-correction node includes: A standard pre-correction action setting sub-node is used to locally set M groups of standard pre-correction actions for M groups of thermal map quantization feature thresholds.

[0034] An action extraction sub-node is used to extract M standard pre-correction actions from the M groups of standard pre-correction actions by using the intersection relationship of the M real-time thermal map quantization features in the M groups of thermal map quantization feature thresholds.

[0035] A conflict compensation sub-node is used to perform conflict compensation on the M standard pre-correction actions to obtain the first pre-correction action.

[0036] The cross-level pre-correction sub-node is used to perform cross-level pre-correction on the second Gaussian setting reference using the first pre-correction action to obtain the second Gaussian joint debugging parameter.

[0037] Furthermore, the dynamic tuning parameter update channel is used to: input the spatial mapping result of mapping the instantaneous impurity concentration data to spatial coordinates into a predefined impurity concentration-color scale mapping table for gradient coloring synthesis to obtain the first impurity distribution heat map.

[0038] Furthermore, the multi-modal iron powder component characteristics include a particle size distribution curve, a saturation magnetization intensity distribution, a coercivity distribution, and an initial impurity spectrum.

[0039] Furthermore, the control parameter mapping module includes: The first matching unit is used to, after analyzing the particle size distribution curve and locating the median particle size position and the proportion of the median particle size distribution width, match the first multi-level initialization magnetic separation control parameter in the residence time spectrum mapping table according to the median particle size position and the proportion of the median particle size distribution width.

[0040] The second matching unit is used to match the second multi-level initialization magnetic separation control parameter in the field strength response spectrum mapping table according to the calculated central tendency value of the saturation magnetization intensity distribution and the proportion of the high-value area of the coercivity distribution.

[0041] The third matching unit is used to, after extracting the impurity concentration group corresponding to the core impurity group from the initial impurity spectrum, match the third multi-level initialization magnetic separation control parameter in the element removal response spectrum mapping table.

[0042] The conflict parameter arbitration unit is used to perform conflict parameter arbitration on the first multi-level initialization magnetic separation control parameter, the third multi-level initialization magnetic separation control parameter, and the third multi-level initialization magnetic separation control parameter, and output the N-level reference magnetic separation control parameter.

[0043] Through the subsequent detailed description of a multi-level magnetic separation method for high-purity iron powder in this specification, those skilled in the art can clearly know a multi-level magnetic separation device for high-purity iron powder in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, refer to the description in the method section.

[0044] Embodiment 2, based on the same inventive concept as the multi-level magnetic separation device for high-purity iron powder in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a multi-level magnetic separation method for high-purity iron powder, and the method includes: Cross-device invocation of multi-modal iron powder component characteristics; The characteristics of the multimodal iron powder components refer to the different physical and chemical characteristics of the iron powder samples obtained through multiple testing devices, including the particle size distribution curve, saturation magnetization intensity distribution, coercivity distribution, and initial impurity spectrum. For example, the particle size distribution curve of the iron powder is obtained through a particle size analyzer, the saturation magnetization intensity distribution of the iron powder is obtained through a magnetization intensity testing device, the coercivity distribution of the iron powder is obtained through a coercivity testing device, and cross-device invocation means that the data between different testing devices can be shared and combined with each other, so as to generate complete and multi-angle characteristics of the multimodal iron powder components.

[0045] Perform multi-level magnetic separation control parameter mapping based on the characteristics of the multimodal iron powder components to generate N-level reference magnetic separation control parameters. Among them, each level of reference magnetic separation control parameters consists of a Gaussian setting reference, a Gaussian adjustable range, and an execution time reference.

[0046] Perform multi-level magnetic separation control parameter mapping based on the characteristics of the multimodal iron powder components, aiming to generate accurate control parameters for each magnetic separation level to optimize the magnetic separation process. Exemplarily, according to the particle size distribution curve of the iron powder, the magnetic separation parameters for different particle size segments are determined. For example, for finer particles, a higher magnetic field intensity and a shorter execution time are required to avoid over-purification of the particles due to excessive magnetic field intensity; for the saturation magnetization intensity distribution, iron powder particles with a higher magnetization intensity will be affected by a stronger magnetic force, so the magnetic separation parameters for these particles will be different, and the corresponding magnetic field intensity and execution time are calculated according to the saturation magnetization intensity distribution; for the coercivity distribution, particles with a larger coercivity are more difficult to remove, so a longer magnetic separation time or a stronger magnetic field is required, and the magnetic separation parameters are adjusted according to the coercivity distribution; for the initial impurity spectrum, the initial impurity spectrum provides the concentration distribution of impurities. By matching with the core impurity group, the removal target of the impurities is obtained, and the control parameters of each level of magnetic separation are adjusted accordingly.

[0047] Based on the above mapping, N-level reference magnetic separation control parameters are generated. The divided magnetic separation levels can include a pre-selection level, a fine-selection level, and a reverse-selection level. Among them, the Gaussian setting reference is the set value of the initial condition and serves as the starting standard for the magnetic separation operation; the Gaussian adjustable range refers to the range of magnetic field intensity that can be adjusted in each magnetic separation level, representing the adjustment space in the magnetic separation process; the execution time reference is the time requirement for each level of magnetic separation operation to ensure that the magnetic separation process is completed within the specified time.

[0048] This parameter mapping method ensures that the magnetic separation process can be adjusted specifically at each stage, so that the finally obtained iron powder has a higher purity.

[0049] When the magnetic separation hardware system uses N execution time references as the magnetic separation time limit and performs the N-level magnetic separation treatment process of the iron powder to be purified according to N Gaussian setting references: Step a: According to the results of synchronous laser-induced breakdown spectroscopy analysis of the LIBS probe, perform dynamic closed-loop control of single-level magnetic separation parameters within N Gaussian adjustable intervals.

[0050] Step b: According to the impurity distribution heat maps at the end of the N execution times, perform joint debugging and optimization of the cross-level Gaussian setting benchmarks.

[0051] Iteratively execute until N-level iron powder magnetic separation is completed to produce iron powder with the target purity.

[0052] The execution time benchmark is the time requirement for each level of magnetic separation operation. Each level of magnetic separation operation has a predetermined duration, which controls the duration of the magnetic separation process. Using the N execution time benchmarks of the N-level magnetic separation process as the magnetic separation time limit, it ensures that the magnetic separation process at each level is completed within the specified time. The Gaussian setting benchmark is the set value of the initial conditions, usually based on the Gaussian distribution. During the magnetic separation process, the N Gaussian setting benchmarks of the N-level magnetic separation process are used to determine the control strategies at each stage. The magnetic separation control at each level is optimized according to the Gaussian setting benchmark, enabling each level of magnetic separation operation to be maximally adjusted according to the current iron powder characteristics. The N-level magnetic separation process for the iron powder to be purified includes: LIBS (Laser-Induced Breakdown Spectroscopy) is a technique commonly used to analyze the composition of substances. It uses a laser to excite the surface of the substance, generating a plasma and obtaining elemental composition information through spectral analysis. In this process, it is particularly applied to the real-time analysis of surface impurity concentrations. The LIBS probe detects the impurity concentration in the iron powder to be purified in real time, generating a laser-induced spectrum to obtain the distribution of different impurity elements in the iron powder.

[0053] The Gaussian adjustable interval refers to the range of magnetic field intensities that can be adjusted in each magnetic separation level, representing the adjustment space in the magnetic separation process. Within these intervals, the magnetic separation hardware system makes dynamic adjustments according to the LIBS analysis results. Among them, the dynamic closed-loop control of single-level magnetic separation parameters means that according to the impurity concentration data obtained by the LIBS probe, the parameters of the current magnetic separation level are adjusted in real time. By monitoring the impurity concentration in real time and feeding it back to the control system, the magnetic separation parameters such as the magnetic field intensity and execution time can be dynamically adjusted to optimize the magnetic separation effect. Through this dynamic closed-loop control, it can respond in real time to changes in the impurity concentration in the iron powder, precisely adjust the magnetic separation parameters, ensure that the magnetic separation at each level reaches the optimal effect, and avoid unnecessary over- or under-magnetic separation, thereby improving the purity of the iron powder.

[0054] At the end of each magnetic separation level, impurity distribution heat maps are generated. These impurity distribution heat maps show the distribution of impurities at different execution time points, reflecting the impurity removal effect after each level of magnetic separation operation and helping to identify which magnetic separation stages still need to be optimized.

[0055] Cross - level cascade optimization refers to optimizing and adjusting the Gaussian setting benchmark at the end of the execution time of all magnetic separation levels, in combination with the impurity distribution heat map of each level. Specifically, during the processing of each magnetic separation level, the magnetic separation parameters of the next level are adjusted according to the information feedback of the impurity distribution heat map. The optimization process involves adjusting the Gaussian setting benchmark based on the information of the impurity distribution heat map to ensure that the magnetic separation at each level can effectively remove impurities, and the processing parameters of subsequent levels can be coordinated with the operations of the previous levels, thereby further improving the purity. Cross - level optimization ensures the coordination between magnetic separation operations at each level, enabling the system to precisely adjust parameters at each stage, avoiding parameter conflicts or mismatches between different levels. By providing real - time feedback on impurity distribution and adjusting the setting benchmark, the magnetic separation process can be gradually optimized to ensure that the purity of the final iron powder meets the target requirements.

[0056] The magnetic separation process is continuously executed and the parameters of subsequent stages are adjusted according to the results of the previous stage. This iterative execution ensures that the magnetic separation at each level can be optimized based on real - time feedback data. The output data of each stage is fed back into the system to further optimize the magnetic separation parameters. By continuously adjusting the magnetic separation parameters, impurities can be removed to the maximum extent during the multi - level magnetic separation process, making the purity of the final iron powder higher and higher. After N - level iron powder magnetic separation, the finally produced iron powder will meet the required purity standard, obtaining iron powder with the target purity.

[0057] Furthermore, the method further includes: After running the magnetic separation hardware system with the first Gaussian setting benchmark of the first - level reference magnetic separation control parameters, start the feeder to convey the iron powder to be purified into the magnetic separation hardware system for pre - selection - level magnetic separation processing; during the pre - selection - level magnetic separation processing, synchronously run the LIBS probe to perform laser - induced breakdown spectroscopy analysis on the conveyed iron powder, and output a real - time surface impurity concentration sequence; according to the multi - impurity fluctuation characteristics of the real - time surface impurity concentration sequence, with the first Gaussian adjustable interval as a constraint, perform dynamic parameter adjustment and update of the first Gaussian setting benchmark until it stops when reaching the first execution time benchmark, generating the first impurity distribution heat map.

[0058] The pre - selection - level magnetic separation processing is the first step in the magnetic separation process, aiming to remove the relatively simple and easily separable impurities in the iron powder. This stage usually deals with the coarse - grained impurities in the iron powder and those components that can be effectively removed by simple magnetic separation. The first - level reference magnetic separation control parameters provide preliminary magnetic separation settings for the pre - selection - level magnetic separation processing. Running the magnetic separation hardware system with the first Gaussian setting benchmark of the first - level reference magnetic separation control parameters, in the magnetic separation hardware system, the feeder is responsible for conveying the iron powder to be purified into the magnetic separation hardware system. The conveying volume and speed of the feeder need to be adjusted according to the processing capacity of the magnetic separation equipment and the characteristics of the iron powder to ensure that the iron powder can enter the magnetic separation system evenly.

[0059] During the pre-selection magnetic separation process, the LIBS probe continuously detects the impurity concentration in the iron powder to be purified, generates laser-induced spectroscopy, and thus obtains the distribution of different impurity elements in the iron powder. Synchronous operation means that while the pre-selection magnetic separation process is ongoing, the LIBS probe will continuously analyze and obtain the impurity concentration data on the surface of the iron powder in real time. This process is synchronized with the magnetic separation process to ensure that any impurity changes at each stage can be accurately captured. The real-time surface impurity concentration sequence is the data collected by the LIBS probe at each moment, representing the change in the impurity concentration on the surface of the iron powder. This data sequence provides information on the concentration changes of different impurity elements on the surface of the iron powder, helping to analyze which impurities are removed during the magnetic separation process and which ones remain.

[0060] The multi-impurity fluctuation characteristic refers to the pattern of change in the concentration of different types of impurities on the surface of the iron powder over time. Since the iron powder contains multiple impurity elements, the fluctuation characteristics of the impurity concentration are often diverse. Different impurities exhibit different fluctuation characteristics at different time intervals. For example, some impurities may be rapidly removed in a short period, while others may be removed more slowly.

[0061] The Gaussian adjustable range refers to the range of magnetic field strength that can be adjusted during the magnetic separation process. In this step, the first Gaussian adjustable range serves as an adjustment range to limit the dynamic adjustment of the magnetic separation parameters. This constraint ensures that during the dynamic adjustment process, the magnetic separation parameters do not deviate from the set range, thus avoiding over-regulation of the system. With the first Gaussian adjustable range as the constraint, dynamic parameter adjustment and update are performed on the first Gaussian setting benchmark. The dynamic parameter adjustment and update are based on the impurity concentration data to ensure that the magnetic separation process can effectively remove different types of impurities. The goal is to continuously optimize the magnetic separation process based on real-time data and improve the impurity removal efficiency. At each time point, the magnetic separation parameters are adjusted according to the current change in impurity concentration to ensure that each stage of the magnetic separation process can adapt to the different characteristics of the impurities.

[0062] The first execution time benchmark is the maximum duration of the pre-selection magnetic separation. When the time in the pre-selection magnetic separation process reaches the first execution time benchmark, the adjustment process stops, and preparations are made to enter the next stage of processing. At the end of the first execution time benchmark, a first impurity distribution heat map is generated based on the impurity removal situation during the magnetic separation process. The heat map is a visualization tool that shows the changes in impurity distribution at each stage during the magnetic separation process. The first impurity distribution heat map presents the distribution of impurity concentration through a color scale, where high-concentration areas are displayed in different colors for easy analysis and optimization of the magnetic separation process. The first impurity distribution heat map is used to further analyze which areas have poor impurity removal effects, so as to make corresponding adjustments in subsequent magnetic separation processes.

[0063] Furthermore, the method further includes: Perform cross-level pre-correction on the second Gaussian setting benchmark based on the first impurity distribution heat map to obtain the second Gaussian joint adjustment parameter; during the process of performing secondary magnetic separation on the magnetic separation hardware system using the second Gaussian joint adjustment parameter, perform parameter adjustment and update of the second Gaussian joint adjustment parameter according to the surface impurity concentration data synchronously detected by the LIBS probe until the magnetic separation duration reaches the second execution time benchmark, and output the second impurity distribution heat map; perform dynamic adjustment of the magnetic separation parameters in the single-level magnetic separation process based on the surface impurity concentration iteration, and perform cross-level Gaussian benchmark pre-joint adjustment based on the hierarchical impurity heat distribution until the N-level benchmark magnetic separation control parameter is executed to completion, and produce the target purity iron powder.

[0064] Cross-level pre-correction is to adjust the control parameters of secondary magnetic separation according to the first-level magnetic separation result (i.e., the first impurity distribution heat map). By analyzing the characteristics of the impurity distribution in the first heat map, the requirements for secondary magnetic separation treatment can be inferred, and relevant control parameters such as magnetic field strength and execution time can be adjusted. The second Gaussian setting benchmark is the control benchmark for secondary magnetic separation (such as the fine separation level). Based on the data in the first heat map, the second Gaussian setting benchmark is optimized to ensure that residual impurities can be better removed during the secondary magnetic separation process. The second Gaussian joint adjustment parameter is the magnetic separation control parameter obtained through cross-level correction to ensure that the secondary magnetic separation process can adapt to the result of the first-level magnetic separation.

[0065] After obtaining the second Gaussian joint adjustment parameter, use these optimized parameters to run the magnetic separation hardware system for secondary magnetic separation treatment. This process is executed at the secondary magnetic separation level and aims to further remove the remaining impurities. The LIBS probe runs synchronously during the secondary magnetic separation process to monitor the concentration data of the impurities on the surface of the iron powder in real time. These data can reflect the efficiency of impurity removal at this stage and which impurity components still have problems in removal.

[0066] Perform parameter adjustment and update of the second Gaussian joint adjustment parameter according to the surface impurity concentration data collected in real time, which means adjusting the magnetic separation parameters such as magnetic field strength and execution time in real time according to the current impurity removal situation to improve the magnetic separation effect. The second execution time benchmark is the set secondary magnetic separation treatment time. The duration of the secondary magnetic separation process is controlled according to this benchmark. When the secondary magnetic separation treatment time reaches the second execution time benchmark, the secondary magnetic separation process automatically stops and is ready to enter the next stage. At the end of the secondary magnetic separation treatment, a second impurity distribution heat map is generated. This heat map shows the distribution of impurities in each region during the secondary magnetic separation process, which provides intuitive visual data for subsequent analysis.

[0067] The surface impurity concentration is the concentration data of the impurities on the surface of iron powder obtained by real-time monitoring with a LIBS probe. During the single-layer magnetic separation process, according to the surface impurity concentration, the magnetic separation parameters are dynamically adjusted. The magnetic separation parameters include magnetic field strength, execution time, etc. This dynamic adjustment is achieved through real-time feedback to ensure that impurities can be maximally removed in each layer of the magnetic separation process and the efficiency of the process is maintained.

[0068] The thermal distribution of impurities at each level is used to represent the distribution of impurities in different magnetic separation levels. The heat map can intuitively show the effect of removing impurities in each magnetic separation stage and help judge which areas have poor impurity removal effects. The cross-level Gaussian benchmark pre-adjustment is to iteratively adjust the Gaussian benchmark and other magnetic separation control parameters according to the thermal distribution of impurities at each level generated after each stage of magnetic separation processing. This means not only adjusting the magnetic separation parameters of the current level, but also adjusting the magnetic separation parameters of subsequent stages according to the impurity distribution in the previous stage to ensure the coordination between magnetic separation operations at each level. Through iterative optimization, the parameters in the magnetic separation process are optimized level by level to ensure the maximization of the impurity removal effect.

[0069] The N-level benchmark magnetic separation control parameters are continuously iteratively executed until the magnetic separation processing of all levels is completed. The magnetic separation at each stage is adjusted according to the results of the previous stage, so as to ensure that the effect and purity of magnetic separation at each level are gradually improved. By iteratively executing the magnetic separation processing of all levels and dynamically adjusting the magnetic separation parameters according to the results of each level, iron powder with the target purity is finally obtained, thus improving the overall effect of magnetic separation.

[0070] Furthermore, according to the multivariate impurity fluctuation characteristics of the real-time surface impurity concentration sequence, with the first Gaussian adjustable interval as the constraint, the dynamic parameter adjustment update of the first Gaussian setting benchmark is executed until it stops when reaching the first execution time benchmark, and the first impurity distribution heat map is generated. The method includes: Interactively obtain multiple groups of Gaussian adjustment actions of various iron powder impurities in multiple impurity fluctuation scenarios to construct a Gaussian adjustment action library; input the multivariate impurity fluctuation characteristics as multivariate retrieval conditions into the Gaussian adjustment action library to parallelly match the multivariate Gaussian adjustment actions of the multivariate impurity fluctuation scenarios; perform a union solution on the multivariate Gaussian adjustment actions to locate the first real-time adjustment action; use the first real-time adjustment action to execute the parameter adjustment update of the first Gaussian setting benchmark; when the dynamic parameter adjustment update of the first Gaussian setting benchmark reaches the end of the first execution time benchmark, drive the LIBS probe to collect instantaneous impurity concentration data and synthesize the first impurity distribution heat map.

[0071] Multiple iron powder impurities refer to different types of impurity components in iron powder. These impurities have different physical and chemical properties. For example, the impurities include elements such as silicon, phosphorus, and aluminum, and their removal effects will be affected by parameter changes during the magnetic separation process; multiple groups of impurity fluctuation scenarios refer to different modes of concentration changes of impurities during the magnetic separation process. These fluctuation scenarios reflect the removal characteristics of impurities. For example, some impurities may be removed quickly, while some may be removed slowly. The removal performance of each impurity is different under different conditions, so different adjustment strategies are required. The Gaussian adjustment action library generates a set of selectable magnetic separation adjustment actions by collecting the fluctuation scenarios of multiple impurities. These adjustment actions are based on the Gaussian distribution, and each Gaussian adjustment action represents a specific control action, such as increasing the magnetic field strength, extending the magnetic separation time, etc. By analyzing different impurity fluctuation scenarios, selective adjustment can be carried out under different fluctuation scenarios.

[0072] The multivariate impurity fluctuation characteristics refer to the concentration fluctuation characteristics of multiple impurity elements during the magnetic separation process. The multivariate impurity fluctuation characteristics are input into the Gaussian adjustment action library as retrieval conditions. Through the parallel matching mechanism, considering the fluctuation characteristics of multiple impurities simultaneously, the corresponding Gaussian adjustment actions are queried. The multivariate Gaussian adjustment actions are a set of Gaussian distribution control parameter adjustment strategies generated for multiple impurity fluctuation scenarios. These actions not only include increasing or decreasing the magnetic field strength, but also include adjustments of time, changes in separation intensity, etc., to ensure that each impurity can obtain the optimal removal effect according to its fluctuation characteristics.

[0073] After the multivariate Gaussian adjustment actions are matched, the union solution of the obtained adjustment actions is performed. This means combining multiple Gaussian adjustment actions to find the adjustment strategy most suitable for the current magnetic separation process. During the union solution process, the maximum value of the magnetic field increasing actions is taken as the reference offset, the minimum value of the magnetic field decreasing actions is taken as the protection threshold, and the median value of the adjustable range is taken for the conflicting actions. Specifically, the magnetic field increasing action refers to the action of enhancing the magnetic field strength. When the magnetic field strength needs to be increased, the maximum value is selected as the offset to ensure that the magnetic force can be effectively increased to remove difficult-to-separate impurities. The reference offset refers to the value that is maximally adjusted within the control range of the Gaussian distribution, which is used to enhance the adjustment ability of the system; the magnetic field decreasing action refers to the action of reducing the magnetic field strength. When it is detected that the removal efficiency of some impurities is high, or a strong magnetic field may cause over-separation, the minimum value is selected as the protection threshold to avoid over-magnetic separation caused by too strong a magnetic field; the conflicting action refers to when there are two operations that need to be adjusted with each other (such as increasing and decreasing the magnetic field) in the same scenario, the median value of the adjustable range is selected as a compromise value to ensure that the system will not be excessive or insufficient during the adjustment process. Through the union solution of multiple adjustment actions, the first real-time adjustment action is finally determined.

[0074] The first Gaussian setting reference is the preliminary control reference for the primary magnetic separation process. The first real-time adjustment action is used to update the parameters of the first Gaussian setting reference. Parameter update means adjusting the initial first Gaussian setting reference to adapt to the current impurity concentration, iron powder characteristics, and magnetic separation effect. Through this adjustment, the magnetic separation process will become more flexible and efficient, capable of making precise control for the characteristics of different impurities to ensure better removal effect.

[0075] During the magnetic separation process, the dynamic parameter update continues until the preset first execution time reference is reached. When the magnetic separation process reaches the end of the first execution time reference, the LIBS probe is activated to drive it to collect instantaneous impurity concentration data. Based on the instantaneous impurity concentration data, the first impurity distribution heat map is synthesized and generated to show the impurity distribution of each position of the iron powder during the entire magnetic separation process.

[0076] Furthermore, the second Gaussian setting reference is cross-level pre-corrected based on the first impurity distribution heat map to obtain the second Gaussian joint adjustment parameter. The method includes: Pre-construct a heat map feature detection network, where the heat map feature detection network includes M heat map feature recognition channels; after loading the first impurity distribution heat map into the heat map feature detection network, feature parallel recognition is performed through the M heat map feature recognition channels, and M real-time heat map quantization features are output; after pre-correction action matching based on the M real-time heat map quantization features, the second Gaussian setting reference is cross-level pre-corrected to obtain the second Gaussian joint adjustment parameter.

[0077] Construct a heat map feature detection network. The heat map feature detection network consists of M heat map feature recognition channels, which are specifically used for analyzing and processing the features in the heat map. The M heat map feature recognition channels are multiple channels in the network that process the heat map in parallel. Each channel is responsible for extracting a certain type of feature in the heat map, such as the change trend of concentration, the impurity distribution in the hot spot area, etc. These heat map feature recognition channels process the input heat map in different ways to identify different features. Through the parallel processing of multiple channels, the network can extract the information in the heat map more comprehensively and accurately.

[0078] Input the first impurity distribution heat map into a pre - constructed heat map feature detection network. Through M heat map feature recognition channels, different features are recognized in parallel. For example, concentration hot spot recognition is carried out to identify areas with higher impurity concentration, helping to judge which areas have not effectively removed impurities; impurity removal trend analysis is carried out to analyze the removal trends of different areas during the magnetic separation process, helping to judge which impurities have been removed and which still remain; removal efficiency analysis is carried out to analyze the impurity removal efficiency of each area, helping to judge whether the magnetic separation operation needs to be further optimized. Parallel recognition means that these recognition tasks are carried out simultaneously rather than sequentially. Through parallel processing, various features in the heat map can be extracted in a shorter time.

[0079] Quantify the results recognized by each channel into a feature value, forming M real - time heat map quantization features. These features include impurity concentration, removal efficiency, the size of the hot spot area, etc. Based on these features, the effect of the magnetic separation process can be judged, and it can be decided whether to adjust the magnetic separation parameters or carry out further optimization.

[0080] The pre - correction action refers to making a preliminary adjustment to the current magnetic separation parameters during the magnetic separation process according to the real - time analyzed feature data. By analyzing the M heat map quantization features, it can be judged which magnetic separation parameters need to be adjusted in advance to optimize the effect of the subsequent magnetic separation stage. The pre - correction action matching refers to searching in a preset correction action library according to the M real - time heat map quantization features and selecting the most suitable correction action for the current magnetic separation stage.

[0081] Cross - level pre - correction refers to adjusting the control parameters of the next magnetic separation level (i.e., the second - stage magnetic separation). Although the adjustment in the current stage is based on the data of the current level, the cross - level correction will affect the subsequent magnetic separation levels, ensuring the coordination of control strategies between different levels. According to the M real - time heat map quantization features and the matched pre - correction actions, cross - level pre - correction is carried out on the second Gaussian setting benchmark. This correction is to ensure that the second - stage magnetic separation can make full use of the effect of the first - stage magnetic separation and further optimize on this basis. The second Gaussian coordinated adjustment parameters are the adjusted parameters obtained based on the cross - level pre - correction, and these parameters will be used as the control benchmark for the second - stage magnetic separation process.

[0082] Furthermore, after performing pre - correction action matching based on the M real - time heat map quantization features, cross - level pre - correction is carried out on the second Gaussian setting benchmark to obtain the second Gaussian coordinated adjustment parameters. The method includes: Local settings of M groups of standard pre-correction actions for the quantization feature thresholds of M heat maps; extracting M standard pre-correction actions from the M groups of standard pre-correction actions by using the intersection relationship of the M real-time heat map quantization features in the M groups of heat map quantization feature thresholds; performing conflict compensation on the M standard pre-correction actions to obtain the first pre-correction action; and performing cross-level pre-correction on the second Gaussian setting benchmark by using the first pre-correction action to obtain the second Gaussian joint adjustment parameter.

[0083] The M groups of heat map quantization feature thresholds refer to specific thresholds set by the system for classifying and judging the impurity distribution in the heat map. These thresholds determine which regions or features need to be adjusted or optimized. These thresholds are usually set based on empirical data, experimental results, or optimization goals. For example, a specific impurity concentration value may be considered a sign of insufficient removal, or when the impurity concentration in a certain region is higher than the set threshold, it is determined that the region needs further optimization. The M groups of standard pre-correction actions are a series of pre-set correction actions according to the set M groups of heat map quantization feature thresholds. These correction actions are classified based on different impurity concentrations and heat map features, and corresponding magnetic separation adjustment strategies are provided. For example, if the impurity concentration in a certain region exceeds the set threshold, the standard pre-correction actions include increasing the magnetic field strength, extending the magnetic separation time, or adjusting other control parameters.

[0084] The intersection relationship refers to the overlapping part of different real-time heat map quantization features under their respective heat map quantization feature thresholds. For example, some heat map features indicate that the impurity concentration in a specific region is too high, and there is an intersection with the concentration feature threshold. In this case, it is considered that the region needs special treatment. The M standard pre-correction actions are the correction actions selected from the pre-set M groups of standard pre-correction actions according to the intersection relationship. By analyzing the intersection of different heat map quantization features, the most suitable correction actions are extracted from the pre-set action library to ensure precise adjustment for different impurity features.

[0085] During the magnetic separation process, some conflicts may occur among the M standard pre-correction actions. For example, the removal of some impurities may require an increase in the magnetic field strength, while other impurities may overreact due to the too strong magnetic field, resulting in a decrease in the removal efficiency. In this case, there is a conflict between the operation of increasing the magnetic field strength and the operation of reducing the magnetic field strength. The purpose of conflict compensation is to solve these conflicts and ensure that the magnetic separation process does not lead to a decrease in efficiency due to the inconsistency between different correction actions by merging or adjusting the operation strategies.

[0086] By analyzing the characteristics and effects of M standard pre-correction actions, determine which correction actions conflict with each other. For example, if one action requires increasing the magnetic field and another requires decreasing the magnetic field, then based on specific factors such as impurity concentration and removal efficiency, select the optimal adjustment method. During this process, merge or adjust the conflicting actions to ensure that the final adjustment action can remove impurities without causing losses in other treatment effects. The finally generated first pre-correction action will be an integrated and conflict-free optimization strategy.

[0087] Apply the first pre-correction action to perform cross-level pre-correction on the second Gaussian setting benchmark. In this way, ensure that on the basis of the first-stage magnetic separation, the second-stage magnetic separation can be fully optimized, and the magnetic separation processes of each stage can cooperate with each other to achieve the final optimization effect. The second Gaussian joint adjustment parameter is the adjustment parameter obtained by applying the first pre-correction action. The second Gaussian joint adjustment parameter will be used as the control benchmark for the second-stage magnetic separation to ensure that the magnetic separation process can further optimize and remove impurities on the basis of the first-stage magnetic separation.

[0088] Furthermore, input the spatial mapping result of spatially mapping the instantaneous impurity concentration data into a pre-defined impurity concentration-color scale mapping table for gradient coloring synthesis to obtain the first impurity distribution heat map.

[0089] The instantaneous impurity concentration data is the concentration data of the impurities on the surface of iron powder collected in real time by the LIBS probe, which reflects the impurity distribution on the surface of iron powder at the current moment. The spatial coordinate mapping is to map the instantaneous impurity concentration data into a spatial coordinate. This process associates the concentration value of the impurity with its specific position in the magnetic separation equipment. Through this mapping, the concentration information of the impurity will be presented as a spatial distribution to help the system identify the changes in impurity concentration in different regions.

[0090] The impurity concentration-color scale mapping table is a predefined table that corresponds impurity concentrations to color scales (i.e., colors). Different impurity concentration values are mapped to different colors in the color scale to more intuitively represent the distribution of impurities. For example, higher impurity concentrations are mapped to red, and lower concentrations are mapped to green or blue. Gradient coloring synthesis refers to combining the spatial coordinate mapping results with the impurity concentration-color scale mapping table to generate a heat map with gradient colors. This color gradient reflects the change in impurity concentration, facilitating the quick identification of areas on the iron powder surface with higher impurity concentrations and areas with better removal effects. For example, the high-concentration areas of the heat map (i.e., where more impurities have not been removed) appear dark red, while the low-concentration areas (i.e., where impurities have been better removed) appear light green or blue. Through this gradient color method, the spatial information of the impurity distribution is visually presented. Through the above steps, the first impurity distribution heat map is finally obtained. It is a visual graph that shows the spatial distribution of impurity concentrations on the iron powder surface, providing a basis for subsequent magnetic separation adjustment.

[0091] Furthermore, the multi-modal iron powder component characteristics include a particle size distribution curve, saturation magnetization intensity distribution, coercivity distribution, and initial impurity spectrum.

[0092] The multi-modal iron powder component characteristics include a particle size distribution curve, saturation magnetization intensity distribution, coercivity distribution, and initial impurity spectrum. Among them, the particle size distribution curve is a graph of the particle size distribution in iron powder, reflecting the proportion of particles with different particle sizes in the iron powder. The particle size distribution is usually measured by methods such as sieving or laser diffraction. The particle size distribution is very important for the magnetic separation process because particles of different sizes respond differently to the magnetic field. Larger particles are more easily separated by magnetic separation, while smaller particles require a stronger magnetic field or a longer processing time; the saturation magnetization intensity refers to the maximum magnetization intensity that iron powder can reach under the action of an external magnetic field. This characteristic indicates the magnetization ability of iron powder in a magnetic field and is usually related to factors such as the chemical composition and crystal structure of the iron powder; the coercivity is the magnetic intensity that a magnetic material still retains when the external magnetic field is removed. In the case of iron powder, the coercivity distribution shows the ease with which iron powder particles regain their magnetism during the demagnetization process. Particles with high coercivity in iron powder are often more difficult to remove by magnetic separation. Therefore, during the magnetic separation process, it is necessary to adjust the magnetic field strength or processing time to remove these high-coercivity particles; the initial impurity spectrum is a graph of the initial concentration distribution of all impurity elements in iron powder. It includes the initial concentration information of all impurity elements (such as silicon, aluminum, calcium, etc.) in the iron powder and is used to determine which impurities are the target removal objects and which need special treatment during the magnetic separation process. Through the multi-modal iron powder component characteristics, it is possible to more effectively identify the behaviors and removal difficulties of different iron powder particles and adjust parameters such as the magnetic field strength and execution time accordingly, thereby improving the impurity removal efficiency and the purity of the final produced iron powder.

[0093] Furthermore, based on the characteristics of the multi-modal iron powder components, multi-level magnetic separation control parameters are mapped to generate N-level reference magnetic separation control parameters. The method includes: After analyzing the particle size distribution curve and locating the position of the median particle size and the proportion of the median particle size distribution width, based on the position of the median particle size and the proportion of the median particle size distribution width, the first multi-level initialization magnetic separation control parameters are obtained by matching in the residence time spectrum mapping table; based on the calculated central tendency value of the saturation magnetization intensity distribution and the proportion of the high-value region of the coercivity distribution, the second multi-level initialization magnetic separation control parameters are obtained by matching in the field strength response spectrum mapping table; after extracting the impurity concentration group corresponding to the core impurity group from the initial impurity spectrum, the third multi-level initialization magnetic separation control parameters are obtained by matching in the element removal response spectrum mapping table; conflict parameter arbitration is performed on the first multi-level initialization magnetic separation control parameters, the third multi-level initialization magnetic separation control parameters, and the third multi-level initialization magnetic separation control parameters, and the N-level reference magnetic separation control parameters are output.

[0094] The particle size distribution curve is a statistical graph of the particle sizes of iron powder particles, showing the distribution of particles with different particle sizes in the entire iron powder sample. Usually, the particle size distribution curve will show different particle size segments of the particles and their corresponding quantity or mass distributions. Analyzing the particle size distribution curve determines the particle size distribution of the particles in the iron powder, especially the median particle size of the particles, as well as the position of the median particle size and the proportion of the median particle size distribution width. Among them, the median particle size refers to the particle size value in the middle after sorting the particle size distribution by size; the median particle size distribution width refers to the width of the particle size range, that is, the difference between the maximum particle size and the minimum particle size, describing the broadness of the particle size distribution; the position of the median particle size is the midpoint in the particle size distribution, which divides the particle size distribution curve into two parts, such that half of the particles are smaller and the other half are larger; the proportion of the particle size distribution width indicates the broadness of the particle size distribution. If the proportion is large, it means that the particle size difference is large. If the proportion is small, it means that the particle sizes are relatively uniform.

[0095] The residence time spectrum mapping table is a predefined table used to calculate the residence time of particles with different particle sizes in the magnetic separation process based on the particle size distribution (especially the position of the median particle size and the proportion of the median particle size distribution width). The residence time refers to the time that the particles stay in the magnetic separation system. Larger particles have a shorter residence time, and smaller particles have a longer residence time. Based on the position of the median particle size and the proportion of the median particle size distribution width, the matching residence time parameters are found in the residence time spectrum mapping table to obtain the first multi-level initialization magnetic separation control parameters for the subsequent magnetic separation process to appropriately process particles of different particle sizes.

[0096] The saturation magnetization intensity distribution refers to the magnetization intensity distribution of iron powder particles under the action of an external magnetic field. The central tendency values (such as the average value, peak value, etc.) refer to the degree of concentration of the magnetization intensity in the saturation magnetization intensity distribution. A higher central tendency value indicates that most particles have strong magnetism, while a lower central tendency value indicates that the particles have weak magnetism. The coercivity distribution refers to the distribution of the magnetic intensity retained by iron powder particles after removing the external magnetic field. The proportion of the high-value area represents the proportion of particles with higher coercivity among the particles. Particles with higher coercivity are usually more difficult to be removed by magnetic separation and require a stronger magnetic field or a longer processing time.

[0097] The field strength response spectrum mapping table is a predefined table used to adjust the magnetic field strength in the magnetic separation process according to the magnetic properties of the particles (such as saturation magnetization intensity and coercivity distribution). The selection of the magnetic field strength directly affects the magnetic separation efficiency. By analyzing the central tendency value of the saturation magnetization intensity distribution and the proportion of the high-value area of the coercivity distribution, the most suitable magnetic field strength control parameters are found in the field strength response spectrum mapping table, and the second multi-level initialization magnetic separation control parameters are obtained to optimize the magnetic separation process and ensure that the magnetic field strength matches the magnetic properties of the iron powder particles.

[0098] The initial impurity spectrum refers to the concentration distribution of various impurity elements in iron powder before magnetic separation treatment. From the initial impurity spectrum, the impurity elements that are most challenging to the magnetic separation process are extracted, which are called the core impurity group. These impurity elements are the components that need to be removed with emphasis in the magnetic separation process. The impurity concentration group refers to the concentration information of these core impurities, reflecting the distribution and concentration level of these impurities in the iron powder.

[0099] The element removal response spectrum mapping table is a predefined table that correlates the impurity concentration with the magnetic separation control parameters (such as magnetic field strength, processing time, etc.). This table helps the system calculate the required magnetic separation parameters according to the concentration distribution of different impurities in order to effectively remove specific impurities. According to the impurity concentration group extracted from the core impurity group, the matching magnetic separation parameters are found in the element removal response spectrum mapping table, and the third multi-level initialization magnetic separation control parameters are obtained. This process provides targeted control parameters for the subsequent magnetic separation steps to optimize the removal of these core impurities.

[0100] During the magnetic separation process, different initialization magnetic separation control parameters may conflict with each other. For example, some parameters suggest increasing the magnetic field strength, while others suggest decreasing the magnetic field strength. Conflict parameter arbitration is to solve these conflicts and find an optimal adjustment strategy. The arbitration methods include selecting the optimal parameters, making a compromise, or adjusting the parameters according to the actual situation. Usually, these conflicts are resolved through weighted average, priority sorting, or other strategies. After the conflict is resolved, N-level benchmark magnetic separation control parameters are generated according to the arbitration results, and these parameters will be used as the control benchmark for the entire magnetic separation process.

[0101] In summary, the multi-stage magnetic separation method for high-purity iron powder provided by the embodiments of the present application has the following technical effects: By cross-device calling of multi-modal iron powder component characteristics, the physical and chemical characteristics of iron powder can be comprehensively understood, which provides accurate data support for the subsequent magnetic separation process and ensures that the magnetic separation parameters can be adjusted specifically during the magnetic separation process; according to the multi-modal iron powder component characteristics of iron powder, N-level reference magnetic separation control parameters are generated through mapping of multi-stage magnetic separation control parameters. The control parameters of each level include Gaussian setting reference, Gaussian adjustable range, and execution time reference, ensuring that the magnetic separation process can accurately process iron powder with different particle sizes and magnetic strengths, so as to effectively classify and process iron powder, thereby improving the purity; by using the N execution time references as the magnetic separation time limit, the processing time of each stage of magnetic separation is accurately controlled, avoiding over-processing or under-processing. Within the time limit of each magnetic separation level, the magnetic field strength and processing time are adjusted according to the results of the previous stage, so as to gradually remove impurities in the iron powder; during the magnetic separation process, the concentration of impurities on the surface layer of iron powder is detected in real time through LIBS probe synchronous laser-induced breakdown spectroscopy analysis, and the magnetic separation parameters can be dynamically adjusted according to real-time data to form a closed-loop control. This dynamic closed-loop control can respond to changes in impurity concentration in real time, ensuring that the best removal effect can be achieved at each magnetic separation level; at the end of each magnetic separation stage, an impurity distribution heat map is generated. Through the impurity distribution heat map, the control parameters can be optimized in the subsequent magnetic separation stage. Through the joint adjustment and optimization of the Gaussian setting reference, the operations of each magnetic separation level are ensured to be coordinated, further improving the efficiency of removing impurities; by iteratively executing the magnetic separation process and continuously optimizing the control strategy until the required target purity is reached, the results of each stage will affect the control strategy of the subsequent stage, thereby ensuring that the entire magnetic separation process can efficiently remove impurities and finally produce high-purity iron powder.

[0102] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-stage magnetic separation device for high-purity iron powder, characterized in that, The device includes: A component characteristic calling module for cross-device calling of multimodal iron powder component characteristics; A control parameter mapping module for performing multistage magnetic separation control parameter mapping based on the multimodal iron powder component characteristics to generate N-level reference magnetic separation control parameters, where each level of reference magnetic separation control parameters consists of a Gaussian setting reference, a Gaussian adjustable range, and an execution time reference; A magnetic separation processing module for the magnetic separation hardware system to perform an N-level magnetic separation process on the iron powder to be purified based on N execution time references and N Gaussian setting references. The magnetic separation processing module includes: A closed-loop control unit for performing dynamic closed-loop control of single-layer magnetic separation parameters within N Gaussian adjustable ranges according to the synchronous laser-induced breakdown spectroscopy analysis results of the LIBS probe; A joint debugging and optimization unit for performing joint debugging and optimization of cross-layer Gaussian setting references according to the impurity distribution heat maps at the ends of N execution times; An iterative execution unit for iteratively executing until the N-level iron powder magnetic separation is completed to produce iron powder with the target purity.

2. The multi-stage magnetic separation device for high-purity iron powder according to claim 1, characterized in that, The joint debugging and optimization unit further includes: A preselected-level magnetic separation processing channel for starting the feeder to convey the iron powder to be purified into the magnetic separation hardware system to perform preselected-level magnetic separation processing after running the magnetic separation hardware system with the first Gaussian setting reference of the first-level reference magnetic separation control parameters; A spectroscopy analysis channel for synchronously running the LIBS probe to perform laser-induced breakdown spectroscopy analysis on the conveyed iron powder during the preselected-level magnetic separation process and output a real-time surface impurity concentration sequence; A dynamic parameter adjustment and update channel for performing dynamic parameter adjustment and update of the first Gaussian setting reference based on the multivariate impurity fluctuation characteristics of the real-time surface impurity concentration sequence and with the first Gaussian adjustable range as a constraint until it stops when reaching the first execution time reference to generate a first impurity distribution heat map.

3. The multi-stage magnetic separation equipment for high-purity iron powder according to claim 2, characterized in that, The iterative execution unit further includes: A cross-level pre-correction channel for performing cross-level pre-correction on the second Gaussian setting reference based on the first impurity distribution heat map to obtain second Gaussian joint debugging parameters; A parameter adjustment and update channel for performing parameter adjustment and update of the second Gaussian joint debugging parameters during the secondary magnetic separation process of running the magnetic separation hardware system with the second Gaussian joint debugging parameters according to the surface impurity concentration data synchronously detected by the LIBS probe until the magnetic separation duration reaches the second execution time reference and outputting a second impurity distribution heat map; A Gaussian reference pre-joint debugging channel for performing dynamic adjustment of magnetic separation parameters in the single-layer magnetic separation process based on the iterative execution of surface impurity concentrations and performing cross-layer Gaussian reference pre-joint debugging based on the hierarchical impurity heat distribution until the N-level reference magnetic separation control parameters are executed to completion to produce the iron powder with the target purity.

4. The multi-stage magnetic separation equipment for high-purity iron powder according to claim 2, characterized in that, The dynamic parameter adjustment and update channel includes: A Gaussian adjustment action library construction node for interactively obtaining multiple groups of Gaussian adjustment actions of various iron powder impurities in multiple impurity fluctuation scenarios to construct a Gaussian adjustment action library; A parallel matching node for inputting the multivariate impurity fluctuation characteristics as multivariate retrieval conditions into the Gaussian adjustment action library to parallelly match multivariate Gaussian adjustment actions of multivariate impurity fluctuation scenarios; Union solution node, used to perform union solution on the multivariate Gaussian adjustment actions to locate the first real-time adjustment action; Parameter adjustment update node, used to perform parameter adjustment update of the first Gaussian setting benchmark by using the first real-time adjustment action; Data acquisition node, used to drive the LIBS probe to collect instantaneous impurity concentration data and synthesize the first impurity distribution heat map when the dynamic parameter adjustment update of the first Gaussian setting benchmark reaches the end of the first execution time benchmark.

5. The multi-stage magnetic separation device for high-purity iron powder according to claim 3, characterized in that, The cross-level pre-correction channel includes: Thermal feature detection network construction node, used to pre-construct a thermal feature detection network, where the thermal feature detection network includes M thermal map feature recognition channels; Feature parallel recognition node, used to load the first impurity distribution heat map into the thermal feature detection network, and then perform feature parallel recognition through the M thermal map feature recognition channels to output M real-time thermal map quantization features; Cross-level pre-correction node, used to perform cross-level pre-correction on the second Gaussian setting benchmark after performing pre-correction action matching based on the M real-time thermal map quantization features to obtain the second Gaussian joint adjustment parameter.

6. The multi-stage magnetic separation device for high-purity iron powder according to claim 5, characterized in that, The cross-level pre-correction node includes: Standard pre-correction action setting sub-node, used to locally set M groups of standard pre-correction actions for M groups of thermal map quantization feature thresholds; Action extraction sub-node, used to extract M standard pre-correction actions from the M groups of standard pre-correction actions by using the intersection relationship of the M real-time thermal map quantization features in the M groups of thermal map quantization feature thresholds; Conflict compensation sub-node, used to perform conflict compensation on the M standard pre-correction actions to obtain the first pre-correction action; Cross-level pre-correction sub-node, used to perform cross-level pre-correction on the second Gaussian setting benchmark by using the first pre-correction action to obtain the second Gaussian joint adjustment parameter.

7. The multi-stage magnetic separation device for high-purity iron powder according to claim 4, characterized in that, The dynamic parameter adjustment update channel is used to: input the spatial mapping result of the instantaneous impurity concentration data for spatial coordinate mapping into a predefined impurity concentration-color scale mapping table for gradient coloring synthesis to obtain the first impurity distribution heat map.

8. The multi-stage magnetic separation device for high-purity iron powder according to claim 1, characterized in that, The multimodal iron powder component characteristics include particle size distribution curve, saturation magnetization intensity distribution, coercivity distribution, and initial impurity spectrum.

9. The multi-stage magnetic separation device for high-purity iron powder according to claim 8, characterized in that, The control parameter mapping module includes: The first matching unit, used to analyze the particle size distribution curve, locate the median particle size position and the proportion of the median particle size distribution width, and then match the first multi-level initialization magnetic separation control parameter in the residence time spectrum mapping table according to the median particle size position and the proportion of the median particle size distribution width; The second matching unit, used to match the second multi-level initialization magnetic separation control parameter in the field strength response spectrum mapping table according to the calculated central tendency value of the saturation magnetization intensity distribution and the proportion of the high-value area of the coercivity distribution; The third matching unit, used to extract the impurity concentration group corresponding to the core impurity group from the initial impurity spectrum, and then match the third multi-level initialization magnetic separation control parameter in the element removal response spectrum mapping table; A conflict parameter arbitration unit is used to perform conflict parameter arbitration on the first multi-stage initialization magnetic separation control parameter, the third multi-stage initialization magnetic separation control parameter, and the third multi-stage initialization magnetic separation control parameter, and output the N-level reference magnetic separation control parameter.

10. A multi-stage magnetic separation method for high-purity iron powder, characterized in that, Implementing a multi-stage magnetic separation device for high-purity iron powder according to any one of claims 1-9, the method includes: Cross-device call of multi-modal iron powder component characteristics; Performing multi-stage magnetic separation control parameter mapping based on the multi-modal iron powder component characteristics to generate N-level reference magnetic separation control parameters, where each level of reference magnetic separation control parameter consists of a Gaussian setting reference, a Gaussian adjustable range, and an execution time reference; When the magnetic separation hardware system uses N execution time references as the magnetic separation time limit and performs the N-level magnetic separation process of the iron powder to be purified according to N Gaussian setting references: Step a: Perform single-layer magnetic separation parameter dynamic closed-loop control within the N Gaussian adjustable ranges according to the synchronous laser-induced breakdown spectroscopy analysis results of the LIBS probe; Step b: Perform coordinated optimization of the cross-layer Gaussian setting reference according to the impurity distribution heat maps at the ends of the N execution times; Iteratively execute until the N-level iron powder magnetic separation is completed, and produce iron powder with the target purity.

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