Intelligent function optimization method for inductively coupled plasma mass spectrometer

Abstract

The invention relates to the technical field of mass spectrometers, in particular to an intelligent function optimization method for an inductively coupled plasma mass spectrometer, which comprises the following steps: S1, multi-parameter real-time monitoring: acquiring plasma torch temperature, ion lens voltage and mass spectrum detector noise parameters in real time through an embedded sensor array; s2, intelligent control adjustment: dynamically adjusting the power of a radio frequency generator and the carrier gas flow by adopting a self-adaptive fuzzy PID controller; s3, stability prediction modeling: establishing a plasma stability prediction model based on a deep belief network DBN, and outputting optimization parameters according to real-time acquired data; s4, virtual verification optimization: simulating the influence of parameter adjustment on mass spectrum resolution through a digital twin system; through multi-sensor monitoring, self-adaptive control and digital twinborn verification, the analysis precision and stability are remarkably improved, the matrix interference problem is effectively solved through the intelligent pretreatment technology, reliable operation of equipment is guaranteed through a three-level emergency mechanism, and manual intervention and maintenance cost are greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of mass spectrometry technology, specifically to an intelligent function optimization method for an inductively coupled plasma mass spectrometer. Background Technology

[0002] Inductively coupled plasma mass spectrometry (ICP-MS) is an inorganic element and isotope analysis and testing technology developed in the 1980s. It combines the high-temperature ionization characteristics of ICP-MS with the sensitive and rapid scanning advantages of mass spectrometer through a unique interface technology, forming a highly sensitive analytical technique. According to CN115763208A, an inductively coupled plasma mass spectrometry (ICP-MS) system and its operation method are disclosed. This technology discloses "an ICP-MS system and its operation method, comprising an ion acquisition device, a first deflection component, multiple processing branches, a second deflection component, a mass analyzer, and a detector. The first deflection component is used to transport sample ions acquired by the ion acquisition device to each processing branch, and the processing branches are used to selectively remove interfering ions from the sample ions. The second deflection component is used to receive sample ions output from several processing branches and transport them to the mass analyzer, the mass analyzer is used to perform mass number screening on the sample ions passing through the collision reaction cell, and the detector is used to perform ion detection on the mass number-screened sample ions." This technology has the technical effect of "removing any different interfering ions from the acquired sample ions through multiple processing branches, significantly improving the accuracy and efficiency of the analysis." Existing ICP-MS instruments rely heavily on operator experience during parameter adjustment, making it difficult to achieve accurate and rapid parameter optimization. Especially when faced with complex samples or changing operating conditions, manual adjustments often suffer from lag and subjectivity, leading to unstable instrument performance. Furthermore, existing technologies have significant limitations in analyzing high-matrix samples, and traditional dilution methods cannot adapt to changes in sample composition in real time, resulting in signal saturation or decreased sensitivity. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an intelligent function optimization method for inductively coupled plasma mass spectrometers. Through multi-sensor monitoring, adaptive control, and digital twin verification, it significantly improves analytical accuracy and stability. Intelligent pretreatment technology effectively solves matrix interference problems, and a three-level emergency mechanism ensures reliable equipment operation, greatly reducing manual intervention and maintenance costs.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent function optimization method for an inductively coupled plasma mass spectrometer, comprising the following steps: S1, Real-time monitoring of multiple parameters: Real-time acquisition of plasma torch temperature, ion lens voltage and mass spectrometer detector noise parameters through an embedded sensor array; S2, Intelligent Control and Adjustment: Adaptive fuzzy PID controller is used to dynamically adjust the power of the radio frequency generator and the carrier gas flow rate; S3, Stability Prediction Modeling: A plasma stability prediction model is established based on a deep belief network (DBN), and optimized parameters are output based on real-time acquired data. S4, Virtual Verification Optimization: Closed-loop optimization is achieved by simulating the impact of parameter adjustments on mass spectrometry resolution through a digital twin system.

[0005] Preferably, the operations performed by the adaptive fuzzy PID controller in S2 include: Dynamic modeling phase: Establish a library of nonlinear mapping relationships between plasma ionization efficiency and radio frequency power; Parameter optimization stage: The proportional, integral, and derivative coefficients of the PID controller are adjusted online using the stochastic gradient descent method with a driving term; Fuzzy inference stage: When a change in the sample matrix is ​​detected, the fuzzy inference mechanism is activated to correct the control parameters.

[0006] Preferably, the deep belief network model in S3 includes: Multimodal input layer: Through a spatiotemporal feature coding module, a cross-scale feature extraction network, and a data quality verification unit, it achieves collaborative processing of the temporal characteristics, spatial distribution, and outliers of sensor data; Physical constraint feature fusion layer: Combining plasma physics knowledge embedding module, adaptive attention mechanism and uncertainty propagation channel, it realizes dynamic optimization of feature weights and quantifies the impact of measurement error; Multi-task output layer: integrates working parameter prediction branch, state evaluation branch and confidence evaluation unit to realize the synchronous generation of optimized control parameter values, stability scores and reliability indicators.

[0007] Preferably, the digital twin system in S4 includes: Multiphysics simulation engine: Through plasma multi-scale modeling units, cross-platform solver interfaces and real-time data assimilation modules, it realizes the dynamic fusion of electromagnetic-thermal-fluid coupling simulation and experimental data; Ion Motion Simulator: Based on first principles, an ion dynamics framework is constructed, an adaptive grid is used to track the trajectory of charged particles, and the space charge effect and collision model are integrated. Intelligent optimization decision-making unit: Combining multi-objective parameter search algorithms, constraint processing mechanisms, and parallel computing architecture, it efficiently solves for the optimal instrument operating parameters within a safe range.

[0008] Preferably, the plasma multi-scale modeling unit in the multiphysics simulation engine is implemented through the following coupling equations: Electromagnetic field governing equations: Solve the generalized Maxwell equations that include nonlinear permeability, where the current density includes conduction current, convection current and electron inertia terms, and the permeability is dynamically calculated using plasma frequency and driving frequency; Thermodynamic governing equations: The evolution of electron temperature and heavy particle temperature is solved independently using a dual-temperature model, and energy exchange is achieved through electron-ion collision terms, where radiation loss terms are calculated based on the collision radiation model; Fluid dynamics equations: Solve the modified Navier-Stokes equations, introduce a turbulent viscosity model to characterize plasma flow characteristics, and couple the Lorentz force term generated by the electromagnetic field; Cross-scale coupling term: The behavior of microscopic particles is correlated with macroscopic fluid parameters through electron-ion collision frequency. The collision frequency is calculated using the Coulomb logarithm correction formula, and its parameters are related to plasma density and temperature in real time.

[0009] Preferably, the sample introduction stage further includes the following steps: A1, Component Pre-analysis: Real-time detection of sample composition using laser-induced spectroscopy, analysis of principal and interfering elements, and generation of a two-dimensional distribution map of ionization energy-interference mode; A2, Dynamic parameter scheduling: Based on the pre-analysis results, an element-specific timing control strategy is established to implement differentiated ion lens voltage regulation and dynamic allocation of integration time; A3, Intelligent Dilution Control: Automatically triggers the cascade dilution algorithm for high matrix samples, adjusts the dilution curve through real-time signal feedback, and activates the anti-saturation protection mechanism in advance.

[0010] Preferably, the generation of the two-dimensional distribution map in A1 includes: Spectral data preprocessing: Wavelengths are calibrated using standard atomic emission lines, and background radiation is eliminated using an adaptive algorithm; Element identification and quantization: Convolutional neural networks are used to analyze feature spectral profiles, and the concentration and intensity response relationship is constructed by combining the standard addition method; Interference mode analysis: By calculating the spectral line overlap coefficient matrix and predicting double-charged ion interference, an interference correlation model between elements is established; Spectral visualization: The ionization energy, interference intensity and element concentration are correlated in three dimensions to form a two-dimensional distribution spectrum.

[0011] Preferably, when the plasma scintillation frequency is detected to exceed the threshold, a level-three emergency response mechanism is activated: Level 1 response: Automatic injection of argon stabilizer; Secondary response: Switch to backup ion optical path; Level 3 response: Triggers the self-cleaning electrode procedure and re-ignites.

[0012] This invention provides an intelligent function optimization method for inductively coupled plasma mass spectrometry. Compared with existing technologies, it has the following advantages: 1. By constructing a complete closed-loop control system, intelligent management of the entire process from data acquisition to parameter optimization is achieved. Real-time monitoring of key plasma parameters via a distributed sensor network, combined with an adaptive fuzzy PID control algorithm, enables dynamic adjustment of the instrument's operating status, significantly reducing the need for manual intervention. A deep belief network model integrates multi-source data and physical constraints to generate highly reliable optimization parameters, while a digital twin system verifies the parameter adjustment effect through virtual simulation, forming an intelligent closed loop of monitoring-control-prediction-verification. This systematic intelligent optimization scheme not only significantly improves the instrument's automation level but also ensures the stability and reliability of the analysis process, providing strong support for the accurate detection of complex samples.

[0013] 2. Through multi-scale modeling and intelligent preprocessing technology, many problems in traditional analysis are effectively solved; through laser-induced spectral pre-analysis and two-dimensional distribution map generation in the sample introduction stage, the elemental composition and interference patterns can be accurately identified, providing a scientific basis for subsequent parameter optimization; dynamic parameter scheduling and intelligent dilution control automatically adjust the working mode according to different sample characteristics, significantly improving the adaptability to high matrix samples; at the same time, multi-physics coupling simulation and ion motion simulation ensure the accuracy of parameter optimization, enabling the instrument to maintain optimal performance under various complex working conditions, and greatly improving the reliability and repeatability of analytical results.

[0014] 3. Through a three-level emergency response mechanism and a self-learning control strategy, the stability and maintainability of the instrument are significantly improved. When a plasma anomaly is detected, the system can automatically initiate a graded processing procedure, from temporary stabilization to complete recovery, effectively avoiding unplanned downtime. The adaptive control algorithm can continuously optimize parameters based on historical data and real-time status, extending the stable operating time of the equipment. In addition, the predictive maintenance function of the digital twin system can detect potential problems in advance, guide preventive maintenance, and significantly reduce operation and maintenance costs. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method steps of the present invention; Figure 2 This is a flowchart of the sample introduction stage in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 - Figure 2 This invention provides a technical solution: a method for intelligent function optimization of an inductively coupled plasma mass spectrometer, comprising the following steps: S1, Real-time monitoring of multiple parameters: Real-time acquisition of plasma torch temperature, ion lens voltage and mass spectrometer detector noise parameters through an embedded sensor array; S2, Intelligent Control and Adjustment: Adaptive fuzzy PID controller is used to dynamically adjust the power of the radio frequency generator and the carrier gas flow rate; S3, Stability Prediction Modeling: A plasma stability prediction model is established based on a deep belief network (DBN), and optimized parameters are output based on real-time acquired data. S4, Virtual Verification Optimization: Closed-loop optimization is achieved by simulating the impact of parameter adjustments on mass spectrometry resolution through a digital twin system.

[0018] In this implementation scheme, key parameters such as plasma temperature field, ion lens voltage, and detector noise are acquired in real time through a distributed sensor network. Subsequently, a fuzzy PID control algorithm with self-learning capability is used to dynamically adjust parameters such as radio frequency power, carrier gas flow rate, and lens voltage. A prediction model based on a deep belief network integrates multi-source sensor data and physical constraints to output the probability distribution of optimized parameters. Finally, multi-physics coupling simulation and ion trajectory simulation are performed through a digital twin system to verify the impact of parameter adjustment on mass spectrometry resolution. This achieves closed-loop management of the entire process from data acquisition and intelligent control to virtual verification, significantly improving the instrument's analytical performance and operational intelligence.

[0019] Specifically, the operations performed by the adaptive fuzzy PID controller in S2 include: Dynamic modeling phase: Establish a library of nonlinear mapping relationships between plasma ionization efficiency and radio frequency power; Parameter optimization stage: The proportional, integral, and derivative coefficients of the PID controller are adjusted online using the stochastic gradient descent method with a driving term; Fuzzy inference stage: When a change in the sample matrix is ​​detected, the fuzzy inference mechanism is activated to correct the control parameters.

[0020] In this embodiment, an adaptive fuzzy PID control algorithm is used to achieve intelligent dynamic adjustment of the parameters of the inductively coupled plasma mass spectrometer. During the dynamic modeling stage, a time-varying nonlinear model of RF power and ionization efficiency, as well as a multidimensional response relationship between carrier gas flow rate and stability, are established to update system characteristic parameters in real time. During the parameter optimization stage, an improved stochastic gradient descent algorithm is used to dynamically adjust the PID control parameters according to the plasma state. The proportional term responds to fluctuations, the integral term incorporates historical error attenuation weights, and the derivative term integrates noise suppression functionality. During the fuzzy inference stage, intelligent identification of sample matrix changes is used, and an adaptive correction control strategy based on a fuzzy rule base is employed to achieve a smooth transition of the operating point. This significantly improves the instrument's adaptability to different operating conditions and the accuracy of parameter adjustment, providing a reliable guarantee for the stable operation of the plasma mass spectrometer.

[0021] Specifically, the deep belief network models in S3 include: Multimodal input layer: Through a spatiotemporal feature coding module, a cross-scale feature extraction network, and a data quality verification unit, it achieves collaborative processing of the temporal characteristics, spatial distribution, and outliers of sensor data; Physical constraint feature fusion layer: Combining plasma physics knowledge embedding module, adaptive attention mechanism and uncertainty propagation channel, it realizes dynamic optimization of feature weights and quantifies the impact of measurement error; Multi-task output layer: integrates working parameter prediction branch, state evaluation branch and confidence evaluation unit to realize the synchronous generation of optimized control parameter values, stability scores and reliability indicators.

[0022] In this embodiment, an intelligent feature processing and decision-making system for an inductively coupled plasma mass spectrometer is constructed using a multimodal deep learning architecture. The multimodal input layer achieves deep extraction of the temporal characteristics and spatial distribution features of sensor data through spatiotemporal feature encoding and cross-scale analysis networks, and ensures input reliability by combining data quality verification. The physical constraint feature fusion layer uses the basic physical equations of plasma as regularization constraints for the network, dynamically optimizes feature weight allocation through an adaptive attention mechanism, and quantifies the uncertainty propagation of measurement errors. The multi-task output layer synchronously generates optimized values ​​of instrument control parameters, plasma stability scores, and result confidence assessments, forming a complete prediction-evaluation decision-making closed loop.

[0023] Specifically, the digital twin system in S4 includes: Multiphysics simulation engine: Through plasma multi-scale modeling units, cross-platform solver interfaces and real-time data assimilation modules, it realizes the dynamic fusion of electromagnetic-thermal-fluid coupling simulation and experimental data; Ion Motion Simulator: Based on first principles, an ion dynamics framework is constructed, an adaptive grid is used to track the trajectory of charged particles, and the space charge effect and collision model are integrated. Intelligent optimization decision-making unit: Combining multi-objective parameter search algorithms, constraint processing mechanisms, and parallel computing architecture, it efficiently solves for the optimal instrument operating parameters within a safe range.

[0024] In this embodiment, a multiphysics simulation engine is used to achieve cross-scale modeling of plasma by coupling electromagnetic field, thermodynamic and fluid dynamics equations, and supports hybrid solutions using commercial software and proprietary algorithms. At the same time, experimental measurements are dynamically fed back to the simulation boundary through real-time data assimilation. The ion motion simulator constructs an ion dynamics framework based on first principles, uses an adaptive grid to track the trajectory of charged particles, and accurately considers space charge effects and collision processes. The intelligent optimization decision system uses a multi-objective parameter search algorithm to simultaneously optimize key indicators such as resolution, sensitivity and stability under the condition of meeting equipment safety constraints, and accelerates the solution process with the help of a parallel computing architecture.

[0025] Specifically, the plasma multi-scale modeling unit in the multiphysics simulation engine is implemented through the following coupling equations: Electromagnetic field governing equations: Solve the generalized Maxwell equations that include nonlinear permeability, where the current density includes conduction current, convection current and electron inertia terms, and the permeability is dynamically calculated using plasma frequency and driving frequency; Thermodynamic governing equations: The evolution of electron temperature and heavy particle temperature is solved independently using a dual-temperature model, and energy exchange is achieved through electron-ion collision terms, where radiation loss terms are calculated based on the collision radiation model; Fluid dynamics equations: Solve the modified Navier-Stokes equations, introduce a turbulent viscosity model to characterize plasma flow characteristics, and couple the Lorentz force term generated by the electromagnetic field; Cross-scale coupling term: The behavior of microscopic particles is correlated with macroscopic fluid parameters through electron-ion collision frequency. The collision frequency is calculated using the Coulomb logarithm correction formula, and its parameters are related to plasma density and temperature in real time.

[0026] In this embodiment, by integrating the interaction mechanisms of electromagnetic fields, thermodynamics, and fluid dynamics, and accurately describing the physical behavior of plasma at different scales, a comprehensive digital characterization of the instrument's operating state is achieved. Among them, the unique dual-temperature model effectively solves the limitations of the traditional single-temperature assumption in high-temperature plasma environments, while the improved turbulence model significantly improves the calculation accuracy under low-pressure conditions. By establishing a fully coupled solution framework of electromagnetic fields, thermodynamics, and fluid dynamics, the system can reflect the impact of plasma parameter changes on mass spectrometry performance in real time, providing a reliable theoretical basis for the intelligent control and optimization decision-making of the instrument.

[0027] Specifically, the sample introduction stage also includes the following steps: A1, Component Pre-analysis: Real-time detection of sample composition using laser-induced spectroscopy, analysis of principal and interfering elements, and generation of a two-dimensional distribution map of ionization energy-interference mode; A2, Dynamic parameter scheduling: Based on the pre-analysis results, an element-specific timing control strategy is established to implement differentiated ion lens voltage regulation and dynamic allocation of integration time; A3, Intelligent Dilution Control: Automatically triggers the cascade dilution algorithm for high matrix samples, adjusts the dilution curve through real-time signal feedback, and activates the anti-saturation protection mechanism in advance.

[0028] In this embodiment, the analytical performance and intelligence level of the inductively coupled plasma mass spectrometer (ICP-MS) are significantly improved through a sample pretreatment optimization scheme. A complete intelligent processing chain is constructed in the sample introduction stage: firstly, real-time component analysis of the sample is achieved through laser-induced spectroscopy, generating a comprehensive spectrum that includes elemental ionization characteristics and interference modes; then, instrument parameters are dynamically adjusted based on pre-analysis data, and precise differentiated control is implemented for different elemental characteristics; at the same time, an intelligent dilution system automatically adapts to high-matrix samples to ensure signal quality. This systematic pretreatment method not only effectively solves the problems of severe sample matrix interference and reliance on experience for parameter settings in traditional analysis, but more importantly, it realizes intelligent decision-making throughout the entire process from sample introduction to parameter optimization.

[0029] Specifically, the generation of the two-dimensional distribution map in A1 includes: Spectral data preprocessing: Wavelengths are calibrated using standard atomic emission lines, and background radiation is eliminated using an adaptive algorithm; Element identification and quantization: Convolutional neural networks are used to analyze feature spectral profiles, and the concentration and intensity response relationship is constructed by combining the standard addition method; Interference mode analysis: By calculating the spectral line overlap coefficient matrix and predicting double-charged ion interference, an interference correlation model between elements is established; Spectral visualization: The ionization energy, interference intensity and element concentration are correlated in three dimensions to form a two-dimensional distribution spectrum.

[0030] In this embodiment, the two-dimensional distribution map generation technology significantly improves the sample analysis capability and data processing efficiency of the inductively coupled plasma mass spectrometer. A multi-step intelligent processing method is employed: first, the raw spectral data undergoes precise calibration and noise reduction to ensure data quality; then, deep learning algorithms are used to achieve accurate element identification and quantitative analysis; next, a comprehensive interference correlation model is established to effectively identify and eliminate various spectral line interferences; finally, complex multidimensional information is integrated into an intuitive two-dimensional distribution map. This systematic data processing flow not only greatly improves the accuracy and reliability of elemental analysis but, more importantly, achieves an intelligent transformation from raw data to decision-making basis, providing a scientific and intuitive reference for subsequent parameter optimization.

[0031] Specifically, when the plasma scintillation frequency is detected to exceed the threshold, a level-three emergency response mechanism is activated: Level 1 response: Automatic injection of argon stabilizer; Secondary response: Switch to backup ion optical path; Level 3 response: Triggers the self-cleaning electrode procedure and re-ignites.

[0032] In this embodiment, a three-level emergency response mechanism significantly enhances the self-regulation and recovery capabilities of the inductively coupled plasma mass spectrometer under abnormal operating conditions. By monitoring the plasma scintillation frequency in real time, the system intelligently judges the unstable state and adopts a progressive, graded treatment strategy: first, plasma fluctuations are quickly smoothed by injecting a specific stabilizer; when the primary response is insufficient, the backup ion path is automatically switched to maintain ion transport efficiency; finally, the system performance is completely restored through electrode self-cleaning and intelligent restart procedures. This graded response mechanism not only effectively avoids the overreaction or underreaction problems that may be caused by traditional single treatment methods, but more importantly, it establishes a complete solution from temporary stabilization to fundamental recovery, greatly reducing the risk of instrument downtime due to plasma instability.

[0033] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the intelligent functions of an inductively coupled plasma mass spectrometer, characterized in that: Includes the following steps: S1, Real-time monitoring of multiple parameters: Real-time acquisition of plasma torch temperature, ion lens voltage and mass spectrometer detector noise parameters through an embedded sensor array; S2, Intelligent Control and Adjustment: Adaptive fuzzy PID controller is used to dynamically adjust the power of the radio frequency generator and the carrier gas flow rate; S3, Stability Prediction Modeling: A plasma stability prediction model is established based on a deep belief network (DBN), and optimized parameters are output based on real-time acquired data. S4, Virtual Verification Optimization: Closed-loop optimization is achieved by simulating the impact of parameter adjustments on mass spectrometry resolution through a digital twin system.

2. The intelligent function optimization method for an inductively coupled plasma mass spectrometer according to claim 1, characterized in that: The operations performed by the adaptive fuzzy PID controller in S2 include: Dynamic modeling phase: Establish a library of nonlinear mapping relationships between plasma ionization efficiency and radio frequency power; Parameter optimization stage: The proportional, integral, and derivative coefficients of the PID controller are adjusted online using the stochastic gradient descent method with a driving term; Fuzzy inference stage: When a change in the sample matrix is ​​detected, the fuzzy inference mechanism is activated to correct the control parameters.

3. The intelligent function optimization method for an inductively coupled plasma mass spectrometer according to claim 1, characterized in that: The deep belief network model in S3 includes: Multimodal input layer: Through a spatiotemporal feature coding module, a cross-scale feature extraction network, and a data quality verification unit, it achieves collaborative processing of the temporal characteristics, spatial distribution, and outliers of sensor data; Physical constraint feature fusion layer: Combining plasma physics knowledge embedding module, adaptive attention mechanism and uncertainty propagation channel, it realizes dynamic optimization of feature weights and quantifies the impact of measurement error; Multi-task output layer: integrates working parameter prediction branch, state evaluation branch and confidence evaluation unit to realize the synchronous generation of optimized control parameter values, stability scores and reliability indicators.

4. The intelligent function optimization method for an inductively coupled plasma mass spectrometer according to claim 1, characterized in that: The digital twin system in S4 includes: Multiphysics simulation engine: Through plasma multi-scale modeling units, cross-platform solver interfaces and real-time data assimilation modules, it realizes the dynamic fusion of electromagnetic-thermal-fluid coupling simulation and experimental data; Ion Motion Simulator: Based on first principles, an ion dynamics framework is constructed, an adaptive grid is used to track the trajectory of charged particles, and the space charge effect and collision model are integrated. Intelligent optimization decision-making unit: Combining multi-objective parameter search algorithms, constraint processing mechanisms, and parallel computing architecture, it efficiently solves for the optimal instrument operating parameters within a safe range.

5. The intelligent function optimization method for an inductively coupled plasma mass spectrometer according to claim 4, characterized in that: The plasma multi-scale modeling unit in the multiphysics simulation engine is implemented through the following coupling equations: Electromagnetic field governing equations: Solve the generalized Maxwell equations that include nonlinear permeability, where the current density includes conduction current, convection current and electron inertia terms, and the permeability is dynamically calculated using plasma frequency and driving frequency; Thermodynamic governing equations: The evolution of electron temperature and heavy particle temperature is solved independently using a dual-temperature model, and energy exchange is achieved through electron-ion collision terms, where radiation loss terms are calculated based on a collision radiation model; Fluid dynamics equations: Solve the modified Navier-Stokes equations, introduce a turbulent viscosity model to characterize plasma flow characteristics, and couple the Lorentz force term generated by the electromagnetic field; Cross-scale coupling term: The microscopic particle behavior is correlated with macroscopic fluid parameters through electron-ion collision frequency, where the collision frequency is calculated using the Coulomb logarithm correction formula, and its parameters are related to plasma density and temperature in real time.

6. The intelligent function optimization method for an inductively coupled plasma mass spectrometer according to claim 1, characterized in that: The sample introduction stage also includes the following steps: A1, Component Pre-analysis: Real-time detection of sample composition using laser-induced spectroscopy, analysis of principal and interfering elements, and generation of a two-dimensional distribution map of ionization energy-interference mode; A2, Dynamic parameter scheduling: Based on the pre-analysis results, an element-specific timing control strategy is established to implement differentiated ion lens voltage regulation and dynamic allocation of integration time; A3, Intelligent Dilution Control: Automatically triggers the cascade dilution algorithm for high matrix samples, adjusts the dilution curve through real-time signal feedback, and activates the anti-saturation protection mechanism in advance.

7. The intelligent function optimization method for an inductively coupled plasma mass spectrometer according to claim 6, characterized in that: The generation of the two-dimensional distribution map in A1 includes: Spectral data preprocessing: Wavelengths are calibrated using standard atomic emission lines, and background radiation is eliminated using an adaptive algorithm; Element identification and quantization: Convolutional neural networks are used to analyze feature spectral profiles, and the concentration and intensity response relationship is constructed by combining the standard addition method; Interference mode analysis: By calculating the spectral line overlap coefficient matrix and predicting double-charged ion interference, an interference correlation model between elements is established; Spectral visualization: The ionization energy, interference intensity and element concentration are correlated in three dimensions to form a two-dimensional distribution spectrum.

8. The intelligent function optimization method for an inductively coupled plasma mass spectrometer according to claim 1, characterized in that: When the plasma scintillation frequency is detected to exceed the threshold, a Level 3 emergency response mechanism is activated: Level 1 response: Automatic injection of argon stabilizer; Secondary response: Switch to backup ion optical path; Level 3 response: Triggers the self-cleaning electrode procedure and re-ignites.

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