Inductively coupled plasma atomic emission spectrometer sampling device and method

Abstract

The invention discloses a sampling device and method for an inductively coupled plasma atomic emission spectrometer. The device comprises a dual-mode atomizer, a heater and a control box, the method comprises the following steps: 1, pretreating a sample; 2, monitoring and feeding back sample parameters after atomization; 3, optimizing sample parameters after atomization; and 4, data storage. By arranging the dual-mode atomizer, the atomization mode can be dynamically selected according to the sample type, for a low-viscosity sample, the system adopts a pneumatic atomization mode, and a liquid sample is atomized into uniform aerosol by utilizing high-pressure carrier gas; for a high-viscosity or high-salt sample, the system is automatically switched to ultrasonic atomization, the surface tension of the sample is reduced through ultrasonic vibration, and atomization is achieved; experimental parameters and monitoring data are stored in a memory, and experimental settings are continuously optimized by utilizing statistical analysis and a machine learning model, so that data support is provided for long-term experiments; and powerful evidences are provided for anomaly analysis and traceability.

Description

Technical Field

[0001] This invention belongs to the field of spectrometer technology, and in particular relates to a sample introduction device and method for an inductively coupled plasma atomic emission spectrometer. Background Technology

[0002] In traditional inductively coupled plasma atomic emission spectrometry (ICP-AES) sample introduction devices, aerosol nebulization technology is mainly used to convert liquid samples into aerosols via an atomizer, which are then transported to the plasma region by a carrier gas. However, this traditional sample introduction method has the following problems: 1. Low sample utilization: Traditional atomization technology can only effectively utilize 10%-20% of the sample, with most of the sample wasted on the tube wall or during the atomization process; 2. Uneven atomization efficiency: Due to the uneven distribution of atomized particle size, the composition of the sample entering the plasma fluctuates, affecting the stability and sensitivity of the analysis; 3. Viscous samples are difficult to handle: Injection of high-salt, viscous or particulate samples can easily lead to pipeline blockage, limiting the wide application of the system; 4. Difficulty in optimizing injection parameters: Traditional devices lack real-time monitoring and adjustment functions, making it difficult to dynamically optimize injection parameters based on sample characteristics.

[0003] Therefore, a sample introduction device is needed to solve the above problems. Summary of the Invention

[0004] The technical problem this invention aims to solve is to address the shortcomings of the prior art by providing a sample introduction device and method for inductively coupled plasma atomic emission spectrometry (ICP-AES). By setting a dual-mode nebulizer, the system can dynamically select the nebulization mode according to the sample type. For low-viscosity samples, the system uses pneumatic nebulization, employing high-pressure carrier gas to atomize the liquid sample into a uniform aerosol. For high-viscosity or high-salt samples, the system automatically switches to ultrasonic nebulization, using ultrasonic vibration to reduce the sample surface tension and achieve nebulization. Simultaneously, a heater provides a stable temperature-controlled environment for the sample, preventing crystallization of high-salt samples from affecting the nebulization effect. By storing experimental parameters and monitoring data in a memory, the system continuously optimizes experimental settings using statistical analysis and machine learning models, providing data support for long-term experiments. Furthermore, the stored historical data provides strong evidence for anomaly analysis and source tracing.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a sample introduction device and method for an inductively coupled plasma atomic emission spectrometer, characterized in that: the method includes the following steps: It includes a dual-mode atomizer, a heater disposed on one side of the dual-mode atomizer for heating the dual-mode atomizer, and a control box disposed at the bottom of the dual-mode atomizer; The dual-mode atomizer includes a pneumatic atomizer and an ultrasonic atomizer. Both the pneumatic atomizer and the ultrasonic atomizer are connected to an exhaust pipe. A mass flow meter is installed at the connection between the exhaust pipe and the pneumatic atomizer, and a mass flow meter is installed at the connection between the exhaust pipe and the ultrasonic atomizer. A temperature sensor and a pressure sensor are installed inside the exhaust pipe. A laser particle size analyzer is also connected to the exhaust pipe. The control box contains a circuit board, which integrates a controller and a memory connected to the controller. The signal output terminals of the mass flow meter, temperature sensor, pressure sensor, and laser particle size analyzer are all connected to the signal input terminal of the controller.

[0006] The above-mentioned sample introduction device for an inductively coupled plasma atomic emission spectrometer is characterized in that: a display is provided on the control box.

[0007] The present invention also provides a method for sample introduction using an inductively coupled plasma atomic emission spectrometer sample introduction device, characterized in that the method includes the following steps: Step 1, Sample Pretreatment: Determine the atomization mode to be selected based on the sample type. When the sample is a low-viscosity sample, mix the sample with high-pressure carrier gas and send it into the pneumatic atomizer. When the sample is a high-viscosity or high-salt sample, send the sample into the ultrasonic atomizer and start the heater at the same time. Step 2: Monitoring and feedback of atomized sample parameters: The laser particle size analyzer monitors the aerosol particle size distribution of the atomized sample in real time, and acquires and collects particle size range and average particle size data; the mass flow meter, temperature sensor, and pressure sensor acquire the delivery rate, temperature, and pressure parameters of the atomized sample in real time, respectively; the monitored data are processed by the fluid dynamics equations, and if the data do not meet the optimal conditions for entering the plasma, proceed to step 3; otherwise, proceed to step 4. Step 3: Optimization of sample parameters after atomization: The flow trajectory and particle size distribution of the atomized sample are calculated using a fluid dynamics model to predict the optimal sample parameters after atomization; then, the optimal parameter model is obtained using a machine learning algorithm. Combined with the obtained optimal sample parameters after atomization, the carrier gas flow rate, sample flow rate and atomizer vibration frequency are dynamically optimized to adjust the sample's operating state in the atomizer in real time. Step 4: Data storage: Synchronously store the various parameters of the atomized sample and the optimized atomized sample parameters into the memory.

[0008] The above-mentioned method for sample introduction using an inductively coupled plasma atomic emission spectrometer is characterized in that: in step two, the optimal conditions for entering the plasma are that the aerosol particle size of the atomized sample is concentrated between 1µm and 10µm, and the average aerosol particle size of the atomized sample is 5µm with RSD < 10%; the delivery rate of the atomized sample is in the range of 0.5L / min to 1L / min, the temperature of the atomized sample is 25℃, and the pressure of the atomized sample remains stable.

[0009] The above-mentioned method for sample introduction using an inductively coupled plasma atomic emission spectrometer sample introduction device is characterized in that: in step three, the selected fluid dynamics equation is... ;in, The average particle size of the sample after atomization. For liquid surface tension, For the density of the liquid, The velocity of the sample after atomization.

[0010] The above-mentioned method for sample introduction using an inductively coupled plasma atomic emission spectrometer sample introduction device is characterized in that: in step three, the model is trained and optimized using historical data stored in the memory in the machine learning algorithm to obtain the optimal parameter model.

[0011] Compared with the prior art, the present invention has the following advantages: 1. This invention, by setting a dual-mode nebulizer, can dynamically select the nebulization mode according to the sample type. For low-viscosity samples, the system adopts pneumatic nebulization, using high-pressure carrier gas to atomize the liquid sample into a uniform aerosol. For high-viscosity or high-salt samples, the system automatically switches to ultrasonic nebulization, using ultrasonic vibration to reduce the surface tension of the sample and achieve nebulization. At the same time, the heater provides a stable temperature control environment for the sample, preventing crystallization of high-salt samples from affecting the nebulization effect.

[0012] 2. This invention optimizes the parameters of the atomized sample, and the optimized aerosol is transported to the plasma torch core by a carrier gas. Due to the uniform particle size distribution and precise parameter adjustment, the aerosol is completely vaporized in the plasma, ensuring the sensitivity and stability of the analytical signal. Moreover, the entire process does not require manual intervention, and the system automatically adjusts to adapt to sample changes, improving experimental efficiency and result reliability.

[0013] 3. This invention uses a CFD model to calculate optimal atomization parameters and a PID control algorithm to optimize carrier gas flow rate, sample flow rate, and atomizer frequency in real time, significantly improving atomization efficiency and sample injection stability. Compared with existing technologies, this invention improves the uniformity of atomizer particle size distribution by approximately 30% and reduces injection error to below 1%.

[0014] 4. This invention utilizes a laser particle size analyzer to monitor particle size data in real time and combines machine learning to optimize sample injection parameters. This invention achieves significant improvements in both signal intensity and analytical stability. Test results show that the signal intensity is improved by approximately 25%, and the relative standard deviation of repeatable analyses is less than 2%.

[0015] 5. This invention optimizes experimental parameters by introducing data storage and machine learning models, reducing the need for repeated debugging in traditional experimental methods. Related experiments show that the optimized experimental procedure reduces debugging time by 50% and minimizes sample and reagent waste.

[0016] 6. This invention supports dual-mode selection of pneumatic atomization and ultrasonic atomization, and is suitable for the detection of high viscosity and complex matrix samples by combining a sample pretreatment module; tests show that the sample fit rate reaches more than 98% for industrial wastewater samples with high viscosity.

[0017] 7. The sample introduction device of the present invention combines a hydrodynamic model and real-time particle size monitoring, which significantly improves the plasma excitation efficiency; when analyzing complex samples (such as liquids containing suspended particles or high salt content), the detection limit is reduced by about 20%, which significantly improves the reliability of the analysis.

[0018] 8. This invention stores experimental parameters and monitoring data in a memory, and continuously optimizes experimental settings using statistical analysis and machine learning models, providing data support for long-term experiments; at the same time, the stored historical data provides strong evidence for anomaly analysis and source tracing.

[0019] In summary, this invention, by setting up a dual-mode nebulizer, can dynamically select the nebulization mode according to the sample type. For low-viscosity samples, the system adopts pneumatic nebulization, using high-pressure carrier gas to atomize the liquid sample into a uniform aerosol. For high-viscosity or high-salt samples, the system automatically switches to ultrasonic nebulization, using ultrasonic vibration to reduce the surface tension of the sample and achieve nebulization. At the same time, the heater provides a stable temperature control environment for the sample, preventing crystallization of high-salt samples from affecting the nebulization effect. By storing experimental parameters and monitoring data in the memory, the experimental settings are continuously optimized using statistical analysis and machine learning models, providing data support for long-term experiments. In addition, the stored historical data provides strong evidence for anomaly analysis and source tracing.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the dual-mode atomizer of the present invention.

[0022] Figure 2 This is a schematic diagram showing the connection relationship between the dual-mode atomizer and the control box of the present invention.

[0023] Figure 3 This is a circuit block diagram of the present invention.

[0024] Figure 4 This is a flowchart of the method of the present invention.

[0025] Explanation of reference numerals in the attached figures: 1—Dual-mode atomizer; 2—Control box; 3—Pneumatic atomizer; 4—Heater; 5—Laser particle size analyzer; 6—Display; 7—Mass flow meter; 8—Temperature sensor; 9—Pressure sensor; 10—Memory; 11—Ultrasonic nebulizer; 12—Exhaust pipe; 13—Controller. Detailed Implementation

[0026] like Figures 1 to 3 The sample introduction device for an inductively coupled plasma atomic emission spectrometer shown includes a dual-mode nebulizer 1, a heater 4 disposed on one side of the dual-mode nebulizer 1 for heating the dual-mode nebulizer 1, and a control box 2 disposed at the bottom of the dual-mode nebulizer 1. The dual-mode atomizer 1 includes a pneumatic atomizer 3 and an ultrasonic atomizer 11. Both the pneumatic atomizer 3 and the ultrasonic atomizer 11 are connected to an exhaust pipe 12. A mass flow meter 7 is installed at the connection between the exhaust pipe 12 and the pneumatic atomizer 3, and at the connection between the exhaust pipe 12 and the ultrasonic atomizer 11. A temperature sensor 8 and a pressure sensor 9 are installed inside the exhaust pipe 12. A laser particle size analyzer 5 is also connected to the exhaust pipe 12. The control box 2 is equipped with a circuit board, on which a controller 13 and a memory 10 connected to the controller 13 are integrated. The signal output terminals of the mass flow meter 7, the temperature sensor 8, the pressure sensor 9, and the laser particle size analyzer 5 are all connected to the signal input terminal of the controller 13.

[0027] In practical use, this invention can dynamically select the atomization mode according to the sample type by setting a dual-mode atomizer 1. For low-viscosity samples, the system adopts pneumatic atomization, using high-pressure carrier gas to atomize the liquid sample into a uniform aerosol. For high-viscosity or high-salt samples, the system automatically switches to ultrasonic atomization, using ultrasonic vibration to reduce the surface tension of the sample and achieve atomization. At the same time, the heater 4 provides a stable temperature control environment for the sample, preventing crystallization of high-salt samples from affecting the atomization effect.

[0028] This invention optimizes the parameters of the atomized sample, and the optimized aerosol is transported to the plasma torch core via a carrier gas. Due to the uniform particle size distribution and precise parameter adjustment, the aerosol is completely vaporized in the plasma, ensuring the sensitivity and stability of the analytical signal. Moreover, the entire process requires no manual intervention, and the system automatically adjusts to adapt to sample changes, improving experimental efficiency and result reliability.

[0029] This invention utilizes a CFD model to calculate optimal atomization parameters and a PID control algorithm to optimize carrier gas flow rate, sample flow rate, and nebulizer frequency in real time, significantly improving atomization efficiency and sample injection stability. Compared to existing technologies, this invention improves the uniformity of nebulizer particle size distribution by approximately 30% and reduces injection error to below 1%.

[0030] This invention utilizes a laser particle size analyzer to monitor particle size data in real time and combines machine learning to optimize sample injection parameters, resulting in significant improvements in both signal intensity and analytical stability. Test results show that the signal intensity is improved by approximately 25%, and the relative standard deviation (RSD) of repeated analyses is less than 2%.

[0031] This invention optimizes experimental parameters by introducing data storage and machine learning models, reducing the need for repeated debugging in traditional experimental methods. Related experiments show that the optimized experimental procedure reduces debugging time by 50% and minimizes sample and reagent waste.

[0032] This invention, by supporting dual-mode selection of pneumatic atomization and ultrasonic atomization, and combined with a sample pretreatment module, is suitable for the detection of high-viscosity and complex matrix samples. Tests show that the sample fit rate reaches over 98% for high-viscosity industrial wastewater samples.

[0033] The sample introduction device of this invention, combining a hydrodynamic model and real-time particle size monitoring, significantly improves plasma excitation efficiency. When analyzing complex samples (such as liquids containing suspended particles or high salinity), the detection limit is reduced by approximately 20%, significantly improving the reliability of the analysis.

[0034] This invention stores experimental parameters and monitoring data in the memory 10, and continuously optimizes the experimental setup using statistical analysis and machine learning models, providing data support for long-term experiments. Simultaneously, the stored historical data provides strong evidence for anomaly analysis and source tracing.

[0035] It should be noted that heater 4 provides a stable temperature environment for the dual-mode nebulizer 1, reducing the risk of crystallization in high-salt samples. Laser particle size analyzer 5 monitors the atomized particle size distribution in real time, ensuring the uniformity of the aerosol entering the plasma. Mass flow meter 7 monitors the liquid and carrier gas flow rates. Temperature sensor 8 and pressure sensor 9 detect the internal temperature and pressure of the sample introduction device, providing stable operating conditions.

[0036] Specifically, the memory 10 records the injection parameters, particle size distribution, and analysis results for each experiment, supporting traceability of the experimental process and subsequent optimization. Furthermore, operational data such as sample flow rate, temperature, and pressure are also integrated and stored, providing fundamental data support for further improving experimental accuracy.

[0037] In this embodiment, a display 6 is provided on the control box 2.

[0038] like Figures 1 to 4 The method for sample introduction using an inductively coupled plasma atomic emission spectrometer sample introduction device, as shown, includes the following steps: Step 1, Sample Pretreatment: Determine the atomization mode to be selected based on the sample type. When the sample is a low-viscosity sample, mix the sample with high-pressure carrier gas and send it into the pneumatic atomizer. When the sample is a high-viscosity or high-salt sample, send the sample into the ultrasonic atomizer and start the heater 4 at the same time. Step 2: Monitoring and feedback of atomized sample parameters: The laser particle size analyzer 5 monitors the aerosol particle size distribution of the atomized sample in real time, and acquires and collects particle size range and average particle size data; the mass flow meter 7, temperature sensor 8, and pressure sensor 9 acquire the delivery rate, temperature, and pressure parameters of the atomized sample in real time, respectively; the monitored data are processed by the fluid dynamics equations, and if the data do not meet the optimal conditions for entering the plasma, proceed to step 3; otherwise, proceed to step 4. Step 3: Optimization of sample parameters after atomization: The flow trajectory and particle size distribution of the atomized sample are calculated using a fluid dynamics model to predict the optimal sample parameters after atomization; then, the optimal parameter model is obtained using a machine learning algorithm. Combined with the obtained optimal sample parameters after atomization, the carrier gas flow rate, sample flow rate and atomizer vibration frequency are dynamically optimized to adjust the sample's operating state in the atomizer in real time. Step 4: Data storage: Synchronously store the various parameters of the atomized sample and the optimized atomized sample parameters into the memory 10.

[0039] In step one, for low-viscosity samples, the system employs pneumatic atomization, using high-pressure carrier gas to atomize the liquid sample into a uniform aerosol. For high-viscosity or high-salt samples, the system automatically switches to ultrasonic atomization, using ultrasonic vibration to reduce the sample's surface tension and achieve atomization. Simultaneously, the heating unit provides a stable temperature control environment for the sample, preventing crystallization of high-salt samples from affecting the atomization effect.

[0040] In step two, the atomized aerosol immediately enters the real-time monitoring and feedback module. A laser particle size analyzer measures the aerosol particle size distribution, obtaining particle size range and average particle size data. The sample flow rate monitoring unit and temperature and pressure sensors record key parameters such as sample delivery rate, temperature, and pressure. The monitoring module analyzes this data in real time to ensure that the atomized particle size distribution is uniform and meets the optimal conditions for plasma entry, preventing large droplets from entering and causing instability in the analysis signal. In step three, a fluid dynamics model is used to simulate the sample flow and particle size distribution in the atomizer. Machine learning algorithms (such as random forests or gradient boosting trees) are combined to optimize the atomization parameters based on historical and real-time monitoring data. Finally, based on closed-loop PID control, dynamic optimization is achieved by adjusting the carrier gas flow rate, sample flow rate, and atomizer frequency. When training and optimizing the model using machine learning algorithms, the model's feature variables are particle size, flow rate, carrier gas ratio, and atomizer frequency; the target variables are signal strength and analysis stability. The optimal parameter combination is predicted by fitting the feature variables and target variables using a random forest algorithm. In step four, the atomized sample parameters are synchronously stored in the memory 10, recording the injection parameters and particle size distribution data for each experiment, supporting the traceability and optimization of experimental results. The sample flow rate, temperature, pressure, and other operational data are also integrated and stored, providing fundamental data support for further improving experimental accuracy.

[0041] In practical use, the optimized aerosol is delivered to the plasma torch core via a high-pressure carrier gas. Due to the uniform particle size distribution and precise parameter adjustment, the aerosol completely vaporizes in the plasma, ensuring the sensitivity and stability of the analytical signal. The entire process requires no manual intervention; the system automatically adjusts to adapt to sample changes, improving experimental efficiency and result reliability. Simultaneously, through a remote communication interface, this sample introduction device system can connect to a cloud platform or external computer, supporting remote real-time monitoring of the experimental process. It provides a clear view of the aerosol particle size distribution and sample introduction status, allowing users to manually adjust parameters or activate the automatic optimization function based on real-time feedback data. This remote control capability not only improves operational convenience but also enables real-time diagnosis and adjustment of abnormal situations, ensuring long-term stable operation of the equipment. Through precise sample pretreatment, real-time monitoring, intelligent optimization, and remote control, the entire sample introduction process enables the system to automatically adapt to different types of samples and complex experimental conditions, significantly improving sample utilization, detection sensitivity, and experimental efficiency while reducing the need for manual intervention.

[0042] It should be noted that by calculating the optimal atomization parameters through a fluid dynamics model and optimizing the carrier gas flow rate, sample flow rate, and nebulizer frequency in real time using a PID control algorithm, this invention significantly improves atomization efficiency and sample injection stability. Compared with existing technologies, this invention improves the uniformity of nebulizer particle size distribution by approximately 30% and reduces injection error to below 1%.

[0043] By utilizing a laser particle size analyzer to monitor particle size data in real time and combining this with machine learning to optimize injection parameters, this invention achieves significant improvements in both signal intensity and analytical stability. Test results show that the signal intensity is improved by approximately 25%, and the relative standard deviation (RSD) of repeated analyses is less than 2%.

[0044] By introducing data storage and machine learning models to optimize experimental parameters, this invention reduces the need for repeated debugging in traditional experimental methods. Related experiments show that the optimized experimental procedure reduces debugging time by 50% and minimizes sample and reagent waste.

[0045] By supporting dual-mode selection of pneumatic atomization and ultrasonic atomization, and combined with a sample pretreatment module, this invention is suitable for the detection of high-viscosity and complex matrix samples. Tests show that the sample fit rate reaches over 98% for high-viscosity industrial wastewater samples.

[0046] The sample introduction device of this invention, combining a hydrodynamic model and real-time particle size monitoring, significantly improves plasma excitation efficiency. When analyzing complex samples (such as liquids containing suspended particles or high salinity), the detection limit is reduced by approximately 20%, significantly improving the reliability of the analysis.

[0047] This invention stores experimental parameters and monitoring data in data units, and continuously optimizes experimental settings using statistical analysis and machine learning models, providing data support for long-term experiments. Simultaneously, the stored historical data provides strong evidence for anomaly analysis and source tracing.

[0048] In this embodiment, in step two, the optimal conditions for entering the plasma are that the aerosol particle size of the atomized sample is concentrated between 1µm and 10µm, and the average aerosol particle size of the atomized sample is 5µm with RSD < 10%; the delivery rate of the atomized sample is in the range of 0.5L / min to 1L / min; the temperature of the atomized sample is 25℃; and the pressure of the atomized sample remains stable.

[0049] In practical use, when the data of the atomized sample meet the optimal conditions for entering the plasma, the plasma load can be made uniform and the energy balanced, and the entry of atomized sample droplets with a diameter >20µm can be avoided, which may cause signal drift or plasma extinction.

[0050] In this embodiment, in step three, the selected fluid dynamics equation is: ;in, The average particle size of the sample after atomization. For liquid surface tension, For the density of the liquid, The velocity of the sample after atomization.

[0051] In this embodiment, in step three, the machine learning algorithm uses historical data stored in memory 10 to train and optimize the model to obtain the optimal parameter model.

[0052] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A sample introduction device for an inductively coupled plasma atomic emission spectrometer, characterized in that; Includes a dual-mode atomizer (1), a heater (4) disposed on one side of the dual-mode atomizer (1) for heating the dual-mode atomizer (1), and a control box (2) disposed at the bottom of the dual-mode atomizer (1); The dual-mode atomizer (1) includes a pneumatic atomizer (3) and an ultrasonic atomizer (11). Both the pneumatic atomizer (3) and the ultrasonic atomizer (11) are connected to an exhaust pipe (12). A mass flow meter (7) is installed at the connection between the exhaust pipe (12) and the pneumatic atomizer (3), and a mass flow meter (7) is installed at the connection between the exhaust pipe (12) and the ultrasonic atomizer (11). A temperature sensor (8) and a pressure sensor (9) are installed inside the exhaust pipe (12). A laser particle size analyzer (5) is also connected to the exhaust pipe (12). The control box (2) is equipped with a circuit board, on which a controller (13) and a memory (10) connected to the controller (13) are integrated. The signal output terminals of the mass flow meter (7), the temperature sensor (8), the pressure sensor (9), and the laser particle size analyzer (5) are all connected to the signal input terminal of the controller (13).

2. The sample introduction device for an inductively coupled plasma atomic emission spectrometer according to claim 1, characterized in that: The control box (2) is equipped with a display (6).

3. The method for sample introduction in an inductively coupled plasma atomic emission spectrometer according to claim 1, characterized in that: The method includes the following steps: Step 1, Sample pretreatment: Determine the atomization mode to be selected based on the sample type. When the sample is a low viscosity sample, mix the sample with high pressure carrier gas and send it into the pneumatic atomizer. When the sample is a high viscosity or high salt sample, send the sample into the ultrasonic atomizer and start the heater (4) to heat it. Step 2, Monitoring and Feedback of Sample Parameters after Atomization: The laser particle size analyzer (5) monitors the aerosol particle size distribution of the atomized sample in real time, and acquires and collects particle size range and average particle size data; the mass flow meter (7), temperature sensor (8), and pressure sensor (9) acquire the delivery rate, temperature, and pressure parameters of the atomized sample in real time; the monitored data are processed by the fluid dynamics equation, and if the data do not meet the optimal conditions for entering the plasma, step 3 is executed; otherwise, step 4 is executed. Step 3: Optimization of sample parameters after atomization: The flow trajectory and particle size distribution of the atomized sample are calculated using a fluid dynamics model to predict the optimal sample parameters after atomization; then, the optimal parameter model is obtained using a machine learning algorithm. Combined with the obtained optimal sample parameters after atomization, the carrier gas flow rate, sample flow rate and atomizer vibration frequency are dynamically optimized to adjust the sample's operating state in the atomizer in real time. Step 4: Data storage: Synchronously store the various parameters of the atomized sample and the optimized atomized sample parameters into the memory (10).

4. The method for sample introduction in an inductively coupled plasma atomic emission spectrometer according to claim 3, characterized in that: In step two, the optimal conditions for entering the plasma are that the aerosol particle size of the atomized sample is concentrated between 1µm and 10µm, the average aerosol particle size of the atomized sample is 5µm, and the RSD is <10%; the delivery rate of the atomized sample is in the range of 0.5L / min to 1L / min, the temperature of the atomized sample is 25℃, and the pressure of the atomized sample remains stable.

5. The method for sample introduction in an inductively coupled plasma atomic emission spectrometer according to claim 3, characterized in that: In step three, the selected fluid dynamics equation is: ;in, The average particle size of the sample after atomization. For liquid surface tension, For the density of the liquid, The velocity of the sample after atomization.

6. The method for sample introduction in an inductively coupled plasma atomic emission spectrometer according to claim 3, characterized in that: In step three, the machine learning algorithm uses historical data stored in memory (10) to train and optimize the model to obtain the optimal parameter model.

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