153results about How to "Fast analysis" patented technology

The invention relates to the technical field of ecological environment monitoring, and discloses intelligent eagle equipment and an ecological environment monitoring method, and the intelligent eagle equipment comprises a data obtaining module which is used for obtaining ecological environment images, temperature data and point cloud data of a target area at different distances through employing a multi-focal-segment fusion algorithm; the data processing module is used for performing parallel processing on the acquired data by adopting a quantum circuit algorithm; the remote transmission module is used for transmitting the collected data and the processed data to a ground control station in real time so as to make decisions and determine monitoring task requirements through the ground control station; the monitoring path planning module is used for receiving monitoring task requirements, selecting a path planning algorithm according to terrain complexity, performing path planning on a target area in combination with the collected data, and converting a path planning result into a control instruction through a ground control station; and the unmanned aerial vehicle platform comprises a composite wing unmanned aerial vehicle and a multi-rotor unmanned aerial vehicle, and is used for carrying other modules and receiving the control instruction of the ground control station to carry out flight monitoring.

The invention discloses a Petri network-based fault diagnosis method and system and a storage medium, and the method comprises the steps: S1, obtaining a time sequence signal of substation equipment, the time sequence signal comprising alarm information and accident and displacement information; s2, on the basis of the time sequence signal of the substation equipment, constructing a fault judgment rule base and setting a front part weight, a rule starting threshold value and a back part credibility; s3, establishing a fuzzy function based on the preceding component weight, the rule starting threshold and the consequent component credibility; s4, on the basis of the Petri network and the fuzzy function, a network topology model of the substation equipment is established, and the network topology model of the substation equipment comprises a fault intelligent judgment rule base; and S5, based on the network topology model of the substation equipment, obtaining a current time sequence signal of the substation equipment, and judging a fault condition of the substation equipment. By establishing the network topology model of the substation equipment, the fault diagnosis analysis speed is increased, the fault diagnosis efficiency is improved, and the diagnosis effect of the power system is optimized.

The invention discloses a method for locating the position of a software problem source code through a log and electronic equipment, and the method comprises the steps: obtaining log output points of target software, creating an index record for the log output points, detecting the source code position information of each log output point, analyzing the log content of each log output point based on the source code position information, extracting and generating characteristic values corresponding to the log content, constructing a log index database, receiving an actual running log, extracting the characteristic values of the log, performing matching in the log index database to determine the position of a source code corresponding to a log output point, and positioning the specific position of the source code according to a matching result. According to the method, the code performance and readability are improved, the method does not depend on code position information output in the log, the requirement for the log format is lowered, the code readability is enhanced, and the integrity of the log is improved by scanning the code and proposing suggestions for improving the log.

The invention discloses an AI-based customer behavior analysis and prediction system and method, and the system comprises a data collection module which is used for automatically collecting customer behavior data from a plurality of data sources; the data processing module is used for cleaning and standardizing the collected data so as to facilitate analysis; the AI model training module is used for training a machine learning model by utilizing the processed data; the prediction analysis module is used for predicting future behaviors of the customer by using the trained AI model; the user interface module is used for displaying a prediction result and allowing a user to adjust prediction parameters; the report generation module is used for automatically generating a report containing prediction information according to the selection of the user; the customer behavior data can be automatically collected from multiple data sources, the customer behavior mode can be learned and recognized through advanced data processing and the AI model training module, the prediction accuracy can be improved, the user experience can be improved, and enterprises can better adapt to market changes and customer requirements.

The invention relates to the technical field of water quality analysis, in particular to a water quality evaluation method based on data analysis, which comprises the following steps: based on a satellite remote sensing technology, analyzing water spectral characteristics by adopting a deep neural network algorithm, and generating water characteristic data. According to the method, the satellite remote sensing technology and the deep neural network are combined, the accuracy of water body spectral feature analysis is improved, particulate matter in water is classified through image recognition, the analysis speed is increased, the result accuracy is improved, environmental DNA sampling is used for evaluating the biological diversity, and basic data are generated for microbial activity analysis; a micro-fluidic chip and a fluorescence labeling method are used for improving the activity analysis sensitivity of microorganisms and algae, a support vector machine algorithm is used for improving the water quality detection accuracy, an autoregression integral moving average model and a random forest algorithm are combined for deep water quality change analysis, and a comprehensive risk assessment model is used for comprehensively assessing the water quality risk. And a basis is provided for accurate water quality management.

The invention discloses a cloud computing analysis system and method based on a high performance liquid chromatograph, and belongs to the technical field of analytical chemistry and information. The system comprises a data acquisition module, a data transmission module, a cloud storage module, a data analysis module, a user interface module and a real-time analysis and feedback module. The data acquisition module is used for acquiring data detected by the high performance liquid chromatograph; the data transmission module is used for determining a protocol adopted by data transmission; the cloud storage module is used for storing the data transmitted to the cloud; the data analysis module is used for training an intelligent clustering analysis model and inputting data for automatic data analysis; the user interface module is used for providing an interface for a user to check an analysis result; and the real-time analysis and feedback module is used for carrying out real-time analysis and judgment on the data. Automatic data analysis is realized by using a machine learning training model, and manual intervention is reduced.

The present invention provides a multifunctional agricultural robot, which includes a fertilizer and soil mixing mother bin and a driving module, wherein the fertilizer and soil mixing mother bin is installed above the driving module through a quick locking device; the driving module is a wheeled or crawler driven chassis, and the fertilizer and soil mixing mother bin includes: a rotating bucket, a closed soil conveyor belt, a human proximity safety sensor, a laser radar / RFID / visual recognition system, a fertilizer bin module, a liquid foliar fertilizer or pesticide module, a soil bin module, a fertilizer / soil mixing module, a robot control center, a soil feeding and paving device, a weighing module and a mixed fertilizer and soil feeding port. The present invention realizes the one-time completion of the whole process of trenching and soil digging, real-time nutrient detection and analysis, fertilizer distribution, fertilizer mixing, fertilization, and automatic backfilling. It can be operated in different terrains and soils such as orchards, small plots of land, tea gardens, hills, mountains, etc., which are inaccessible to large machinery. It has many advantages such as fast analysis speed, simple operation, no waste, and no pollution.

The invention provides a method for calculating the loosening and collapsing depth of top arch surrounding rock. The method comprises the steps that S1, the arch crown height a, the tunnel radius b and the boundary angle theta 1 of an equivalent four-center arc are obtained; s2, on the basis of the data obtained in the step S1, drawing is conducted through a four-center method, and an elliptical caving arch is formed; s3, carrying out stress balance analysis on the self-stabilizing surrounding rock in the vertical direction to obtain a balance equation; s4, the balance equation is analyzed, a transcendental equation only containing one unknown quantity is obtained, and the unknown quantity is the included angle theta h between the surrounding rock friction force direction of the self-stabilizing surrounding rock and the vertical direction; and S5, solving the equilibrium equation through a numerical analysis method to obtain the slump depth. The cavern top arch surrounding rock collapse cavity depth can be accurately predicted, the surrounding rock supporting force is determined, and a reasonable dynamic design basis is provided for tunnel construction penetrating through a fault fracture zone.

The invention relates to the technical field of traditional Chinese medicine detection, and particularly provides a construction method of specific chromatograms of common carpesium fruit and a preparation thereof. Comprising the following steps: by taking octadecylsilane chemically bonded silica as a filling agent and a mobile phase comprising methanol and an acid-containing aqueous solution, carrying out gradient elution, and screening through a large number of experiments to obtain a specific elution procedure, so as to obtain 13 or more common characteristic peaks, and realizing the purification of the chlorogenic acid, the caffeic acid, the chlorogenic acid, the cryptochlorogenic acid, the 1, 4-dicaffeoylquinic acid, the 1, 4-dicaffeoylquinic acid, the 1, 4-dicaffeoylquinic acid, the 1, 4-dicaffeoylquinic acid and the 1, 4-dicaffeoylquinic acid. The method can effectively separate characteristic peaks of 2, 5-dicaffeoylquinic acid, cosmosiin and carabrone, is good in peak shape, stable in base line, short in detection time, provides a basis for quality detection and control of the common carpesium fruit and the preparation thereof, can fully reflect the integrity and characteristics of the common carpesium fruit and the preparation thereof, is simple to operate, and is suitable for industrial production. The method is suitable for quality control of different preparation types of common carpesium fruit.

The invention relates to the technical field of chips, in particular to a chip x-state propagation root cause analysis method, electronic equipment and a medium, and the method comprises the steps: S1, obtaining chip IP information in a to-be-tested chip design; s2, simulation is carried out based on the to-be-tested chip design, and output ports of all minimum composition units for outputting x states and the corresponding time when the x states appear for the first time in the simulation process are recorded in a preset first list; s3, determining the minimum composition unit appearing in the x state for the first time as a first target minimum composition unit, and determining other minimum composition units in a preset first list as second target minimum composition units; s4, determining the IPn containing the second target minimum composition unit as a target IPn; and S5, obtaining the first target minimum composition unit and the waveform information of each target IPn in parallel, and carrying out x-state propagation root cause analysis. According to the invention, the chip x-state propagation root cause analysis speed is improved, and the chip verification efficiency is improved.

The invention discloses a satellite remote sensing image transmission and storage method and system for tobacco planting analysis, and relates to the technical field of image data processing. Comprising the following steps: identifying and marking abnormal images in multiple frames of collected tobacco satellite remote sensing images, and performing fuzzy processing on all images except the abnormal images in the tobacco satellite remote sensing images; according to the method, the abnormal images such as the vegetation density, the growth state and the lesion position in the tobacco satellite remote sensing image are identified and marked, and then the abnormal images are taken as boundaries to perform fuzzy processing on the non-abnormal image segments in the tobacco satellite remote sensing image, so that key details are prevented from being lost, and the accuracy of the tobacco satellite remote sensing image is improved. The transmission quantity of redundant data of the tobacco satellite remote sensing image is reduced, so that the subsequent processing of the tobacco satellite remote sensing image can be analyzed based on the abnormal image, the subsequent analysis speed of the tobacco satellite remote sensing image is improved to a certain extent, and the analysis and processing accuracy of the subsequent tobacco satellite remote sensing image is improved.

The invention discloses an underground equipment fault tracing method based on a large language model. The method comprises the steps that fault signals and fault reasons and solutions corresponding to the fault signals are arranged according to a maintenance ledger; predicting possible fault signals and corresponding fault reasons and solutions thereof; establishing a fault traceability database through the fault signals, the fault reasons and the solutions; establishing a pre-training model based on the BERT large model, and performing pre-training through the fault traceability database; establishing a fault traceability network, and accessing the fault signal of the underground equipment to the fault traceability network; the fault traceability network is used as an input end to access the trained BERT large model; and when the underground equipment sends out a fault signal, the BERT large model correspondingly outputs a fault reason and a solution. According to the method, the fault signals are extracted and analyzed through the BERT large model, the corresponding fault reasons and solutions are output, the analysis speed of the fault reasons and the output speed of the solutions are increased, and the fault processing speed is increased.

The invention discloses a regional water environment quality evaluation system and method based on artificial intelligence, and relates to the technical field of water environment evaluation.The regional water environment quality evaluation method comprises the steps that pollution detection is conducted on a river channel in a to-be-detected region through a sensor, and obtained pollution detection feature information is bound with the corresponding river channel position in the to-be-detected region; calculating a single-factor pollution index of each pollutant based on the pollution detection feature information; based on the corrected Nemerow index method, analyzing the comprehensive pollution index of each position in the river channel; constructing a pollution index change model of each position in the river channel through the comprehensive pollution index, and analyzing an association score between the river channel sections by using the pollution index change model in combination with the position information of the river channel; predicting the comprehensive pollution index; and analyzing the pollution index change value of the river channel section based on the prediction result. The accuracy of system analysis is improved; and automation of water environment quality assessment and early warning is realized.

The invention relates to the field of big data processing technology and financial science and technology, and discloses an insurance claim settlement video frame filtering method, device, equipment and medium, a to-be-filtered target video in insurance claim settlement is acquired, each frame of image in the target video is segmented into N sub-images, and N is greater than or equal to 2; performing graying processing on each sub-image to obtain a pixel difference value between each sub-image in each frame of image and the corresponding sub-image of the previous frame of image, and filtering each sub-image in each frame of image according to the pixel difference value and a preset dynamic threshold value; and recombining all the filtered sub-images to generate a new video frame image. According to the scheme of the invention, the problem that the frame filtering efficiency is low when the insurance claim settlement video frame is filtered by the existing frame filtering method is solved.

The invention discloses a method and a system for identifying years of dried orange peel by a multi-dimensional atlas model and a storage medium. The method comprises the following steps: obtaining the composition and content of flavonoid components, amino acids, organic acids and volatile components of dried orange peel in different known years; taking the composition and content of flavonoid components, amino acids, organic acids and volatile components of the dried orange peel as samples in the data set; training an orthogonal partial least square discriminant analysis model by using the data set to obtain a year OPLS-DA standard model of the dried orange peel; and inputting the composition and content of the flavonoid components, the amino acids, the organic acids and the volatile components of the to-be-identified pericarpium citri reticulatae into the pericarpium citri reticulatae year OPLS-DA standard model to obtain the year of the to-be-identified pericarpium citri reticulatae. According to the method, the fingerprint spectrum is constructed by utilizing data of flavonoid components, amino acids, organic acids and volatile components of the pericarpium citri reticulatae, and the aging year of the to-be-identified pericarpium citri reticulatae can be accurately predicted in combination with the orthogonal partial least squares discriminant analysis model.

The invention discloses an ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometer) method for determining the content of cerium in aviation gear steel. The method comprises the following steps: setting working parameters of an inductively coupled plasma emission spectrometer; determining the blank solution and the cerium standard solutions under different concentrations by using an inductively coupled plasma emission spectrometer, and drawing a cerium element concentration standard curve; determining a to-be-determined sample solution by using the inductively coupled plasma emission spectrometer, and then determining the content of the cerium element in the to-be-determined solution by combining the cerium element concentration standard curve. According to the method, the content of the cerium element in the aviation gear steel is accurately measured, meanwhile, it is guaranteed that other elements are accurately measured, no element loss is generated, and compared with a steel mill test result, a sulfuric acid-phosphoric acid fuming method, a perchloric acid fuming method and a spectrophotometric method, the difference is small, precision is high, and accuracy and reliability are achieved.

The present application belongs to the field of metal ion concentration detection, and particularly relates to a method for detecting the concentration of vanadium ions in the negative electrolyte of a vanadium battery by two-step potentiometric titration. In view of the problems of inaccurate measurement results, large errors and complicated operation of the negative electrolyte of the vanadium battery, the present application detects the concentration of vanadium ions in the negative electrolyte of the vanadium battery by two-step potentiometric titration. In the first step, the negative electrolyte is added to a solution containing excess pentavalent vanadium and sulfuric acid, and the pentavalent vanadium ions oxidize the divalent and trivalent vanadium ions in the solution into tetravalent vanadium ions, and at the same time, part of the pentavalent vanadium ions are reduced into tetravalent vanadium ions. Subsequently, the concentration of tetravalent vanadium ions in the solution is titrated with potassium permanganate to solve the problem of exposure of divalent vanadium ions to oxidation. In the second step, the concentration of vanadium ions in the negative electrolyte is directly titrated with potassium permanganate. The results prove that the present application has small detection result deviation, high precision and accuracy, and the instrument is light and portable, and is suitable for real-time monitoring of energy storage power stations.

The invention provides a spark source atomic emission spectrometry determination method for acid-soluble aluminum and inclusion aluminum, and belongs to the technical field of assessment of inclusions in steel. The spark source atomic emission spectrometry determination method for acid-soluble aluminum and inclusion aluminum comprises the following steps: establishing a calibration curve of all aluminum, then determining integral intensities of inclusion aluminum, all aluminum and acid-soluble aluminum in a sample under corresponding conditions, and calculating the intensity of the inclusion aluminum by using a difference value between the integral intensities; selecting two standard samples with adjacent strength according to the strength of aluminum inclusion in the sample, and calculating a calibration coefficient K according to the aluminum content Al% lt; the excitation rate F is calculated according to the peak integral intensity of the inclusion aluminum and the integral intensity of the total aluminum in the 0.0007% standard sample, the inclusion aluminum content Als% is equal to K * F * (IAlt-IAls), and the content of the acid-soluble aluminum is equal to the difference between the content of the total aluminum and the content of the inclusion aluminum. According to the method, the accuracy of a measurement result is effectively improved while high analysis speed, low cost and simplicity and convenience in operation are ensured.

The invention provides a method for measuring the water hardness of a seawater desalination project, which comprises the following steps: calibrating an electrode by using standard solutions with different calcium and magnesium ion concentrations to obtain standard curves in different concentration ranges, with potential values in the standard curves as ordinates and concentrations as abscissas; and inserting the calibrated water hardness composite electrode into a to-be-detected seawater desalination engineering water quality sample, and calculating the hardness concentration of the to-be-detected seawater desalination engineering water quality sample through the potential value. The hardness concentration of the water quality sample of the seawater desalination project to be measured is measured by using the water hardness composite electrode, so that the problem of hidden dangers in the aspects of safety, quality and environmental protection existing in measurement of the water quality hardness of the seawater desalination project by using an existing chrome black T method and an acid chrome blue K method is solved.

The application discloses an automatic analyzer for detecting aniline compounds in liquid and a determination method, and realizes automatic and rapid determination of aniline compounds in liquid samples through the design, connection and control of flow paths and structures, and the analysis processes such as automatic sampling, reagent adding, mixing reaction and spectrophotometric detection, so that reagent consumption and waste liquid discharge are reduced, and the analysis detection sensitivity, accuracy and precision are improved.

The invention belongs to the technical field of aviation fuel inerting system design, and discloses a Simulink-based airplane inerting system performance model and an analysis method thereof. The model comprises a pipeline element mathematical model, an isolating valve element mathematical model, a resistance element mathematical model, a heat exchanger element mathematical model, a double-flow control valve mathematical model and a hollow fiber membrane neural network assembly. According to the method, parameterized models of all the parts of the aircraft inerting system are established, and the models are simplified by combining engineering experience based on the technical characteristics of all the parts, so that the model analysis speed is increased while the precision is ensured; parameterized model matching of all parts of the system is established based on Simulink, the influence of parameters of all the parts on the performance of the system can be dynamically simulated with high precision, early design index distribution is facilitated, and the design iteration period is shortened; an interface model among the components is established, and the input and output cross-linking relationship of the components is identified, so that the integrity of technical index requirements of the system and the equipment is ensured; and 4, performance prediction is carried out based on existing test data by using a neural network method, and the problems of difficult performance analysis and low accuracy of the heat exchanger and the hollow fiber membrane are solved.

The invention discloses a method for detecting ferrous oxide in sintered ore, which is characterized by comprising the following steps of: (1) grinding a certain amount of sintered ore to be detected by using a grinding machine to prepare a sample to be detected with a certain granularity; (2) preparing the prepared to-be-tested sample into a to-be-tested tabletting sample through a press machine; and (3) placing the prepared tabletting sample to be measured in a sample box of an X-ray fluorescence spectrophotometer, and measuring to obtain the content of ferrous oxide. The invention provides the method for detecting the ferrous oxide in the sintered ore by adopting the X-ray fluorescence spectrophotometer, the analysis method is simple and easy to master, the analysis speed is high, and the field production requirement is met.

The present application relates to the field of biotechnology, and particularly relates to a method for identifying a cleavage site of a nucleic acid nicking enzyme. The present application discloses a method for identifying a cleavage site of a nicking enzyme, which comprises the following steps: obtaining nucleic acid sequence data before and after enzyme cleavage by using mNGS sequencing, analyzing second-generation sequencing data by using Bowite2 and samtools software, and obtaining the cleavage site of the nicking enzyme according to single-base sequencing depth and analysis. The present application does not need to determine the sequence of the nucleic acid to be cleaved, has short time consumption and low budget, and has a good application prospect.

The application relates to the technical field of power distribution control, in particular to an active power distribution network fault self-healing system and a method thereof, which comprises a data sensing module, a fault positioning module, an isolation control module, a fault self-healing module and a power supply recovery module, and realizes intelligent processing of the whole process from fault detection to power supply recovery through cooperation of the multiple modules. The system can quickly position faults based on FTU layout structure and operation data, optimizes analysis efficiency in combination with a GPU computing power dynamic distribution mechanism, and realizes accurate isolation of a fault area, self-healing repair and smooth grid recovery by using intelligent strategy generation and digital twin technology. The application effectively improves the power supply reliability, system stability and operation intelligent level of the power distribution network.

The invention provides a top arch surrounding rock collapse depth calculation method considering excavation unloading stress redistribution. The top arch surrounding rock collapse depth calculation method comprises the following steps that S1, the height of a collapse arch before excavation is calculated through a triangle formula; s2, the tunnel radius, the surrounding rock volume weight, the cohesive force and the internal friction angle are collected; and S3, stress decomposition and stress redistribution analysis are carried out after excavation, and the caving depth of the top arch surrounding rock is obtained. The cavern top arch surrounding rock collapse cavity depth can be more accurately predicted, the surrounding rock supporting force is determined, and a reasonable dynamic design basis is provided for tunnel construction penetrating through a fault fracture zone.

The invention relates to the technical field of clinical medical examination, in particular to an immunochromatography test strip for enhancing near-infrared two-region fluorescence through supramolecular host-guest interaction and a preparation method of the immunochromatography test strip. The invention provides a preparation method of an immunochromatographic test strip. The preparation method comprises the following steps: preparing a rare earth nanoparticle (RENPs) labeling solution; preparing a microsphere combination pad; preparing a sample pad; coating an antibody with a nitrocellulose membrane; and assembling and manufacturing the card. The aggregation phenomenon and partial fluorescence quenching effect caused in the surface modification process of the rare earth microspheres are reduced by utilizing an efficient surface modification strategy of a supramolecular host compound, the fluorescence luminescence property of the rare earth microspheres is effectively improved, and meanwhile, the antibody labeling efficiency of the rare earth microspheres is further improved by utilizing a supramolecular self-assembly strategy; the prepared test strip is obviously improved in analysis sensitivity, has the advantages of simplicity in operation, low cost and short detection time, and is suitable for rapid and sensitive detection of low-concentration markers in complex biological samples.

The invention relates to a method for determining the total amount of impurity elements in a natural high-purity quartz mineral, which comprises the following steps of: preparing a laser sheet of the quartz mineral, observing and photographing by using a polarizing microscope, designing an analysis test point on a picture, and carrying out point-by-point analysis and determination on a laser ablation and inductively coupled plasma spectrum according to the design point. Accurate determination of impurity elements of quartz minerals in a natural high-purity quartz raw material is realized, comprehensive analysis and evaluation are carried out, and grade characteristics of the quartz minerals in the high-purity quartz raw material are further determined. The method is suitable for analysis and determination of impurity elements in natural quartz minerals, has the advantages of low detection limit, high sensitivity, wide linear range and the like, can be applied to effective and rapid evaluation, quality monitoring and the like of high-purity quartz raw materials, and provides quartz mineral raw materials with stable grades for the high-purity quartz sand industry.

This invention discloses a method for quantitatively detecting muscle inositol in blood. It employs an electrochemical method and specifically includes the following steps: a) preparation of a series of standard working solutions; b) preparation of a blank sample solution; c) plotting a standard curve using differential pulse voltammetry (DPV) with the blank sample solution and the series of standard working solutions; d) preparation of the test sample solution; e) determination of the muscle inositol content in the test sample. Experiments have demonstrated that the method of this invention yields accurate and reliable results and can be used for clinical blood testing.

The invention belongs to the technical field of detection, and particularly relates to a method for detecting the content of tributyl phosphate. The method for detecting the content of tributyl phosphate comprises the following steps: extracting a liquid to be detected by using water, and layering to obtain an upper oil phase; and mixing the upper oil phase with isooctane, performing gas chromatography detection, and obtaining the content of tributyl phosphate according to the obtained peak area and a preset standard curve. According to the method, water is adopted as an extraction solvent, interfering substances such as phosphoric acid can be removed, TBP can be concentrated, and corrosion to a chromatographic column can be reduced due to acidity reduction. An upper-layer oil phase obtained after water extraction and isooctane are mixed and diluted, so that the viscosity of a sample can be reduced, and the separation effect on a chromatographic column can be improved; meanwhile, the blockage of a chromatographic column can be reduced, and the resolution ratio of a chromatographic peak is improved, so that the content of tributyl phosphate can be more accurately measured.

This invention relates to the field of medical data processing technology, and discloses an Alzheimer's disease prediction method, system, device, and medium, comprising: obtaining spectral data of a target group in a non-invasive manner; training an Alzheimer's disease prediction model based on the spectral data; the target group includes healthy individuals and individuals with Alzheimer's disease. This invention non-invasively obtains samples from the target group, utilizing the advantages of laser-induced breakdown spectroscopy (LAS) for simultaneous detection and analysis of all elements at high speed. It achieves qualitative or quantitative analysis of the chemical composition of material surfaces by measuring the emission spectrum of micro-plasma, accurately and rapidly obtaining spectral data of the target group. The Alzheimer's disease prediction model trained based on the spectral data can effectively assist doctors in early screening of individuals, enabling early intervention for patients with early-stage Alzheimer's disease, and has broad application prospects.