448 results about "Sample mass" patented technology

The invention relates to the technical field of deep learning and small sample learning, in particular to a small sample capacity training method based on deep learning, which comprises the steps of 1, cross-domain data adaptation and feature alignment, 2, meta-knowledge distillation and prototype enhancement, 3, attention-guided small sample fine adjustment, and 4, model uncertainty quantification and iterative optimization. According to the small sample capacity training method based on deep learning, through cross-domain feature alignment, meta-knowledge distillation, prototype enhancement and dynamic iterative optimization, the problems of model overfitting and weak generalization ability in a small sample scene are solved, high-precision model training when the sample size is less than or equal to 50 is realized, and the training efficiency is improved. The method is suitable for data scarce scenes such as medical images and minority language processing.

The invention provides a soil nutrient content prediction method and system, and relates to the technical field of soil nutrient circulation.The soil nutrient content prediction method comprises the steps that to-be-detected soil sample data are obtained, the to-be-detected soil sample data are input into a soil nutrient content prediction model, and prediction parameters of target nutrients in a to-be-detected soil sample are obtained; wherein the soil nutrient content prediction model is a model obtained after training of a joint random forest algorithm and a depth distribution function. And based on the prediction parameter of the target nutrient, obtaining a prediction result of the target nutrient in the to-be-detected soil sample data so as to evaluate change characteristics of the target nutrient in the current to-be-detected soil sample data. On the basis, the problems that in the prior art, in the aspect of deep soil nutrient prediction, the data quality is low, and the sample size is limited can be solved, the effectiveness and accuracy of deep soil nutrient prediction are improved, and the blank in the prior art is filled up.

The invention discloses a seismic activity parameter determination method and system based on incomplete seismic data, and relates to the technical field of engineering seismology, and the method comprises the steps: collecting seismic data, and constructing a historical directory; simulating a seismic directory according to the seismic activity parameters and the activity model; similarity comparison is carried out on the simulated seismic catalog and the historical catalog; and determining a seismic activity parameter combination according to a similarity result between the directories. According to the method, the seismic activity parameters are determined based on the incomplete seismic data, and the sample size is increased by coupling various seismic data, so that the defect of a historical seismic directory can be overcome, and the length of available data can be prolonged; a Monte Carlo method is adopted to simulate a seismic directory so as to obtain a characteristic index sample matrix, and Euclidean distance is adopted to carry out mapping similarity analysis on a historical directory and a simulated directory so as to give a parameter optimization result; the earthquake information is utilized as much as possible, and excessive constraints are avoided.

The invention provides a large model optimization method and device and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a basic large model pedestal which comprises a basic large model and a basic reward model for rewarding the output of the basic large model; forming generalization data by using high-quality samples of a plurality of professional scenes of the target domain, and performing generalization training on the basic large model to obtain a generalization large model of the target domain; using high-quality samples and non-high-quality samples of each professional scene in the target field to form professional data, and performing vertical training on the basic reward model and the generalization large model to obtain a special large model of each professional scene in the target field; and deploying the special large models to each professional scene of the target field for application. The generalization ability of the large model is improved by adopting the thought of meta-learning, the available sample size during vertical training is increased by adding the reward model, and the problem of training imbalance caused by small high-quality sample size is avoided.

The invention discloses a farmland soil sampling space optimization method based on local heterogeneity, and belongs to the field of soil sampling space optimizing.The method comprises the steps that the heterogeneity level of soil in a research area is calculated through a variable coefficient; according to the soil heterogeneity level of the research area, dividing the research area into spatial continuous sub-areas with similar soil heterogeneity level by using a heterogeneity change algorithm; and on the basis of the environment covariables, a method of combining a fuzzy c-mean classifier and a heterogeneity change algorithm is adopted, geographic and feature spaces are optimized, key sampling positions in continuous sub-regions of each space are identified, and farmland soil sampling space optimization is completed. According to the method, the problem that the influence of the soil local heterogeneity level on sample size distribution and spatial layout optimization is not considered in an existing method is solved.

The invention discloses a material structured data-oriented generation and screening method and device. The method comprises the steps of S1, acquiring material structured data; s2, according to the material structured data, judging whether treatment is needed or not by evaluating the ratio of the characteristic quantity to the sample quantity and the insight of a data set, and if treatment is needed, performing the step S3; s3, effectively generating the structural data of the material by using the depth generative model oriented to the structural data; s4, the generated samples are evaluated through multiple dimensions and weighted integration is carried out; and S5, according to an evaluation result, performing multi-dimensional high-quality sample screening by fusing domain knowledge. By adopting the technical scheme of the invention, effective generation of high-quality material structured data is realized.

The invention relates to a small sample HRRP radar target identification method, device, equipment and medium, after normalization processing is carried out on a one-dimensional high-resolution range profile sequence of an unknown target with a small sample size, radar scattering physical prior is utilized to construct a static adjacency matrix, then a dynamic adjacency matrix is generated through a feature enhancement network, and the dynamic adjacency matrix is obtained. And after the static adjacent matrix and the dynamic adjacent matrix are fused, a hierarchical adaptive meta-learning graph neural network is adopted to carry out target identification training, so that high-precision identification of a position target is realized under data of fewer samples.

The invention relates to the technical field of material physical property testing, and discloses an injectable hydrogel wound dressing tensile strength testing method, which comprises: establishing a correlation model of the mass and the maximum fracture load of a standard sample at different temperatures, and the correlation model of the initial rigidity and the temperature; measuring the mass and the actual maximum fracture load of the to-be-measured sample, and determining the actual initial rigidity of the to-be-measured sample; according to the method, the actual initial stiffness is calculated, the equivalent temperature of the test is converted by utilizing the actual initial stiffness, the standard theoretical maximum fracture load is calculated in combination with the sample mass and the corresponding model, and the performance is judged by comparing the two, so that the measurement of the geometric dimension of an irregular sample is avoided by constructing the correlation between the mass and the mechanical performance; the quality control problem of an in-situ forming material is solved, and the reliability of a judgment result in an industrial environment is further ensured by utilizing mechanical response data of the same test through an integrated temperature self-adaptive calibration mechanism.

The invention discloses a full-automatic dyeing system and a dyeing technology.The dyeing system comprises a processing cavity, an incubation area is arranged in the processing cavity, the incubation area is provided with a plurality of incubation positions, the number of the incubation positions is n, the dyeing system further comprises a transfer waiting area, the transfer waiting area is arranged in the processing cavity, and the transfer waiting area is arranged in the processing cavity; the transfer waiting area is used for placing slides to be processed and slides to be sent out after processing, before the full-automatic dyeing system starts to work, the number of the slides in the transfer waiting area is N, and N / n is larger than or equal to 1; the reagent bin is arranged in the treatment cavity, a refrigeration mechanism is arranged in the reagent bin, and a plurality of small reagents can be contained in the reagent bin. Through the arrangement of the transfer waiting area and the reagent bin, the off-machine time of workers is prolonged, samples can be continuously processed under the unattended condition at night, the sample processing amount per day is increased, and therefore the high-throughput effect is achieved.

The invention discloses a cloud top height quality inspection method and device, computing equipment and a storage medium, and belongs to the technical field of atmospheric detection and remote sensing, and the method comprises the steps: taking laser radar cloud profile data as a detection truth value, taking imaging type radiometer cloud product data as to-be-inspected data, and carrying out the quality inspection of the to-be-inspected data on the basis of the time-space matching of the two types of data; the method comprises the following steps: screening out unqualified data through a space-time uniformity test, and carrying out layer-by-layer and step-by-step analysis on qualified matching data to count a total sample size, correct detection, missing detection, false detection, a correct screening sample size and a proportion, a correct identification sample size of each cloud type including cirrus cloud and lower cloud, an error identification sample size and a corresponding cloud top height difference; therefore, complete and accurate cloud top height quality detection is realized.

The invention discloses a coal seam gas content determination method. The method comprises the following steps: collecting a coal sample and placing the coal sample in a coal sample closed desorption container for closed normal-pressure desorption; opening the corresponding branches based on the change of the gas flow; working condition flow data and corrected gas flow data in the standard state are determined based on the laminar flow sensors on the branches and measurement data of the environment temperature sensor and the environment pressure sensor, and then the loss amount is calculated; the coal sample crushing device crushes the coal sample, and the corresponding branch is opened again based on the change of the gas flow until the desorption work is stopped; based on measurement data of each sensor, the coal sample mass is calculated to finally determine the coal seam gas content. The parameters such as the temperature, the pressure and the pressure difference of fluid flowing through the laminar flow sensor are measured in real time, the flow in a standard state is obtained after calculation and correction, and the measured gas flow and volume data are more scientific and accurate through temperature and pressure compensation.

The invention relates to a bioelectrical impedance spectroscopy noninvasive blood glucose detection method, and relates to the technical field of noninvasive blood glucose detection. The method comprises the following steps: firstly, acquiring a bioelectrical impedance spectrum of the forearm of a human body by using a bioelectrical impedance sensor, then, reducing the difference of bioelectrical impedance between different subjects and sessions by using a proposed baseline correction method, and finally, expanding and balancing a sample based on an Optuna hyper-parameter optimization algorithm. Frequency and feature selection and regression model establishment are integrated into one evaluation index, and overall optimization of hyper-parameters is achieved. Specifically, the problems of small sample size and large blood glucose extreme value estimation deviation are simultaneously solved by using a data generation method based on K nearest neighbor in combination with a Clark error grid evaluation index and sample density weighting. Secondly, according to prior information of a data structure, using sparse group minimum absolute shrinkage with a weight proportion threshold value and a selection operator to simultaneously select frequency and characteristics which contribute to modeling to the greatest extent; and finally, an ensemble learning algorithm XGBoost is selected and used to establish a final model. The overall optimization method improves the robustness of the model and the accuracy of the estimation result.

The invention provides a joint optimization method for a stacking sequence and a grabbing posture of a neutron measurement system, and relates to the field of data processing. According to the method, multi-source monitoring data of a neutron measurement system are collected, shadow voxels are constructed in combination with a three-dimensional space model, a neutron resonance shadow domain is formed through clustering superposition, and a dangerous concentration cloud cluster and a safe rarefied area are marked; mapping candidate stacking positions and postures according to sample mass distribution, geometric dimensions, centroid positions and irradiation sensitive characteristics, generating a stacking mode and determining a stacking sequence; performing path risk assessment on the grabbing postures, and selecting the grabbing postures of which the crossing risk meets a threshold value; superposing micro-random jitter on the path to suppress resonance phase locking; and abnormal events are monitored in real time during operation, and a risk domain is updated to execute hierarchical unloading control, so that the instantaneous impact and damage risk is reduced. According to the technical scheme, the probability of instantaneous impact risks caused by stacking overall micro-drift and grabbing track overlapping can be effectively reduced, and therefore the safety of the system is guaranteed.

The invention provides a method for exploring performance goodness and generalization ability of various machine learning models in runoff sediment simulation. According to the method, the time sequence change trend of a river long-time sequence hydrological factor, a meteorological factor, a vegetation factor, an artificial activity factor and a runoff sediment transport change correlation factor is determined. Then identifying the correlation between each factor and the runoff sediment change, and selecting an important influence factor as an input factor of a machine learning model; the performance goodness of different machine learning models in simulation of runoff and sediment transport is evaluated, a sample data set is randomly divided to eliminate the influence of limited sample size and sample sequence on a simulation result, and each model is repeatedly trained and tested to determine the empirical risk, predictive ability and generalization ability of each model. And sorting the contribution degrees of the input factors in the runoff and sediment transport simulation process by applying a local interpretation method so as to reveal the main driving factors influencing the water and sediment change of the drainage basin.

The invention discloses an index transaction monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of transaction monitoring. The threshold range is dynamically generated based on the historical business data to adapt to the change of data distribution, the adaptive statistical test algorithm is selected in combination with the data sample size and the distribution characteristics to improve the accuracy of anomaly judgment, and personalized early warning output is performed according to the region and the business type of the auditing point. According to the technical scheme, data dynamic change and business scene diversity can be flexibly coped with, so that the technical problem that the reliability of an early warning result is reduced in the face of missing data, abnormal values and complex seasonal fluctuation, and then the real-time control and decision-making efficiency of an operator on a business operation state is influenced can be solved; the technical effects of improving the accuracy and reliability of business development index abnormity identification, reducing the false report and missing report rate, and enhancing the real-time control capability and decision-making efficiency of the operator for the business operation state are achieved.

The invention discloses a large-scale customization quality prediction method based on transfer learning. The invention designs a large-scale customization quality prediction method for solving the problems that structures among variables are difficult to excavate and the quality of products with few samples is difficult to predict due to modular production, customized assembly and inconsistent number of different product samples in large-scale customization production. The method comprises the following steps: firstly, mining a causal relationship among source domain variables by utilizing a PC causal discovery method aiming at source domain data with a richer sample size, carrying out structure migration on a part of fixed structures from a source domain to a target domain by utilizing mechanism knowledge in order to obtain the causal relationship among target domain variables, and carrying out target domain few-sample amplification through a generative adversarial network; a causal relationship is complemented in a target domain after variable amplification through a PC causal discovery method, a target domain variable causal graph structure is obtained, then a heterogeneous causal graph attention network is utilized to carry out source domain and target domain model training, and target domain quality prediction is carried out through an MAML transfer learning method with MMD distance weighting. Aiming at the characteristics of large-scale customized production of customized products, the transfer learning-based quality prediction method suitable for large-scale customized production is formed.

The invention relates to a port crude oil unloading speed regulation and control prediction method based on physical constraints, and belongs to the technical field of crude oil storage and transportation automation control, and the method comprises the following steps: S1, constructing a coupling equation of pipeline outlet pressure from an oil transfer arm to a reservoir area and storage tank flow speed; s2, constructing a physical constraint neural network model; s3, constructing a loss function formula of the physical constraint neural network model based on the coupling equation; and S4, performing adaptive training on the physical constraint neural network model based on historical data, and updating network parameters of the model. According to the method, the physical law of pipeline fluid movement is fused into the loss function in the form of constraint conditions in the model training stage, the degree of dependence of the model on the pure data sample size is remarkably reduced, and accurate regulation and control of the oil discharge rate under the complex working condition are achieved.

The invention belongs to the technical field of uncertainty probability characterization analysis, and discloses a thermoplastic composite material leading edge structure design allowable value probability characterization method and system under a small sample condition, and the method comprises the steps: defining a plurality of candidate probability distribution models; fitting each model based on the original sample data and calculating an AIC value and a BIC value; a dynamic weight factor alpha is calculated according to the sample size n, and then a hybrid information criterion HIC value is calculated; generating a plurality of sample sets through Bootstrap self-service sampling, recalculating the HIC value on each sample set, and counting the selected optimal frequency of each model; and determining an optimal probability distribution model according to the frequency, wherein the optimal probability distribution model is used for representing a design allowable value. According to the method, the dynamic weight factor alpha is introduced, AIC and BIC criteria are effectively unified, optimal balance between prediction precision and model complexity is achieved under the condition of small samples, and engineering practicability and robustness are remarkably improved.

The embodiment of the invention provides a method and device for determining the working life of a sealing ring in a high-temperature liquid environment, and the method comprises the steps: determining a simulation condition and a simulation parameter, and determining a sealing element service life test for a plurality of groups of sealing elements based on the simulation condition and the simulation parameter; testing a plurality of groups of sealing elements at a plurality of time points based on the sealing element service life test, and determining service life test data; wherein the plurality of time points are in one-to-one correspondence with the plurality of groups of sealing elements; determining a material aging formula, and determining an aging function based on the material aging formula, a particle swarm optimization algorithm and life test data; an aging characteristic threshold is determined, and a life of the seal is determined based on the aging characteristic threshold and the aging function. The embodiment of the invention adopts a group of temperature life tests to realize the evaluation of the service life of the sealing O ring, so that the sample size and the test cost are reduced.

The invention discloses a sample size calculation method and system for quantitative consistency evaluation. The method comprises the following steps: firstly, determining MDL and a clinical consistency boundary value, then collecting pre-test data, and determining a required sample size through iterative calculation. Then, verification samples are extracted from the overall data for consistency analysis, and the robustness of a calculation result of the verification sample size is simulated through Bootstrap; according to the method, MDL and least square regression are fused for the first time, a sample size formula for quantitative consistency evaluation is constructed, the reliability and effectiveness of clinical test data are ensured by providing a new sample size calculation method, more accurate sample size estimation is provided for clinical research, and therefore the quality of clinical decisions is improved.

The invention discloses a product production management and control method, device and equipment and a storage medium, and relates to the technical field of informatization management and optimization in the manufacturing industry, and the method comprises the steps: obtaining the quality parameters of a to-be-detected product batch, and calculating the sample size of the to-be-detected product batch based on the quality parameters, the quality parameters comprise a nominal defective rate, a maximum error and a confidence level; sampling products with the number corresponding to the sample size are extracted from the to-be-detected product batch for quality detection, so that a sampling quality detection result of the to-be-detected product batch is obtained; and outputting a production decision of the batch of products to be inspected based on the sampling quality inspection result, and updating the quality parameters based on the production decision. According to the method, the needed sample size is calculated through the quality parameters, and it is ensured that expected detection precision can be achieved with the minimum sample size in each time of detection. Therefore, not only is the unnecessary detection times reduced, but also the detection cost is reduced.

The invention discloses a rapid yield analysis method based on xgboost proxy model truncation sampling, and the method comprises the following steps: generating a first random sample point set, and obtaining a training set through simulation; constructing a proxy simulator, and training the proxy simulator by using the training set; generating a second random sample point set, and inputting the second random sample point set into the proxy simulator to obtain a pseudo data set; establishing a filter, determining two hyperspherical surfaces based on the pseudo data set, dividing a process parameter space, evaluating a safety coefficient and the probability that a sample point enters a real simulator, and simulating the sample point of the process parameter space according to the probability to obtain a real sample set; iteratively optimizing the hyper-spherical surface and the proxy simulator by using the real sample set until convergence to obtain a final safety coefficient; and generating a third random sample point set, carrying out truncation sampling and failure discrimination by combining a real simulator and an agent simulator, and carrying out statistics on the final yield. According to the method, the xgboost network is adopted as an agent model to reduce the simulation cost, the sample size is reduced through truncation sampling, and rapid and accurate prediction of the yield of the integrated circuit is realized.

The invention provides an oil-containing shale porosity evaluation method, and belongs to the technical field of shale oil-gas exploration and development, and the method comprises the steps: obtaining a plurality of samples of a target oil-containing shale; measuring the free hydrocarbon content of the first sample; measuring the total volume, the skeleton volume and the sample mass of the second sample; measuring the crude oil density of the region where the target oil shale sample is located; determining the porosity value of the target oil-containing shale when the target oil-containing shale does not contain oil according to the total volume and the skeleton volume of the second sample, and calculating the porosity value of the target oil-containing shale when the target oil-containing shale contains oil according to the crude oil density, the free hydrocarbon content of the first sample, the total volume of the second sample, the skeleton volume and the sample mass; and determining the porosity of the target oil-containing shale sample according to the porosity value when the oil is not contained and the porosity value when the oil is contained. According to the method provided by the invention, the accuracy of porosity evaluation is improved, and meanwhile, as the test equipment is easily carried to the well site, the requirement of instantly testing the porosity in the well site is met.

The invention discloses a method for determining porosity of a shale oil sample in an effective pore space. The method mainly solves the problem that the porosity of an effective storage space cannot be accurately measured due to cracks caused by unsmooth oil-gas separation of the existing oil-containing shale and loss of clay water. The method is characterized by comprising the following steps: S1, preparing a shale sample; s2, putting the sample into a saturated solution for self-absorption saturation, and recording the self-absorption saturation time; s3, measuring the crane scale mass of the sample in the saturated liquid and the wet sample mass of the sample full of the saturated liquid, measuring the density of the saturated liquid, and calculating the total volume of the sample; s4, performing oil removal, ventilation and dehumidification on the sample; s5, measuring the mass of a dry sample and the volume of a sample skeleton, and measuring the mass of particles and dust in the sample bag; and S6, calculating the porosity of the shale sample. According to the method, the influence caused by cracks generated by dehydration of the sample is eliminated, the effective porosity of the shale oil sample in which the clay adsorbed water is retained in situ and the pore water is removed can be accurately determined, the experimental period is short, and the accuracy is high.

The invention belongs to the technical field of medical data processing, and discloses a preoperative multi-complication risk prediction method and system based on structured clinical data, and the method comprises the steps: inputting the causal association between risk factors and complication nodes into the edge of a knowledge graph, calculating the statistical correlation between all complications, and supplementing the statistical correlation into the knowledge graph, and performing network embedding training on the knowledge graph to form a first-stage model, performing preliminary risk assessment on complications, modeling the knowledge graph in a graph neural network mode, and performing joint training with the first-stage model to form a second-stage model to output a final complication probability. According to the method, the interpretability and cross-domain consistency of the model can be improved through deep fusion of the medical knowledge graph and the multi-relational graph convolutional network, stability and calibration performance are still kept in a specialist with scarce sample size, and the problem that a traditional black box model cannot be interpreted is avoided; and the practical application value can be evaluated conveniently.

The invention discloses a CAD (Computer Aided Design) model gap defect detection method and device based on deep learning, which are mainly applied to the field of computer aided design and engineering. The method comprises the following six core steps: firstly, constructing a data set covering various typical gap defects, and expanding the sample size through a data amplification technology; then coding processing is carried out on geometric features of the model, and a deep convolutional neural network is built on the basis; and finally completing model training and gap defect detection. A corresponding device is composed of a data acquisition module, an amplification module, a geometric feature coding module, a detection analysis module and the like, and a matched computer readable storage medium stores program codes for realizing the method. According to the method, the gap defect detection precision is remarkably improved, the adaptability to the complex geometrical morphology is enhanced, and the model generalization ability is effectively improved. Popularization and application of the method can promote intelligent upgrading of a detection process, reduce dependence on experience of engineers, improve detection efficiency and provide technical support for automatic geometric cleaning.

The invention discloses a cognitive function screening system and method based on an MMSE (Minimum Mean Square Error) prediction model, and relates to the technical field of data analysis, the method comprises the following steps: collecting physical examination index data, removing missing records, adopting a multiple interpolation method for interpolation, and obtaining multiple sets of complete data sets; a continuous prediction model is constructed, MMSE continuous prediction values are obtained, and a sensitivity analysis report is generated; constructing a first-stage classification model, adaptively dividing an optimal threshold combination, and dividing a sample into a high-confidence region, a to-be-discriminated region and a low-confidence region; if the sample size of the to-be-discriminated region is higher than a preset training threshold value, constructing an enhanced feature set, and constructing a second-stage classification model; when the prediction probability reaches the optimal re-discrimination threshold value, the classification result in the first stage is corrected, and otherwise, the classification result is maintained; if not, maintaining the classification result; and integrating the classification results to obtain a final classification result of all the samples.

The invention discloses gynecological tumor sampling equipment capable of avoiding secondary pollution of samples, and belongs to the technical field of gynecological tumor sampling. Gynecological tumor sampling equipment capable of avoiding secondary pollution of samples comprises a positioning device, a penetrating type penetrating hole is formed in the positioning device, a clamping groove is formed in the surface of one end of the positioning device, a clamping block matched with the clamping groove is clamped in the clamping groove, and a penetrating type fixing pipe is fixedly installed on the surface of the clamping block. Compared with the traditional sampling equipment, the gynecological tumor sampling equipment capable of avoiding secondary pollution of the sample has the advantages that the arc-shaped cutter annularly cuts a to-be-cut sample tissue by adopting rotary motion, so that the to-be-cut sample is sampled at one time by the arc-shaped cutter, and the sample is prevented from falling back due to adhesion of the sampled sample and the uncut tissue; the basic requirement of pathological detection on the sample size is met, the tumor sampling speed and efficiency are improved, the real situation of the tumor is comprehensively and accurately reflected, and the illness state of a patient is accurately judged.

The invention relates to an immunotherapy prognosis evaluation system based on combined penalty likelihood modeling, and belongs to the technical field of precision medicine. Based on a generalized linear hybrid model, various types of clinical endpoints, such as objective remission rate and disease-progression-free lifetime, are effectively integrated. By introducing the random effect, the model can capture potential correlation between end points, and the statistical effectiveness of analysis is enhanced. For the colinearity between clonality mutation features, key variables are screened through a penalty likelihood method, the influence of multiple colinearity is reduced, and the explanatory property and prediction accuracy of the model are improved. In order to overcome the challenge of limited sample size, cross-subgroup data fusion likelihood modeling is introduced, the strategy not only retains the model specificity of each subgroup, but also improves the adaptability of the model to small sample data and the prediction accuracy through information sharing among the subgroups.

The invention provides a method and a system for automatically marking text data by using a large language model, and belongs to the technical field of large language models and online public opinion monitoring and marking. According to the method and the system for automatically marking the text data by using the large language model, related personnel can more effectively analyze characteristics, trends and potential risks by generating high-quality applicable scene related data and formulate accurate monitoring and intervention measures. By increasing the sample size, the generalization ability of the machine learning model can be improved, the adaptability to different application scenes can be enhanced, and the model can respond more quickly when facing diversified bad techniques in the application scenes. According to the method and the system for automatically marking the text data by using the large language model, powerful data support can be provided for identifying, early warning and fighting against bad behaviors in an applicable scene, so that the legal rights and interests of related personnel are better protected, and the stability and the safety of the market are maintained.