690 results about "Screening method" patented technology

The invention discloses a BERT model-based resume screening method and system, and belongs to the technical field of deep learning and text classification. The method comprises the following steps of: obtaining post demand text data and job seeker resume text data, and preprocessing the post demand text data and the job seeker resume text data; based on a TFIDF-LDA model, subject term extraction and category feature expansion are carried out on the preprocessed text vector; performing word extraction vocabulary filtering on the subject word vector after category feature expansion based on N-gram of information entropy; a deep learning matching degree calculation module is combined to calculate the comprehensive matching degree of the posts and the job seekers, and the resumes of the job seekers are preliminarily screened in combination with a set threshold value; and carrying out resume text classification based on the FE-BERT model to obtain a resume screening result. According to the method, the problems that the ability of candidates is difficult to accurately evaluate and different post requirements cannot be adapted or the talent screening accuracy is low due to the reasons of inaccurate keyword matching, incapability of understanding contexts and the like in the existing resume screening method can be solved.

The invention discloses a multi-mode teenager idiopathic scoliosis screening method based on a back RGB-D image, and belongs to the technical field of image processing and scoliosis screening, and the method specifically comprises the steps: collecting an RGB-D image pair of a subject in an Adams anteflexion posture, carrying out the manual marking of an RGB color image, constructing and training a back key point detection model, and carrying out the detection of the back key point. And establishing a calculation rule of a maximum trunk rotation angle, a minimum trunk rotation angle and a back contour asymmetry index, and establishing an AIS classification rule based on the RGB-D data set, thereby completing AIS classification of the to-be-detected subject. According to the non-contact radiation-free AIS screening method, comprehensive utilization of back two-dimensional and three-dimensional information is achieved by means of data complementation of the RGB mode and the D mode, the problem of feature deficiency in a single mode is solved, and the method has the advantages of being fast, efficient, robust and objective.

The invention provides a method for predicting the quality of a sparse small sample industrial process based on a stacked network model. The method comprises the following steps: collecting end point quality report data of an industrial production process; performing hierarchical processing on the acquired data according to the missing rate, and removing abnormal data in combination with a quartile method and production experience; generating a synthetic data expansion small sample data set by adopting a conditional generative adversarial network; obtaining a first-layer basic model based on an accumulated contribution rate screening method of an SHAP value; constructing a first layer of a stacked integrated learning model and adjusting hyper-parameters by using Bayesian optimization; constructing a Ridge meta learning device to integrate the output of the basic model and constructing a second-layer network; a six-fold cross validation training model is adopted; predicting performance through a multi-index quantitative model based on the test set; and verifying the prediction precision of the end-point phosphorus content and the temperature by using real converter production data. The method can realize high-precision prediction of the end point quality index of the complex industrial generation process, and is beneficial to ensuring the product quality and improving the production efficiency.

The invention discloses an Alzheimer's disease early screening method based on magnetic resonance imaging, and relates to the technical field of magnetic resonance imaging, and the method comprises the steps: obtaining information identity data and magnetic resonance scanning image data of a to-be-detected person; preprocessing the magnetic resonance scanning image data, and calculating a brain area volume and a DTI-ALPS index according to the preprocessed magnetic resonance scanning image data; constructing a brain network and calculating global network attributes and regional node attributes according to the brain network; substituting the information identity data, the brain area volume, the DTI-ALPS index, the global network attribute and the area node attribute of the to-be-tested person into an Alzheimer's disease screening model to calculate diagnosis parameters; and comparing the diagnosis parameter with the optimal diagnosis threshold value to obtain a screening result. According to the method, the brain colloidal lymphatic network function is evaluated by means of multiple high-sensitivity nerve image indexes, and the early screening capability of the Alzheimer's disease is improved.

The invention discloses an Alzheimer's disease auxiliary screening system based on intelligent interaction, particularly relates to the field of computer-aided diagnosis, and is used for solving the problem that in an existing screening method, it is difficult to quantify cognitive performance differences of life knowledge and professional knowledge at the same time. The method comprises the following steps: constructing a task interaction group covering life knowledge and professional knowledge, collecting and coding multi-dimensional interaction operation of a testee in a task process, and generating an interaction vector matrix; further performing jump point identification, vector residual calculation and invalid operation density statistics on the interaction behavior through a preset task logic chain, and quantifying the operation deviation condition of the interaction behavior; respectively generating life and professional cognitive performance indexes based on the analysis result, and constructing a cognitive deviation evolution curve of a difference value of the life and professional cognitive performance indexes along with time change in a set period; and finally, through identifying a continuous rising trend in a cognitive deviation evolution process, auxiliary screening and early warning of asymmetric degradation of cognitive competence are realized.

The invention provides a multi-modal large model incremental training data screening method, and relates to the technical field of data processing, and the method comprises the steps: executing modal structure analysis on newly added multi-modal data, extracting each modal vector, calculating a semantic matching degree, and removing samples lower than a preset first threshold value; calculating a multi-level semantic distance between a sample embedding vector and a historical clustering center in a unified semantic space, and dividing a core semantic region sample, a boundary semantic region sample and a discrete semantic region sample according to the change rate of the multi-level semantic distance; performing semantic fine-grained alignment on the boundary semantic region samples, when multimodal unstable distribution is detected, executing local context reconstruction to repair semantic deviation, and if the multimodal unstable distribution is still unstable, removing the semantic deviation; performing multiple rounds of small-batch reasoning, calculating a semantic stability coefficient based on a semantic prediction result, and when the semantic stability coefficient is lower than a preset second threshold value, determining that the sample is a potential drift sample and removing the potential drift sample; constructing an incremental training data set; according to the method, the autonomy and accuracy of incremental training data screening are improved.

The invention discloses an intelligent literature screening method and system based on zero sample voters. The method comprises the following steps: expanding a literature set based on seed literatures, constructing a classification system comprising a plurality of zero sample voters, screening literatures related to a target topic through a majority voting mechanism, and iteratively expanding the literatures to obtain more literatures related to the target topic. The zero sample voting device integrates subject term matching, language model pre-training, multi-large language model voting and a semantic embedding vector technology, adaptively determines a threshold value in combination with an improved binary search algorithm, and realizes high-precision classification without training data. The system supports parallel processing and intelligent API management, and the processing efficiency of a large-scale literature set is remarkably improved. The method is suitable for the fields of academic research, literature retrieval and the like, can quickly construct a large-scale high-correlation literature information base, and particularly meets the retrieval requirements of emerging technologies or interdisciplinary themes.

The invention discloses microminiature needle body high-speed visual screening equipment and a screening method thereof, and belongs to the technical field of industrial automatic detection and sorting. The equipment comprises an equipment frame, a feeding module, a visual identification module, a grabbing module, a control module and a material receiving module. The feeding module is used for conveying to-be-screened needle bodies. The visual identification module performs image acquisition and model identification on the needle body; the grabbing module is provided with a plurality of grabbing clamping jaws, can move in the horizontal direction and the vertical direction under the driving of the control module, and grabs a plurality of needle bodies of the same model at the same time at a time. The material receiving module comprises a plurality of material receiving boxes and a transposition driving mechanism for driving the material receiving boxes to move so as to align the target material receiving box to the grabbing module. The equipment disclosed by the invention can be widely applied to high-efficiency sorting of micro-miniature needle bodies with the diameters ranging from 0.3 mm to 3mm and the lengths ranging from 3mm to 77mm, has high precision, high speed and high reliability, and is suitable for large-scale production and multi-variety and small-batch production requirements.

The invention discloses an early lung cancer screening method based on transfer learning. The method comprises the following steps: S1, obtaining preprocessed three-dimensional volume data; s2, generating a multi-scale enhanced feature map; s3, obtaining a preliminary micro-nodule detection result; s4, obtaining a first-stage optimization model; s5, performing second-stage training on the first-stage optimization model by adopting a double-normal-form transfer learning strategy to obtain a transfer optimization model; s6, embedding an adversarial domain adaptive module in the migration optimization model to obtain a cross-domain robust model; and S7, in a clinical reasoning stage, outputting a micro-nodule detection result for the new chest low-dose spiral CT original image data by using the cross-domain robust model. According to the invention, the model can adaptively emphasize the local gradient difference between the lung micronodule and the background texture, thereby improving the discrimination capability of the weak boundary region.

The invention discloses a grain defective product detection method combining fingerprint spectrum and finite element analysis, and particularly relates to the technical field of grain quality detection.The grain defective product detection method comprises the following steps that grain chemical and physical characteristics are extracted through a fingerprint spectrum and a finite element model, and key characteristic vectors are obtained through dimension reduction screening; on the basis of defective grain grade labels, mutual information is calculated, cross-modal analysis is carried out, and a strong dependency feature group is identified; feature sparsity and stability evaluation are combined, features are optimized by adopting a self-adaptive screening method, a food quality knowledge graph is finally constructed, and intelligent detection and quality evaluation of defective food are realized; according to the method, the accuracy of a detection model, namely a grain quality knowledge graph, is improved through feature sparsity and feature stability evaluation, an automatic grain defective product detection and recognition mechanism is constructed by utilizing a multi-modal information fusion method of fingerprint spectrum and finite element analysis and combining a mutual information analysis and dynamic feature screening algorithm, and the detection accuracy is improved. And the dependence on manual intervention and experience judgment is reduced.

The invention relates to the technical field of biological information, in particular to a translation efficiency prediction method, a screening method, a model training method and electronic equipment. The translation efficiency prediction method is realized based on a translation efficiency prediction model, and the translation efficiency prediction model comprises an embedding module, a coding module and a prediction module. The method comprises the following steps: acquiring sequence data of messenger ribonucleic acid; extracting features of the sequence data through the embedding module to obtain feature representation of messenger ribonucleic acid; the feature representation is input into the coding module, the input feature representation is subjected to convolution processing through the coding module, a convolution result is coded based on an attention mechanism, and the updated feature representation is output according to a coding result; and performing translation efficiency prediction on the updated feature representation through the prediction module to obtain a translation efficiency prediction result. According to the embodiment of the invention, the translation efficiency of the messenger ribonucleic acid can be accurately predicted with high throughput, the screening efficiency is improved, and the screening cost is reduced.

The invention relates to the technical field of artificial intelligence and thermal power production fault detection, in particular to a thermal power production fault detection data measuring point screening method and system based on a clustering algorithm, and the method comprises the steps: obtaining historical fault data of thermal power plant production equipment, and selecting related measuring points to form a multi-dimensional time series data set; and performing embedding processing on the time sequence data by using the time sequence large model to generate a feature vector with a set dimension. And then, carrying out clustering analysis on the feature vectors by adopting a clustering model to obtain a clustering result. And finally, screening out the measuring points related to the fault points of the production equipment of the thermal power plant according to a clustering result, obtaining a corresponding screening result, and providing support for equipment fault prediction and diagnosis.

The invention relates to a large law model fine tuning data screening method and device and a medium, and belongs to the technical field of artificial intelligence. Generating and forming a preference data set, training a legal quality scoring model by using the preference data set, and dynamically adjusting weights of a simple compliance sample and a complex dispute sample by using a balance focus loss function; performing quality scoring on the candidate law fine tuning data set, calculating a complexity score of the candidate law fine tuning data set, and generating a quality-complexity scoring data set of the candidate law fine tuning data set; and constructing a dependency tree of a grammar dependency relationship, performing clustering analysis on vectorized dependency tree features, and performing cluster division and sorting on candidate law fine adjustment data sets so as to merge the candidate law fine adjustment data sets into final fine adjustment sub-data sets and complete screening. The problems of semantic distortion and training deviation caused by a traditional screening method are effectively solved, and a systematic solution is provided for efficient training of a large model in the legal vertical field.

The invention provides an abnormal value screening method based on particulate matter component reconstruction, and relates to the technical field of environment monitoring. The method comprises the following steps: obtaining standard ion component data, standard carbon component data and standard inorganic element component data; obtaining a plurality of reconstruction component data; target PM2.5 data are determined; determining a doubt reconstruction component data judgment result of the plurality of reconstruction component data at each moment; determining a univariate time sequence abnormal point judgment result of each kind of reconstruction component data at each moment; determining a multivariable time sequence abnormal point judgment result of the multiple reconstruction component data at each moment; and determining whether the reconstruction component data at each moment is abnormal data. According to the method, the component reconstruction characteristics under different pollution levels can be evaluated, and the understanding of the model on the relationship among the particulate matter components is enhanced, so that the stability and accuracy of identifying whether the reconstructed component data is abnormal data are improved, the calculation efficiency is optimized, and environment monitoring auditing personnel are assisted to make decisions.

The invention discloses a primary tea grade screening method, a medium and equipment. The method comprises the following steps: acquiring original primary tea information containing a first visible light image, first near infrared spectrum information and first three-dimensional form information according to a preset sampling frequency; performing illumination compensation, geometric correction and multi-source registration processing on the original raw tea information to obtain standard raw tea information; extracting static quality parameters such as strip crimpness from the registered second visible light image, analyzing and acquiring physical and chemical quality parameters from the second near infrared spectrum information, and extracting structural features such as strip compactness from the second three-dimensional form information; finally, grade judgment is conducted through the cascade decision model, and the sorting device is controlled to execute corresponding sorting actions. Fusion analysis and intelligent grading of multi-dimensional features of the raw tea are achieved, and the sorting precision and efficiency are effectively improved.

According to the low-concentration flue gas CO2 absorbent screening method, system, equipment and medium disclosed by the embodiment of the invention, the RF for feature screening and the XGBoost for performance prediction are combined to construct a high-precision physical property prediction model, so that the dependence on experimental data is reduced; by introducing an NSGA-II algorithm of an adaptive constraint processing mechanism, weights of key features are dynamically adjusted, a global optimal solution is ensured, the accuracy of the model is effectively improved, and industrial requirements are met. Finally, the low-concentration flue gas CO2 absorbent formula which is excellent in performance and economical and feasible is screened out, an efficient absorbent design scheme is provided for industrial application, the efficiency and economical efficiency of the CO2 capturing process are improved, the industrialization process of the CO2 capturing technology is promoted, and powerful technical support is provided for achieving the purposes of carbon emission reduction and carbon neutralization.

The invention discloses an EUR prediction model construction method and system of a tight gas reservoir gas well, and relates to the field of oil and gas field development technology and artificial intelligence cross technology, and the method comprises the steps: collecting multi-source data; screening the multi-source data by adopting a three-level feature screening method, and removing redundant features; constructing a heterogeneous integrated model comprising a plurality of machine learning models, inputting the screened features into the heterogeneous integrated model to carry out EUR prediction, obtaining respective prediction errors of different machine learning models, correcting each EUR prediction result based on the prediction errors in combination with a material balance equation MBE, a yield attenuation model DCA and fracture network simulation, and carrying out EUR prediction. And carrying out weight distribution on each machine learning model after the EUR prediction result is corrected by adopting an error reciprocal weighting method to generate a final combined EUR prediction model. According to the method, an optimization strategy combining data driving and physical constraint is adopted, and the precision and reliability of EUR prediction are improved.

The invention provides a seed screening method and system based on a laser radar technology, and relates to the technical field of agricultural automation, and the method comprises the steps: extracting geometric features and defect features from optimized point cloud data; extracting mildew and insect pest spectral features from the optimized spectral data; a multi-dimensional feature set is obtained; calculating seed plumpness, roundness and surface roughness based on the optimized point cloud data; calculating a sag index and a crack feature based on point cloud edge detection; fusing the geometric features, the defect features and the pest spectral features to generate a comprehensive score; a machine learning model is utilized to execute final grading, and a first probability predictor based on laser radar features and a second probability predictor based on pest spectral features are trained respectively; and inputting the probability outputs of the two predictors into a second-layer grading model of the meta-learning architecture, and outputting a final quality grade. According to the method, efficient and accurate seed screening is realized through automatic process and algorithm design.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-mode early cognitive impairment screening method and system based on medical history information.The method comprises the steps that a first electrode plate and a second electrode plate are arranged at the forehead position and the occipital position of the brain of a user to be detected in advance; the third electrode plate and the fourth electrode plate are arranged on the left side of the brain side by side, and the fifth electrode plate and the sixth electrode plate are arranged on the right side of the brain side by side. And obtaining a disturbance coefficient set of the to-be-detected user under the reference detection mode combination, inputting the disturbance coefficient set into a pre-trained early cognitive impairment screening model, and predicting to obtain a prediction probability value of the early cognitive impairment of the to-be-detected user, and by adopting the scheme to screen the early cognitive impairment, the accuracy is high, the universality is strong, and the accuracy is high. The method can be applied to screening of people in communities, nursing homes and the like on a large scale.

The invention discloses a wolfberry screening method and system based on image analysis, and particularly relates to the technical field of wolfberry screening. Lycium barbarum samples with different growth environments, maturity, varieties and drying degrees are selected, images are shot under various illumination conditions, and data diversity is ensured; the image is preprocessed, surface texture roughness features and color distribution uniformity features are extracted, and support is provided for accurate classification; constructing a data prediction model based on the extracted features, evaluating the accuracy of the algorithm for identifying the appearance difference of the Chinese wolfberry fruits, and if the algorithm is high in accuracy, directly applying to a production line to realize automatic sorting of qualified and unqualified Chinese wolfberry fruits; and if the accuracy is low, the problem of misjudgment caused by natural differences of the Chinese wolfberry fruits is effectively solved by predicting the abnormal degree of the accuracy, dynamically adjusting the identification strategy and improving the screening precision, the accuracy and the automation level of Chinese wolfberry fruit screening are improved, the waste of high-quality Chinese wolfberry fruits is reduced, and the enterprise income and the brand reputation are remarkably improved.

The invention relates to a method for screening foot spine deformity of children and adolescents based on artificial intelligence and computer vision technologies. The method comprises the following steps: firstly, acquiring foot three-dimensional data of children and teenagers, and constructing a three-dimensional foot spine model based on the data; thirdly, processing the data of the three-dimensional foot spine model to obtain key geometric features; and calculating a malformation index by using a specific formula according to the characteristics, comparing the malformation index with a normal parameter threshold range, and marking the characteristic value as a potential malformation characteristic value if the threshold value is exceeded. And then, inputting the potential malformation characteristic value into a three-dimensional convolutional neural network to obtain a malformation type classification label. And generating a deformity degree score value by means of a clustering algorithm according to the label. And finally, inputting the score value into a trained three-dimensional foot ridge model to obtain a foot suggestion report comprising targeted suggestions. According to the method, the potential features of the foot spine deformity can be identified more accurately, the screening accuracy is greatly improved, the screening efficiency is effectively improved, and a personalized correction scheme can be formulated.

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 the field of noninvasive prenatal diagnosis, and particularly discloses a preeclampsia noninvasive screening method based on deep sequencing 8bp oligonucleotide double-fragment characteristics, which comprises the following steps: collecting preeclampsia and healthy pregnant woman peripheral blood samples, and extracting free DNA for high-throughput sequencing; the method comprises the following steps: extracting core 8-mer sequences' GTGCGCCC 'and' GATGGGGT 'in a long fragment of 150-200bp through bioinformatics analysis; an integrated support vector machine, K-nearest neighbor, extreme gradient lifting, a random forest and a multi-layer perceptron are combined with a logistic regression element classifier to construct a stacking model, the frequency of a core sequence is normalized, machine learning analysis is carried out, and the preeclampsia risk is predicted. According to the invention, two 8bp oligonucleotide characteristic fragments are specifically screened, and a deep learning architecture of multi-model fusion is combined, so that the limitations of low specificity and invasive detection of a traditional screening method are effectively broken through.

The invention discloses an AI large model-based arrival person screening method and device, a medium and equipment, and belongs to the field of screening, and the method comprises the steps of firstly obtaining screening conditions input by a user, including text content, image data and qualification information requirements, and to-be-screened arrival person data; next, performing word segmentation, keyword extraction and semantic matching on the text content of the person arrival data by utilizing a preset semantic analysis model in combination with BiLSTM, CRF and LDA technologies, and outputting a semantic score; meanwhile, note styles are recognized through the text classification model, logic judgment is conducted, and styles and logic verification scores are obtained. In addition, element detection and style verification are carried out on the image data through the multi-modal recognition model, and image matching scores are output; the qualification scoring module calculates qualification scores according to a preset weight formula. And finally, fusing the multi-dimensional scores to generate a comprehensive score, and carrying out accurate screening on the arriving persons according to the comprehensive score.

The invention relates to the field of computer vision algorithms, in particular to a defect detection data screening method based on a Pontryagin maximum principle, which comprises the following steps of: training a training data set and testing an evaluation data set to obtain a model reference index; based on a preset proxy data set, respectively calculating definition, labeling integrity and distribution deviation degree indexes to obtain an initial quality weight vector; iteratively updating the basic model parameters and reversely iteratively updating the target vector to obtain a sample quality score; a training scoring device scores and sorts full samples of the training data set; and dividing the sorted full samples into a plurality of candidate screening intervals, and screening high-quality data to train a final model. According to the method, the entropy weight method is adopted to weight the three dimensions to obtain the initial quality weight, so that the initial weight of the sample can reflect the own basic quality difference, the problem that the traditional uniform weight ignores the sample quality difference is avoided, and the accuracy of the sample quality score is further improved.

The invention discloses a visual analysis and screening method for fish maw appearance features, and relates to the technical field of computer vision and intelligent sorting, and the method comprises the steps: carrying out the polarization reflection feature analysis of a fish maw main region image, constructing a surface direction-reflection relation tensor, and carrying out the clustering to generate an anisotropic reflection texture feature pattern; gradient pyramid fusion is carried out on the anisotropic reflection texture feature map and the RGB image, contour information is extracted, and a fish maw boundary enhanced image is generated; inputting the fish maw boundary enhanced image into a contour detection network, performing automatic modeling, outputting a high-precision boundary thermodynamic diagram, and generating a high-precision boundary contour diagram; and carrying out feature fusion on the anisotropic reflection texture feature map and the high-precision boundary contour map, constructing a fish maw appearance description vector, carrying out appearance grade classification through a multi-layer neural network model in combination with a historical evaluation sample, and outputting a fish maw sample classification result. The accuracy and efficiency of fish maw quality detection are remarkably improved.

The invention relates to the technical field of information, and discloses an instruction data screening method and device, a medium and a product. The method comprises the following steps: determining a first confusion degree and a second confusion degree according to a pre-trained lightweight general language model and a target sample set; the first confusion degree is used for representing the prediction confusion degree under the instruction condition; the second confusion degree is used for representing the prediction confusion degree under the instruction-free condition; determining an information gain score according to the first confusion degree and the second confusion degree; the information gain score is used for quantifying the contribution degree of the instruction content to reduction of the response content generation difficulty; determining a training data set according to the information gain score and the target sample set; the training data set is used for improving the generalization ability and instruction following precision of the large language model in the instruction fine tuning stage. The technical problems of high deployment threshold, high cost, supervision dependence, low training efficiency and poor model generalization ability can be at least solved.

The invention relates to a high-throughput screening method and system for a self-assembled single-layer thermal interface material and a medium. The high-throughput screening method comprises the following steps: constructing an interface heat transport simulation system based on a self-assembled monomolecular layer; screening set preparation is carried out on the end group structure, and an end group screening set and a pre-calculation set are constructed; carrying out molecular dynamics simulation on the self-assembled monomolecular layer corresponding to the pre-calculation set to obtain a physical association index; training a machine learning model by taking the physical association index as a machine learning objective function; carrying out machine learning prediction on the uncalculated screening candidate data set based on the trained machine learning model, and carrying out parallel screening of physical association indexes to obtain a simulation verification set; and carrying out molecular dynamics simulation for verification. Compared with the prior art, high-throughput screening of the self-assembled single-layer thermal interface material is achieved, and the method has important significance on solid-liquid interface heat conduction characteristic regulation and control design under data driving.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a target data sample set construction and screening method, device, equipment and medium, and the method comprises the steps: obtaining a target type description, carrying out semantic extension to generate an extension description, generating a reference image sample based on an image generation model, real data units are screened through feature extraction and similarity comparison, and a target data sample set is constructed in combination with knowledge base verification. According to the method, by introducing semantic extension, reference image generation, cross-domain feature comparison and knowledge base consistency verification, real samples highly fitting target type semantics are automatically screened from massive original data, so that the manual annotation dependence is reduced, the illegal sample construction efficiency is improved, and the manual annotation time is shortened. And the training quality and the expansion capability of a subsequent detection model are enhanced.

The embodiment of the invention provides a feature screening method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining an initial feature set and a target dependent variable; taking each abnormal value threshold in the plurality of abnormal value thresholds as a current abnormal value threshold, and executing the following feature screening operation to obtain a plurality of feature sets: according to a correlation coefficient between each initial independent variable and a target dependent variable and the current abnormal value threshold, performing linear regression processing on each initial independent variable in sequence to obtain a plurality of feature sets; and obtaining a saliency probability value of each initial independent variable under the current abnormal value threshold, so as to screen the initial independent variables from the initial feature set, obtain a feature set corresponding to the current abnormal value threshold, and select a target feature set. The problem that the reliability of a prediction model is low when data containing abnormal values are used for training is solved, and the effect of improving the prediction efficiency of the model is achieved.