491 results about "Screening method" patented technology

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 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 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 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 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 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 invention discloses a meta-model decision-making-based intelligent prediction method for inclusion quality in an electroslag remelting process. The method comprises the following steps of: constructing a sample data set containing process and component characteristics and target variables; obtaining a first-layer basic model based on an SHAP value cumulative contribution rate screening method; a heterogeneous learner is adopted to construct a first layer structure of the stacked integrated learning model, and Bayesian is adopted to carry out adjustment and optimization; adopting logistic regression as a meta-learner to construct a second-layer structure of the stacked integrated learning model; training by adopting a five-fold cross validation strategy, and predicting the performance by using a multi-index quantitative model; and deploying the D-type inclusion prediction model in the electroslag remelting process to an actual process, collecting process parameters as input data in real time by using a multi-sensor group, performing D-type inclusion risk prediction of a corresponding heat, and outputting an inclusion risk prediction result. According to the method, the accuracy and reliability of D-type inclusion prediction can be remarkably improved.

The invention discloses a long-tail scene data screening method and device and a readable storage medium, and the method comprises the steps: inputting intelligent driving data and prompt words into a visual language model, so that the visual language model carries out the long-tail scene recognition of the intelligent driving data according to a task set by the prompt words, the recognition result is output according to an output format set by the cue word, the output format comprises long-tail scene information, the long-tail scene information comprises a long-tail scene category and a long-tail scene confidence coefficient, the long-tail scene category is an enumeration field, and the long-tail scene confidence coefficient is an enumeration field. The visual language model carries out identification item by item according to enumeration values included in the enumeration fields and outputs corresponding enumeration values; and if the type of the long-tail scene in the recognition result is a set enumeration value and the confidence coefficient of the long-tail scene is greater than a first preset confidence coefficient, taking the intelligent driving data corresponding to the recognition result as long-tail scene data. According to the method and the device, the accuracy of screening the long-tail scene data is greatly improved.

The invention discloses a key index screening method and system for investigation and monitoring of watershed agricultural non-point source pollution, and relates to the technical field of watershed agricultural non-point source pollution prevention and control. The method comprises the following steps: acquiring multi-source spatio-temporal data of a target area about watershed agricultural non-point source pollution; the multi-source spatio-temporal data comprises water quality monitoring data, geographic spatial data, yearbook statistical data and meteorological data; constructing a feature variable set and a target variable set by using a source-migration-sink theory based on the multi-source spatio-temporal data; inputting the feature variables in the feature variable set and the target variables in the target variable set into a pre-trained machine learning model to obtain a feature importance value of the contribution degree of each feature variable to each target variable; and screening out key indexes in the feature variable set according to the feature importance value. According to the method, a scientific and unified index system can be established, the monitoring cost is remarkably reduced, and the objectivity and comparability of evaluation are improved.

The invention discloses a chronic obstructive pulmonary disease patient screening system for the respiratory medicine department, particularly relates to the field of computer-aided diagnosis, and is used for solving the technical problems that an existing screening method is low in screening efficiency, low in patient adaptability and difficult to popularize on a large scale in a basic level. According to the system, a standardized patient medical event sequence is constructed by obtaining a target population medical record containing medicine distribution and disease diagnosis codes; identifying a characteristic medication and diagnosis mode before definite diagnosis of the chronic obstructive pulmonary disease based on a sequence pattern mining technology; constructing a medical knowledge graph by using the modes, obtaining a feature embedding vector representing a chronic obstructive pulmonary disease medical trajectory through graph neural network learning, and establishing a high-risk digital portrait; inputting the medical sequence of the person to be screened into a feature extraction model based on same map training, and generating an individualized risk probability prediction value by calculating the matching degree of the medical sequence and the digital portrait; and a screening result report is automatically generated, so that efficient and noninvasive early risk screening is realized.

The invention discloses an unmarked steel rail surface defect screening method based on self-supervised learning, and relates to the technical field of steel rail maintenance. Comprising the following steps: S100, acquiring steel rail surface image data and carrying out data preprocessing to generate an enhanced image pair; s200, constructing a defect screening basic feature encoder through a multi-scale visual pre-training model, and generating a final multi-scale fusion feature vector based on the enhanced image pair; and S300, constructing a dynamic pseudo tag generation unit, and calculating the cosine similarity between the final multi-scale fusion feature vector and the nearest neighbor normal sample feature vector. According to the method, a multi-scale visual pre-training framework is constructed, deep visual features representing the normal state and the abnormal state of the surface of the steel rail are automatically learned from massive original steel rail images on the premise that manual labeling is not needed, and a dynamic pseudo-label generation mechanism and a cross-scene migration adaptation unit are combined, so that the real-time performance of the system is improved. High-precision automatic screening of steel rail surface defects is achieved, and the generalization ability of the model in a complex environment is improved.

The invention relates to an industrial big data feature screening method and device, equipment, a storage medium and a program product. Comprising the following steps: acquiring multi-source data of an industrial production process to form an original feature matrix; performing preprocessing and dimension reduction processing on the original feature matrix to obtain a dimension-reduced feature subset; calculating mutual information to screen to obtain a candidate feature pool; performing importance evaluation by using a plurality of preset models to obtain a plurality of groups of importance sequences; performing superposition integration according to the multiple groups of importance sequences to obtain an integrated importance sequence; fusing the multi-stage results to obtain a comprehensive score of each feature; and performing screening processing according to the comprehensive score of each feature in the candidate feature pool to obtain a key feature set. Through multi-angle and multi-model collaborative evaluation, a complex feature relationship in industrial big data is comprehensively and effectively mined, a key feature set with a prediction value is accurately identified, and a solid foundation is laid for subsequently constructing a high-performance industrial prediction and diagnosis model.

The invention relates to a urine specific protein detection sample quality screening system and screening method. The screening method comprises the following steps: firstly, creating a urine specific protein quality model; then collecting an image of a to-be-detected sample; and finally, analyzing and evaluating the sample to be detected according to the sample quality model and the image of the sample to be detected, and converting urine colors (such as light yellow, deep yellow, hematuria and the like) into quantifiable numerical values by collecting the image of the sample (urine). Compared with traditional naked eye observation, judgment deviation caused by subjective factors such as experience and ambient light of detection personnel is avoided, and the detection result is more objective and repeatable; according to the method, the sample image is converted into quantifiable numerical values so as to carry out numerical analysis on the detection data of the color, clarity and turbidity of the sample, hematuria and urine with high turbidity in the sample are automatically screened out, manual operation steps are reduced, the detection efficiency is improved, and the method is particularly suitable for clinical or large-scale screening scenes.

The invention discloses a self-adaptive multi-dimensional evaluation high-quality scientific and technological information screening method and system, and relates to the technical field of information screening, and the method comprises the following steps: collecting data from a database by using an API interface and a web crawler technology, and establishing a multi-source data set; establishing a preprocessing data set; performing feature extraction on the preprocessed data set, and establishing a multi-dimensional feature set after standardization processing; carrying out dimension evaluation under a six-dimensional evaluation channel on the multi-dimensional feature set, and establishing a dimension score; and after the dynamic weight mapped by the dimension score is adaptively configured, weighted calculation is executed, and a scientific and technological information screening result is generated. The technical problems that in the prior art, due to the fact that the scientific and technological information screening dimension is single, the evaluation weight is fixed and different scenes are difficult to adapt, the screening result is insufficient in accuracy and comprehensiveness are solved, and the purposes of achieving self-adaptive multi-dimensional evaluation and high-quality screening of the scientific and technological information and improving the screening efficiency are achieved. And the accuracy and comprehensiveness of scientific and technological information screening are improved.

The invention discloses a screening method of insulating gas decomposition product sensing materials based on machine learning, and relates to the technical field of functional material design and gas sensing. The method provided by the invention comprises the following steps: selecting a first candidate sensing material according to an insulating gas and a decomposition product; doping elements or embedding atoms into a central cavity of the first candidate sensing material to obtain a second candidate sensing material, and constructing a candidate sensing material library; obtaining the most stable adsorption configuration and adsorption energy of gas molecules, constructing feature descriptors, forming an initial data set, and dividing a training set and a test set; constructing and training a model, and evaluating and screening out an optimal model by using a test set; predicting and screening the second candidate sensing material by using the optimal model to obtain a third candidate sensing material; and performing DFT calculation verification and performance index evaluation on the third candidate sensing material, and outputting a final candidate sensing material. The method provided by the invention is high in prediction accuracy and reliability, and a novel sensitive material can be found.

The invention discloses an automatic testing and screening method for electronic components, which relates to the technical field of electronic engineering and microelectronics and comprises the following steps: generating a multi-dimensional stress field through a temperature control module, a humidity control module and an electromagnetic interference simulation module; calling a preset extreme working condition combination sequence based on the target application scene of the component; under a dynamic coupling environment, dominant parameters and recessive parameters including a dielectric relaxation spectrum, a carrier mobility transient response and a thermoacoustic emission signal are synchronously collected; inputting the hidden parameter time sequence data into physical failure models including an electrochemical corrosion model and a thermal mechanical fatigue model; dynamically adjusting the passing threshold of the test item according to the model output; the step of applying accelerated degradation excitation to the screened component comprises the following steps of: circulating a set temperature for a set number of times; applying a bias voltage-temperature stress; and repeating the step S2 to monitor the implicit parameter drift amount, and eliminating the device with out-of-tolerance drift. The device guarantee is provided for the fields of new energy automobiles, aerospace electronics and the like.

The invention discloses a ship loading and unloading monitoring method and system based on point cloud data analysis, and relates to the technical field of point cloud data analysis, and the method comprises the steps: carrying out the point cloud data collection for ship loading and unloading, carrying out the normalization operation of a whole point cloud, constructing a point cloud deep learning model, screening a hatch point cloud subset, and calculating the number of neighborhood points of the point cloud, and density constraint is carried out on the point cloud, an outer envelope boundary rectangle based on the point cloud is used as spatial distribution region limitation, and region division is carried out based on point cloud coordinates according to the spatial distribution of the cabin. According to the method, through combination of a density constraint screening method after normalization, effective points which are relatively uniformly distributed are reserved while low-density noise points in point clouds are removed, isolated points and sparse region points which deviate from a physical structure of a hatch are eliminated through density constraint, and through a closed-loop adjustment mechanism of orthogonal verification after normal vector normalization, a high-density noise point in the point clouds is eliminated. And a pseudo-orthogonal phenomenon caused by a calculation error or an environmental influence is avoided.

The invention discloses a feature data screening method and device for power grid fault assessment, and belongs to the field of power systems, and the method comprises the steps: firstly obtaining a local bus set of a power grid and a candidate feature set corresponding to the set; performing a fault simulation experiment on the power grid according to a preset power system simulation platform to obtain characteristic quantity data corresponding to each characteristic quantity and form a characteristic quantity data set; according to a Mahalanobis distance principle and a Spearman level correlation coefficient calculation method, sorting each characteristic quantity data in the characteristic quantity data set to obtain an intermediate candidate characteristic set; screening each intermediate candidate feature in the intermediate candidate feature set by adopting an incremental feature subset strategy to obtain a plurality of final candidate features; and finally, performing fault assessment on the power grid according to each final candidate feature and the local bus set, and outputting a fault assessment result, so that the accuracy of power grid fault assessment can be improved through implementation of the method.

The invention provides a multi-element machine learning model-based cross-species lung disease characteristic gene screening method and system, an electronic system and a storage device. The method comprises the following steps of: acquiring single cell / transcriptome data related to mouse lung diseases from a public database and preprocessing the single cell / transcriptome data; training the model by adopting six machine learning algorithms and outputting a gene importance score; calculating the weight according to the model performance and normalizing the score; and integrating the cross-species scores through a weighted fusion formula, and outputting a feature gene list and a visual report. The system comprises a data acquisition and preprocessing module, a multi-element machine learning model training module, a weight calculation and normalization module, a cross-species comprehensive scoring module and a result output module. The screening accuracy, stability and generalization ability are improved through multi-algorithm integration and cross-species fusion, and the method can be widely applied to the fields of mechanism research of lung diseases, diagnosis marker development and drug target verification.

The invention provides an SAR target screening method based on binary super-dimensional calculation, and relates to the technical field of real-time target detection. The method comprises the following steps: directly carrying out real and virtual part solution and sign function quantization on I / Q dual-channel complex data of an original echo of a synthetic aperture radar (SAR) to obtain a binary fusion matrix; constructing an abnormal profile based on a preset background statistical model, and adaptively dividing the quantized data into multiple segments according to the abnormal profile; performing super-dimensional mapping on each segment through an independent random binary projection matrix, generating segmented super-vectors, and splicing the segmented super-vectors into a complete sample super-vector; and finally, target discrimination is realized by calculating the Hamming distance between the target and a pre-stored background / target prototype super vector. The quantization logic can be dynamically switched according to the target size, and the problem that a single quantization strategy is poor in adaptability is effectively solved. And through an adaptive segmentation mechanism of energy perception, the weak target characterization capability is effectively enhanced, and the screening accuracy under a strong clutter background is improved.

The invention discloses a method for automatically screening common errors in a BIM model in engineering. The method comprises the following steps: opening the BIM model, and opening an automatic screening device for the common errors of the BIM model; selecting the BIM model to be subjected to error screening from the BIM models; selecting a function module of error type screening to be executed in the BIM model common error automatic screening device, and operating the device; the device circularly traverses the selected BIM model and sequentially compares BIM model data according to a set error screening rule, if the BIM model data accords with the error screening rule, error prompt information is generated, and if the BIM model data does not accord with the error screening rule, the step is quitted; the BIM model checking efficiency is greatly improved, and meanwhile the problems that manual checking is low in efficiency, high in omission ratio and high in misjudgment rate are solved.

The application discloses a kind of general-purpose and safety performance big model training data metric and screening method. By constructing a machine learning model, a multidimensional feature vector is input, and the real comprehensive quality score is labeled for training. The machine learning model acts as a low-cost, high-efficiency proxy model, which only needs to perform feature extraction on the new data set to predict its quality score, thereby successfully replacing and bypassing the traditional process of supervised fine-tuning, benchmark evaluation and score calculation, which has high computational cost and high time consumption, achieving millisecond-level data set screening. This method can overcome the defects of weak correlation between existing data set indicators and model final performance, ignoring the trade-off between generality and security, and subjective index weight allocation, achieving objective and comprehensive evaluation of data set quality.

The embodiment of the invention discloses a key soil monitoring point screening method based on multiple machine learning methods, and the method comprises the steps: obtaining soil ecological risk values of a plurality of monitoring points in a to-be-monitored region, and values of multiple related factors; screening out a plurality of main driving factors of the soil ecological risk through a random forest method; fitting a prediction model which takes the soil ecological risk as a dependent variable and takes the multiple main driving factors as independent variables, and deleting redundant point locations in the prediction model; taking the point position deletion rate, the prediction precision of the prediction model after deletion and the space coverage uniformity of the remaining monitoring point positions as optimization objectives, and executing a Bayesian optimization algorithm to update hyper-parameters in the random forest method and prediction model fitting process; and returning to execute the random forest method according to the new hyper-parameter until the optimal target is realized. And executing the operation again according to the optimal hyper-parameter to obtain a prediction model with an optimal monitoring point position. According to the embodiment, the accuracy and representativeness of point location screening can be improved.

The invention discloses an e-commerce user screening method and device based on big data, and a medium, and relates to the technical field of big data analysis, and the method comprises the steps: extracting the spatial-temporal characteristics of video frame sequence data through a three-dimensional convolutional neural network, coding a user behavior sequence through gating circulation, obtaining a behavior vector, and carrying out the recognition of the behavior vector; fusing the behavior vector and the spatio-temporal characteristics of the video frame sequence data by using a multi-head attention layer to obtain an encrypted video characteristic vector, and extracting a user portrait label, an industry classification label and a content vertical classification label of a business order account at the same time to obtain an account attribute label; receiving the encrypted video feature vector and the account attribute tag, and performing secure aggregation by adopting a Paillier homomorphic encryption algorithm to generate a business order portrait vector; calculating a user causal effect value according to the historical user behavior data; by introducing a causal inference double-tower model, multi-dimensional feature weighted sorting is performed on a preliminary recommendation list, so that the e-commerce user screening accuracy and the system intelligence level are remarkably improved.

The invention provides a laying hen feed raw material sample screening method and system based on multi-source variability, and relates to the technical field of data processing analysis, and the method comprises the steps: obtaining multi-source attribute data and conventional component content data of raw materials, mapping the attribute data into tensor modal dimensions through multi-source variability tensor construction processing, and obtaining a multi-source variation tensor model; the component data is used as a characteristic component, and a multi-source variability tensor is obtained through decoupling and compression. Variability spectrum decomposition processing is carried out, local rank spectrum decomposition is carried out along producing areas, time and component dimensions, and a variability spectrum vector set is obtained; and identifying a candidate modeling sample set through multi-scale extremum and sparsity screening. And evaluating the contribution degree and sensitivity of the sample to a standard ileum amino acid digestibility prediction equation through a leave-one-out method and sensitivity analysis, and screening out an optimal modeling sample. According to the method, the multi-source variation information of the raw materials can be integrated, the variation spectrum is comprehensively covered with the minimum sample size, and the precision and generalization ability of the prediction model are remarkably improved.

The invention discloses a construction method and application of an animal model of Parkinson's disease, and relates to the technical field of animal models of neurodegenerative diseases. The animal model of the Parkinson's disease induces alpha-synuclein to be transmitted along an intestine-brain axis. The construction method comprises the following steps: (1) providing an SD (Sprague Dawley) rat; and (2) performing intragastric administration on the SD rat with rotenone according to the dosage of 30mg / kg / day, so as to obtain the animal model of the Parkinson's disease. The Parkinson's disease modeling method successfully simulates sequential transmission of alpha-syn along the intestine-brain axis, has the advantages of strong targeting, low death rate, endogenous pathology generation and the like, and provides a reliable platform for PD mechanism research and treatment development. According to the screening method of the medicine for treating the Parkinson's disease, the blocking effect of the medicine on PD pathology source transmission can be accurately evaluated by detecting the deposition amount of alpha-syn at multiple parts, and the defects of a traditional model are overcome; the method is suitable for research and development requirements of various drugs, and the application range is far better than that of a screening model only aiming at a single pathological link.

The invention discloses an intelligent depression screening method and system based on causal learning and an interpretable federation mechanism, relates to the technical field of depression risk assessment, and solves the problems of feature redundancy, lack of privacy protection, insufficient interpretability and insufficient interpretation credibility of a machine learning model in the prior art. The method comprises the steps of preprocessing collected original high-dimensional data, performing feature screening by utilizing a CIIG algorithm, performing training and optimization by adopting multiple classifiers to screen out a final depression classification and recognition model with the best effect, and generating explainable diagnosis output conforming to causal constraints based on a causal topological structure and an SHAP contribution value. Meanwhile, a federal causal consistency updating mechanism is adopted to carry out multi-node cooperative training and structure maintenance on the model so as to reduce input parameters and maintain or improve recognition performance, and an interpretable diagnosis mechanism based on causal topology driving improves result interpretability; and federal learning that data heterogeneous distribution fusion of multiple medical institutions and original data are not out of the local is supported.

The invention relates to the technical field of oil and gas development, in particular to a carbonate reservoir oil well parameter screening method and a geological well selection method. The carbonate reservoir oil well parameter screening method comprises the steps that dependent variable parameters and a plurality of independent variable parameters are screened out, test values of all variable parameters of a plurality of sets of known oil wells are obtained, a dimensionless regression model is established, estimated values of dimensionless regression coefficients before all the independent variable parameters are calculated through a least square method, and the estimated values of the dimensionless regression coefficients before all the independent variable parameters are calculated. And evaluating the dimensionless regression model, determining dimensionless regression coefficients conforming to evaluation as influence factors, screening out key influence factors, and determining well selection key parameters. According to the method, key well selection parameters are rapidly screened out through the dimensionless regression model, the average level of variable parameters does not need to be found, processing is more convenient, a foundation is provided for rapid well selection, the well selection efficiency is effectively improved, and meanwhile the requirement for high recovery efficiency can be met.