664 results about "Logistic regression" patented technology

The invention discloses an IT asset fault propagation prediction method and system based on dynamic evolution of a knowledge graph, and relates to the technical field of cloud computing and large-scale IT operation and maintenance management. Through an asynchronous message bus and a logic clock, the knowledge graph is updated immediately when resources are abnormal and a scheduling event occurs; the knowledge graph uniformly integrates physical connection, logic dependence and multi-copy redundancy, so that the cross-machine-room asset relationship is clear at a glance. And then, based on a weighted logistic regression model, node features and relation weights in the knowledge graph are fused, the node fault probability is accurately calculated, the limitation of traditional single-dimensional analysis is solved, self-healing operation is supported, end-to-end intelligent operation and maintenance from fault detection to prediction and early warning to closed-loop self-healing are realized, and the fault detection efficiency is improved. The problems that in a cross-machine-room and multi-live-site environment, resource topology is split, real-time state and alarm information cannot be fused with an asset dependence model, and large-scale real-time deployment of a traditional single-dimensional fault analysis and high-complexity prediction algorithm is difficult are effectively solved.

The invention relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention provides a shield adaptability adjusting method based on a stratum structure, which comprises the following steps: acquiring geological parameters of a construction area of a shield tunneling machine through drilling and geophysical prospecting, and arranging sensors at key positions of the shield tunneling machine to acquire construction parameters of the shield tunneling machine in real time; a convolutional neural network CNN is adopted to analyze the geological parameters to identify stratum features, and a geological model is constructed based on the identified stratum features; constructing a disaster risk index system, and predicting a disaster risk based on the geological model and the construction parameters by adopting Logistic regression and a random forest algorithm; according to the predicted disaster risk, the cutterhead configuration of the shield tunneling machine is dynamically adjusted, and tunneling parameters are optimized or a muck improvement scheme is adopted; by monitoring geological parameters, construction parameters and environmental changes in real time, a multistage early warning mechanism is adopted to trigger emergency response measures, and construction safety is guaranteed. According to the method, the safety and efficiency of shield construction can be improved, the geological disaster risk is reduced, and reliable technical guarantee is provided for underground space development.

The invention relates to the technical field of unit detection, in particular to a unit magnetic variable on-line monitoring method and system, and the method comprises the steps: carrying out the multi-source data collection through a sensor, carrying out the correction of the multi-source data through a multi-dimensional calibration mechanism, synchronizing a timestamp through a dual-synchronization system, and carrying out the frequency-band-divided conditioning and standardization processing; environmental noise in the standardized multi-source data is eliminated through an intelligent algorithm, feature vectors are extracted, and purified feature vectors are obtained; screening effective abnormal features through an isolation forest algorithm; and inputting the effective abnormal features into an LSTM prediction model to obtain a corrected prediction value, substituting the corrected prediction value into a logistic regression formula to obtain a fault prediction probability of fault occurrence, calculating a health degree score, and performing graded early warning according to the health degree score. According to the scheme, through multi-dimensional calibration, working condition adaptive feature extraction and graded early warning, the unit monitoring data precision, the fault feature recognition accuracy and the operation and maintenance decision efficiency are improved.

The invention discloses an equipment abnormity monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent operation and maintenance of the Internet of Things, and the method comprises the steps: collecting monitoring data to generate a high-dimensional original matrix, carrying out the optimization through employing a GCN model and combining with ACO, carrying out the updating through a comparison learning model and an FCM algorithm, and carrying out the searching of global optimum through employing a VAE model and combining with a PSO algorithm. The method comprises the steps of performing classification optimization based on K-means clustering and BSO, performing MLE calculation, updating dynamic causal KG through Granger causal test, generating a multi-modal result array through NSM, a scoring formula, a naive Bayesian model, a Mahalanobis distance formula and a logistic regression model, and performing optimization by using a fuzzy rule and GWO. According to the method, the GCN model is combined with the adaptive optimization algorithm, the precision and response speed of anomaly monitoring are improved, optimization is carried out by using the fuzzy rule and introducing the GWO based on multi-modal causal reasoning, and the reliability and efficiency of anomaly monitoring are improved.

The invention discloses an on-load tap-changer vibration fault diagnosis algorithm based on tensor feature and adaptive weighted Stacking integration, relates to the technical field of on-load tap-changer fault diagnosis, and is used for improving the fault diagnosis precision. Comprising the following steps: S1, data acquisition; s2, feature extraction; the method comprises the following steps: extracting multi-scale time-frequency characteristics of an on-load tap-changer vibration signal by using wavelet scattering transform WST, and realizing low-rank decomposition and dimensionality reduction characterization of high-dimensional characteristics by combining a non-negative tensor decomposition model NTF; s3, fault diagnosis; a multi-base learner Stacking integration framework is adopted, and a prediction matrix is generated through K-fold cross validation; through a swarm intelligent optimization algorithm SRA, hyper-parameters and fusion weights of all base learners are adjusted, L2 regularization suppression over-fitting is introduced, and finally fault classification is realized by adopting a logic regression element learner with Softmax cross entropy. According to the invention, through fault diagnosis of multi-model adaptive fusion and optimization, the fault identification precision, stability and on-line monitoring capability are improved.

The invention discloses a health care service data management method based on machine learning, and relates to the technical field of data management. The method comprises the following steps: collecting health care service data in real time, constructing a health care knowledge graph, and carrying out feature dynamic alignment on the health care knowledge graph by adopting agency attention and multi-scale contrast learning to obtain a dynamic alignment feature vector; based on the dynamic alignment feature vectors, training CatBoost, XGBoost and a random forest model through Bayesian optimization, and obtaining a health prediction model through stacking generalization fusion of a logistic regression model; inputting the dynamic alignment feature vector into a health prediction model, and outputting to obtain a prediction result; corresponding service measures are executed according to the prediction result, new data are collected again after the service measures are executed, the health care knowledge graph is updated, and therefore management of health care service data is achieved.

The invention discloses a real-time power generation load estimation method and system for photovoltaic power generation, relates to the technical field of photovoltaic power generation load estimation, and aims to solve the problems of state judgment delay and model mismatching and misestimation caused by short-time cloud shadow rapid movement. The method comprises the following steps: collecting power, current, voltage, irradiance and temperature signals through a multi-array sensor, separating a high-frequency characteristic signal from a low-frequency characteristic signal through high-pass / low-pass filtering, and generating a static baseline multi-dimensional characteristic vector; based on the power spectrum entropy, adaptively adjusting a time window and extracting dynamic characteristics; fusing the static and dynamic characteristics to generate discrimination input; performing state judgment by adopting a logistic regression model, and starting extended Kalman filtering online correction when an energy residual exceeds a threshold; and model drift is detected based on CUSUM and Shewhart algorithms, and an incremental training set is automatically constructed to update model parameters. And high-precision and low-delay real-time power generation load estimation and operation and maintenance support under a short-time cloud shadow condition are realized.

The invention relates to a construction method of a metabolic syndrome phlegm syndrome lipid metabolism characteristic diagnosis model. The construction method comprises the following steps: S1, acquiring a serum sample; s2, carrying out lipid metabolite analysis through a full-quantitative lipidomics formula; s3, carrying out primary screening on differential metabolites; s4, optimizing the characteristic indexes by using a random forest algorithm and stepwise regression; and S5, constructing a differential metabolite diagnosis model through Logistic regression analysis. By analyzing differential lipid metabolites of patients with MetS phlegm syndromes and non-phlegm syndromes, the invention reveals that abnormal accumulation of lipid and lipid metabolites may be the core pathological parenchyma of MetS phlegm syndromes. By integrating lipid metabonomics data, using a random forest algorithm, stepwise regression and other methods to screen feature difference lipid metabolites and construct a MetS phlegm syndrome specific diagnosis model, a novel combined biomarker and evidence-based basis are provided for early diagnosis of MetS phlegm syndromes, and a scientific basis can also be provided for objective diagnosis of traditional Chinese medicine phlegm syndromes.

The invention discloses a high-temperature equipment fault identification method and system based on cooperative imaging, and particularly relates to the technical field of fault identification, and the method comprises the steps: building an offline ROI identification model in combination with historical data, achieving the initial ROI region configuration of high-temperature equipment before the high-temperature equipment is online, and achieving the fault identification of the high-temperature equipment through an online mechanism driven by a multi-modal image and real-time data in the operation process. And continuously and dynamically updating the ROI, performing first-order and second-order differential analysis on temperature gradients of the standard high-temperature equipment and the actual equipment in a monitoring interval based on ROI imaging data of the standard and actual high-temperature equipment, and determining image information of the ROI by using KL divergence calculation based on a kernel density estimation result of a grayscale image, geometric features and spatial positions of initial defects of the high-temperature equipment are extracted, PCA analysis and Euclidean distance calculation are combined, a logistic regression model is constructed, influence information of the ROI is determined, and the fault identification comprehensiveness and accuracy can be improved.

The invention discloses an HER2 state recognition method and system based on an IHC and HE bimodal image, and relates to the medical image processing and analysis technology, and the method comprises the steps: carrying out the preprocessing of a full-slice image WSI containing IHC and HE, and cutting the WSI into designated image blocks; constructing a multi-task learning framework to classify image blocks obtained by cutting the IHC image, and determining HER2 expression intensity in the tumor region; calculating the area ratio of the image block corresponding to each grade in the tumor region to calculate an IHC HER2 score; taking each image block of the cut HE image as the input of a Prov-GigaPath model, and aggregating the multi-dimensional features of each image block by using an ABMIL model, and taking the aggregated multi-dimensional features as an HE HER2 score; and converting the IHC HER2 score and the HE HER2 score into one-hot codes, and outputting an HER2 judgment result by using a logistic regression model. The HER2 state interpretation result can be quickly output, a pathologist is assisted in diagnosis, and the diagnosis efficiency and accuracy are improved.

The invention discloses an electrical equipment defect detection method, system and equipment based on multi-modal alignment, and particularly relates to the technical field of electrical equipment detection. Obtaining a spatial feature difference and a time feature difference between every two pieces of modal data; performing logistic regression processing on the spatial feature difference and the time feature difference to obtain an alignable coefficient between each two modal data, and determining a to-be-aligned data combination for electrical equipment defect detection based on the alignable coefficients; and performing data alignment on the to-be-aligned data combination to obtain aligned modal data, and performing defect detection analysis on the power equipment by using the time sequence model according to the aligned modal data to obtain a defect detection result of the power equipment.

The invention discloses a financial anomaly detection and risk early warning system based on Bayesian deep learning, and relates to the technical field of deep learning, and the system comprises an information contribution weight analysis unit which is used for selecting financial indexes used for measuring financial robustness from historical data of a rolling window formed by a plurality of recent continuous report periods, and outputting the selected financial indexes to a database; for each financial index, normalizing the value of the mutual information into a weight; the pressure score construction unit is used for estimating the probability of occurrence of abnormity at the current period by using a logistic regression model which introduces nonlinear logic Student-t likelihood in the same rolling window; the risk early warning unit is used for calculating potential loss of monetization; and if the monetization potential loss exceeds a set loss threshold value, the system sends out early warning information. According to the method, accurate modeling and uncertainty quantification of the abnormal probability are realized, default opening and loss rate parameters are introduced, and the risk prediction result is monetized into potential loss for dynamic early warning triggering.

The present invention relates to the field of tissue-derived cell identification, in particular to a method for identifying tissue-derived cells in the body fluid based on single-cell sequencing technology. In the present invention, on the basis of high-throughput single-cell RNA sequencing data of cell samples obtained from body fluids, by means of reference component analysis (RCA), expression of known tissue-related marker genes, and a tissue-derived cell prediction model constructed based on logistic regression, the specific identification of tissue-derived cells in body fluids is achieved.

The invention provides an electric motor coach whole vehicle OTA upgrading control method based on a BMS and a VCU, a temperature control dynamic threshold value and a multi-sensor weighted scoring algorithm are adopted, the BMS adaptively adjusts an SOC threshold value according to the battery temperature, the VCU synthesizes the vehicle speed, a hand brake and other states to generate upgrading condition evaluation, a user portrait database and a logistic regression model are combined, and the whole vehicle OTA upgrading control method based on the BMS and the VCU is obtained. Differentially pushing popup windows and predicting user cancel behaviors, implementing a block verification and low-temperature delay write-in strategy, dynamically optimizing remaining time display and low-bandwidth mode switching by a VCU, constructing a multi-stage rollback strategy library, automatically selecting a rollback path according to failure reasons, and cooperating with a safe restart mechanism of key state linkage; and in combination with NLP analysis user feedback driving system iteration, the low-temperature environment compatibility, the user interaction experience and the upgrading success rate are effectively improved, and a closed-loop optimized OTA upgrading ecological system is formed.

The invention relates to the technical field of data processing, and provides a missing data interpolation method and system based on a generative adversarial network. The method comprises the following steps: clustering a missing data matrix to obtain a clustering cluster containing a cluster label; based on the clustering cluster, performing classification prediction on a data feature vector corresponding to the missing data matrix through a logistic regression algorithm to obtain a cluster label prediction model; performing probability distribution modeling on the data feature vector through a Gaussian mixture model to obtain a probability model; training a generative adversarial network framework based on the cluster label prediction model and the probability model to obtain an interpolation model; and interpolating data to be interpolated through the interpolation model to obtain an interpolation data matrix. According to the invention, the interpolation precision and stability of nonlinear data are improved.

The invention relates to the technical field of electric energy loss analysis, and discloses a power grid electric energy loss analysis system, method and device based on machine learning and a medium. Acquiring electric energy loss data of each power transmission line of the power grid in the target area in the current monitoring period, and respectively comparing and analyzing the electric energy loss data with the electric energy loss data of each power transmission line in each historical monitoring period in the current year and the electric energy loss data of each power transmission line in each historical same period in a preset historical year period; obtaining a first electric energy loss increase evaluation index and a second electric energy loss increase evaluation index, evaluating the electric energy loss increase condition, constructing an electric energy loss discrimination vector, and discriminating whether the electric energy loss meets the standard through a logistic regression model, thereby improving the evaluation accuracy of the electric energy loss of the power transmission line. The subjectivity and uncertainty of manual judgment are reduced, a manager can quickly know the electric energy loss trend of the power transmission line, the electric energy loss of the power transmission line is reduced, and normal operation of a power grid is ensured.

The invention discloses a COPD-FE risk prediction method based on disease and symptom combination, and is applied to the technical field of chronic obstructive pulmonary disease risk prediction. Comprising the following steps: acquiring chronic obstructive pulmonary frequent acute exacerbation influence factor data of a patient; the influence factors are screened through LASSO regression and an improved Boruta algorithm respectively; the LASSO independent influence factors and the Boruta independent influence factors are combined in different modes, a Logistic regression model and an XGBoost model are trained, and a plurality of COPD-FE risk prediction models are obtained; and evaluating the performance of all the COPD-FE risk prediction models, and selecting the COPD-FE risk prediction model meeting the requirement to predict the COPD-FE risk. According to the method, clinical data of patients are collected, a risk prediction model is constructed in combination with a feature selection method and machine learning, and an optimal model is screened out through comprehensive evaluation.

The invention relates to the technical field of remote sensing image recognition, in particular to a method for determining a moso bamboo forest snow disaster affected area based on a GEE platform. The method mainly solves the problems of low efficiency, high cost and difficulty in realizing large-range rapid evaluation caused by dependence on manual field investigation in the prior art. According to the technical scheme, the method comprises the following steps: firstly, obtaining a multi-temporal Sentinel-2 image before and after a snow disaster, and carrying out the preprocessing of the multi-temporal Sentinel-2 image; extracting spectral bands, vegetation indexes and texture features to form an initial feature set; then, a key feature variable combination is determined through statistical significance filtering and machine learning optimization; on this basis, establishing a logistic regression discrimination model and determining an optimal classification threshold; and finally, carrying out snow disaster state classification on the moso bamboo forest region by utilizing the trained model, and generating a disaster region spatial distribution diagram.

The invention discloses a risk assessment system and method for analyzing abnormal sleep breathing of children based on CBCT dual-channel, and the method comprises the steps: collecting local three-dimensional image data of a maxillofacial region through CBCT, and extracting the sagittal area, volume and multi-dimensional angle and distance parameters of an upper airway through head correction and anatomical mark positioning; and constructing a logistic regression clinical prediction model combining single-factor and multi-factor logistic regression to realize risk prediction. Standardization and equal-interval sampling are synchronously carried out on image data, a multi-channel three-dimensional image data cube is generated, and deep learning classification is realized by inputting the multi-channel three-dimensional image data cube into a 3D ResNet network based on identical fast connection. And finally, performing weighted collaborative analysis on results of the two diagnosis channels, and outputting children obstructive sleep apnea risk classification. Through the parameter driving and image learning dual-channel fusion design, the accuracy and applicability of early screening and risk assessment of children obstructive sleep apnea are improved.

The invention provides an analysis method and system for a soil microbial community structure and a medium, and relates to the technical field of ecological environment monitoring. According to the method, environmental parameters, namely pH, organic carbon, moisture and oxidation reduction potential, of a soil sample are collected and subjected to normalization processing, phylum abundance, dominant phylum information and Shannon index classification community structure categories are obtained in combination with high-throughput sequencing, the support probability of the environmental parameters for classification is calculated through a multi-term logistic regression model, and the classification result is obtained. A D-S evidence theory is utilized to fuse multi-source BPA (basic probability allocation), joint confidence distribution is output, a conflict factor threshold value is set, an artificial review prompt is triggered under a certain condition to avoid errors caused by environmental parameters, finally, to-be-detected soil parameters are input into a pre-training model, confidence distribution is output in real time, and the detection accuracy is improved. And judging the structure of the microbial community in combination with a threshold rule, and outputting health, risk, transition state or uncertainty.

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 provides a large language model generation code detection method and system, which is applied to the technical field of artificial intelligence, and comprises the following steps: obtaining a to-be-detected code; a to-be-detected code is input to a trained shared encoder, a code feature vector is obtained, the shared encoder is obtained through multi-target joint training, and the multi-target joint training is used for optimizing classification loss, domain confrontation loss, comparison loss and difficult sample loss at the same time; l2 normalization is carried out on the code feature vector, and the code feature vector is mapped to a hyperspherical space to obtain spherical embedding; the sphere is embedded and input into a sphere category classifier based on sphere logistic regression for classification processing, a detection result of the to-be-detected code output by the sphere category classifier is obtained, the detection result comprises AI generation and human writing, and a decision boundary of the sphere category classifier is an intersection line of a hyperplane and a hypersphere for classification. According to the invention, the AI generation code and the human compiled code can be accurately distinguished.

The invention discloses a management decision support method for intelligent operation and maintenance and fault prediction of engineering equipment, and belongs to the technical field of operation and maintenance management and intelligent decision of the engineering equipment. According to the method, equipment fault features are extracted and classified through a deep belief network (DBN), and text diagnosis is refined in combination with TF-IDF and cosine similarity; analyzing the importance and cause of the fault by using a Bayesian network; predicting a fault and a decline trajectory based on the decision tree and logistic regression; and constructing an intelligent operation and maintenance decision support system of ontology integration. According to the method, multi-source data are integrated, accurate fault diagnosis, advanced prediction and intelligent decision making are achieved, the problems that traditional operation and maintenance depend on experience, precision is low and cost is high are solved, and the operation and maintenance efficiency and reliability of engineering equipment are improved.

The invention discloses a disastrous big wave occurrence probability and wave height combined prediction method, device and equipment, a medium and a product, and relates to the technical field of meteorological ocean forecasting. The method comprises the following steps: acquiring an effective wave height measured value and environmental factor data of a to-be-predicted region in a historical time period; key environment factors are screened through spatial correlation analysis and linear fitting verification, a first training data set and a second training data set are constructed, and a multiple linear regression model and a logic regression model are trained respectively to obtain a wave height prediction model and a big wave occurrence probability prediction model; and inputting the key environmental factors of the current time period of the to-be-predicted region into the model to obtain the predicted occurrence probability of the disastrous waves and the predicted value of the significant wave height in the predicted time period. According to the method, the key environmental factors are screened and the two models are used for respective prediction, the influence of the various environmental factors on the big waves is considered, the occurrence probability and the wave height of the disastrous big waves can be predicted more accurately, and the ocean safety is effectively guaranteed.

The invention discloses a context poisoning attack defense method oriented to an RAG application, and relates to the technical field of RAG. the method comprises the following steps: inputting a target query statement, and retrieving the target query statement to obtain multiple pieces of context information; taking representative sentences in the retrieved context information, and identifying and filtering potential malicious template clusters; the big language model gives all candidate answers according to existing context information, the logarithmic probability of all contexts to different candidate answers is calculated, and after the influence of parameter knowledge of the big language model is removed from the logarithmic probability, the support degree of all contexts to different candidate answers is obtained; the whole logarithmic probability vector is used as a support degree distribution condition of the context to the candidate answers; identifying a single piece of harmful information from the support degree distribution condition of the context to the candidate answers through a logistic regression model so as to filter wrong answers; according to the attack defense method provided by the invention, centralized injection of multiple malicious texts and sparse injection of a small number of malicious texts can be defended.

The invention relates to the technical field of medical data analysis and clinical risk prediction, provides a construction method of a glucocorticoid induced diabetes risk prediction model based on an LASSO algorithm, and belongs to the technical scheme of data processing executed on computing equipment. Clinical data of inpatients receiving systemic glucocorticoid treatment are collected, key variables are screened through LASSO regression, and a Logistic regression model is constructed to achieve risk prediction. The model is composed of four conventional clinical indexes, has good distinction degree and calibration degree, and finally realizes individualized risk assessment through column diagram form output. The method is simple, practical and accurate, and is suitable for SDM prediction and intervention management of clinical high-risk groups.

The invention relates to the technical field of biomedical engineering, and discloses a method and system for early warning and analyzing high-risk groups with high altitude polycythemia, and the method comprises the steps: screening a subject data set from an extremely high altitude area; performing structured processing on the subject data set to obtain a structured feature matrix, and extracting a core prediction factor from the structured feature matrix; candidate early warning models of the core predictive factors are generated, and the candidate early warning models comprise a logistic regression model, an XGBoost model and a random forest model; screening an optimal early-warning model from the candidate early-warning models, and establishing a high altitude polycythemia early-warning system of the subject data set through the optimal early-warning model; and effect verification is carried out on the high altitude polycythemia early warning system so as to realize high altitude polycythemia high-risk group early warning analysis processing of the subject data set. According to the method, the core problem that the early warning result is one-sided and unreliable due to three defects of data dimension missing, static evaluation limitation and extensive privacy mechanism can be solved.

The invention provides a light color cast correction method and device based on a target area, and is applied to the technical field of data processing. The method comprises the following steps: acquiring a light original image and a shooting environment parameter through an image acquisition module; a light source point is positioned by using a YOLO algorithm, a white connected domain is extracted by combining an HSV threshold value, a luminous body contour is obtained through closed contour search, and whether correction is needed or not is judged according to the proportion of 80%; a U-Net model with specific parameters is adopted to segment outer-layer halo, and a color reference annular area is obtained through XOR operation; on the basis of pixel data of the region, through formula calculation, abnormal value filtering and class logistic regression function fitting, deducing and compensating an HSV (Hue, Saturation and Value) parameter; and performing non-target area invariance on the color cast area of the inner layer of the light source, and finally outputting a corrected image and verifying the effect, thereby realizing accurate color cast correction.

The invention provides a causal relationship and time sequence characteristic-considered cascading failure rapid screening method and system, and relates to the technical field of power grid cascading failures, and the method comprises the steps of obtaining cascading failure scene data; based on cascading failure scene data, extracting a causal relationship between an initial short-circuit fault and a subsequent fault by using logistic regression and a neural network model; different states of the fault event are converted into graph structures, time delays between the different states of the fault event are obtained based on cascading fault scene data statistics, and time sequence features in the transient evolution process of the cascading fault event are extracted; the probability that the initial fault causes a subsequent fault event is obtained based on a causal relationship, the duration of different states of the fault event is generated by adopting a Monte Carlo sampling method based on time sequence characteristics, and a cascading fault scene is generated; and carrying out risk assessment and probability assessment on the cascading failure scene generated based on the causal relationship and the time sequence characteristics, and finally screening to obtain a high-risk cascading failure scene.