822 results about "Logistic regression" patented technology

The invention discloses an antagonism prompt detection method and device based on a large language model and a medium, and belongs to the technical field of artificial intelligence safety. The technical problem to be solved by the invention is how to overcome the defects of static rule lagging, high manual maintenance cost and insufficient context understanding in the security protection process of a large language model in the prior art, and dynamic, real-time and high-precision antagonism prompt detection is realized. According to the technical scheme, the method comprises the following steps: feature extraction: comprehensively analyzing semantic information, structural information and context information in a user text, extracting semantic features, structural features and context features, performing Min-Max normalization on the semantic features, the structural features and the context features, and splicing the semantic features, the structural features and the context features into a 128-dimensional joint vector, performing feature selection on the joint vector through an L1 regularization logistic regression model, compressing to 10-dimensional core features, and removing redundant information; carrying out resistance scoring; and dynamically defending.

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 relates to the technical field of electric power operation and maintenance management, in particular to a transformer area intelligent operation and maintenance device and method based on a machine learning expert model, and the device comprises a logistic regression machine learning module and a robot process automation module. Real-time perception and data analysis of a business scene are realized by means of artificial intelligence, total data information of a master station measurement domain and an operation and maintenance work task is dynamically collected, incremental data is continuously utilized for data analysis and rule mining, a machine learning expert model is introduced, and the operation and maintenance efficiency is improved. A transformer area intelligent operation and maintenance system integrating human-computer interaction such as operation and maintenance index intelligent monitoring and analysis, intelligent abnormity diagnosis, intelligent operation list generation, intelligent dispatching, intelligent path planning and field operation guidance is constructed, service data is analyzed in real time, meanwhile, a robot process automation technology is integrated, automatic operation of a part of processes is achieved, and operation efficiency is improved. And support is provided for on-site acquisition operation and maintenance work.

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 dynamic library management method and system based on artificial intelligence, and relates to the technical field of library management, and the method comprises the following five steps: firstly, constructing a reader portrait through collecting basic information, borrowing history and other data of a reader by using TF-IDF analysis and a machine learning algorithm; secondly, a positioning tag and a sensor are deployed to be combined with a book RFID tag, a path is optimized according to reader behavior data, and the book retrieval efficiency is improved; monitoring and analyzing borrowing behavior data and reader portraits, establishing a borrowing risk assessment model, assessing risks by adopting a logistic regression algorithm, providing decision suggestions for workers, quantitatively adjusting the borrowing quantity, and reducing the risk of overdue or damaged books; through application of the artificial intelligence technology, automation, intellectualization and individuation of library management are realized, the management efficiency and the service quality are remarkably improved, and the operation effect of the library is enhanced.

The invention relates to the technical field of medical emergency monitoring, in particular to an ICU emergency response system which comprises a preliminary risk assessment module, an emergency recognition module, a treatment scheme optimization module, a vital sign monitoring module, an emergency response coordination module, a disease evolution prediction module, a resource allocation management module and a medical decision support module. According to the method, feature engineering and logistic regression analysis are combined with medical history and real-time vital signs of a patient, emergency medical events are recognized by applying pattern recognition and natural language processing, a treatment scheme is optimized by using a support vector machine and a decision tree, personalized treatment suggestions are provided, and the time sequence and a biometric method are used for finely monitoring the vital signs; health risks are early warned, resources are reasonably allocated in emergency response through a resource scheduling algorithm and a priority queue, illness state prediction is assisted through machine learning and probability statistics, resource utilization is improved through linear programming and a resource optimization strategy, and waste is reduced.

The invention relates to the technical field of case retrieval and analysis, in particular to a method and a system for retrieving and analyzing unhealthy asset cases. The method comprises the following steps of performing cleaning, standardization and feature extraction on collected enterprise financial data, market information and historical cases; performing primary screening on the cleaned structured data based on a dynamic weight logistic regression rule, performing feature extraction on the high-risk candidate case set to obtain structured features # imgabs0 # of the high-risk candidate case set, and constructing a multi-layer knowledge graph based on an SPO triple; taking the spliced mixed feature # imgabs1 # as the input of a graph enhanced mixed gradient lifting model, and obtaining a finely screened high-risk case; and a progressive training strategy is adopted to train the graph enhanced mixed gradient lifting model, and the contribution of the structured features and the graph features to the prediction result is balanced. A multi-layer knowledge graph with space-time attributes introduced is constructed, the risk propagation weight is dynamically calculated in combination with a space-time diagram attention network, and the dynamism and accuracy of risk path identification 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 belongs to the technical field of health assessment, and particularly relates to an aging clock construction method based on a causal machine joint model. The method comprises the steps of 1, obtaining a multi-modal data set of a target crowd based on multi-modal data fusion and standardization preprocessing; 2, performing two-stage screening on the multi-modal data set to obtain core features; 3, analyzing interaction among multiple groups of schools based on the extended proportion advantage logistic regression hybrid model, and then carrying out P value calibration based on a fused saddle point approximation method; 4, constructing an aging clock based on a mixed causal effect graph model, evaluating a multi-dimensional causal relationship, and calculating the biological age of the individual; according to the method, clinical phenotype data and multi-omics data are fused, a machine learning and causal inference combined method is adopted, the individual aging degree and health risk are accurately analyzed, a high-performance aging clock model is provided for primary medical institutions, and scientific basis and decision support are provided for health management and early disease intervention.

The invention relates to the technical field of artificial intelligence, finance and medical health, and provides a retrieval method, device and equipment based on attention guidance and a medium. Training data is constructed according to the correlation between each specified lexical element and a retrieval question and a retrieval answer and the attention feature vector of each specified lexical element; a logistic regression classifier is trained to obtain a detector, and the light-weight detector can be used for recognizing utilized lexical elements in real time in the reasoning process, so that the higher-degree explanation of the generation process is realized; when the retrieval model is used for retrieval, the detector is used for detecting the target lexical elements which are being used in real time, the original probability distribution is corrected according to the utilization probability of each target lexical element so as to control the retrieval model to output the retrieval result, and the output probability of the used lexical elements is dynamically enhanced. Therefore, the attention mechanism of the guide model is dynamically focused on the context information highly related to the user query, and the context illusion phenomenon is fundamentally reduced.

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 invention discloses a memory anomaly prediction method based on multi-algorithm decision, and belongs to the technical field of fault prediction. The method comprises the steps that data information generated in the running process of a server is obtained, preprocessing and feature extraction are conducted on error data, a feature set is obtained, and the feature set comprises counting dimension features, component dimension features, statistical dimension features and equipment configuration dimension features; the feature set is input into a trained basic prediction model, a first prediction probability, a second prediction probability and a third prediction probability are obtained, and the basic prediction model comprises a random forest sub-model, a lightweight gradient elevator sub-model and an extreme gradient lifting sub-model; obtaining a first prediction probability value based on the first prediction probability, the second prediction probability and the third prediction probability; inputting the feature set into a depth time sequence extraction network, and obtaining target features based on a feature masking method; inputting the target features into a gating circulation unit to obtain a fourth prediction probability value; obtaining a decision matrix based on the first prediction probability value and the fourth prediction probability value; and inputting the decision matrix into the trained decision optimizer logistic regression model to obtain a memory anomaly prediction result. According to the method, the accuracy and reliability of memory anomaly prediction are improved.

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 analysis, in particular to a method for constructing an acute pancreatitis severity prediction model based on a liquid neural network, which comprises the following steps: firstly, extracting and preprocessing patient data, and then dividing AP patients into a severe AP group and a mild AP group; screening two groups of characteristic indexes with differences through nonparametric test analysis to carry out machine learning modeling in the next step; in order to prevent overfitting, five-fold cross check is adopted to train the model, and optimal feature combinations under respective machine learning models (a liquid neural network, a decision tree, a random forest, Logistic regression and an XGBoost algorithm) are obtained by drawing AUC values under an ROC curve for comparison. The method comprises the following steps: firstly, training a liquid neural network model, then verifying verification data by using respective optimal prediction models obtained by training, and finally, analyzing influence weights of different features in the liquid neural network model through SHAP visualization and carrying out visualization so as to provide visual prediction reference for users such as doctors and the like.

The invention discloses a landslide risk and risk evaluation method suitable for a tectonic active area, and belongs to the field of geological disaster risk evaluation, and the risk evaluation method is characterized in that risk evaluation is superposed with vulnerability evaluation. The risk evaluation method comprises the following steps: calculating a deterministic coefficient value of each evaluation factor, and weighting to a grid layer of each evaluation factor to generate a deterministic coefficient grid layer of the evaluation factor; importing the deterministic coefficient layer into a logistic regression model, and generating a landslide risk evaluation result based on a logistic regression-deterministic coefficient coupling model; the vulnerability evaluation method is an analytic hierarchy process. By coupling a deterministic coefficient model and a logistic regression model, a linear relationship of multiple evaluation factors is effectively processed, and nonlinear contribution of a single evaluation factor to landslide development is captured, so that the landslide risk evaluation precision of a geological environment complex region and a structure active region is improved; and the evaluation result can more objectively and truly reflect the landslide risk of the active fault zone and the nearby area.

The invention discloses an efficient intelligent network attack classification tool based on a federated learning framework, which comprises a central server and a plurality of end-side nodes, local models are deployed in each end-side node, a central model is deployed in the central server, and the models comprise multi-task logistic regression, random forest, XGBoost and long and short-term memory neural network classifiers; the local model collects local network flow data, performs model training and abnormal data detection and classification in combination with a D-S evidence theory optimization algorithm and a four-class complementary classifier, and transmits encrypted abnormal data and related key update parameters to the central server; the central model receives and aggregates the updated parameters, performs attack type classification based on abnormal data in combination with a D-S evidence theory optimization algorithm after updating the global model, and re-allocates a classification result and the updated global model to each end side node; the method has high robustness and high efficiency, the adaptability is enhanced, and the recognition capability of attack scenes is improved.

The invention relates to the technical field of data processing, in particular to an egg crack detection method based on a sound wave signal, and the method comprises the following steps: obtaining a sound wave signal on the surface of an egg and a mechanical vibration signal of a surrounding environment, and obtaining a sound wave signal on the surface of the egg based on the frequency difference and amplitude difference between each frequency and a main frequency in a frequency domain signal of the mechanical vibration signal; determining a first index of each frequency, calculating an average difference between every two amplitude values of all the sound wave signals at the same frequency to obtain a second index of each frequency, and based on the first index and the second index, classifying whether the sound wave signals at each frequency are interfered by mechanical vibration noise by using a logistic regression model. And sound wave signals which are interfered by mechanical vibration noise in a classification result are removed, and whether cracks exist on the surface of the egg or not is judged by utilizing the residual sound wave signals through a pre-trained crack detection model. According to the invention, accurate detection of cracks on the surface of the egg can be realized.

The invention discloses a financial big data risk analysis and early warning system based on artificial intelligence, and belongs to the technical field of financial analysis. A data acquisition module is used for collecting financial data from a bank transaction system, a security transaction platform, a credit rating mechanism and a third-party financial data provider; information of transaction details, a debt sheet, market quotation data, macroeconomic indicators and customer credit records is covered; and the risk assessment module is used for fusing artificial intelligence algorithms such as logistic regression, a decision tree, a support vector machine, a neural network and a deep learning model and calculating a risk score in combination with a dynamic weight formula, and the dynamic weight formula dynamically adjusts the weight of each risk index according to parameters such as a fluctuation ratio, a market uncertainty index and a macroeconomic index. The method has the advantage of improving the communication efficiency among the modules and the task scheduling reasonability.

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.