171 results about "Data complexity" patented technology

The invention relates to the technical field of man-machine interaction, in particular to a mixed interaction system based on an AI large model, which comprises a multi-modal question acquisition module, a preprocessing and classification module, a retrieval and analysis module, a mixed processing module, a result integration and output module, an interaction module and an answer display module, the problem preprocessing and classification module performs text cleaning, semantic analysis and classification, and the hybrid processing module distributes tasks to natural language processing or AI large models according to a data complexity threshold value, so that the problem processing flow is optimized, the system performance is improved, and the system performance is improved. According to the system, model parameters are optimized based on a machine learning classification algorithm and transfer learning training, classification accuracy is ensured, the system adapts to problem changes, through cooperation of all the modules, the existing technical problems are effectively solved, the man-machine interaction efficiency and quality are improved, the system has wide application prospects in multiple fields, and the man-machine interaction technology is promoted to be developed to be intelligent and efficient.

The invention discloses a multivariable time sequence prediction method based on GRU and a computer program product. The method comprises the following steps: firstly, introducing the dynamic characteristics of self-adaptive different time sequences of self-defined time sequence decomposition, and splitting an original sequence into trend, seasonal and residual components so as to reduce the data complexity and improve the interpretability; afterwards, a value embedding module is used for unifying feature representation so as to ensure that the model fully captures the time dependency relationship; in the modeling stage, the model adopts a multi-channel recurrent neural network to independently model the three types of components so as to reduce the interference between modes and improve the learning ability. In the prediction stage, a feature splicing strategy is adopted, information of each component is integrated, and richer time sequence representation is provided. In addition, a segmented prediction strategy is designed for the model, the prediction process is divided into multiple time periods, prediction information is combined, error accumulation is reduced, and the stability and robustness of long-sequence prediction are improved. Experimental results show that the prediction precision can be improved on different data sets.

The invention discloses a data asset classification method and system based on big data, and the method comprises the steps: carrying out the standardization processing of multi-source data assets in an enterprise business system, constructing a knowledge graph through correlation analysis, dynamically updating the graph according to the flow condition of the data assets, determining key data assets according to the use frequency of the data assets, and carrying out the classification of the key data assets. The method comprises the steps of obtaining a knowledge graph, obtaining an association path and association strength from the knowledge graph, carrying out foreground prediction, clustering prediction results according to data complexity, calculating a data asset evaluation average value of each cluster as a clustering center index, classifying all data assets according to the clustering center index, and forming a data asset portrait. And mutually isolated distributed storage is executed, and a minimum storage principle is followed to optimize life cycle management of data assets. According to the method, the utilization rate of data assets is increased, data security management is enhanced, an enterprise is supported to make a more intelligent data-driven decision, and meanwhile, the method has relatively good interpretability.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention relates to the technical field of wind speed prediction, in particular to a wind speed prediction method and system based on entropy clustering, and the method comprises the following steps: carrying out the multi-scale decomposition of a wind speed sequence through a permutation entropy algorithm, generating subsequences, carrying out the sliding window probability distribution calculation of a plurality of subsequences, setting a dynamic classification threshold value based on a normalized entropy value, and carrying out the calculation of the sliding window probability distribution. And dividing the sub-sequence set into an entropy classification result set. According to the method, a classification threshold value is dynamically set through sliding window probability distribution, the sensitivity of fixed entropy division to data complexity change is overcome, nonlinear mapping between an original sequence and entropy components is established through GRU training, the feature extraction depth and low component error transmission are enhanced, high, medium and low entropy components are modeled through Bi-RNN, LSTM and SVR, feature extraction with different complexities is matched, and the accuracy of feature extraction is improved. Chaotic mapping optimization fusion weight, multi-target optimization balance component complementarity and dynamic error reference are combined with feedback to adjust a confidence interval, error distribution adaptive tracking is realized, and prediction reliability is enhanced.

The invention discloses a new energy weather forecast method based on numerical weather forecast and AI revision, and particularly relates to the technical field of weather forecast, and the method comprises the steps: obtaining the historical actually measured weather data of a new energy power station, correcting the numerical weather forecast data of multiple sources through employing an artificial intelligence algorithm, and building a correction model; inputting the current numerical weather forecast data into the correction model for correction, extracting the internal structure characteristics of the correction data in combination with a high-dimensional geometric mapping and manifold learning method, and evaluating the matching degree between the manifold distribution of the correction data and the historical data; analyzing and evaluating the data complexity by using multi-scale entropy, and ensuring that the data meets a preset standard; on the basis of matching degree and complexity evaluation, selecting suitable numerical weather forecast data for new energy short-term and medium-term meteorological parameter forecast; the method improves the prediction accuracy and applicability, and is suitable for the generation power prediction and optimization scheduling of the new energy power station.

The embodiment of the invention discloses a multi-modal large model optimization method and device for a complex task and a medium, and relates to the technical field of multi-modal large models.The method comprises the steps that multi-modal input data are received, modal specific feature extraction is carried out on the multi-modal input data, and initial feature representation of each modal is generated, the multi-modal input data comprises image data, text data and audio data; generating a dynamic parameter adjustment strategy based on a pre-acquired task type and input data complexity, and adjusting calculation parameters of a multi-modal encoder in the multi-modal large model according to the dynamic parameter adjustment strategy to determine a corresponding optimization modal processing sub-module; and performing cross-modal fusion by optimizing the modal processing sub-module and the initial feature expression to generate a fused multi-modal feature expression, and performing semantic analysis and reasoning on the multi-modal feature expression by using a pre-trained multi-modal large model to generate a task output result.

The invention provides a battery health state prediction method and system, and the method comprises the steps: simulating the operation condition of an energy storage battery through a battery charging and discharging circulation experiment platform, and obtaining the characteristic value of the charging and discharging process of the battery; respectively calculating correlation coefficients corresponding to the characteristic values through a Pearson correlation analysis method and a Spearman correlation analysis method, and selecting the characteristic values of which the correlation coefficients are higher than a preset value as input characteristic values; dividing the input characteristic value into a training set and a test set according to a preset proportion, and training and testing the CNN-LSTM neural network based on the training set and the test set to obtain a CNN-LSTM prediction model after network parameter optimization; and estimating the health state of a battery to be detected through the CNN-LSTM prediction model. Through the scheme, the model setting process can be simplified, the model data complexity is reduced, and the battery health state prediction precision and speed can be improved.

A method for visually identifying crop rows includes acquiring imagery from at least one imaging device operatively connected to an agricultural vehicle while the agricultural vehicle is traversing a field, the imagery includes plants arranged in rows, processing the imagery at a computing device to preserve spatial information along a first dimension while reducing data complexity along a second dimension to generate processed data, analyzing the processed data to identify locations of the rows within the field. The method may include dividing the imagery into multiple sections along a direction of travel of the agricultural vehicle and for each section, performing statistical analysis to generate a one-dimensional intensity profile, applying an edge detection operation to identify predominantly vertical features within the imagery and filtering out predominantly horizontal features to isolate plant stalks, and / or identifying spatial relationships between neighboring rows and using the spatial relationships to validate row identification.

The invention discloses an artificial intelligence large model reasoning acceleration method and system based on a GPU and an NPU, and relates to the technical field of large models. The invention discloses an artificial intelligence large model reasoning acceleration system based on a GPU (Graphics Processing Unit) and an NPU (Network Processing Unit). The system comprises a reasoning information shunting module and a reasoning data acceleration module, according to the method, by combining input data features, model structure dynamic control and heterogeneous computing resource collaborative scheduling, highly-adaptive large model reasoning optimization is realized, accurate matching of computing resources and data complexity is realized, and the overall reasoning throughput and response speed are remarkably improved; a dynamic scheduling mechanism based on a strategy output model is adopted, GPU / NPU calculation tasks are reasonably distributed, resource idling and congestion are avoided, the hardware utilization rate is improved, and the method is particularly suitable for multi-task concurrent and high-frame-rate video scenes.

The invention relates to a ship user behavior self-learning recommendation system based on a large model, and relates to the technical field of ship informatization. According to the system, through collection and fusion of multi-source heterogeneous ship user behavior data, a large-scale pre-training language model (large model) is utilized to carry out deep understanding and semantic mining on massive ship field text information and user behavior sequences, and a ship field knowledge graph or semantic vector space is constructed. The large model can identify and predict potential demands, behavior patterns and preference changes of ship users, and generates highly personalized, accurate and prospective ship service, product, route or information recommendations in combination with real-time operation data and external environment factors. Besides, a user feedback self-learning mechanism is introduced into the system, recommendation strategies and model parameters are continuously optimized according to interaction behaviors and explicit evaluation of the users in modes of reinforcement learning or continuous learning and the like, and intelligent iteration of the system and continuous improvement of the recommendation effect are achieved. According to the method, the challenges of a traditional recommendation system in the aspects of data complexity, semantic gaps and dynamic demand adaptability in the ship field are effectively solved, and the ship operation efficiency and the user satisfaction degree are remarkably improved.

The invention discloses a distributed task processing method and device based on spatio-temporal data complexity and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: extracting a feature vector of spatio-temporal data of a digital twin model, and inputting the feature vector into a trained random forest model; through the trained random forest model, performing classification prediction on the spatio-temporal data according to the feature vector, and outputting a category label corresponding to the feature vector, the category label representing the complexity of the spatio-temporal data; mapping the category labels of the spatio-temporal data into weight coefficients, and formulating distributed task queues based on the weight coefficients, so that the difference of the sum of weights of the task queues of each processing node is minimum to ensure load balance among the distributed task queues; and distributing the distributed task queue to each cluster node for distributed processing. The technical effect of improving the distributed task processing efficiency is achieved.

The invention relates to the technical field of smart park data monitoring, and discloses an artificial intelligence smart park multi-source data monitoring and analysis method. The method comprises the following steps: constructing a park operation state basic model; when the model is identified to be abnormal, data dynamic change characteristics are extracted by utilizing a spatio-temporal data fusion technology, and an abnormal event mode in a specific region is identified by combining deep belief network deep analysis; calculating data complexity by using permutation entropy, optimizing and analyzing an abnormal propagation path in combination with an A star algorithm, and evaluating a diffusion range and an influence path; recognizing a comprehensive risk area, analyzing the environment and equipment state characteristics of the area through a multispectral imaging technology, and evaluating the operation abnormity in combination with real-time multi-source data; and performing accurate intervention on the comprehensive risk area based on the abnormal condition. According to the method, effective integration and analysis of multi-source data of the smart park are realized, abnormity is accurately identified, risks are mastered, a scientific means is provided for park management, and stable and efficient operation of the park is guaranteed.

The invention provides a significant wave height prediction method and device and electronic equipment, and relates to the technical field of sea wave data processing. The method comprises the following steps: acquiring an effective wave height data set for wave height prediction; determining model input data based on the correlation; carrying out two-layer decomposition on the effective wave height; wherein the primary decomposition comprises empirical mode decomposition based on a self-adaptive noise complete set and K-means clustering of mode components; the secondary decomposition comprises the step of optimizing variational mode decomposition of parameters based on a crown porcupine optimization algorithm; inputting model input data into a bidirectional long-short-term memory network based on an attention mechanism for training; the method comprises the steps of firstly screening data with relatively high correlation with the significant wave height as input data to reduce the calculation amount, then performing two-layer decomposition on the significant wave height to reduce the data complexity, and finally fully extracting data features through a bidirectional long-short-term memory network based on an attention mechanism to comprehensively predict the data so as to improve the prediction accuracy of the significant wave height. And the accuracy of predicting the significant wave height is improved.

The invention provides a financial risk prediction method based on a multi-objective ensemble learning algorithm, and aims to solve the problems of multi-risk type comprehensive evaluation, data complexity and nonlinear relation processing, dynamic change adaptability and the like in risk prediction in the financial field. According to the method, a financial time sequence is divided into a plurality of time blocks through a space-time block data preprocessing technology, and a heterogeneous model library is independently trained, so that the adaptability and prediction precision of a model are improved. Meanwhile, an NSGA-II multi-objective optimization algorithm is combined with Gibbs distribution to dynamically distribute weights, the prediction precision and the engineering practicability are balanced, and dynamic optimization of economic indexes and engineering indexes is achieved. In addition, a time decay and mixed attention mechanism is designed, multi-model prediction results are fused, market dynamic changes are further captured, and the accuracy and real-time performance of risk prediction are improved. According to the method, the precision and the real-time response capability of financial risk prediction are effectively improved.

The invention discloses a foot tub parameter intelligent adjustment method and system based on machine learning. The method comprises the steps of data collection, data screening, foot tub parameter prediction model establishment, global parameter optimization and foot tub parameter adjustment. The invention relates to the technical field of smart homes, in particular to a foot tub parameter intelligent adjusting method and system based on machine learning, according to the scheme, data screening is carried out by dividing data neighborhoods and defining data complexity and local density, and the accuracy and reliability of abnormal value recognition are improved; a model is established by designing an excitation function, a front double transformation layer, a residual adjustment layer and a loss function, so that the expression ability, prediction accuracy and prediction error control ability of the model are improved; parameters are optimized by designing a parameter search function and a search rule, and the search direction and step length are dynamically adjusted, so that the search process is more efficient, the anti-interference capability of the search process is enhanced, and local optimum can be jumped out.

The invention discloses a thermal load prediction method and system based on multi-stage fusion optimization, and the method employs a gray wolf optimization algorithm to replace a conventional empirical method to optimize key parameters of variational mode decomposition, and automatically determines an optimal mode number and a penalty factor through multi-objective optimization. The obtained parameter combination is introduced into a variational mode decomposition algorithm, and then the variational mode decomposition algorithm is utilized to decompose thermal load historical data into a plurality of stable mode components, so that the complexity and noise interference of the data are reduced, and the decomposition quality of variational mode decomposition is remarkably improved; then, the hyper-parameters of the long-short-term memory network are optimized by combining a sparrow search algorithm, so that an optimal hyper-parameter combination is found, and finally, a thermal load data set is predicted through a long-short-term memory model under the obtained optimal hyper-parameter combination.

The invention belongs to the technical field of scene recognition, and discloses a sequence multi-modal scene recognition method based on a state space model. Laser point clouds and visual images in the tracks are jointly coded to form multi-modal sequence data, the multi-modal sequence data are processed into unique global descriptors through a global descriptor coding network, and the global descriptors of the two tracks are used as maps and queries respectively; and in the query process, finding the most similar data in the map through a nearest neighbor search algorithm to complete scene recognition. According to the invention, fusion coding is carried out on laser point cloud and image data, the data dimension is increased, and the data complexity is compressed. A global descriptor is obtained through a single frame module and a sequence module based on a state space model. The calculation efficiency is improved through the cross scanning design in the single-frame module, and the position recognition problem of track change in a scene is solved through the sequence full combination representation strategy in the sequence module. The method has the characteristics of high calculation efficiency, high accuracy and robustness.

The invention belongs to the technical field of data security, and particularly relates to a non-intrusive power load identification method and system based on an intelligent fusion terminal, and the method comprises the steps: collecting power load data, and dividing an analysis window based on the power load data; calculating a volatility index corresponding to the analysis window based on the data in the analysis window so as to represent the data complexity in the analysis window; determining the number of iterations for encryption based on the volatility index; and executing a compression algorithm on the data in the analysis window to generate compressed data, and executing multiple rounds of encryption perturbations determined by the number of iterations on the compressed data to generate a dynamically encrypted compressed data stream. According to the method, the privacy protection intensity can be adaptively adjusted according to the dynamic complexity of the data, and the privacy leakage risk caused by the fact that the static encryption intensity cannot adapt to the data dynamics in the prior art is solved.

The invention provides a residual terrain correction estimation method based on PINNs and a residual terrain model, and relates to the technical field of geodetic survey, a data set is randomly divided into a training data set and a test data set, a residual terrain gravity anomaly of each data set is obtained through RTM as an output, the network architecture of the PINNs is a full-connection neural network, and the network architecture of the PINNs is a full-connection neural network. Associating the output quantity and the gravitational potential quantity of the RTM model according to a basic equation of physics geodetics to serve as a boundary value condition; and taking the weighted sum of the physical loss function and the data loss function as a total loss function and an initial full-connection neural network weight parameter of the full-connection neural network, training the constructed full-connection neural network through a training data set, and achieving the requirements of small calculation amount, low required data complexity and high precision through the method.

The invention discloses an intelligent workshop digital twin modeling and simulation system, and belongs to the technical field of intelligent manufacturing. The system comprises a multi-dimensional workshop modeling and management module, an intelligent analysis and decision module and a data management module. The multi-dimensional workshop modeling and management module dynamically displays and generates a scheduling flow through two-dimensional and three-dimensional models which are highly consistent with the actual workshop layout in combination with a production scheduling scheme; the intelligent analysis and decision module processes and analyzes data by applying a scheduling algorithm, optimizes a production process and updates scheduling information in real time; the data management module is based on a server, transmits and integrates data of the multi-dimensional workshop modeling and management module and the intelligent analysis and decision support module, realizes seamless communication between the modules, and ensures consistency and real-time performance of the data. The combination of the three modules effectively solves the problems of insufficient real-time performance, difficulty in comprehensive reaction of data complexity, poor user experience and the like in the prior art.

The invention discloses a large-model multi-agent framework-driven medical data analysis method, which comprises the following steps of: S1, based on collected electronic medical record data of a patient, constructing a medical knowledge graph in an auxiliary manner by virtue of a large language model, and storing the medical knowledge graph in a graph database; s2, constructing a graph retrieval enhancement generation agent, combining the graph retrieval enhancement generation agent with a medical knowledge graph, and performing preliminary analysis on the input data; s3, the assessment agent assesses the data complexity according to the original medical data and the analysis result in S2, and the recruitment agent creates a corresponding number of medical expert agents according to the assessment result; and S4, designing a multi-agent cooperative analysis algorithm, calculating a weighted similarity analysis matrix, judging the consistency of analysis results of the medical expert agent group, and finally obtaining a reliable analysis conclusion. According to the method, the illusion problem of a large model is effectively relieved, and the quality, accuracy and credibility of medical data analysis are remarkably improved.

The invention relates to the technical field of medical data processing, in particular to a radiology department image data analysis system, which comprises a self-matching texture recognition module, an image data analysis module, a search window size and shape adjustment module, a window area structure fitting degree calculation module, a self-matching texture recognition module and a self-matching texture recognition module, wherein the self-matching texture recognition module collects image data, analyzes texture types in images, adjusts the search window size and shape and calculates the window area structure fitting degree according to texture features; and aggregating the data of the same-type regions. According to the method, more refined data analysis is realized by adjusting the size and shape of the search window and accurately calculating the structural fitting degree of the region according to the texture features, the objectivity of data analysis is optimized by gathering the data of the similar region and updating the weight of the fitting degree, the texture features and edges are effectively processed by applying an iterative soft threshold algorithm, and the accuracy of data analysis is improved. Compared with the prior art, the method has the advantages that data complexity is reduced, processing speed is increased, in addition, topological structure analysis strengthens intuitive understanding of a complex pathological structure, global mode features are analyzed through a graph network, diagnosis accuracy is enhanced, and data support is provided for disease prediction.

The invention discloses an impossible difference-intermediate encounter attack password analysis method, which comprises the following steps of: obtaining an impossible difference partition device of a certain block cipher, respectively expanding forwards and backwards for several rounds according to the difference characteristics of the head and the tail of the impossible difference partition device, and recording corresponding parameters; selecting a plaintext to attack, encrypting to obtain a corresponding ciphertext, and calculating data complexity required by the attack according to corresponding parameters; a key involved in the forward extension part is guessed, partial encryption is performed on the plaintext, pairing is performed by using data after partial encryption, and ciphertext data pairs corresponding to data pairs satisfying forward differential propagation are stored in a hash table; a key related to the backward expansion part is guessed, partial decryption is carried out on the ciphertext, pairing is carried out by using the partially decrypted data, online matching is carried out on a ciphertext pair corresponding to a data pair satisfying backward differential propagation and a ciphertext data pair in a hash table, and a wrong key is eliminated; and carrying out exhaustion search on the remaining candidate keys to find a correct key.

The invention discloses an intelligent analysis method and system for gas station operation data, and relates to the technical field of operation data analysis, and the method comprises the steps: collecting gas station data for preprocessing, generating symbolized sequence data for the preprocessed data through employing a symbolized approximate aggregation SAX algorithm, and calculating the similarity of the symbolized sequence data by using a dynamic time warping (DTW) algorithm, constructing an accumulative error matrix, and backtracking an optimal alignment path to obtain high-similarity sequence data. According to the method, a continuous time sequence is converted into a discrete symbol sequence through the application of a symbolic approximate aggregation algorithm, the data complexity is remarkably reduced, the trend characteristics of original data are reserved, a symbol quantile interval is generated through a cumulative distribution function of Gaussian distribution and an inverse cumulative distribution function of standard normal distribution, and the probability that the symbol quantile interval is larger than the discrete symbol sequence is reduced. The symbolization process has a strict statistical basis, and the frequent pattern is extracted by using an Apriori algorithm through a high-similarity sequence data set.

The invention provides a method and device for analyzing a video and electronic equipment. The method comprises the following steps: acquiring video image data and a video analysis task; performing image sparse processing on the video image data by using a target detection model according to the video analysis task to generate sparse image data; performing feature extraction on the sparse image data by using a visual embedding model to generate visual embedding features; according to a multi-modal cue word template corresponding to the video analysis task, constructing multi-modal dialogue data based on the visual embedding features; and processing the multi-modal dialogue data by utilizing a visual language model to generate an analysis result corresponding to the video analysis task. The analysis result of the visual language model on the multi-modal dialogue data is the result of the corresponding video analysis task, and the data volume and the data complexity needing to be processed in the multi-modal feature extraction process are reduced through sparsification, so that the processing efficiency is improved, and the processing efficiency of the video analysis task is improved by utilizing the visual language model.

The invention discloses a dynamic resource optimal configuration method for multi-source data fusion, and relates to the technical field of optimal configuration, and the method comprises the steps: obtaining multi-source data, including business demand data, resource state data and environmental condition data, and carrying out different processing according to the type of the business demand data; for the first type of service demand data matched with the service demand template, calculating a deviation value of the first type of service demand data relative to a preset service standard, and adjusting a resource allocation direction according to the deviation value; and for the mismatched second type of service demand data, identifying a service operation scene, and adjusting a resource allocation direction or pausing unnecessary resource supply according to the scene and the data complexity, in addition, receiving an instruction for starting a dynamic resource optimization configuration function before data is acquired, the invention also provides an electronic device and a readable storage medium for realizing the method, and the method can effectively fuse multi-source data, realize dynamic optimal configuration of resources, and improve resource utilization efficiency and business operation flexibility.

The invention provides a wheel life prediction and operation and maintenance plan decision method and device and a storage medium. The method comprises the following steps: acquiring data related to the remaining life of a wheel; generating a wheel residual life based on the wheel life related data and a prediction model obtained by pre-training; obtaining wheel turning repair related data, and generating a wheel turning repair plan based on the wheel turning repair related data; updating the remaining service life of the wheel based on the wheel turning repair plan; and generating an operation, maintenance and / or purchase plan corresponding to the updated wheel remaining life based on the updated wheel remaining life. According to the technical scheme, the remaining service life of the wheel can be estimated only by using a small amount of data, the data complexity is reduced, the prediction accuracy is improved, the turning wheel operation plan is flexibly generated, the potential influence of the turning wheel on the remaining service life is accurately reflected, the timeline of the maintenance task is automatically generated or optimized, and the maintenance efficiency is improved. The accurate matching of the maintenance activity and the actual demand is ensured, and the non-planned downtime is reduced.

The present invention discloses a brain image classification method based on discretized data, includes: dividing an original brain image dataset into an original training set, an original validation set, and an original test set; constructing a multi-objective function including an information loss before and after dataset discretization, a classification error rate, and a discrete data complexity, and obtaining a discretization scheme; discretizing the original training set, the original validation set and the original test set according to the discretization scheme; performing feature selection on a discrete training set and a discrete validation set, and performing feature reduction on the discrete training set, and a discrete test set using the feature selection result to obtain a reduced discrete training set and a reduced discrete test set; and training a classifier using the reduced discrete training set to classify the reduced discrete test set, to obtain a brain image data classification result.

The invention belongs to the technical field of network security, and discloses a security event association aggregation and maliciousness analysis method. Preprocessing the security event log set to obtain a security event context sequence and an embedded vector thereof; obtaining an initial vector by the embedding vector of the security event context sequence through an encoder; the decoder calculates an attention vector and predicts event probability distribution according to the embedded vector and the initial vector decoded in the previous step; iteratively training until a termination condition is met, and calculating the total attention distribution of each event according to the obtained attention vector and the security event context sequence; an improved DBSCAN method is adopted, and a data set Z formed by total attention distribution of all events is clustered; and extracting a sample from each cluster, and performing event maliciousness analysis according to an LDA topic model. According to the method, the workload of further analysis can be effectively reduced, event sequences with similar context features are clustered, and the data complexity and calculation cost of subsequent analysis are reduced.