209results about How to "Strong explainability" patented technology

The invention relates to the technical field of remote water meter abnormity early warning, and discloses a remote water meter abnormity early warning method and system based on historical water volume, and the method comprises the steps: dividing a user water meter into three classes A, B and C according to the installation time of the user water meter, and employing a differentiated abnormity detection strategy. For the A-type users, establishing a water consumption prediction model based on historical consumption data of the A-type users; for the B-type users, finding similar reference water meters from the A-type users by using a K-means clustering algorithm; and for the C-type users, monitoring whether the accumulated water consumption for continuous N days is completely equal or not after the C-type users are installed. After real-time usage data is obtained, usage deviations of various users are calculated respectively, if the deviations exceed a set threshold value, abnormal early warning is triggered, and the early warning level is dynamically adjusted according to the number of abnormal duration days. According to the method, technologies such as machine learning and clustering analysis are comprehensively utilized, a set of comprehensive, flexible and efficient water consumption abnormity early warning system is constructed, and the refinement and intelligence level of urban water supply management can be remarkably improved.

The invention relates to the technical field of computer vision and deep learning, and relates to a deep forgery detection method based on mask supervision. According to the technical scheme, an image is input to a preprocessing module for preprocessing; the processed image is respectively input into a mask supervision module for extracting spatial domain features and a frequency domain feature extraction module for extracting frequency domain features; and the spatial domain features and the frequency domain features are connected and input to a classification module to obtain a final prediction result. The method has the beneficial effects that the attention of the model to the irrelevant region is supervised and corrected through the mask, and a pixel-level label is used for guiding the model to face the synthesis region of the face, so that more accurate extraction of spatial features is ensured; the depth forgery detection method based on mask supervision has remarkable advantages in the aspects of detection accuracy, robustness, generalization ability, calculation efficiency, interpretability, overall performance optimization and the like, and a new solution is provided for the field of depth forgery detection.

The invention provides an electric vehicle charging load space-time prediction method based on hybrid model driving, and relates to the field of electric vehicle charging, and the method comprises the steps: building a user travel model based on a Markov chain; constructing a multi-angle traffic road network model and a load space-time prediction model based on multi-graph feature fusion; acquiring user historical travel data, electric vehicle charging load historical data, traffic network historical information and external historical data of the to-be-predicted area; generating a user MDP trip chain according to the user historical trip data of the to-be-predicted area through a user trip model; extracting a multi-angle spatial relation graph from the traffic road network historical information of the to-be-predicted area through the multi-angle traffic road network model; the electric vehicle charging load of the to-be-predicted area is predicted through the load space-time prediction model based on multi-graph feature fusion according to the user MDP trip chain, the multi-angle spatial relation graph and the external historical data, and the method has the advantage of improving the accuracy of electric vehicle charging load prediction.

The invention discloses a meta-cognition level prediction method based on voiced thinking driven retrieval enhancement, which is characterized in that an information extraction method in a deep learning technology is adopted to extract answer features, a voiced thinking response analysis library is constructed, a correlation mode between various answer contents and corresponding meta-cognition scores is established, and a prediction result is obtained. The method comprises the following steps: establishing a mapping model, bridging the internal relation between the answer content and the meta-cognitive level by using the established mapping model, matching the sound thinking answer with the existing answer sample in a sound thinking response analysis library to realize the prediction of the meta-cognitive level, and specifically comprising the steps of data collection, data enhancement, big five personality correction, mapping model establishment, matching, retrieval and the like. Compared with the prior art, the method has the advantages that a more scientific and targeted problem design scheme can be provided on the basis of real-time evaluation of the meta-cognition level of the student when the type of problems proposed to the student is determined, so that the method has remarkable advantages in the aspects of education evaluation and personalized learning support.

The invention relates to a transformer comprehensive safety assessment method and system based on symbol regression. The method comprises the following steps: acquiring monitoring variables of an electrical signal dimension, an equipment state dimension and an operation environment dimension of a transformer; based on the maximum mutual information coefficient, screening the dimension monitoring variables to obtain a plurality of monitoring variable sets with closely associated variables; performing sign regression on the plurality of monitoring variable sets to obtain a nonlinear function expression between any reference monitoring variable and other monitoring variables in the monitoring variable sets, namely a mutual check function, and realizing sign regression by using a genetic algorithm; establishing a single-variable fault probability function according to the influence of the multi-dimensional monitoring variable on the transformer equipment; and building a system dynamics model based on the mutual verification function and the fault probability function to realize comprehensive safety evaluation of the transformer. Compared with the prior art, the method has the advantages of improving the safety assessment level of the transformer, being high in interpretability and the like.

The invention particularly relates to a pose calculation method and device based on point cloud and an article grabbing system. The method comprises the steps of collecting a current scene image to be processed and a corresponding scene depth image, and performing image recognition on the scene image to determine position information of a target to be captured; wherein the scene image comprises at least one target to be captured; acquiring corresponding scene point cloud data based on the scene depth image, and determining at least one target point cloud data corresponding to the to-be-captured target in combination with the position information of the to-be-captured target; performing plane fitting processing on the target point cloud data to obtain a corresponding target fitting plane; projecting the target point cloud data to the corresponding target fitting plane to obtain a projection point cloud, and calculating a convex polygon corresponding to the projection point cloud; calculating a target quadrangle corresponding to the convex polygon with dense point clouds; and determining the pose information of the target to be grabbed according to the target quadrangle.

The invention relates to the field of automatic driving, and provides an out-of-distribution point cloud object detection method based on fuzzy membership supervision and evidence deep learning. Comprising the following steps: obtaining a candidate foreground object by using a 3D target detection model; foreground object point cloud feature extraction; calculating a fuzzy membership degree based on fuzzy C-mean clustering; the fuzzy membership degree value is used as mark information, and evidence deep learning model training is carried out; and inferring an uncertain value of a foreground object by using the trained evidence deep learning model, and judging whether the object belongs to an out-of-distribution sample or not. According to the method, the fuzzy membership degree obtained by fuzzy clustering is used as supervision information, evidence deep learning is used for training on the point cloud target detection data set, and the uncertain value predicted by the evidence deep learning is used for point cloud open set target detection, so that robustness and interpretability are high; and the open set detection effect can be ensured in a large-scale sparse point cloud scene.

The invention relates to an electric power quantity accumulated temperature effect duration prediction method and device and a medium, and the method comprises the following steps: obtaining original data, dividing the original data into a plurality of groups of data, and carrying out the wavelet transformation of temperature-sensitive load data and temperature data in a high-temperature time period in each group, so as to obtain time-frequency distribution data; performing causal inference by using a frequency domain causal analysis method and integrating the time-frequency distribution data to obtain a causal effect of the temperature on the temperature-sensitive power and electric quantity data, and determining occurrence time of an explicit causal relationship; daily load data, daily electric quantity data and daily temperature data of multiple consecutive days are collected as to-be-analyzed data; and predicting the duration of the accumulated temperature effect according to the data to be analyzed by using a high-order Granger causality method. Compared with the prior art, the method has the advantages of high accuracy, high interpretability and the like.

The invention provides an industrial glass defect detection method based on Visionmaster. The industrial glass defect detection method is provided by aiming at the pain point of current glass defect detection and combining the actual demand of an enterprise. A traditional industrial glass defect detection method has many problems and deficiencies, such as poor environmental adaptability, lack of robustness, low precision, dependence on manual adjustment and the like. In order to solve the problems, the innovation comprises the following steps: preprocessing an image, detecting whether the glass is transversely broken or longitudinally divided, detecting glass edge defects, detecting glass surface defects, and summarizing results to judge the qualification of the glass. Firstly, noise interference is removed through an image processing technology, the position and the edge of a glass area are obtained, and an image enhancement means is used for improving the contrast ratio, so that an image is clearer; based on Visionmaster, whether glass is transversely cut or longitudinally divided, whether edge defects exist and whether surface defects exist are judged by utilizing a traditional visual technology and setting a proper threshold range. And finally, determining whether the industrial glass is qualified or not by summarizing detection results of all links. And if not, determining the defects of the glass. The industrial glass defect detection method has the advantages that the coding difficulty is reduced, the parameter adjustment process in the detection process is simplified, and the accuracy, universality and anti-interference capability of industrial glass defect detection are improved; and meanwhile, the labor cost is reduced, the product quality is better guaranteed, and the detection speed and efficiency are improved. In addition, the method has high interpretability and adaptability, and is suitable for complex and changeable industrial field environments. According to the method, while the production efficiency is improved, challenges existing in a traditional method are effectively handled, and a more effective and reliable detection method is provided for the industrial glass production process.

The invention discloses a tracked vehicle driving state prediction model training method, a tracked vehicle driving state prediction method and a tracked vehicle driving state prediction product, and relates to the technical field of tracked vehicle intelligent driving. Inputting the measurable control vector data into the trained transmission-track model to obtain actual control vector data; acquiring state vector data of the tracked vehicle at the previous moment, and sequentially calculating prior estimations of the state vector data and the measurement vector data to obtain prior state estimation and prior measurement estimation; converting the priori state estimation, the priori measurement estimation and the actual control vector data into a differential sequence; and training the recurrent neural network by using the differential sequence to obtain a tracked vehicle driving state prediction model. According to the method, the neural network with a good nonlinear fitting characteristic is combined into the Kalman filtering algorithm, effective state estimation of a strong nonlinear system is realized, a Kalman filtering state estimation principle is integrated, and to-be-learned parameters of the neural network are reduced.

The invention discloses an electrical coupling system operation reliability evaluation method, which comprises the steps of analyzing a network topology, an energy flow direction and an element logic relationship of an electrical coupling system, and constructing a Bayesian network model of the system from a power supply source to a load point; establishing an element reliability model, and obtaining different failure rate information of the non-key element by changing different age limits and load rates; taking different failure rate information of the non-key elements as input data of nodes of the Bayesian network model, outputting an analytical model coefficient, and obtaining a reliability index in combination with a key element combination state probability; training an LLSVM model by taking different failure rate information of the non-critical elements as input and taking an analytical model coefficient as output; and during online application prediction, obtaining different failure rates of corresponding non-key elements by combining element long-term and short-term prediction results with the element reliability model, inputting the different failure rates into the trained LLSVM model, and outputting a reliability index. According to the method, effective, rapid and efficient reliability evaluation of the electrical coupling system is realized.

The invention provides a method and a system for diagnosing and estimating the state of health of a fuel cell based on polarization characteristics, and relates to the technical field of fuel cells, and the method comprises the following steps: constructing a proton exchange membrane fuel cell model based on a multi-condition multi-physical field, extracting health state characteristics of the to-be-diagnosed battery from the proton exchange membrane fuel cell model under a preset typical working condition; and inputting the health state characteristics into a trained fuel cell health state diagnosis model to obtain the health state of the to-be-diagnosed battery evaluated by the fuel cell health state diagnosis model. The method not only can accurately diagnose the health state of the fuel cell, but also is low in implementation difficulty and cost and relatively high in interpretability, so that the dependence of high-precision diagnosis of the health state of the fuel cell on expert experience is reduced.

The invention discloses a data-driven calculation method, device and equipment for hydrogen storage capacity of a solid-state hydrogen storage device, and belongs to the technical field of solid-state hydrogen storage. The method comprises the steps that distributed parameters of the solid-state hydrogen storage device are obtained through a multi-physical-field full-coupling model of the solid-state hydrogen storage device, and the multi-physical-field full-coupling model is constructed based on structural characteristics of the solid-state hydrogen storage device; obtaining a dynamic response database of the multi-physics field full-coupling model, wherein the dynamic response database is used for representing a corresponding relation between distributed parameters of the multi-physics field full-coupling model and the hydrogen storage capacity of the solid hydrogen storage device; performing dynamic identification on the multi-physical field full-coupling model through a dynamic response database to obtain an identification model of the solid hydrogen storage device; and inputting the target distributed parameters of the solid-state hydrogen storage device into the identification model, and performing data driving calculation on the target distributed parameters through the identification model so as to output the predicted hydrogen storage amount of the solid-state hydrogen storage device. According to the method, the calculation cost and the solving time can be reduced.

The invention provides a process identification method based on model features, which comprises the following steps that: a process knowledge graph is constructed by extracting a relation node triple in a standard process description text, and the node types of the graph comprise feature nodes, process nodes, equipment nodes, cutter nodes and clamp nodes; the relation between every two adjacent nodes comprises an available process, a preorder process, available equipment, initial machining, tool assembling and clamp assembling. Obtaining a geometric model of a to-be-machined mechanical part, extracting geometric feature attributes of the to-be-machined mechanical part, and determining a feature topological relation to obtain machining features; and starting from the feature nodes corresponding to the machining features by adopting an inverted sequence search method, screening out nodes meeting the machining requirements of the to-be-machined mechanical part from the process knowledge graph, and obtaining a complete process scheme. The method can deeply excavate the correlation between the geometric features of the part model and the machining process, improves the process identification accuracy, and is wide in applicability.

The invention discloses a construction method of a dam multi-measuring-point deformation deep learning monitoring model, and the method comprises the steps: carrying out the clustering analysis of dam deformation data through employing a K-means algorithm, so as to recognize a spatial mode of a monitoring region; in each clustering region, based on the interaction of dependent variables between the monitoring points, a long-short-term memory network of a coupling attention mechanism is adopted to construct a deformation prediction model; sequencing and evaluating the measuring points and the influence factors of the measuring points in different areas by applying an interpretability analysis technology; according to the method, clustering analysis, an attention mechanism, deep learning and other algorithms are effectively integrated, and a dam multi-measuring-point deformation monitoring model which is high in prediction precision and high in explanation capacity is successfully constructed. The model provides a new thought and method for reasonably analyzing and predicting the overall deformation characteristics of the dam. The application of the method not only improves the prediction accuracy, but also enhances the transparency and credibility of the model.

The invention provides a fracture detection method based on gridding and a DBSCAN clustering algorithm. The fracture detection method comprises the steps of obtaining post-stack seismic data as input data of a subsequent calculation step; carrying out fracture sensitive attribute calculation on the post-stack seismic data; clustering analysis is carried out on fracture detection attributes through a DBSCAN algorithm so as to realize automatic tracking of fracture detection results, on this basis, through gridding processing, a reasonable threshold value is set for tracked fracture data according to geometric topology information such as an inclination angle, an inclination angle and a length in combination with prior geological knowledge, cleaning of the fracture detection results is realized, and the fracture detection results are cleared. And finally, a fracture detection data body with high precision and high signal-to-noise ratio is obtained. The method is suitable for the seismic data processing field with a complex fracture system and a low seismic data signal-to-noise ratio. Compared with the prior art, the method has the advantages that the finally obtained fracture detection result is high in signal-to-noise ratio, high in resolution, obvious in linear feature, good in continuity, high in interpretability and better in conformity with geological knowledge of a work area.

The invention provides a wired table recognition method based on morphology, and the method comprises the following steps: S1, carrying out the graying processing of all read-in to-be-detected three-channel images, obtaining a gray image, carrying out the binarization processing of the obtained gray image, and obtaining a binary image of an original image; and S2, carrying out corrosion and expansion operation on the binary image by using a detection core, and forming an Excel format file by identifying a text table format and the like in the image so as to solve the problem of a special-shaped table. The problem that in the prior art, table recognition is always a research difficulty in the field of document recognition is solved, and the method is high in running speed, high in stability and good in interpretability. And the method is innovative, and can be used for well processing conditions such as special-shaped tables, cross-page disconnection, text cross-line, text wireless frame package and the like. The method can be popularized, and has good compatibility for identification of different types of wired tables. The method can be deployed in a server, a personal pc (personal computer) and embedded equipment, and is high in practical applicability.

The invention discloses an unmanned aerial vehicle obstacle avoidance method, system and device based on reinforcement learning and differential programming and a medium. The obstacle avoidance method comprises the following steps: constructing an unmanned aerial vehicle state matrix s; constructing a task neural network module, and obtaining an expected flight vector fexp of the unmanned aerial vehicle according to the unmanned aerial vehicle state matrix s; constructing a differentiable safety module, correcting an expected flight vector fexp of the unmanned aerial vehicle according to the obstacle position, and obtaining an actual flight direction vector ffly of the unmanned aerial vehicle; the task neural network module and the differentiable safety module are combined in a mode that the task neural network module is in front of the differentiable safety module, an unmanned aerial vehicle obstacle avoidance strategy network is constructed, an unmanned aerial vehicle simulation flight environment is established, the unmanned aerial vehicle obstacle avoidance strategy network is trained, and an available unmanned aerial vehicle obstacle avoidance strategy is obtained; the system, the device and the medium are used for bearing and implementing the method. Safe flight of the unmanned aerial vehicle is achieved in the simulation environment, the advantages of an intelligent algorithm and a traditional obstacle avoidance algorithm are integrated, and therefore the safety of the unmanned aerial vehicle in the task execution process is improved.

The invention provides a multi-task casting blank quality defect prediction method and device based on adaptive loss, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a continuous casting production sample data set; preprocessing the continuous casting production sample data set to obtain a preprocessed sample data set; an initial multi-task learning model based on TabNet is constructed; according to the preprocessed sample data set, training an initial multi-task learning model based on the TabNet to obtain a trained multi-task learning model based on the TabNet; acquiring continuous casting production data to be predicted; preprocessing the continuous casting production data to be predicted to obtain preprocessed continuous casting production data to be predicted; and inputting the preprocessed continuous casting production data to be predicted into the trained multi-task learning model based on the TabNet to obtain an internal quality prediction result of the continuous casting billet. By adopting the method, the performance of predicting the internal quality defects of the continuous casting billet can be improved.

The invention relates to a credit distribution method in multi-agent cooperation, and the method comprises the following steps: obtaining joint data of a multi-agent system, the joint data comprising observation information and execution actions of each agent; the joint data are input into a discriminator model, and the discriminator model carries out modeling on an interaction dependency relationship among multiple agents based on an interaction dependency relationship modeling module of an attention mechanism; generating an auxiliary reward signal corresponding to each agent through a discriminator model; generating a fused reward signal based on the auxiliary reward signal and a global reward signal from the environment; and training and updating the multi-agent strategy model by using the fused reward signal so as to optimize the cooperation strategy of the multi-agent system. By introducing a credit distribution mechanism based on a discriminator model, automatic decomposition and optimization generation of individual reward signals are realized, so that the learning efficiency and stability of a multi-agent system in a complex cooperative task are remarkably improved.

The present invention belongs to the field of geological engineering construction, and discloses an automatic analysis method and system for advanced geological prediction of tunnels using elastic wave method, which solves the problems of less training samples, poor universality of intelligent model engineering, and low automation level in the existing research on the application of artificial intelligence to advanced geological prediction analysis of tunnels. The present invention obtains elastic wave method parameter data by identifying elastic wave method parameter images, and establishes an elastic wave method parameter database; combines rock mechanics theory, geological disaster mechanism, etc. to establish a mapping relationship between key geophysical parameters of elastic wave method, geological indicators, and geological quality, to establish a classifier classification principle; finally, a machine learning method is used to construct a classifier model according to the classifier classification principle, and training and test samples are obtained based on the elastic wave method parameter database to train and verify the model. The model can be used to realize real-time automatic analysis of the entire process, thereby improving the accuracy and efficiency of the analysis.

The invention discloses a series fault arc identification method suitable for a low-voltage alternating-current power distribution system, which is different from a traditional fault arc detection method and comprises the following steps of: judging whether an arc fault occurs in a line or the line is in a normal state by using zero-sequence current as a detection signal; according to the method, the selected characteristic quantities accurately describe four stages of arc starting, arc burning, arc extinguishing and zero stopping of the fault arc, and a good fault arc detection effect is achieved for a low-voltage alternating-current power distribution system with complex scenes including multiple loads, multiple arc generation modes, multiple line topologies and the like.

The invention discloses a multi-modal perception and agent collaborative urban operation event handling method, which belongs to the technical field of artificial intelligence, and adopts the technical scheme that multi-modal front-end equipment is deployed to collect data, a fusion confidence coefficient is generated based on multi-source data, and an event work order is generated when the fusion confidence coefficient reaches a threshold value; the method comprises the following steps: classifying work orders by utilizing an urban management knowledge graph, determining responsibility subjects and disposal guidance, and determining priorities by integrating risks and regions through a weight model; abstracting various resources as disposal resources, and realizing work order and resource matching assignment under the constraint of response time limit; when a complex event condition is met, disassembling into sub-work orders according to a knowledge graph and carrying out parallel processing; the processing resources upload processing feedback, automatic verification is carried out based on computer vision and multi-source evidence, closed loop is qualified, if not, processing or evidence supplementation is carried out, and a closed loop result is archived and used for continuously optimizing the model and scheduling. The beneficial effect of the invention is that the urban operation event handling method based on multi-modal perception and intelligent agent cooperation is provided.

The invention relates to a battery spare part prediction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring various data of a plurality of in-use battery packs to obtain total data; wherein the plurality of in-use battery packs are arranged in different electric equipment; determining a failure change relationship according to the total data; the failure change relationship comprises a relationship that the failure probability of the battery pack changes along with the first survival duration; according to the failure change relationship, predicting the failure number of the plurality of in-use battery packs in a preset future time period; and determining the number of battery pack spare parts required in the preset future time period according to the failure number. According to the invention, the accuracy of battery spare part prediction can be improved, so that spare parts can be better supplied.

The invention discloses an Alzheimer's disease diagnosis and prediction method based on multi-source knowledge guidance, and belongs to the technical field of medical artificial intelligence assistance. Comprising the following steps: acquiring a nuclear magnetic resonance image, brain statistical data and clinical data of a participant; processing the nuclear magnetic resonance image by using a dual-path feature extraction strategy, obtaining global brain features and local brain features, carrying out collaborative learning, and generating fused visual features; the brain statistical data and the fused visual features are fused and then input into a trained diagnosis module for processing, and a diagnosis result at the current moment is obtained; and taking the clinical data as guidance, combining the diagnosis result at the current moment, and utilizing the trained prediction module to obtain the disease progress at the future moment. The multi-modal data can be integrated, the accuracy and comprehensiveness of Alzheimer's disease diagnosis and prediction are improved, and the problems that the accuracy of existing Alzheimer's disease diagnosis is not high, and clinical requirements are difficult to meet are solved.

The invention provides a multi-stage high-speed compressor pneumatic instability early warning method and system based on deterministic learning, and the method comprises the steps: arranging sensor measurement points at a multi-stage high-speed compressor in a multi-angle manner of combining a whole ring with a sector, and obtaining the pulsating pressure data in a pneumatic instability test; according to the method, the aerodynamic instability internal nonlinear dynamic system is learned according to pulsating pressure data by utilizing a deterministic learning modeling method, a learning result serves as holographic expression of a fast time-varying instability mode, more essential and comprehensive dynamic characteristics in the development process of aerodynamic instability complex dynamic phenomena can be described, and good interpretability is achieved. On the basis, an online early warning method for pneumatic instability is developed, an early warning signal can be given out before instability occurs only through single-measuring-point pulsating pressure data of the multi-stage high-speed gas compressor, and therefore a certain application value is achieved, and a real-time monitoring method with practical significance is expected to be provided for safe and stable operation of a high-performance aero-engine.

The invention discloses a smart retail recommendation method and system based on graph decoupling representation learning, and belongs to the technical field of smart city recommendation. Aiming at the problems that a current smart retail recommendation system cannot effectively capture complex purchase decisions and behavior changes of consumers and is difficult to meet personalized requirements, the method comprises the following steps: constructing a hypergraph according to user session behavior data, and embedding and constructing an intention hyperedge by taking multiple intentions of a user as commodity nodes to form a complete user behavior hypergraph structure; based on a user behavior hypergraph structure, multiple intentions behind user behaviors are analyzed from microscopic and macroscopic angles, then preference scores of all commodities are calculated for prediction, and thus a smart retail recommendation model is established; and designing a joint optimization objective function, carrying out model training on the objective function to realize recommendation of the smart retail, and providing a recommendation result to the user and a retail platform merchant for use.

The application provides a large model security management method, system, device and medium based on a core particle architecture, which can be applied to the technical field of semiconductor integrated circuits. The method comprises the following steps: setting a physically isolated special security core particle as a hardware trusted root in a heterogeneous integrated system, and connecting the special security core particle with at least one target core particle running a large model through a bus; deploying a lightweight security supervision model with a smaller parameter quantity than the large model in the special security core particle; collecting behavior characteristic data of the target core particle running the large model in real time; performing inference by using the lightweight security supervision model, calculating an abnormality metric between the current behavior of the target core particle and a preset normal behavior model; updating a dynamic trust state of the target core particle based on the abnormality metric; and performing a hardware-level security response operation by the special security core particle in response to the dynamic trust state reaching a predetermined threshold.

The application discloses a ransomware identification method and system based on multi-source data fusion, and relates to the field of ransomware detection. The method obtains file system event data, system resource running indexes, process behavior data and industrial control system behavior data, identifies file features, system running behavior situation features, process features and industrial scene features from the data, judges whether each feature meets the essential behavior features of ransomware encrypted files, and divides the features into strong indication features, medium indication features and basic situation features. Different mapping methods are used to determine sub-risk scores for different features, and finally a weighted scoring mechanism is used to obtain an overall urgency score, based on which a ransomware attack is determined, and then suspicious process operations are automatically triggered and terminated in parallel, and network connections are blocked to prevent the spread of ransomware attacks. The application can accurately identify known ransomware and unknown variants, and has good interpretability.

This application relates to the field of data processing technology, and in particular to a data fusion method and system based on neural pathways. The method includes the following steps: First, acquiring electroencephalogram (EEG) data and eye-tracking (EMT) data, aligning them using timestamps, and preprocessing them separately; then, extracting EEG features based on the preprocessed EEG data; extracting EMT features based on the preprocessed EMT data; next, fusion of features based on the EEG and EMT features by downsampling the data; finally, evaluating the synergy of the fused features. This application constructs a complete evaluation system by building a framework from a stimulus-visual paradigm to a signal acquisition platform and analysis methods related to neural structures. The time delay between the most relevant points in the time domain of eye-tracking and EEG features is used as an indicator of brain-eye synergy. The difference between these indicators has interpretability based on physiological structures, constituting a novel evaluation indicator for brain-eye synergy when assessing dynamic visual acuity.