468 results about "Unsupervised clustering" patented technology

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention is suitable for the technical field of agricultural intellectualization, and provides an intelligent blueberry disease detection method and system based on multi-modal unsupervised learning, and the method comprises the following steps: carrying out the feature extraction and clustering of preprocessed multi-modal data based on an adaptive contrast deep clustering framework, and obtaining a feature extraction result; obtaining a multi-modal preliminary feature and a preliminary clustering result; based on a multi-modal complementary feature fusion mechanism, according to the multi-modal preliminary features and the preliminary clustering result, carrying out adaptive weighted fusion on the multi-modal preliminary features to obtain multi-modal fusion features; performing unsupervised clustering optimization and disease type identification on the multi-modal fusion features to obtain an unsupervised learning model; and performing deployment and incremental learning on the unsupervised learning model, and detecting the blueberry diseases. According to the method, early-stage accurate detection of blueberry diseases is realized through an unsupervised learning algorithm, a new normal form is provided for intelligent accurate management of blueberries, and the disease prevention and control efficiency and industrial economic benefits are effectively improved.

The invention discloses a distribution box fire early warning method and system based on multi-source information fusion, and particularly relates to the technical field of distribution box fire early warning. Temperature, smoke, acoustic vibration and current harmonic signals are synchronously acquired, time sequence alignment and amplitude normalization are performed, and a dynamic baseline is generated in real time; extracting multi-scale statistics and morphological characteristics according to an adaptive window, and calibrating an operation mode through unsupervised clustering; then retrieving a matched baseline signature in a historical feature library, and calculating a multi-modal standardization deviation of a current window; mapping the deviation index into a graph node, combining a covariance edge weight, a phase synchronization index and a smoke discrete index, obtaining a coupling risk coefficient through a fusion model, and outputting a comprehensive risk index; and finally, carrying out multi-scale rate and acceleration analysis on the comprehensive risk index, triggering three-level early warning of attention, warning and danger by adopting a dynamic threshold, and correcting the threshold by utilizing operation and maintenance feedback self-learning, thereby effectively solving the problems of early warning response lag and unknown early warning level.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

The invention relates to an automatic inspection method and system for intelligent fire fighting equipment of an LNG (Liquefied Natural Gas) filling station, in particular to the field of the intelligent fire fighting equipment of the LNG filling station, a dynamic drifting model is constructed by collecting environmental parameters in real time, and the interference of severe conditions on a sensor is accurately quantified; a time sequence prediction network is used for prospectively capturing a drift trend, and a digital twinning technology is combined to simulate an equipment state in an extreme scene; intelligently identifying an abnormal mode based on unsupervised clustering and dynamically optimizing a multi-source data fusion weight; and finally, through reinforcement learning of a closed-loop iteration drift model and a fusion strategy, the adaptability of the system is continuously improved, misjudgment and missed judgment caused by environmental interference are effectively eliminated in the whole process, the real-time performance and reliability of fire-fighting early warning are remarkably enhanced, the safety accident risk is greatly reduced, and all-weather, high-precision and self-evolution intelligent protection guarantee is provided for the LNG filling station.

The invention discloses a multi-technology fusion multi-source data grading and classifying method and system, and the method comprises the following steps: S1, determining a data source, collecting multi-source data, and carrying out the preprocessing; s2, monitoring by using a CDC technology, and extracting monitored target data; s3, performing feature extraction on the target data, and introducing an EMAP method to perform nonlinear cross projection on a multi-modal feature set; s4, inputting the high-dimensional feature vector into a hybrid classification network, and executing main component molecular space compression and unsupervised clustering; s5, mining high-frequency attribute items by adopting an FP-Growth algorithm, and constructing a privacy attribute set and a non-privacy attribute set; s6, calculating the weighted privacy degree of each privacy cluster, and performing multi-layer privacy level division through a hierarchical mapping network; and S7, carrying out encryption and desensitization processing on the data under each type of labels, and carrying out visual output. According to the invention, the intelligence and security of multi-source data classification and privacy grading are improved.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention discloses a composite material wind power blade damage detection method, and aims to solve the problems of difficulty in quantitative analysis and low distinguishing precision of various damage types in composite material wind power blade damage detection in the prior art. Comprising the following steps that acoustic emission signals in the operation process of the wind power blade are obtained in real time and preprocessed, and the acoustic emission signals comprise parameter data and waveform data; performing unsupervised clustering on the preprocessed parameter data to obtain a preliminary clustering result; training the preprocessed waveform data by adopting an improved multi-branch convolutional neural network, and outputting an accurate classification result; the unsupervised clustering result is verified by using the supervised learning classification result, and the accuracy of the clustering result is evaluated; and fusing the quantitative index accumulated energy, the damage type and the frequency, and constructing a comprehensive damage evaluation model to determine the damage degree. According to the method, the unsupervised clustering method and the supervised learning method are combined, and accurate identification and quantification of the damage are realized.

The invention discloses a data labeling method and system based on user behaviors and attention tracking, and the method comprises the steps: synchronously collecting multi-source behavior signals of a mouse, a keyboard, eye movement and the like of a doctor in real time, combining identity and interface metadata, and carrying out the standardized normalization, abnormality elimination and short time sequence behavior unit division. And extracting individual behavior micro-modes by using unsupervised clustering, and constructing a behavior portrait library. Through multi-modal time sequence modeling and a self-adaptive space-time attention mechanism, behavior characteristics, an interface area and a report text are deeply fused, a multi-level correlation probability is output, and high-precision automatic tagging of content and an image area is realized.

The invention belongs to the technical field of smart cities, and discloses a point cloud-based urban building roof wireframe reconstruction method, which comprises the following steps of: fusing a fast point feature histogram and a multi-scale roof geometric descriptor, generating robust point-by-point features, screening candidate angular point clusters in combination with a classification head, and adaptively segmenting the candidate point clusters based on density parameters by utilizing DBSCAN (Density-Based Spatial Clustering of Applications with Noise), so as to reconstruct a point cloud-based urban building roof wireframe. Initial inflection points are extracted through unsupervised clustering, noise is effectively suppressed, and irregular distribution is adapted; then multi-scale geometric information of an initial inflection point neighborhood is aggregated through an inflection point correction network, offset is learned to correct position deviation, and inflection point positioning precision is improved; and finally, the edge classification network automatically deduces the topological connection of the roof wireframe based on the geometrical relationship and feature relevance of prediction inflection points, so that error accumulation caused by dependence on manual rules in a traditional method is avoided, the generalization ability of a complex roof structure is enhanced, the correction network learns offset through a multi-scale context, and the inflection point positioning precision is remarkably improved.

The invention relates to the technical field of data processing, and discloses a flue gas treatment intelligent regulation and control method and system based on artificial intelligence and a medium. The method comprises the following steps: collecting parameter data of a flue gas system through a sensor network and processing to obtain preprocessed data; using unsupervised clustering to identify working condition features and categories; constructing an emission concentration error active switching control model according to the working condition information; different working condition control parameters are optimized by adopting a differential evolution algorithm; optimized parameters are input into a controller, and modes are dynamically switched according to pollutant errors. The control strategy can be automatically switched according to the working condition characteristics of the flue gas treatment system, the complex and changeable industrial production environment can be effectively dealt with, and the system operation cost is optimized while stable and up-to-standard emission of pollutants is guaranteed.

The invention provides an automatic driving safety test system and method, and relates to the technical field of automatic driving, and the system comprises a conventional traffic scene generation subsystem which is used for generating a conventional driving scene data set in a simulation platform; the safety key scene generation subsystem is used for generating a safety key scene by utilizing an automatic driving scene generation network SAGA based on adversarial disturbance according to the conventional driving scene data set; and the safety key scene clustering analysis subsystem is used for dynamically discovering a new category and optimizing a clustering structure by introducing a progressive unsupervised clustering method according to the generated safety key scene to obtain a scene analysis result. According to the method, the problems of insufficient test scene coverage, weak resistance test capability, low key scene analysis efficiency and the like in the safety verification of the existing automatic driving system are solved.

The invention discloses a high and cold arid region slope soil stability safety risk evaluation system and method, and relates to the technical field of slope engineering. The state of each side slope under multi-dimensional indexes such as freeze-thaw cycle frequency, dry-wet alternation index, wind erosion strength, shear strength, water content, porosity and fracture density is quantified, the feature similarity between any two side slopes is calculated, and then a side slope feature similarity matrix is formed. Based on the matrix, an unsupervised clustering algorithm (such as spectral clustering, similarity propagation and the like) can be adopted to divide a plurality of side slopes into similar subsets with structural characteristics similar to environmental response, and category attribution of risks is achieved. On the basis, structural variation analysis, historical instability statistics and central risk difference extraction are performed on similar slope samples, so that the internal instability tendency of the slope can be identified, and a risk prediction model suitable for the type of slope can be constructed through feature training.

The invention discloses a lithium battery surface defect intelligent detection method based on deep learning, and the method comprises the steps: S1, carrying out the unsupervised clustering auxiliary defect marking of a collected lithium battery surface image, carrying out the defect enhancement processing, and constructing a lithium battery surface image data set; s2, performing self-supervised pre-training on the YOLOv8 model by using an unlabeled defect image in a related field, and reserving a pre-training weight of a backbone network of the YOLOv8 model; s3, constructing an improved YOLOv8 model, and migrating the pre-training weight of the backbone network to the improved YOLOv8 model; s4, training the improved YOLOv8 model by using the lithium battery surface image data set to obtain a defect detection model; and S5, inputting the surface image of the lithium battery to be detected into the defect detection model to obtain a defect detection result. According to the method, high precision and high efficiency of lithium battery surface defect detection are realized.

The invention discloses a lightweight knowledge graph rapid construction method and system based on an NLP technology. The method comprises the steps of preprocessing an unstructured text and segmenting the unstructured text into semantic segments; unsupervised clustering is combined with the contour coefficient to determine the optimal clustering number, and a semantic association fragment cluster is obtained; performing word segmentation, part-of-speech tagging, NER and entity linking on the fragment cluster, extracting an entity and initial relationship, and fusing semantic similarity, TF-IDF word frequency collaboration degree and co-occurrence frequency to calculate a relationship edge weight; constructing a lightweight knowledge graph; in the question and answer stage, questions are disassembled through a process engine, related sub-graphs are retrieved, and answers with reasoning links are generated. The system correspondingly comprises a text preprocessing module, a clustering module, an entity relation processing module, a graph construction module, a question and answer reasoning module and a storage module. According to the method, the construction cost is reduced, the interpretability and the module coupling degree are improved, multiple scenes such as government and enterprise public opinions and medical assistance are adapted, and the problems of weak generalization, poor real-time performance and'black box 'in the traditional technology are solved.

The invention discloses a federated learning backdoor attack defense method based on layer perception detection, which relates to the technical field of artificial intelligence security, and comprises the steps of client training and uploading, malicious client identification, backdoor key layer detection and deletion, robust aggregation updating and adversarial training for enhancing robustness. According to the method, the model parameters uploaded by the clients are subjected to clustering analysis, and the maximum benign cluster is identified by adopting an unsupervised clustering method, so that instability caused by single threshold judgment is avoided, and the benign client and the malicious client can be distinguished; and meanwhile, layer perception detection and elimination are used in the scheme, so that the backdoor introduced by a malicious client can be effectively identified and eliminated on the premise of ensuring the global model precision, and the influence of backdoor attack on the model is inhibited.

The invention provides a scanning method and system for hidden asset identification in an electric power industrial control network, and aims to solve the problem that silent, non-registered or illegal access equipment is difficult to discover by the existing asset surveying and mapping means. According to the method, multiple protocol induced detection messages are injected into a target network, equipment response data are collected, feature vectors are extracted, and abnormal equipment behaviors are identified by using an unsupervised clustering and anomaly detection algorithm; and meanwhile, in combination with a graph neural network and a hidden Markov model, modeling is performed on a network communication chain structure and a historical behavior sequence, and potential hidden asset nodes are deduced. According to the method, active discovery and risk reasoning of hidden assets can be realized on the premise of not influencing industrial control services, and the method is suitable for industrial control scenes such as transformer substations and dispatching centers in the power industry and has high safety, intelligence and feasibility.

The invention relates to the technical field of intelligent driving, and particularly provides a personalized driving behavior identification method based on deep embedded clustering. Extracting a low-dimensional depth feature vector of the driving time sequence segment through a deep neural network auto-encoder, performing end-to-end joint optimization on the auto-encoder and unsupervised clustering by using a deep embedded clustering method, generating a driving style clustering result, and labeling a clustering result label; freezing the trained auto-encoder parameters, constructing a driving style classification model, and establishing a mapping relation between driving time sequence fragments and style labels; and distributing semantic tags for clustering results by extracting interpretable features. According to the end-to-end joint optimization technology based on deep embedded clustering, the convergence speed and generalization ability of the model are remarkably improved, and the driving style identification precision is far higher than that of a traditional machine learning method.

The invention discloses an LCD display screen production quality collaborative management and control method based on a detection data chain, particularly relates to the technical field of industrial production quality management, and is used for solving the problem of quality root cause misjudgment caused by complex association of multi-source data in the existing liquid crystal display screen manufacturing process. The method comprises the following steps of: constructing a detection data chain covering an array, a box forming process and a module process, firstly performing multi-source data correlation analysis to obtain a variable pair set, then analyzing an intermediary influence path between variable pairs to eliminate false correlation, and then determining a real causal direction by applying a causal discovery algorithm based on conditional independence. Carrying out unsupervised clustering analysis on the basis of defect characteristic indexes, verifying the consistency of causal directions in different data subsets, and finally outputting a stable quality root cause analysis result to realize accurate positioning and effective management and control of key process parameters.

The invention relates to the technical field of federated learning security, and discloses a federated learning poisoning defense method based on time-frequency spectrogram and comparative learning, which comprises the following steps: receiving model update uploaded by each client, grouping and vectorizing parameters according to model layers, and generating a time-frequency spectrogram by applying short-time Fourier transform to parameter vectors of each layer; based on the time-frequency spectrogram, constructing a positive sample pair through data enhancement, carrying out difficult negative sample mining, and training an encoder by using a contrast loss function to extract an embedded vector with high discriminant power; and performing unsupervised clustering on the embedded vector by using a DBSCAN clustering algorithm, judging the maximum cluster as a benign client, performing final judgment in combination with historical malicious records, and only aggregating model parameters of the benign client to update a global model. According to the invention, high-precision detection of attack features can be realized, and a more universal, more efficient and more practical federal learning poisoning attack defense method is realized.

The invention discloses an automatic cable surface defect detection method and system based on a convolutional neural network, and belongs to the technical field of computer vision and deep learning, and the method comprises the following steps: collecting an initial qualified segment image during the production of a new batch of cables, and extracting a library texture feature vector through a pre-trained texture feature coding network, unsupervised clustering is carried out, and cluster centers left after the abnormal clusters are removed and statistics of the cluster centers are stored in a normal texture self-adaptive reference library of the batch; collecting a cable surface expansion image on line, dividing the image into image blocks, inputting the image blocks into the texture feature coding network to obtain a detection texture feature vector, obtaining a texture deviation degree according to the detection texture feature vector and each cluster center in the normal texture adaptive reference library, and judging whether the texture deviation degree is a texture abnormal point or not; and carrying out connected domain analysis on texture abnormal points of the same-frame expanded image to obtain candidate abnormal regions. The problem that real defects and instantaneous noise are difficult to distinguish in the prior art is solved.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention provides a dynamic threshold anomaly detection method and system based on deep semantic reconstruction and an isolated forest, and belongs to the technical field of crossing of network security and artificial intelligence. A sliding window segmentation and dynamic zero padding mechanism is adopted to unify texts of any length into standardized segmented data; the method comprises the following steps: constructing a nine-layer depth auto-encoder (four encoding layers, a middle layer and four decoding layers), learning multi-dimensional semantic features of normal data, calculating a reconstruction error matrix of each data segment, extracting error distribution statistics (mean value, variance, skewness and kurtosis), performing unsupervised clustering on the extracted error features by adopting an improved isolated forest algorithm, and obtaining a clustering result; a dynamic detection threshold value is automatically generated, and after semantic reconstruction and feature analysis are carried out on real-time data, whether the real-time data are abnormal or not is judged in combination with the dynamic detection threshold value; a self-adaptive anomaly detection model is constructed through conjoint analysis of a dynamic threshold generation mechanism and deep semantic features, and dependence on artificial experience is reduced while high detection precision is kept.

The invention discloses an aging performance test system for a cable, and relates to the technical field of cable reliability test. The device is applied to a multi-physics field coupling aging cabin to simulate a synergistic aging environment of various stresses such as electricity, heat, machinery and chemistry. The system integrates a high-precision sensing unit, an edge intelligent analysis unit, a man-machine interaction unit and a control feedback unit, and supports multi-dimensional modeling of cable structures, materials and process parameters. Multiple groups of aging test schemes are generated and recommended based on a conditional generative adversarial network and an unsupervised clustering algorithm, multi-source data are collected in real time in the test process, and aging trends are analyzed by using deep learning models such as CNN and LSTM. Carrying out risk grade judgment in combination with a historical model, introducing an anomaly detection mechanism, and realizing atypical aging sample identification based on Mahalanobis distance and local density scoring; intelligent simulation, accurate diagnosis and abnormal feedback of cable aging behaviors are realized, and the authenticity, accuracy and intelligent level of testing are remarkably improved.

The invention provides an automatic driving takeover quality evaluation method based on fusion of driving behaviors and physiological and psychological characteristics, and the method comprises the steps: carrying out the multi-source data collection and time synchronization of the behavior response and physiological change of a driver in the automatic driving takeover process, and obtaining multi-modal data; performing signal cleaning and adaptive segmented filtering on the multi-modal data to obtain filtering data; differential dynamic feature extraction and time window construction are performed on the filtering data, and multi-modal feature fusion is performed after key features are obtained, so that comprehensive features can be obtained; performing unsupervised clustering on the comprehensive features to obtain different takeover modes; and constructing an intermediary effect model based on the structural equation model and a Bootstrap test method, and performing quality evaluation on the takeover mode to obtain an evaluation result. According to the method, an intelligent evaluation framework combining Gaussian mixture model clustering and intermediary effect analysis is introduced, so that multi-dimensional and interpretable modeling and quality judgment of driver takeover behaviors are realized.

The invention relates to an operation and maintenance alarm root cause positioning method and system fusing a knowledge graph and a large model, and belongs to the technical field of data processing and intelligent operation and maintaining.The method comprises the steps that original alarm data are obtained from a plurality of heterogeneous monitoring sources based on alarm data query service of time period parameters and subjected to cleaning and structural processing, and the original alarm data are obtained; obtaining alarm data to be analyzed; performing unsupervised clustering analysis to realize alarm automatic classification and noise filtering to obtain classified alarms and category labels; extracting corresponding object topological relation information from a pre-constructed knowledge graph; and inputting the classified alarms, category labels and object topological relation information into a large language model, guiding the large language model to perform semantic reasoning through a preset cue word project, and generating a structured diagnosis report containing root cause diagnosis, an influence range and repair suggestions. According to the method, the alarm storm is inhibited, the fault root cause is quickly, accurately and interpretably positioned, and the operation and maintenance efficiency and the system stability are improved.

The invention discloses a lithium battery internal short circuit diagnosis method and system based on a dual-state cycle equivalent circuit, and relates to the technical field of lithium battery internal short circuit detection. The method comprises the following steps: firstly, constructing a hybrid model of a battery second-order RC equivalent circuit embedded RNN, updating a conforming physical equation by customizing a cycle unit constraint state, and fitting an OCV-SOC relationship by an MLP subnet; designing a voltage interval weighted loss function to strengthen the precision of a sensitive area, and combining learning rate attenuation and gradient clipping to jointly optimize physical parameters and MLP parameters; predicting a healthy voltage by using the training model and generating a residual error; extracting a residual subsequence by adopting a sliding window, calculating a DTW distance between the residual subsequence and a zero vector, and forming a two-dimensional feature by using a mean value and a maximum value; and realizing automatic fault identification based on Gaussian mixture model unsupervised clustering. According to the method, the physical mechanism and the data driving advantages are fused, the early-stage internal short circuit can be detected with high precision without fault samples, and the problems that a threshold method is high in false alarm rate, a model method is poor in adaptability and data driving depends on labeled samples are solved.

The invention relates to a manufacturing system risk control knowledge matching method based on semantic embedding and clustering analysis, and the method comprises the following steps: collecting and preprocessing risk control text data: collecting unstructured text data of a manufacturing system history record, and obtaining preprocessed risk control text data, constructing a professional corpus for a discrete manufacturing scene; text semantic embedding generation; semantic clustering modeling: performing unsupervised clustering modeling on all semantic vectors, mining semantic association and potential structures between texts, obtaining semantic representations of risk control knowledge through a clustering algorithm, and assisting in generating clustering tags; and a risk knowledge matching mechanism.

The invention relates to the technical field of intelligent switching, in particular to a multi-mode dynamic coupling intelligent switching method and system based on road condition recognition and a vehicle. The method comprises the steps that gradient, vehicle speed, accelerator and brake signals are collected in real time, and a multi-dimensional feature vector fusing driving habits is constructed after preprocessing; a real-time road condition label is automatically generated through unsupervised clustering, and the clustering effectiveness is verified online by using a contour coefficient. Enabling the road condition label and the current power mode to form a system state, and inputting the system state into a self-learning decision network based on reinforcement learning; and the network updates the state-action value matrix through a reward function in a time sequence difference mode, and outputs a target power mode with the highest accumulated reward. And calculating the motion confidence coefficient of the target mode based on the value matrix, if the motion confidence coefficient exceeds a threshold value, generating a switching instruction, and smoothly adjusting the torque by an execution mechanism through a torque transition algorithm to realize impact-free switching. Automatic road condition recognition, self-adaptive decision making and non-inductive execution are achieved, and smoothness and adaptability are improved.

The invention discloses a block chain abnormal transaction behavior identification method, relates to the technical field of block chains, and solves the problem of low detection efficiency in the prior art. The method comprises the following steps: constructing first, second and third transaction network diagrams; according to a biased migration strategy, calculating to obtain an embedded vector of each node in the second transaction network diagram; performing unsupervised clustering on the first transaction network diagram according to the embedded vector; calculating a multi-dimensional suspicious index of each node in the third transaction network graph, obtaining a comprehensive suspicious value through aggregation calculation, generating a series of sub-graphs and the total suspiciousness of each sub-graph, and determining the sub-graph corresponding to the maximum total suspiciousness as a dense suspicious sub-graph; performing maximum flow detection in the dense suspicious subgraph to obtain an abnormal transaction source account set; according to the abnormal transaction source account set and the behavior abnormal account set, the risk level of the account in each cluster is judged; risk assessment and identification of large-scale Ethereum accounts are realized, and the Ethereum supervision capability is enhanced.