2237 results about "Data labeling" patented technology

Systems and methods for data streaming with attribute-based access control. A client apparatus in a data streaming system receives a message via a data stream, form a data stream provider. The message has a schema version of the schema associated therewith. The client apparatus processes the message to retrieve a record, the record including a data item and a data tag corresponding to the data item. The client apparatus, with reference to the schema version, determine whether an identifier (e.g., client ID) of with the client apparatus has permission to access the data item associated with the data tag. After detecting that the identifier does not have permission to access data associated with the data tag, the client apparatus masks the data item to produce a masked data record. The client apparatus releases the masked data record for further processing by the client apparatus.

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

The invention discloses a rolled metal surface defect automatic labeling method based on a multi-task self-adaptive model, which comprises the following steps: S1, collecting a rolled metal surface image, preprocessing and enhancing the rolled metal surface image, performing data labeling, and constructing a rolled metal surface defect data set; s2, constructing a multi-task adaptive model, and training the multi-task adaptive model by using the data set to obtain an automatic defect labeling model; and S3, inputting a to-be-labeled metal surface image into the defect automatic labeling model, and outputting a surface defect labeling result by executing the target detection task. According to the method, a multi-task learning framework is combined, joint training of target detection and instance segmentation tasks of metal surface defects in the rolling technology is achieved, the target detection task is focused in the reasoning stage, the labeling efficiency is improved, the labor cost is remarkably reduced, and the labeling efficiency is improved. The method can be widely applied to industrial defect detection and data automatic labeling scenes of rolling process metal, and is suitable for defect image automatic labeling tasks in a complex industrial environment.

The invention discloses a big data distributed storage and parallel processing collaboration method based on cloud computing. The method comprises the following steps: sensing data stream characteristics in real time through a self-adaptive dynamic partitioning engine, dynamically adjusting a partitioning strategy and generating a metadata label; constructing a node selection model through a comprehensive evaluation algorithm, selecting storage nodes to form an optimal storage cluster, and dynamically adjusting a resource matching weight coefficient based on a load state through a load optimization module; decomposing a data processing task into parallel subtask units, and constructing a dual-objective optimization model; triggering a dynamic rebalance mechanism through a distributed monitoring agent in combination with a hierarchical early warning strategy; and constructing a multi-level cache system to optimize a data access path, outputting a final result, pushing the final result to the user terminal, and updating the knowledge base. Through collaborative optimization of dynamic partitioning, multi-dimensional resource scheduling, elastic scaling and intelligent caching technologies, the resource utilization rate, the load balancing capacity and the stability in a high-concurrency scene of the system are remarkably improved.

The invention provides a marketing strategy optimization method based on big language model analysis and evaluation driving, and the method comprises the steps: obtaining historical data including user behavior data, label data and statistical data, carrying out the analysis, and constructing a basic marketing strategy; user content data are collected in real time through Kafka, deep analysis is conducted on user content through a large language model, and a personalized marketing scheme is matched from a knowledge base based on the deep analysis result of the large language model and basic rule matching; through a feedback circulation mechanism, feedback of the user to marketing activities is collected, and a feedback result is stored in a knowledge base and used for optimizing a marketing strategy and a large language model; meanwhile, the decision-making process is logged, textualized and vectorized and is stored in a vector library, and the decision-making process and the marketing scheme are optimized in combination with a continuously optimized large language model, so that dynamic adjustment and matching are realized. According to the invention, the accuracy and effect of the marketing strategy are improved by using the large language model.

The invention discloses a deep neural network model-based backfill compaction degree quality evaluation method, which comprises the following steps of: acquiring various physical characteristics of soil in a compaction process in real time through a multi-source sensor, and performing data labeling and time-space adaptive normalization processing; based on the position information of the multi-source sensor and the multi-source sensing data, adopting an improved empirical mode decomposition and stochastic resonance enhancement method, and fusing same-order mode components of the multi-source sensor to obtain an intrinsic mode function related to the compactness; in combination with graph convolution operation, stochastic resonance gating, multi-scale time sequence attention, a mixed loss function, a dynamic course learning strategy and the like, training the deep neural network model; and based on the trained model, carrying out backfill compaction degree quality evaluation on the to-be-detected area. According to the method, by collecting multi-source data in real time and combining advanced technologies such as space-time adaptive normalization, empirical mode decomposition and dynamic adaptive graph convolution, efficient and stable backfill compaction degree evaluation is achieved.

The invention relates to an automatic data labeling method of an ISP image signal processing visual sensor. The method comprises the following steps: carrying out noise reduction and signal enhancement on an original image data stream acquired by an ISP image signal processing visual sensor to obtain a standardized image sequence; performing time sequence feature extraction to obtain a time sequence feature tensor; performing spatial feature extraction to obtain spatial-temporal feature representation; inputting a double-fold teacher-student network for semi-supervised training to obtain an initial image annotation model; performing quality evaluation based on the prediction probability distribution and the time sequence consistency to obtain a labeling result with a quality score; and storing a high-quality sample in the labeling result with the quality score into a sample buffer pool, carrying out sample screening based on entropy and distance measurement, and carrying out parameter updating on the initial image labeling model through an incremental learning strategy to obtain a target image labeling model. According to the invention, the reliability evaluation of the labeling result is realized, and the accuracy of automatic labeling is ensured.

The invention provides an AI-assisted large-model intelligent question and answer accuracy improvement method, and belongs to the technical field of intelligent question and answer. Comprising the steps of data collection, data cleaning, data labeling, dynamic knowledge base construction, knowledge base updating trigger mechanism establishment, pre-training language model improvement, post-processing language understanding enhancement, combination of rule-based and machine learning reasoning, reasoning result verification and optimization, dialogue strategy optimization, emotion perception and response, multi-language search and language recognition search. And multi-dimensional evaluation index setting and regular evaluation and feedback improvement are realized. The intelligent question and answer accuracy improving method is high in understanding ability, timely in data updating, outstanding in reasoning ability and high in language recognition and dialogue ability.

The invention discloses a data annotation method and system of a collaborative computing architecture based on quantum computing, and belongs to the field of data annotation. The method comprises the steps that S1, multi-modal data are input and preprocessed; s2, extracting features of each mode after preprocessing; s3, coding the features of each mode into a quantum state, and carrying out mode fusion; s4, performing label reasoning on the quantum state after modal fusion, and performing label constraint optimization by using a quantum approximate optimization algorithm; s5, based on a quantum Bayesian network or an approximate causal graph generation method, generating explanation according to a modal contribution causal path, and deducing marginal contribution of each modal to final label prediction by using a joint probability measurement result; and S6, outputting a labeling result. According to the method, a quantum-classical cooperative computing architecture is designed, the efficiency and accuracy of multi-modal data labeling are remarkably improved, the interpretability, the distributed processing capacity and the high-dimensional feature modeling capacity of the system are enhanced, and a brand new solution thought is provided for development of the multi-modal labeling technology.

The invention discloses a semantic fingerprint adaptive training method for a teaching service robot, and relates to the technical field of education neural network real-time training. Comprising six steps of course version semantic fingerprint injection, real-time drift detection and bucket division positioning, small sample correction and high-level weight patching, hierarchical control incremental learning scheduling, shadow reasoning consistency optimization and learning asset registration and cycle verification and tracking. Measuring drifting in real time by using an information entropy self-adaptive window and multi-scale divergence, generating a lightweight weight patch by small sample contrast learning, and performing online loading; shadow channel parallel reasoning is combined with a grading heat exchange superior weight, four-dimensional learning asset tensor is written into a registry through double-clock witness and chain commitment, random sampling verification and singular value performance verification ensure that assets are consistent with robot online examples, and the comprehensive effects of source traceability, risk self-sensing, model self-repairing and compliance full-chain trace reserving are achieved.

Provided is a detection method of spaceborne GNSS-R original intermediate frequency coherent reflection signals in ocean, polar and inland water areas, including: acquiring spaceborne GNSS-R original intermediate frequency signal data of TDS-1 or CYGNSS and preprocessing the data; selecting coherent detection feature engineering; setting data labels of different scenes and coherent and incoherent reflected signals; dividing a training set and a test set; and training and testing a multimode-oriented hybrid model for coherent and incoherent detection and classification of spaceborne GNSS-R signals, using the training set to train a model, applying a trained detection model to a test data set, and comparing and evaluating obtained detection results with a classical coherent detection algorithm.

The invention relates to the technical field of data annotation, and particularly discloses an intelligent annotation method and system for large model data, and the method comprises the steps: carrying out the confidence evaluation of each piece of unannotated text data in an unannotated text data set through employing a natural language processing technology based on deep learning; according to the method, text data corresponding to the minimum confidence coefficient is selected as representative sample data, corpus expansion is performed on the representative sample data, and then semantic feature extraction and compensation type interactive fusion are performed on the representative sample data and the representative sample data after corpus expansion, so that common information and unique information between the representative sample data and the representative sample data after corpus expansion are fully utilized; therefore, comprehensive semantic understanding and intelligent labeling of the representative sample data are realized. In this way, the efficiency and accuracy of data annotation can be remarkably improved, meanwhile, the requirement for manual intervention is greatly reduced, and the annotation cost is reduced.

According to the OpenCSG large model intelligent data annotation system provided by the invention, by virtue of an automatic annotation technology driven by a large model, the requirement of manual annotation is greatly reduced, and the data annotation efficiency is remarkably improved. The system can generate a high-precision labeling result by using the intelligent labeling module and the automatic quality control module, and ensures the consistency and accuracy of the labeling result through an automatic auditing process. No matter sentiment analysis, entity recognition or complex text classification, the system can efficiently adapt to various types of labeling tasks, and different data processing requirements are met. Through modular design, the system supports flexible expansion, and can meet various data labeling requirements from small-scale test projects to large-scale commercial projects. According to the overall architecture, all the function modules are closely coordinated in the system, so that efficient operation and high-quality output are always kept in a large-scale data annotation project.

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

The invention discloses a multi-label generation matching RAG intelligent question and answer method, device and equipment and a medium, and the method comprises the steps: obtaining original text data corresponding to a target business scene, and carrying out the vectorization processing of the original text data, and determining a to-be-processed text data set; determining a text label set corresponding to the to-be-processed text data set, and establishing a text vector database based on the text label set and the to-be-processed text data set; under the condition that a to-be-processed problem is received, determining a problem text label and a problem text vector corresponding to the to-be-processed problem; determining a to-be-applied text from a text vector database based on the to-be-processed problem text, the problem text tag and the problem text vector; and generating a target question answer corresponding to the to-be-processed question according to the to-be-applied text and a preset cue word template. According to the technical scheme, the data source and the data label are customized according to the business scene and combined with the vector retrieval function, so that the fine granularity and accuracy of the retrieval result are remarkably improved.

The invention provides an anomaly detection multi-classification method based on multi-source operation and maintenance data fusion. Comprising a data input layer, a parallel coding layer realized through dissimilatory multi-modal coding and a hierarchical multi-modal fusion architecture, a space-time feature fusion layer realized through a space-time perception dynamic gating attention enhancement mechanism, and a dynamic decision optimization layer realized through a gradient perception dynamic smooth loss function. The spatio-temporal feature fusion generates a feature representation and weight matrix with a dynamic attention weight through a spatio-temporal perception dynamic gating attention enhancement mechanism, and outputs the feature representation and weight matrix to the dynamic decision optimization layer; and the dynamic decision optimization layer realizes anomaly detection through a classifier taking a gradient perception dynamic smooth loss function as feedback, so that key problems such as multi-source heterogeneous data fusion, time sequence dynamic modeling and data label imbalance are solved, the anomaly detection accuracy and robustness of a training cluster are effectively improved, and the anomaly detection accuracy and robustness of the training cluster are improved. And a reliable technical support is provided for intelligent operation and maintenance of a complex training cluster.

The invention provides a credential platform data migration verification method and system, and relates to the technical field of computers.The method comprises the steps that all to-be-migrated source data in a credential platform database are read, basic information of the source data is obtained, and a data label is set for each piece of data; reading fields and contents in the migration data, and constructing a data similarity verification model; selecting a migration path and a target database according to the migration demand, and dynamically evaluating the similarity between the source data and the target data through a data similarity verification model; verifying the migrated source data based on the target database, and performing incremental verification on data of different fields in the table; the data table of the to-be-migrated source data is verified in real time among different migration nodes, data changes on each node are verified step by step through an increment verification mechanism, if data missing or verification inconsistency occurs in the verification process, an alarm mechanism is triggered, and the problem of data missing or inconsistency can be automatically recognized and corrected.

The invention discloses an automatic data labeling method based on multi-modal fusion and iterative optimization. The method covers core links such as model automatic labeling, uncertainty recognition, expert recheck and correction and model continuous optimization, and multi-modal enhancement, standardization processing and cross-domain knowledge fusion are combined, and through an iteration mechanism of machine labeling, anomaly screening, manual verification and model retraining, the multi-modal enhancement, standardization processing and cross-domain knowledge fusion are combined. And a closed-loop process of machine main label + artificial refinement capable of continuously learning and self-evolving is formed. The method breaks through the limitations of low efficiency, high cost and difficult quality control of the existing expert-dependent labeling, is universal for multi-modal, multi-temporal and multi-organization-level data, effectively improves the data quality, labeling efficiency and model generalization ability, and has good adaptability and generalization performance.

The invention discloses a multi-modal data labeling method and system based on large model pre-labeling, and the method comprises the steps: S1, receiving to-be-labeled multi-modal original data and labeling task definition, and generating a structured task instruction signal; s2, inputting the structured task instruction signal into a multi-modal large model, and generating a pre-labeling result signal containing a preliminary label and a corresponding confidence coefficient thereof; s3, scheduling a manual verification task based on the confidence coefficient in the pre-labeled result signal; s4, according to the manual verification signal, performing parameter fine tuning or prompt optimization on the multi-modal large model, and generating a model optimization signal; and S5, pre-labeling the new multi-modal original data by using the multi-modal large model updated by the model optimization signal, and fusing an artificial verification signal. According to the multi-modal data annotation method and system based on large model pre-annotation, the problems that traditional multi-modal data annotation is low in efficiency, high in cost and difficult to unify in quality can be solved.

The invention belongs to the technical field of artificial intelligence, and discloses a reinforcement learning method for improving mathematical ability of a large language model and a related device. The reinforcement learning method comprises the steps of obtaining a to-be-enhanced large language model and a reinforcement learning data set; performing fine tuning training on the to-be-enhanced large language model by adopting reinforcement learning, and performing process level labeling on answer prediction generated in reinforcement learning by applying Monte Carlo estimation during fine tuning training to obtain a fine-tuned large language model and a labeled data set; and training the process reward model based on the annotation data set to obtain a trained process reward model. According to the technical scheme disclosed by the invention, fine-grained errors existing in the reasoning process can be captured, and the mathematical ability of a large language model is enhanced; in addition, data annotation can be realized while reinforcement learning is carried out, and the collection cost of process-level annotation data is saved.

The invention relates to a big data processing technology, in particular to an intelligent data labeling method and system. The intelligent data labeling method comprises the following steps: preprocessing public opinion data; semantic deduplication of the historical sample library is carried out, and the sample library is updated; an intelligent auxiliary labeling method based on a historical work order library and a large model; model updating and sample increment updating; compared with the prior art, the method has the advantages that a multi-dimensional data cleaning method is adopted, the quality of the to-be-labeled data is improved, and interference of repeated semantic information is reduced through semantic duplicate removal; potential values of similar work orders in historical work orders are mined, a large model cue word project used for auxiliary labeling is constructed, an available auxiliary labeling cue word template is provided, and the cue word construction and auxiliary labeling process is opened; a PDCA thought is adopted, and a data-annotation-model-work order closed-loop management and PDCA mechanism spiral data quality method is constructed.

The invention discloses a multi-scene application data acquisition method based on atomization design. The method comprises the following steps: analyzing reported data into an atomization data structure; performing semantic recognition on the data labels to generate a label semantic mapping result; constructing a layered cache structure; generating a compressed semantic mapping rule by applying a multi-level semantic difference coding algorithm; identifying an optimal query path from the tag affinity matrix; and executing historical data cleaning based on the data value. Intelligent mapping of data labels is achieved through semantic recognition and version control, the storage efficiency is improved through predictive lazy loading and compression coding, the query path is optimized based on the affinity matrix, and the technical problem of multi-scene application data collection is effectively solved.

The invention relates to the technical field of environment monitoring, in particular to a machine room dynamic environment monitoring and management method. Comprises: collecting machine room environment parameter data; adding a data label to each piece of parameter data and recording a timestamp; creating an environment monitoring data frame, and filling basic information; adding an abnormal event indication field in the data frame, and comparing with a preset threshold to mark an abnormal condition; identifying a sudden change trend in the time sequence data and recording a result; calculating relevance indexes, and analyzing data relevance among different sensors; the signaling modules are mapped to different data processing units, and data processing and circulation services of corresponding levels are provided; the data frames are interpreted step by step, and corresponding monitoring and management strategies are executed; dynamic weight distribution is implemented, and the data frame processing priority is adjusted according to historical weight changes and the current state; data frame specific processing fields are registered and tracked. According to the method, real-time monitoring, comprehensive analysis and accurate management of the dynamic environment of the machine room are realized.

The invention provides a data label classification method and system based on artificial intelligence, and the method comprises the steps: obtaining a text data feature and an image data feature, and fusing the two features to form a multi-modal feature; modeling the label hierarchy graph by using a graph neural network, and training the model by taking the multi-modal features as the output to obtain a trained complex model; performing knowledge distillation on the complex model, and compressing the complex model into a lightweight model; correcting the label prediction probability distribution of the lightweight model according to the label hierarchy graph; newly input data are obtained, the newly input data are input into the label hierarchy diagram to be dynamically updated, meanwhile, consistency regularization is designed during updating to adaptively adjust label prediction probability distribution, and finally the weight of the lightweight model is optimized according to the updated label hierarchy diagram. The invention provides an efficient label classification system which can adapt to diversified application scenes on the premise of keeping classification precision.

The invention relates to the technical field of image recognition data processing, in particular to a pipeline element library creating method based on a model training and image recognition technology, which comprises the following steps of: acquiring pipeline element image data with depth information through a multispectral imaging scheme, executing Retinex algorithm illumination equalization and morphological restoration through a cascade preprocessing pipeline, and establishing a pipeline element library. A standardized data set associated with the metadata is generated. A multi-task joint learning framework is adopted to integrate ResNet-50, HRNet and Mask R-CNN networks, a bottom convolution feature extraction layer is shared, oil stain and strong reflection antagonism data synthesized by a generative adversarial network is injected, model parameters are optimized in combination with a progressive training strategy, and a lightweight MobileNetV3 model is output. Model parameters and an index structure are dynamically updated, and system self-calibration is achieved in combination with a cross-device calibration protocol and data consanguinity tracking. According to the method, through automatic data labeling, multi-task feature multiplexing and retrieval feedback closed loop, the construction efficiency of the pipeline element library is effectively improved, and the recognition error rate in a complex environment is reduced.

The invention provides a semiconductor chip automatic test optimization system, and belongs to the technical field of electrical variable measurement. Comprising an acquisition module used for establishing an electrical variable time sequence arranged according to a time sequence, a drift analysis module used for calculating a short-time fluctuation amplitude value, a long-term drift slope and a high-frequency noise amplitude value based on the impedance time sequence arranged according to the time sequence, and a contact judgment module. The evaluation module is used for constructing a contact impedance coefficient based on a short-time fluctuation amplitude value, a long-term drift slope and a high-frequency noise amplitude value in a jth sliding window, and evaluating and optimizing the contact impedance coefficient, and the data marking module is used for marking electrical variable measurement data corresponding to a test channel which is evaluated to be instable in contact as low confidence. According to the system, the ith test channel of the temperature sensor chip can be tested more accurately, the short-time fluctuation amplitude value, the long-term drift slope and the high-frequency noise amplitude value are calculated at the same time, multi-source data participates, and the test accuracy is improved.

According to the big data management-based data labeling method and system provided by the invention, efficient, accurate and intelligent data labeling is realized by integrating multi-virtual machine collaborative labeling, multi-model fusion labeling, adaptive data preprocessing and hierarchical cleaning and a multi-dimensional task scheduling and dynamic load balancing mechanism. The method comprises the steps that self-adaptive preprocessing and layered cleaning are conducted on original big data, noise is removed, missing values are filled up, and format conversion is completed; dividing the preprocessed data into a plurality of subtasks according to data characteristics; distributing the sub-tasks to different virtual machine instances for parallel processing by utilizing a multi-virtual machine technology; labeling the same data fragment on each virtual machine by adopting a multi-model labeling mechanism; adopting a weighted fusion or voting mechanism to generate a comprehensive annotation result for the output results of the multiple models in each virtual machine; and summarizing the labeling results of the virtual machines to form a global data labeling result.

The invention provides a material data curve identification and fitting method based on deep learning, and relates to the technical field of material data extraction, and the method comprises the steps: obtaining an image file containing a plurality of material data curves; separating the material data curve from the image background by adopting an image segmentation technology, and extracting feature points of the material data curve through a convolutional neural network; and taking the feature points as control points, performing parametric fitting on the curve through polynomial or spline interpolation, and generating a smooth continuous curve. The method further comprises the following steps: detecting the position and boundary of a coordinate axis in the image file through an image processing technology; the OCR technology is used to identify the scale label on the coordinate axis, and the deep learning model is combined to analyze the numerical value and unit of the scale. According to the method, through innovative technologies such as multi-modal feature fusion, self-adaptive preprocessing and intelligent data labeling, precise recognition of complex curves, multi-curve separation and association, intelligent recognition of non-standard coordinate axes and efficient real-time processing are achieved.

The present application relates to the technical field of data detection, and discloses an electricity theft behavior detection method, apparatus, and device, and a storage medium. The method comprises: obtaining historical electricity consumption data of an electrical terminal within a first detection period, and assigning a data label to the historical electricity consumption data; performing data preprocessing on missing data in the historical electricity consumption data to obtain preprocessed data; training and testing a preset model on the basis of the preprocessed data and the historical electricity consumption data having the data label to obtain an electricity theft behavior detection model, wherein the preset model is constructed based on OS_CNN technology and AutoXGB technology; and by means of the electricity theft behavior detection model, detecting whether the electrical terminal has an electricity theft behavior. According to the present application, the preset model constructed based on the OS_CNN technology and the AutoXGB technology is trained and tested to obtain the electricity theft behavior detection model, so as to quickly and accurately detect, by means of the electricity theft behavior detection model, whether the electrical terminal has the electricity theft behavior.

A heavy traffic abnormal event detection and state discrimination system belongs to the field of intelligent traffic and comprises a data acquisition and labeling module, an image processing module, a traffic state analysis module, a state determination and abnormal event identification module and a transfer learning module. The data acquisition and labeling module comprises a data acquisition unit and a data labeling unit; the image processing module comprises an image enhancement unit and an image compression and reconstruction unit; the traffic state analysis module comprises a traffic bottleneck identification unit and a traffic flow wave analysis unit; the state judgment and abnormal event recognition module comprises a vehicle statistical unit, a congestion state recognition unit, a state transition analysis unit and a compressed sensing model establishment unit; and the transfer learning module comprises a transfer learning unit. According to the method, the false detection rate of the traffic flow abnormal events in bad weather can be reduced, so that the traffic abnormal events can be detected all day long, and the occurrence probability of the traffic events in bad weather conditions can be reduced.