4117 results about "Confidence value" patented technology

The invention discloses a multi-source heterogeneous data knowledge graph construction method for railway disaster prevention monitoring, and relates to the technical field of knowledge graph construction, and the method comprises the steps: gathering multi-source heterogeneous data related to railway disaster prevention monitoring, and constructing a domain ontology model used for guiding knowledge extraction and fusion; extracting entities, attributes and relationships among the entities from different modal data after standardization preprocessing by using a targeted extraction algorithm; obtaining fused structured knowledge based on a multi-strategy knowledge fusion process of domain ontology constraint and confidence evaluation; the fused structured knowledge is stored in a graph database, and construction of the knowledge graph in the railway disaster prevention monitoring field is completed; through combination of domain ontology construction, a mixed knowledge extraction engine and a multi-strategy knowledge fusion technology, deep semantic fusion of multi-source heterogeneous data in the railway field is realized. The invention aims to construct a knowledge graph capable of comprehensively and accurately reflecting complex characteristics in the railway disaster prevention field.

The invention discloses a multi-modal knowledge graph rule reasoning method and device based on a large model, and the method comprises the steps: carrying out the feature extraction and cross-modal alignment of input text data and image data, and generating a multi-modal feature vector of a unified semantic space; performing knowledge graph storage on the emotion entities and the relationships by adopting an attribute graph model to complete construction of an emotion knowledge graph; generating an interpretable inference rule from the emotion knowledge graph by using a large language model, and eliminating a conflict rule in combination with logic verification; calculating the confidence coefficient of a reasoning path based on an attention mechanism, and carrying out quantitative evaluation on a rule reasoning result; the knowledge graph and the rule base are updated online according to user feedback, and the real-time performance and accuracy of the inference system are optimized through weight adjustment and a forgetting mechanism. According to the method, through innovative technologies such as multi-modal data integration, dynamic knowledge evolution and interpretability reasoning, the limitation of a traditional sentiment analysis method in the aspects of evidence dimension, adaptive capacity, interpretability and the like is broken through.

The invention relates to the technical field of intelligent conference processing, and relates to a conference summary processing method and system using AI, and the method comprises the steps: carrying out the real-time noise suppression of a collected conference audio stream and associated text data through a noise suppression algorithm, and carrying out the cross-modal alignment of the denoised data through a cross-modal alignment algorithm; a domain-specific attention head is inserted into an attention layer of the pre-trained Transform model, a domain-enhanced speech recognition model is constructed, and audio is converted into a text sequence with a speaker tag; adopting a heterogeneous graph neural network to construct a structured topic evolution graph; key decision nodes in the structured topic evolution graph are extracted based on a reinforcement learning strategy, and a final conference summary document is generated. In the decoding stage, the fusion proportion of the acoustic model and the language model is dynamically adjusted based on the real-time acoustic confidence coefficient, the recognition rate of the vocabularies in the professional field is increased, and the problems of frequent term transcription errors and poor semantic coherence in the professional conference are effectively solved.

The invention discloses a building electromechanical BIM model information rapid retrieval method and system, and the method comprises the steps: generating composite retrieval parameters fusing semantic keywords and three-dimensional coordinate constraints according to a multi-mode retrieval instruction inputted by a user; on the basis of the composite retrieval parameters, constructing a dynamic search space by utilizing a hierarchical graph convolutional network, and generating a candidate model index structure of multi-dimensional feature coding; inputting the candidate model index into a multi-objective optimization engine, performing real-time optimization on a search path by adopting a dynamic pruning algorithm driven by reinforcement learning, and outputting a candidate model set of which the confidence coefficient is higher than a preset confidence threshold after pruning; and on the basis of the candidate model set, associated equipment nodes are expanded through a knowledge graph embedding and complementing technology, and an enhanced retrieval result set containing the hidden associated equipment is generated. By utilizing the embodiment of the invention, efficient, multi-dimensional and multi-modal information accurate positioning and quick retrieval can be realized in a large-scale complex BIM model.

The invention relates to the technical field of building information models, augmented reality, synchronous localization and map construction, and provides a deep learning-based SLAM-BIM augmented reality cooperative localization method and system, and the method comprises the steps: introducing a Transform time sequence feature extractor and a geometric relation graph, evaluating a dynamic distribution weight through combining with the confidence, achieving the cross-modal closed-loop detection, and obtaining an SLAM-BIM augmented reality cooperative localization result. A lightweight semantic segmentation network and a feature fusion module are utilized, a dense map with consistent geometric semantics is constructed, a space-time error propagation equation is constructed, online calibration is realized by means of BIM scale prior, an incremental fusion algorithm is designed, a global pose is optimized in combination with AR interaction, and the system fuses SLAM visual trajectory features and BIM semantic geometric features through a deep learning technology. According to the method, the problems that traditional SLAM accumulative errors are large and the BIM fusion precision is low are solved, robust positioning and map construction in a complex scene are achieved, the cooperation precision and real-time performance of SLAM and BIM are improved, and the method is suitable for AR scenes such as building construction and operation and maintenance.

The invention relates to the technical field of power line inspection, and provides a line holographic anomaly detection method and system based on cross-modal intelligent cooperation. The method comprises the following steps: acquiring multi-modal data; performing cross-modal fusion to generate an association tensor; the abnormal joint reasoning uses a time sequence diagram neural network and reinforcement learning to output abnormal confidence; the dynamic knowledge driven decision adaptively adjusts a detection threshold through Bayesian calculation and transfer learning; local real-time response is realized through layered edge calculation; and multi-target collaborative optimization feedback improves the detection precision. The system is composed of a multi-mode perception fusion layer, an intelligent analysis layer, an edge execution layer and an optimization control layer. According to the method, the problems of multi-modal information isolation, response delay and environmental adaptability are solved, and the defect detection rate and the system robustness are remarkably improved.

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

The invention relates to the technical field of data processing, in particular to a multi-modal data alignment method and system, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data; the preprocessed multi-modal data are sent to an event anchor point detection module, and multi-modal emergency detection, anchor point marking and preliminary global alignment are carried out; performing time resampling and numerical value standardization on the multi-modal data after coarse alignment; mapping the resampled and standardized multi-modal data to a unified time axis; performing local fine alignment and global adjustment on the multi-modal feature sequence after unified time axis mapping; and performing confidence evaluation and anomaly correction on the fine alignment result, and outputting a final alignment sequence. According to the invention, a high-precision and multi-modal data real-time alignment method which is light in weight and has adaptive correction capability can be realized on end side equipment with limited resources.

The invention relates to a multimedia equipment control method and system based on adaptive protocol matching. The method comprises the following steps: firstly, collecting communication protocol data and extracting features to form a protocol feature data set; secondly, performing confidence coefficient verification on a data set type identification result, and if the confidence coefficient is lower than a threshold value, extracting a depth feature through a convolutional neural network; and calling a national standard protocol adaptation conversion engine based on a classification result, converting the heterogeneous protocol into data in a unified format, and generating a secure communication data frame through national secret algorithm encryption and bidirectional identity verification. And finally, performing hash check and structured analysis on the data frame, constructing an interaction information packet in combination with user behavior characteristics, and dynamically optimizing a control strategy through real-time feedback data to generate a self-adaptive interaction control scheme. According to the method, through a technical closed loop of intelligent analysis-standard conversion-security reinforcement-closed loop optimization, the high efficiency, the intelligent level and the security performance of multimedia equipment management are remarkably improved.

The invention discloses a decision optimization method fusing enhanced multi-modal learning and a knowledge graph, and relates to the technical field of artificial intelligence and knowledge graphs. According to the decision optimization method for fusing enhanced multi-modal learning and the knowledge graph, dynamic fusion of multi-modal features is realized through a dynamic weight adjustment and semantic alignment constraint mode, semantic precision and interpretability are improved, the semantic deviation problem caused by traditional static feature fusion is effectively solved, and the method has the advantages of being high in robustness and high in reliability. And by recording an intelligent reasoning path selected by a hierarchical reinforcement learning agent, interactive graph structure display can be carried out, the advantage of transparency is achieved, meanwhile, the interpretability of the path is further improved in cooperation with a multi-target reward function, intelligent updating of the knowledge graph is carried out in cooperation with comprehensive confidence, intelligent growth of the knowledge graph is achieved, and the intellectual property of the knowledge graph is improved. And a fine-grained interpretable report is generated through an adversarial training mechanism and anti-factual reasoning, so that the false alarm rate of an output result is further reduced, and the decision transparency is improved.

The invention relates to the technical field of artificial intelligence and Internet of Things, and discloses an AI interaction intelligent module based on a hybrid architecture, comprising a user interaction unit which supports voice, text and image multi-modal input and integrates intention recognition and context understanding algorithms; the data processing unit is used for carrying out structured processing on the electric appliance specification and the historical fault data and constructing a dynamically updated knowledge graph; the hybrid architecture core unit comprises a deep learning subunit for realizing natural language understanding and generation based on a Transform model, and a knowledge reasoning subunit; a fault diagnosis unit; and a feedback optimization unit. According to the method, seamless cooperation of deep learning and symbol logic is realized through a dynamic routing strategy, a high-confidence-coefficient scene generates a response through a Transform model, a medium-confidence-coefficient scene calls a knowledge graph rule for verification, and a low-confidence-coefficient scene supplements information through multiple rounds of interaction, so that the effect of improving balance efficiency and safety is achieved.

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent regulation and control system for an injection molding process of an industrial control system, and aims to solve the problem that high-precision cooperative regulation and control are difficult under material batch fluctuation, mold state change and environmental disturbance. The system comprises a multi-source sensing module, a dynamic modeling module, a self-adaptive decision-making module, an execution feedback module and a knowledge evolution module, and high-stability and high-adaptability intelligent regulation and control of the injection molding process are achieved through a mixed digital twin model integrating a physical mechanism and data driving, confidence-guided multi-objective optimization and continuous evolution of a process knowledge graph.

The invention belongs to the technical field of network security, provides a network attack detection method based on dynamic graph coding, and solves the problems of poor dynamic adaptability of an attack path and missing of timing constraint in the prior art. The method comprises the following steps: constructing a dynamic threat map, extracting a triple of heterogeneous threat intelligence by using a RoBERTa model, and adding a timestamp and a confidence attribute; a dynamic graph encoder for time sequence perception is designed, semantic and evolution laws are fused through periodic time coding and a multi-head time sequence attention mechanism, and feature weights are adjusted in combination with a gating residual layer; an event-driven incremental updating strategy is adopted, and node similarity is calculated to achieve local subgraph updating; a time sequence rule base is established, three-dimensional parameter verification attack chain time sequence logic is defined, and abnormity is judged through conflict scores; and finally, integrating a graph updating module, a dynamic coding module and a constraint analysis module to realize multi-source threat feature matching and attack detection. According to the method, the adaptability of attack path evolution is improved through dynamic graph modeling and real-time increment updating.

The invention provides an APT (Advanced Persistent Threat) attack detection method based on a large language model, which comprises the following steps of: S1, extracting original system call event data from a kernel audit log of an operating system, and preprocessing the data; s2, constructing a multi-model collaborative detection architecture based on a large language model, and realizing fine-grained classification of network entities according to the preprocessed data through prompt construction, model fine tuning and a confidence scoring mechanism; s3, constructing an adaptive graph search algorithm based on multi-modal feature correlation modeling, driving attack path topology reconstruction, and realizing maximum reduction of a malicious sub-graph topology structure; s4, carrying out combination with MITRE ATTamp; the CK tactical knowledge base constructs a cyclic enhancement analysis framework, a cyclic enhancement technology is adopted to drive a large language model to execute hierarchical association reasoning, a mapping relation from malicious subgraphs to attack tactics and tactical chains is derived step by step, and finally an attack report summary and a targeted defense strategy are generated. According to the invention, APT attack detection with high accuracy and high interpretability is realized.

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

The invention provides a multi-modal knowledge extraction method and system based on multi-agent collaborative optimization, and relates to the technical field of knowledge extraction, and the method comprises the steps: carrying out the multi-modal deconstruction of an original document to be extracted; constructing a multi-modal agent, respectively executing feature extraction and preliminary knowledge extraction, and outputting a single-modal multi-component system; based on a cross-modal knowledge graph, mapping information of different modals to a unified semantic node, and establishing cross-modal association and analyzing a logic chain through a graph neural network and a causal reasoning module; dynamically allocating resources according to the importance of map nodes, and screening structured knowledge; and through confidence analysis and node traceability evaluation, an intelligent agent cooperation mechanism is optimized, and increment correction is carried out on a result. According to the method and the device, the technical problem of low knowledge extraction accuracy and efficiency caused by insufficient multi-modal knowledge collaborative mining capability due to knowledge extraction of literatures by adopting a single agent in the prior art can be solved, and the knowledge extraction quality and efficiency are improved.

The invention discloses an article recognition system based on computer vision. The article recognition system comprises a multi-modal data acquisition module, a multi-modal data processing module and a computer vision processing module, wherein the multi-modal data acquisition module is used for acquiring multi-modal data through a multi-modal sensor array; the data preprocessing module is used for standardizing a multi-modal data format and generating a time-space aligned multi-modal tensor; the feature extraction module is used for respectively extracting modal specific features from texture, spectrum and geometric dimensions by adopting ResNet50, 3D-CNN and PointNet + +; the multi-modal fusion module is used for constructing cross-modal joint representation; the adaptive sensing module is used for modeling illumination invariance and scene dynamics based on self-supervised comparative learning and a 3D-STMN space-time memory network, predicting a shielded target trajectory by using Kalman filtering in combination with the shielding sensing propagation module, and generating an environment sensing parameter set; and the recognition engine module is used for integrating YOLOv8 detection, Mask R-CNN segmentation and multi-modal decision tree classification, outputting a target bounding box, a category and confidence in combination with the depth data, and generating three-dimensional space coordinates combined with the depth data.

The invention relates to the technical field of text classification, and provides a text classification method based on a large model and a rule engine, and the method comprises the steps: S1, storing multi-level rule classification labels, and constructing a classification rule template library; s2, receiving text data from various data sources, and preprocessing the text data; s3, performing rule matching on the text data based on the classification rule through a rule engine, and outputting a rule classification result; and S4, when any one of the following conditions is met, large language model classification is triggered: a, a classification rule is not matched; b, matching a classification rule, wherein the rule confidence is smaller than a rule confidence threshold; c, the text data length exceeds the preset text data length; d, matching a specific business scene label; outputting a model classification result; and S5, when the rule engine classification in the S3 and the large language model classification in the S4 are parallel, executing the strategy. The output reliability and the service continuity are guaranteed, and the method is suitable for scenes with high accuracy requirements such as financial compliance examination and the like.

The invention discloses a multi-agent-based gas insulated switchgear fault diagnosis method and system, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: obtaining signal data of target equipment, carrying out the feature extraction of the signal data, and constructing a multi-modal feature matrix; time delay features of acoustic and electromagnetic signals are extracted from the multi-modal feature matrix, a GIS propagation model is established, and the space coordinate position of a liberated power source is solved through a wave field inversion algorithm; combining the space coordinate position and the multi-modal feature matrix into a complete fusion feature vector, inputting the fusion feature vector into a dynamic Bayesian model, and outputting a fault type label and a corresponding confidence coefficient; migrating the dynamic Bayesian model based on a migration learning mechanism, and dynamically updating a classification threshold value; inputting the diagnosis history sequence into a time sequence prediction model, and predicting a future operation state; through multi-modal fusion and intelligent reasoning, GIS fault accurate positioning and prediction are realized, and the problems of low precision and poor adaptability of traditional diagnosis are solved.

The invention discloses a semi-supervised identification method and system for a small target of a container, and belongs to the technical field of artificial intelligence and computer vision, and the method comprises the steps: carrying out the target detection of a container image through a pre-trained target detection model, intercepting a sub-image, and inputting the sub-image into an initial classification model, and obtaining a classification confidence coefficient; the uncertainty of the model on a sample classification result is quantified through a Monte Carlo Dropout method; a feature space distance filtering and dynamic threshold adjusting mechanism is combined, and samples with high confidence, low uncertainty and consistent feature space are screened out to serve as pseudo label data; pseudo label data and initial synthesis data are mixed, and the generalization ability of the model is gradually improved through semi-supervised iterative training. According to the method, the dependence on manual annotation can be remarkably reduced, meanwhile, the distribution difference between synthetic data and real scene data is gradually reduced, and finally, high-precision recognition and strong generalization ability of a classification model in a real scene are achieved.

The invention relates to a bill voucher information extraction method based on multi-mode and OCR model fusion. The method comprises the following steps: S1, obtaining an image of a bill voucher; s2, preprocessing the image; s3, identifying the preprocessed image by using an OCR engine to obtain the text content and the corresponding two-dimensional coordinates of each text block; s4, taking the recognized text segments and the original image as input, performing joint coding by using a pre-trained multi-modal model, evaluating and outputting the matching degree of each text segment and a predefined field category by the model, and determining candidate texts of each field and confidence of the candidate texts; s5, accurately positioning and extracting the key field, and verifying the consistency of the OCR output and the semantic result; s6, if the verification result conflicts or the identification reliability of a certain field is lower than a threshold value, error correction operation is carried out; and S7, outputting the structured bill voucher information. Through multi-modal fusion and iterative correction, the error rate of non-standard voucher information extraction is effectively reduced, and the method is suitable for various voucher formats and complex scenes.

The invention discloses a grabbing posture generation method and system based on a multi-modal large model, and the method comprises the steps: carrying out the cross-modal matching of visual features and semantic features in the multi-modal large model when a voice instruction and an RGB image are inputted, and obtaining the position information of a control function code and a target object; when an RGB image with a hand drawing instruction is input, obtaining position information of a control function code, a target object and a path point; calculating the point cloud data of the target object according to the position information of the target object and the depth information, inputting the ideal point cloud of the target object into a target recognition network model after preprocessing, carrying out the grabbing region recognition of the point cloud of the target object region, outputting a region with high grabbing confidence, and mapping a real coordinate system; constructing a point cloud bounding box, and generating a grabbing posture candidate set; the grabbing posture with the highest quality is selected as the grabbing posture of the robot by calculating the grabbing posture candidate score; and executing a target grabbing task in combination with the control function code and the grabbing path.

The invention relates to the technical field of nondestructive testing, solves the problems of insufficient flaw detection precision and adaptability of a metal component caused by restriction in the prior art, and particularly discloses a flaw detection system and method for the metal component. The flaw detection system comprises a multi-modal sensor group, a cross-modal feature alignment module, a self-adaptive weight distribution module, a defect decision module and a closed-loop control unit, the flaw detection method comprises the following steps: S100, synchronously acquiring data through the multi-mode sensor group; s200, performing space-time registration on the multi-source data of the metal component by using the dynamic calibration matrix; s300, evaluating the confidence coefficient of the sensor in real time, and generating a defect feature map; s400, inputting the defect feature map into a defect classification network, matching a defect map database, and outputting a defect type and a quantization parameter; and S500, automatically adjusting the detection motion track and the sensor according to the defect parameters. According to the multi-method combined detection for the metal component, the detection precision is improved, and a detection result with higher accuracy can be obtained through comprehensive evaluation according to multi-source data.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention relates to the technical field of AI interaction, and discloses an AI multi-modal emotion interaction memory terminal, which realizes microsecond-level synchronization of voice, facial expression and text data through a multi-thread acquisition engine, dynamically allocates each modal weight by adopting a multi-head cross attention mechanism, and adaptively adjusts modal importance based on a conversation context hidden state; when the cross-modal confidence difference exceeds a threshold value, a gating LSTM conflict resolution module is activated, and the multi-source data collaboration problem is solved; the emotional memory modeling constructs an emotional state transition topology based on a graph convolutional network, protects user privacy in combination with a differential privacy mechanism, and realizes associated event storage of millisecond backtracking of short-term memory and long-term memory. The technology integrates multi-modal dynamic perception, privacy security calculation and adaptive learning ability, significantly improves the real-time performance and personification degree of emotion interaction, and can be applied to the fields of intelligent customer service, emotion accompanying, health monitoring and the like.

The invention discloses a credit risk assessment method and system based on a knowledge graph and a storage medium, and the method comprises the steps: carrying out the standardization processing of multi-source credit data through an ontology mapping rule, and obtaining an RDF triple data set; constructing a dynamic knowledge graph containing a guarantee chain, a fund flow direction and a risk factor by adopting a self-organizing algorithm; carrying out modeling through a graph convolution risk propagation operator to obtain a risk state vector; performing time sequence embedding extraction to obtain a five-dimensional comprehensive risk feature vector; and through adaptive attention mechanism aggregation processing, a credit risk assessment result and a confidence interval are obtained. The technical problems of multi-source heterogeneous data fusion, dynamic relation modeling, risk state quantification and multi-dimensional feature aggregation in credit risk assessment based on the knowledge graph are solved.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention discloses an automatic label labeling and classifying method and system oriented to unstructured system documents, and relates to the technical field of artificial intelligence. The method comprises the steps that semantic structure pre-analysis is conducted on an original system text, and a system semantic structure tree is constructed; establishing a system semantic enhancement vector space based on the semantic units and the logic relationship thereof; performing semantic deconstruction on the preset tag and extracting a feature vector; realizing cross-space semantic matching of the document and the tag through a system semantic attention mechanism; a confidence evaluation module is introduced to screen high-confidence labels from the three dimensions of structural integrity, coverage and logic consistency; and outputting a final label and a score through semantic conflict detection and resolution. According to the method, the problems that in the prior art, unstructured system text labeling accuracy is low and large-scale labeling samples are dependent on polysemy ambiguity, high context dependency, complex semantic structure and the like are solved, and labeling accuracy and robustness are remarkably improved.

The invention provides an intelligent detection system for forging defects of a forge piece product, and relates to the technical field of industrial intelligent detection.The intelligent detection system comprises the steps that multi-angle images of a to-be-detected workpiece are collected and then spliced, and a complete surface expansion view of the product is generated; obtaining defect types of the defect candidate regions through region coordinates and region sizes of the defect candidates; obtaining a confidence value of a corresponding defect type judgment result; a lightweight deep network recognition model is called for secondary judgment, new sample data are generated in a manner of supporting manual annotation, and an equipment end is connected with a programmable controller for intelligent defect detection of the workpiece to be detected. According to the invention, the problems of incomplete defect coverage, failure to realize high-precision automatic identification of multiple types of defects and influence on the detection accuracy and the production efficiency caused by diversified and complex forging surface defects and limited image acquisition angles in the prior art can be solved, comprehensive acquisition and splicing of multi-angle images are realized, and the detection accuracy and the production efficiency are improved. The technical effect of improving the defect detection accuracy of the forge piece product is achieved.

The invention relates to the technical field of intelligent bid evaluation, and provides an intelligent multi-dimensional bid evaluation analysis and decision-making method based on big data, which comprises the following steps: acquiring original bid evaluation data from a multi-source heterogeneous data interface, and fusing through semantic role labeling and a timestamp alignment algorithm to generate a time-space association data set. And performing multi-level cleaning to generate a high-confidence bid evaluation data set. And extracting a multi-dimensional index based on the domain knowledge graph, generating a dynamic feature tensor, and dynamically allocating a weight by adopting a coupling attenuation weight model. And constructing a bidder association network, calculating a node influence score, detecting a potential bidding behavior and generating a risk correction coefficient. And injecting the real-time data stream into the dynamic feature tensor, updating the index weight, and generating a three-dimensional scoring vector through a multi-target aggregation decision algorithm. And performing Pareto optimization by using the asymmetric game equilibrium model, and outputting an optimal bid-winning party sequence and a risk early warning report. The bid evaluation efficiency and fairness can be improved, and the bid invitation risk is reduced.