4593 results about "Confidence value" patented technology

A Generative Artificial Intelligence (Gen. AI) based chatbot apparatus implements classical AI models and Generative AI models to answer user queries with results retrieved from relational databases and unstructured knowledge bases. When a user query is received, it is determined if the intent of the user query can be determined with a confidence greater than a configured confidence limit. If yes, a structured query mapped to the intent is employed to answer the user query. If the intent determination confidence is less than the configured confidence limit then Gen. AI-based techniques using a plurality of LLMs are used to respond to the query. A Gen. AI switch is also implemented to switch between the plurality of LLMs to answer user queries with greater accuracy.

The invention provides a data knowledge graph construction method and system driven by a complex ecological smart brain, and relates to the technical field of complex ecology, and the method comprises the steps: collecting scene data through a depth feature sensing network, and generating an initial graph structure; entity similarity is calculated based on the map feature fingerprints, related entity pairs are screened, multi-hop path reasoning is carried out, and entity association confidence indexes are generated; generating a multi-modal knowledge representation vector based on multi-modal feature extraction and a cross-modal attention mechanism; the method comprises the following steps: identifying influence characteristics, calculating a propagation probability in combination with a problem evolution diagram and causal reasoning, performing comparative analysis on the propagation probability and a map characteristic fingerprint to generate an optimization target, generating an error correction strategy by using an Adam optimizer, dynamically adjusting the reasoning depth through a double-DQN network, forming an optimization closed loop, and constructing a data knowledge map. According to the method, the accuracy and integrity of the knowledge graph can be effectively improved, the reasoning ability is enhanced, and the method can better adapt to knowledge discovery and application in a complex ecological scene.

The invention relates to a microchip appearance defect detection method based on a convolutional neural network. The microchip appearance defect detection method comprises the following steps: acquiring a high-resolution microscopic image and carrying out adaptive brightness equalization processing; labeling defect features in the pre-processed image by adopting a layered labeling strategy, and dividing defects into a surface deformation class and a material anomaly class by coarse-grained classification labeling; a double-branch lightweight convolutional neural network model is constructed based on the annotation data, a first branch extracts global semantic features, and a second branch extracts local detail features. In the model training, a defect sensitivity loss function is adopted for optimization, and defect area attention, edge preservation and category balance loss items are included, so that the small-size defect detection capability and the boundary positioning precision are improved, and the problem of sample imbalance is solved. And finally, a detection result including defect types, position coordinates and confidence scores is generated by inputting a to-be-detected preprocessed microscopic image, and the defect detection performance with high precision and strong pertinence is achieved.

The invention provides a decision model construction method based on a big data environment. The method belongs to the economic data decision field. The method comprises the following steps: firstly, acquiring multi-source heterogeneous data including sensor time sequence data, an expert rule base and equipment causal priori knowledge, and constructing a standardized training data set; then, extracting an explicit causal relationship from the historical data by utilizing a causal discovery algorithm, and generating an interpretable knowledge graph in combination with priori knowledge of an equipment manual; thirdly, constructing a neural symbol joint model; neural network parameters and rule confidence coefficients are synchronously adjusted, and collaborative learning of data driving and knowledge driving is realized. And then, carrying out multi-dimensional logic verification on the trained model, including rule conflict detection, anti-factual reasoning and decision path traceability verification. The method can effectively improve the accuracy and interpretability of the decision model, and is suitable for processing decision tasks in a big data environment.

The invention provides a calibration method and system for measurement data of a pressure sensor, and the method comprises the steps: obtaining an original output signal of the pressure sensor and synchronous collection data of environment parameters, and carrying out the preprocessing, and obtaining multi-source sensing data and dynamic environment characteristics; based on multi-source sensing data and dynamic environment characteristics, a multi-error coupled comprehensive error model is constructed, and a dynamic compensation coefficient is generated through online parameter identification; according to the dynamic compensation coefficient and a preset confidence evaluation strategy, dynamic compensation calibration is carried out on the sensing data, a high-precision pressure value is generated, and the high-precision pressure value is used for representing a real environment pressure measurement value. By adopting the method, the accuracy and the reliability of the tester in the calibration of the pressure sensor in a multi-error coexistence and complex environment can be improved, and the precision and the stability of the measurement data calibration of the pressure sensor are improved.

The invention discloses an equipment intelligent safeguard system based on an offline large model, and relates to the field of equipment management and safeguard, and the system comprises a collection module which collects an equipment surface defect image and a time sequence signal through an unmanned plane, collects a text log, processes the defect image, the time sequence signal and the text log, and generates a multi-modal data set; the feature fusion module is used for performing feature extraction on the multi-modal data set and fusing the three features to generate a multi-modal feature; the updating module is used for extracting a fault triple from the semantic vector, constructing a Bayesian causal graph according to the fault triple, dynamically updating node confidence and generating a knowledge graph; the maintenance strategy module is used for matching the multi-modal features with a knowledge graph and positioning a fault root cause; and the lightweight model deployment module is used for pre-training a lightweight model in an edge server, accelerating parameter aggregation by using a quantum annealing algorithm, adjusting and optimizing a global large model according to the aggregated parameters, and generating a strategy in combination with reinforcement learning.

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 network threat multi-modal detection method based on a large model, and belongs to the technical field of network security, and the method comprises the steps: collecting three types of heterogeneous data of NetFlow flow of a network layer, a system call chain sequence of a host layer and a protocol load of an application layer, and carrying out the desensitization processing and feature coding to generate a unified tensor format; the method comprises the following steps: through network security threat intelligence and MITRE ATTamp; performing supervision fine tuning on the large model by using a CK attack chain sample, and constructing a network threat identification special model; cross-device behavior characteristics are extracted through a model self-attention mechanism, and a dynamic behavior map is constructed; and finally, comprehensively evaluating an attack mode matching degree, a node vulnerability mean value, historical alarm association and an attack path risk by adopting a weighted fusion algorithm, and triggering a high-confidence alarm when a comprehensive score exceeds 0.8. According to the method, through multi-modal data fusion and dynamic graph analysis, the detection precision and response efficiency of the complex attack chain are improved.

The invention discloses a multi-source data fusion method and system based on cloud computing, and particularly relates to the technical field of multi-source data fusion, and the method comprises the steps: obtaining the real-time information of multi-source data, generating a health score and a label through a health evaluation model, and marking a data state and an abnormality type; performing format conversion and semantic enhancement processing on the health data to generate structured data; based on a knowledge graph reasoning technology, extracting a logical relationship to generate a fusion rule, and calculating a rule weight and confidence in combination with a health label, a data source confidence and a semantic feature; a fusion result of conflict data is judged through a semantic consistency index, if the fusion result does not conform to expectation, historical data and rules are loaded according to a verification model, a verification score is calculated, an optimization process is triggered after the verification score is compared with a threshold value, health assessment model parameters, a semantic embedding model and fusion rule logic are optimized, and fusion accuracy and reliability are improved.

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 embodiment of the invention discloses an interactive intelligent analysis method and device based on a knowledge graph and a medium, and relates to the technical field of data processing.The method comprises the steps that a natural language query text input by a user is obtained, intention detection is conducted on the natural language query text, the corresponding intention fuzzy confidence degree is determined, and the knowledge graph is obtained according to the intention fuzzy confidence degree; matching a target query channel; when the target query channel is a guide query channel, performing path derivation on the natural language query text through a business knowledge graph constructed by pre-learning to generate a personalized analysis card sequence so as to obtain a target analysis dimension in the personalized analysis card sequence triggered by a user; and under the triggering of the target analysis dimension, switching the guide query channel into a precise query channel, and through the precise query channel, calling a pre-learned query mode knowledge base to generate a target query statement corresponding to the target analysis dimension, so as to determine a corresponding visual analysis result through the target query statement.

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 relates to the technical field of image target detection, and discloses a complex scene small target detection system and method based on mask attention and context feature optimization, and the system comprises a backbone network integrating a mask attention mechanism, a backbone network in which an original image is input into the integrated mask attention mechanism, and a background network in which the original image is input into the integrated mask attention mechanism. Redundant background information is filtered through a mask mechanism, and a multi-scale feature map is output; the context-aware enhanced feature refining encoder is used for carrying out multi-branch processing on the multi-scale feature map, and finally weighted fusion is carried out to generate a multi-scale fusion feature sequence; and the deformable Transform decoder inputs the multi-scale fusion feature sequence, processes the multi-scale fusion feature sequence through a cross attention and self-attention module, constructs a target query, and generates a detection result containing bounding box coordinates, category labels and confidence scores. According to the method, the performance of small target detection is improved by introducing efficient feature extraction, a fusion mechanism and a sampling and attention strategy.

The invention discloses a real-time health risk prediction method and system based on a dynamic knowledge graph, and relates to the technical field of medical information. The method comprises the following steps: carrying out multi-modal fusion and privacy protection preprocessing on medical and nursing heterogeneous data, and realizing semantic consistency of cross-mechanism data based on an entity alignment method of a cross-modal graph neural network; based on a hierarchical federated learning framework, local model parameters are subjected to hierarchical encryption and aggregation through a secure multi-party computing protocol to generate an initial global model, and prediction distribution of the global model is optimized in combination with knowledge distillation of differential privacy constraints; designing a gradient difference dynamic updating trigger mechanism of noise robustness, smoothing noise interference through a sliding window mean value, and realizing adaptive threshold calibration through linkage model performance verification; and light-weight deployment real-time reasoning is realized based on redundant edge pruning of confidence and 8-bit symmetric quantization. On the premise of protecting data privacy, the real-time performance and accuracy of health risk prediction are remarkably improved, and the method is suitable for a cross-institution medical care collaborative decision-making scene.

The invention belongs to the technical field of intelligent navigation, and discloses a navigation obstacle avoidance method and system in a low-confidence and feature similar environment. The method comprises the following steps: acquiring multi-modal sensor data of obstacles in a detection area in a preset low-confidence environment by using a sensor; preprocessing the data of the multi-modal sensor; introducing a generative adversarial network to carry out image enhancement processing on the denoised image; performing cross-domain fusion on the data by using a converter architecture; obtaining an obstacle position, an obstacle type and a confidence degree distribution value based on a deep neural network, and constructing and obtaining an obstacle confidence degree map; dynamically planning an obstacle avoidance path by combining a deep reinforcement learning algorithm based on an obstacle confidence map obtained in real time, and performing auxiliary planning of the obstacle avoidance path based on an MPC model; the method can effectively cope with high challenge scenes such as tunnels and is widely applied to the fields of industrial automation, intelligent transportation and the like.

The invention provides a multi-round dialogue interaction method and system based on context reconstruction and multi-library retrieval, and the method comprises the steps: firstly obtaining a multi-round dialogue data set of a target user, which comprises a plurality of dialogue round sequences composed of user input statements and system response statements; performing context reconstruction processing on the multi-round dialogue data set to generate context reconstruction characteristics of each dialogue round sequence, covering current round semantic focus offset and historical round semantic dependence intensity, and retrieving a matched multi-database retrieval result from a preset heterogeneous database based on a dynamic context window, a candidate response statement set and confidence are included. Performing multi-strategy matching on context reconstruction features and retrieval results, generating a response generation strategy to adjust candidate response semantic priorities and confidence thresholds, and finally feeding back the response generation strategy to a dialogue service system to trigger response optimization operation, and updating heterogeneous database retrieval weights and context window dynamic adjustment parameters.

The invention discloses a self-adaptive learning question answering system and method based on multi-modal interaction, and particularly relates to the technical field of self-adaptive learning question answering. By constructing a modal recognition and preprocessing module, standardization and structuralization of multi-modal input of texts, voices, images and the like are realized; establishing a cross-round modal memory map through time sequence coding and map modeling; dynamically updating a user portrait in combination with user interaction history and current modal characteristics; in the question and answer generation process, current input, a user portrait and a historical graph are fused, intermediate semantic representation is generated through a context enhancement module, and the intermediate semantic representation is combined with a knowledge base to generate answers; meanwhile, a modal confidence degree dynamic evaluation mechanism is introduced, and weights are distributed according to the input quality, the user adaptation degree and the context correlation; and finally, incremental optimization is carried out on the graph structure, the user portrait and the question and answer strategy through a user feedback driving system, and multi-round, multi-mode and self-adaptive intelligent question and answer interaction is realized.

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 provides a valve actuator full life cycle intelligent test system and method. The system comprises a combined load generation module used for generating an initial dynamic proportion of axial thrust and radial disturbance force according to a test protocol instruction, simulating a comprehensive stress state of an actuator under the impact of a pipeline medium, and outputting mechanical data containing thrust fluctuation characteristics in real time; the environment coupling module is used for dynamically adjusting an environment stress condition according to the test protocol instruction; the digital twinborn analysis module is used for comparing the actual displacement track of the actuator with the theoretical model track through a space-time alignment algorithm according to the mechanical data and environmental stress conditions to obtain a deviation feature vector; and the intelligent diagnosis module is used for carrying out dynamic time warping matching on the basis of the deviation feature vector and a preset historical fault database, outputting a fault type identification code and confidence, and generating a test report. According to the scheme, the accuracy of accurate evaluation of the full life cycle state of the valve actuator is improved.

The invention discloses an intelligent digital operation and maintenance management system and method for a charging pile, and relates to the technical field of new energy infrastructure operation and maintenance, and the system comprises an information obtaining module which determines a delay information circulation channel, obtains real-time discussion content, processes a discussion text through employing a word segmentation technology, and carries out the operation and maintenance of the charging pile. If the confidence coefficient of the semantic feature vector is higher than a preset threshold value, the semantic feature vector is judged to be a specific question type through state space definition, classified question labels are obtained, the historical matching module obtains features associated with a historical question library, historical question records are matched, and the matching precision is evaluated through reward function design. Determining a historical solution most similar to the current problem; according to the intelligent digital operation and maintenance management system and method for the charging pile, the accuracy of problem classification and solution matching is continuously improved, and intelligentization and high efficiency of operation and maintenance management of the charging pile are achieved.

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 forklift risk management, in particular to a multi-scene-oriented forklift self-adaptive safety early warning decision-making method, which comprises the following steps of: through multi-scene dynamic modeling, constructing multi-dimensional scene feature vectors according to forklift operation environment spatial features, cargo physical attributes and driver behavior parameters; establishing a scene classification model by means of transfer learning; a multi-modal sensor data fusion technology is utilized, a heterogeneous sensor network is deployed to collect data, and data reliability is optimized through space-time alignment and confidence evaluation; a dynamic safety threshold value is generated through fuzzy logic and reinforcement learning, and a driver behavior correction factor is introduced for real-time adjustment; calculating a comprehensive risk index by using a dynamic weight distribution algorithm, and triggering graded early warning and response; a federated learning closed-loop optimization risk assessment model is adopted, robustness is verified in combination with digital twinning, and cooperative obstacle avoidance of multiple forklifts is achieved through inter-vehicle communication. According to the method, the operation safety and decision-making accuracy of the forklift in a complex and changeable scene are remarkably 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.