795 results about "Causal inference" patented technology

The present invention provides a system and method for causality-augmented generative intelligence capable of autonomously discovering non-obvious actionable insights from heterogeneous and multimodal data sources. The system integrates a data ingestion unit for semantic and temporal harmonization of structured and unstructured datasets, a causal inference processor for constructing a dynamically evolving directed causal knowledge representation using perturbation-based validation, a latent representation processor that combines multimodal semantic embeddings with causal parameters to generate fused latent vectors, and a generative insight processor utilizing causally constrained generative reasoning to synthesize hypotheses anchored to verified cause-effect dependencies. A validation processor performs counterfactual assessment and observational verification to ensure retention of only those insights that remain consistent with causal ground truth.

The invention provides a time sequence knowledge graph federation collaborative optimization method, system and device based on causal inference and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an enhanced knowledge unit with a causal mark through the real-time access of a multi-field heterogeneous data stream and the execution of a space-time alignment operation; by calculating new and old knowledge conflict scores, conflict resolution and version management are realized through a decision tree mechanism, and a time sequence knowledge graph with history tracing is output; node weights are dynamically distributed among distributed nodes based on knowledge entropy, a hierarchical aggregation strategy is adopted to update an entity embedding layer and a relation prediction layer, and a global optimization model is output; the method comprises the following steps: analyzing a natural language query containing an anti-fact condition, extracting a factor sub-graph from a time sequence knowledge graph, executing intervention calculation, and generating an anti-fact influence report, thereby solving the technical problems of a traditional time sequence knowledge graph in the aspects of multi-source heterogeneous data fusion, knowledge conflict resolution and privacy protection; and the accuracy and the interpretability of the knowledge graph are improved.

The invention relates to the technical field of intelligent control of test equipment, and discloses a semiconductor device test equipment control system and method based on industrial data processing, and the method comprises the steps: collecting multi-source heterogeneous data of semiconductor device test equipment, and carrying out the preprocessing; constructing a structural causal model, and performing root cause identification through anti-fact reasoning by using the structural causal model; constructing an abnormal test fingerprint and a knowledge base; generating an intervention scheme, evaluating the generated intervention scheme, and selecting an optimal intervention scheme; processing the detected anomaly, and generating and implementing a preventive control strategy based on historical anomaly data and a causal analysis result; according to the invention, by introducing innovative technologies such as causal inference, anti-factual inference, abnormal test fingerprint identification and Monte Carlo tree search, intelligent control of semiconductor test equipment is realized.

The invention relates to the technical field of robot anomaly prediction, in particular to a robot anomaly prediction method and system based on multi-dimensional fusion and causal inference. The method comprises the steps of performing multi-scale depth state characterization based on acquired robot multi-joint sensing data, and performing dynamic causal graph fusion based on the multi-scale depth state characterization. Comprising the steps of priori knowledge graph construction based on a kinematics chain, dynamic association attention mechanism construction based on data driving, state fusion of knowledge and attention guidance and global state vector generation. Performing hierarchical space-time dependency prediction based on the fused features, wherein the hierarchical space-time dependency prediction comprises robot joint topological graph construction, spatial dependency dynamic modeling, long-range time evolution prediction and future robot health state prediction; the method shows excellent performance in a plurality of core dimensions such as prediction precision, early warning timeliness and diagnosis interpretability, and has extremely high actual deployment value and engineering popularization potential.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention relates to the technical field of data processing and identification, in particular to an intelligent fire fighting equipment fault identification method and system, and the method comprises the steps: carrying out the execution according to a set first period: generating simulation data through a constructed digital twinborn model; acquiring operation data of real equipment through a sensor network; comparing a deviation value between the simulation data and the real equipment data; when the deviation value exceeds a preset threshold value, triggering a causal inference engine; calibrating digital twin model parameters and adjusting equipment operation parameters; the prior art mainly depends on fixed threshold alarm, early progressive faults of equipment are difficult to capture, and response lags behind; according to the scheme, digital twin simulation and active flaw detection are combined, and deep insight of the health state of the equipment is formed by periodically injecting micro-amplitude disturbance signals into the equipment and analyzing the dynamic response characteristics of the equipment; according to the invention, tiny degradation of equipment performance can be captured in a fault incubation period, so that maintenance intervention is triggered in advance, and the advancement and accuracy of fault early warning are remarkably improved.

The invention provides a root cause positioning method which can be applied to the technical field of artificial intelligence. The root cause positioning method comprises the following steps: acquiring data in a configuration management database, a network topology tool, a monitoring system and a work order system to form a multi-source heterogeneous data set; performing knowledge extraction on the multi-source heterogeneous data set, extracting equipment attributes, network topological relations, fault event entities and timestamps, and storing the equipment attributes, the network topological relations, the fault event entities and the timestamps as structured knowledge; mapping real-time index data in the structured knowledge into dynamic attributes of an entity, and constructing a dynamic knowledge graph; based on a graph neural network and in combination with time sequence features, learning a time sequence dependency relationship and a propagation path between fault events in the dynamic knowledge graph; and outputting a root cause entity, a confidence score and a fault propagation path of the fault event through a causal inference algorithm in combination with the multi-dimensional evidence. The invention further provides a root cause positioning device and equipment, a storage medium and a program product.

A computer-implemented system for personalized health optimization constructs a confidence-weighted personal health knowledge graph (PHKG) from heterogeneous data, including wearable sensors, medical devices, lab results, medication logs, and conversational inputs. A multi-stage causal-inference stack identifies modifiable drivers of outcomes using layered methods (e.g., MI, GAM, Neural Granger, DAG-GNN), and simulates candidate interventions. A recommendation engine ranks lifestyle or pharmacologic actions using a benefit-to-friction score, selecting a personalized intervention aligned with user readiness and clinical safety constraints. Interventions may include a minimum effective dose (MED), optimal level, adaptive low-dose, or behavioral challenge. Optional modules include reinforcement learning for timing adaptation and privacy-preserving on-device inference. The system operates across domains including metabolic, cardiovascular, renal, sleep, stress, and medication response, enabling cross-condition synergy evaluation. The architecture is modular, supports runtime plug-in targets, and adapts in real time with or without continuous clinical oversight, depending on deployment.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an accident scene generation method based on a scene knowledge graph and considering accident causes. Comprising the following steps: step 1, modeling a scene knowledge graph; 2, modeling the scene graph time sequence prediction model; 3, modeling the time sequence causal inference model; step 4, modeling the scene graph time sequence decision generation model so as to generate an accident scene; according to the method, the accident scene database with high authenticity, high diversity and accident cause consistency can be constructed under the condition that the accident scene sample data size is limited, the number of accident scenes of the same type in the automatic driving algorithm closed-loop self-evolution cloud database is efficiently expanded, the constructed scene database is used for training the automatic driving algorithm, and the accuracy of the automatic driving algorithm is improved. The adaptive capacity of the self-driving automobile to scenes with the same type of accidents can be effectively enhanced, and safe and reliable operation of the self-driving automobile in the real world is guaranteed.

The invention discloses a financial risk assessment method based on big data, and relates to the technical field of finance, and the method comprises the following steps: S1, obtaining structured data, unstructured data and real-time streaming data of a target entity through a multi-source heterogeneous data collection module; s2, constructing an association relationship graph, and modeling risk propagation paths of a target entity and associated nodes thereof based on a graph neural network; s3, performing feature alignment and joint representation learning on the structured data, the unstructured text data and the time series data through a multi-modal data fusion module; and S4, based on the causal inference model, separating causal features and hybrid variables of the target entity risk event, generating causal risk factors, quantifying risk infection paths between nodes by setting an enterprise guarantee network and a supply chain relation graph dynamically constructed in a graph neural network, effectively identifying hidden risk nodes, and improving the risk assessment efficiency. And the chain reaction risk caused by the default of the associated enterprise is reduced.

The invention relates to the technical field of intelligent equipment autonomous control, and discloses an AIGA-driven intelligent equipment autonomous control and feedback adjustment method, which comprises the following steps of S1, collecting equipment body state parameters and external environment data in real time through a multi-mode sensor array; s2, combining the collected data with a preset user instruction and a real-time environment semantic analysis result; s3, disassembling the task target into a continuous action instruction set; s4, executing the action instruction set through the layered real-time control architecture; s5, monitoring an action execution effect based on the multi-modal sensor array; and S6, analyzing the relevance between the evaluation result and the action instruction through a causal inference engine. According to the method, an equipment action execution result is fed back to an intention generation layer in real time, a deviation source is positioned through a causal inference engine, a control strategy is dynamically adjusted, closed-loop iteration from one-way instruction execution to perception-decision-execution-verification is achieved, and the effect of self-adaptability in a dynamic scene is achieved.

The invention relates to the technical field of urban drainage network optimization design, and discloses a BIM model-based urban drainage network optimization design method and system, and the method comprises the steps: collecting multi-source hydrological data, and carrying out the preprocessing of the multi-source hydrological data; modeling the drainage system into a space-time diagram structure, and predicting the bearing capacity of the drainage system; an overflow risk assessment model is constructed, key influence factors are identified, node risk degrees are quantified, and high-risk areas are identified; constructing a fault model based on a causal graph, identifying a causal chain of an overflow event, and realizing crossing from correlation analysis to causality understanding; performing drainage system parameter optimization by using the BIM model; by fusing multi-source data analysis, a time-space diagram neural network and a causal inference technology, dynamic bearing capacity prediction, accurate risk assessment and abnormal source overflow diagnosis of the drainage system are realized.

The invention is suitable for the technical field of disaster monitoring, and provides a power system-oriented low-orbit satellite multi-source sensing disaster monitoring method, which comprises the following steps of: acquiring heterogeneous observation data and time sequence monitoring data, mapping the acquired data into a space-time diagram structure, and generating a multi-dimensional sensing field of a surrounding environment of power equipment; the method comprises the following steps: analyzing disaster characteristics at a low-orbit satellite end through a lightweight federated learning model based on a multi-dimensional sensing field, generating a semantic instruction which can be executed by a machine, distributing the instruction to an unmanned aerial vehicle cluster and an edge computing node through an inter-satellite link, and triggering a self-adaptive observation strategy; predicting a potential disaster chain reaction based on a pre-trained causal inference model; when a disaster chain triggers a threshold value, hierarchical response is activated autonomously; and a self-adaptive monitoring strategy configuration file is generated according to a disaster response result and is used for task planning of a next monitoring period, so that the early warning precision and response timeliness of disasters such as mountain fire and flood are improved, and meanwhile, the stability of communication and power grid operation in an extreme environment is ensured.

The invention provides an intelligent question number and index management engine and system based on dynamic reward optimization, relates to the technical field of data learning, and is used for enterprise index analysis, attribution diagnosis and decision support. The system is provided with an index governance layer, index caliber, computational logic, blood relationship, credibility score and version information are managed in a unified mode through a dynamic knowledge graph, unified semantic constraint is carried out on a multi-agent analysis process, and index consistency and traceability are guaranteed. The system also establishes a causal cognition module, based on time sequence data and in combination with expert priori, generates and corrects a business index causal directed acyclic graph, realizes root cause positioning and anti-fact simulation, and answers what change is and what intervention is. The multi-agent collaborative analysis core is responsible for natural language intention analysis, index compliance verification, automatic access, causal inference, narrative generation and chart presentation, calculates a multi-target composite reward value based on user feedback and interaction behaviors, and adaptively adjusts output; and the user corrects and writes back to form closed-loop learning.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention discloses an artificial intelligence-based bid invitation file error content optimization method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the causal inference through employing a CIRCA frame, constructing a causal Bayesian network, introducing the Chen's three-dimensional chaotic disturbance, optimizing the edge probability, recognizing potential error nodes through a GNN model, generating a semantic embedding vector through employing SBERT, and carrying out the calculation of the error content of a bid invitation file. The method comprises the following steps of: mapping to a Riemannian manifold, realizing embedded dimension reduction through Riemannian principal component analysis, determining an optimal path leading to an ideal state by using a geodesic path algorithm, and calculating a candidate fitness score by constraining an Abbe structure and conflict strength. According to the method, causal reasoning and chaotic disturbance are combined, so that the accuracy of error positioning and the reliability of classification judgment in the bid invitation file are improved, and the semantic error recognition capability and the global optimality of a correction path are improved through semantic manifold modeling and geodesic path optimization.

The invention provides a multi-modal-based advertisement recommendation method and a related device, and systematically solves the technical bottleneck of a traditional recommendation technology in a complex scene through multi-modal feature alignment, dynamic weight optimization and causal effect decoupling. The method comprises the following steps: firstly, respectively extracting modal features of a video, a text and a user behavior sequence, and constructing a cross-modal contrast loss function to strengthen multi-modal semantic alignment; secondly, dynamically updating each modal weight through back propagation, and realizing advertisement recall and sorting in combination with cross-modal similarity calculation; an anti-fact causal reasoning module is further introduced, interference of environment mixed variables on the recommendation effect is eliminated through tendency score estimation and anti-fact result prediction, and the real causal effect of advertisement exposure is accurately quantified. According to the scheme, representation learning, dynamic decision and causal inference are deeply fused, a generalized and anti-noise technical framework is provided for short video advertisement recommendation, and the performance boundary is remarkably superior to that of traditional collaborative filtering, matrix decomposition and other methods.

The invention relates to the technical field of laser surface modification, and discloses a water turbine laser cladding process parameter intelligent optimization system and method, and the system comprises a data preparation and preprocessing module which is used for obtaining and preprocessing water turbine laser cladding process parameters and corresponding cladding layer quality index data; a physical information guided multi-task prediction network construction and training module; a causal inference analysis module; and a causal perception multi-objective optimization algorithm module. By constructing a multi-task prediction network guided by physical information, the internal physical law of the laser cladding process is fused into a deep learning model, and the accuracy and reliability of predicting a plurality of key quality indexes of the laser cladding layer of the water turbine are remarkably improved. Compared with a traditional pure data driving model, the method can generate a prediction result more conforming to an actual cladding mechanism by means of guidance of physical constraints even under the condition of limited experimental data, and lays a solid data foundation for subsequent process optimization.

The invention discloses a big data platform storage data isolation method in a SaaS mode, and relates to the technical field of big data, and the method comprises the steps: inputting a storage situation data set into a causal graph neural network, capturing a data time sequence mode and causal association features through a feature extraction layer, carrying out the multi-hop neighborhood feature aggregation through a feature fusion layer, and generating a storage anomaly detection vector; inputting the stored anomaly detection vector into a causal inference engine, executing risk quantification by using an improved causal inference tree algorithm, obtaining a causal effect score, carrying out risk division through a three-level threshold, generating an anomaly risk level, carrying out entropy calculation on the anomaly risk level by using a Shannon entropy formula, obtaining an anomaly entropy value, and obtaining an anomaly result. Carrying out interval classification on the abnormal entropy value to form a sensitivity level; according to the invention, through the constructed causal graph neural network, the improved causal inference tree algorithm and the analytic hierarchy process, dynamic identification of abnormal risks is realized, and redundancy isolation of low-risk data is also avoided.

The invention relates to a meteorological data set automatic construction method and system based on modal bridging, and aims to meet the multi-modal large model training requirement in the meteorological field, and the method and system realize automatic conversion from an original meteorological image to a structured expert reasoning text through deep fusion of image and text information. The method comprises five stages of data preprocessing, image-text semantic modeling, causal reasoning generation, consistency screening and parallel processing, key meteorological elements are extracted by using a multi-modal model, a chained thinking process is constructed through a language model with meteorological knowledge, chained reasoning annotation is realized, cross-modal semantic alignment and a multi-round reasoning mechanism are introduced, and a multi-modal semantic model is established. The method is advantaged in that high-quality samples are screened in combination with rules and models, automation, high consistency and good expansibility are realized, manual annotation cost is substantially reduced, and the method is suitable for large-scale meteorological reasoning multi-modal data set construction.

The invention belongs to the technical field of computers, and particularly relates to a software fault repairing method and system fused with intelligent analysis, which comprises the steps of collecting a multi-level running log and performing structured preprocessing, constructing a dynamic calling graph through a time sequence encoder and a graph neural network, inferring a fault root cause in combination with a Bayesian causal inference model, and repairing a fault fault according to the fault root cause. And matching the repair strategy to generate an atomization instruction sequence, and deploying the atomization instruction sequence to a production system after sandbox environment verification. The system comprises a log acquisition module, a feature coding module, a graph construction module, a causal reasoning module, a strategy matching module, an instruction generation module, a sandbox verification module, a deployment feedback module and the like. Through end-to-end intelligent analysis and a closed loop verification mechanism, the fault positioning precision and the repair safety are remarkably improved, system self-evolution is supported, and operation and maintenance are promoted to be transformed from passive response to active autonomy.

The invention discloses a smart park facility predictive maintenance system based on a digital twinborn technology, and relates to the field of smart park facility maintenance. Comprising a data acquisition module, a preprocessing module, a digital twin model construction module, a fault prediction module, a maintenance decision module, a maintenance resource management module, a user interaction module, a system management module, a spatio-temporal data analysis and prediction module and a social-technical system fusion module. The method comprises the following steps: collecting and fusing a risk-dependent frequency modulation rate of quantum sensing, improving speed and precision by means of quantum calculation, fusing a digital twin model into a meta-universe concept and an intelligent agent, predicting a fault by combining quantum machine learning and causal inference, optimizing a maintenance decision based on a game theory and reinforcement learning, and managing resources by using a block chain-Internet of Things fusion technology. The invention discloses a brain-computer interface and holographic projection interaction and quantum encryption dual-protection management system. The system is accurate in data acquisition, vivid in model construction, accurate in fault prediction, scientific in maintenance decision, efficient in resource management, immersive in interactive experience and safe and stable in system, and ensures stable operation of park facilities.

The invention discloses an intelligent hardware equipment monitoring method based on the Internet of Things, which comprises the following steps of: acquiring data such as sensor signals, temperature power consumption of a processor, voltage and current of a power supply module and the like in real time, processing the data by applying technologies such as a filtering algorithm, a causal inference model and time sequence analysis and judging the performance change of a component; and inputting the processed data into a pre-training regression model, predicting the residual life of each component and generating a health score, integrating the health scores of each component, introducing a dynamic correlation analysis and adaptive weight adjustment mechanism, constructing a system-level health state report, and generating a priority maintenance plan for high-risk components based on the report. Intelligent evaluation and prediction of the health state of the hardware component are realized, interruption of operation of the monitoring system due to hardware faults is effectively avoided, and the reliability and maintenance efficiency of the system are improved.

The invention discloses a non-coal mine safety risk dynamic monitoring and early warning system based on a point-surface fusion technology, and relates to the technical field of mine safety monitoring. The system uses four-dimensional space-time coordinate data of ground surface monitoring points to construct a dynamic ground surface model; dynamically inverting a probabilistic three-dimensional underground geomechanical model from the earth surface by adopting a deep learning large model (DSIM), and quantifying uncertainty; online self-adaptive evolution of the DSIM model is carried out by comparing the difference between prediction and actual observation; integrating a causal inference engine and deeply tracing risk driving factors; according to the method, underground state perspective, risk cause traceability and model self-evolution are realized, and the precision, reliability and intelligent decision support level of monitoring and early warning are remarkably improved.

The invention discloses an agricultural decision reasoning large model based on a large language model and a construction method thereof. The method comprises the following steps: constructing a space-ground-air integrated multi-modal agricultural data acquisition and fusion module, integrating data of a ground sensor, unmanned aerial vehicle remote sensing, meteorological sensing and Internet of Things, solving the problem of data isomerism through reinforcement learning and a Transform architecture, and ensuring real-time integrity of the data. In combination with crop simulation model mechanisms such as DSSAT and the like, the inference rule base is dynamically updated to realize rapid iteration of agricultural knowledge; an agricultural feature extraction module and an adaptation layer are integrated, so that professional data understanding is enhanced; a multi-stage training strategy is adopted, and large-scale agricultural text fine tuning is carried out; reinforcement learning is introduced and expert feedback is combined to optimize decision generation, and a causal inference module is innovatively added to improve the inference precision. The model fuses the language ability of a large language model and agricultural knowledge, can accurately predict and diagnose agricultural problems and provide decision suggestions, and significantly improves the intelligence, precision and high efficiency level of agricultural production.

The invention discloses a knowledge model data management system based on artificial intelligence, and relates to the field of data management, and the system comprises the following components: a data collection and storage module, an intelligent prediction and analysis module, an abnormal comprehensive processing module, a knowledge graph and traceability module and a man-machine interaction management module. According to the invention, the intelligent prediction analysis module is combined with a time sequence prediction model and a causal reasoning algorithm, abnormal fluctuation and causal association thereof in data can be accurately judged, the accuracy of anomaly detection is effectively improved, and meanwhile, the knowledge graph and traceability module uses the constructed knowledge graph and reinforcement learning algorithm to improve the accuracy of anomaly detection. According to the method, the relation chain can be quickly traced from the abnormal data, the problem source can be positioned, the abnormal traceability efficiency is remarkably improved, and the functions act together, so that the system can more quickly and accurately discover and process problems when facing a complex data environment, and the stability and reliability of data management are guaranteed.

The invention discloses an industrial data analysis system and method based on digital twinning and causal inference, and the system comprises a physical sensing layer which is used for collecting multi-source heterogeneous data of an industrial site; the digital twinborn platform layer is used for constructing and operating a virtual twinborn model corresponding to the physical entity; the intelligent analysis engine layer is integrated with a causal analysis module and a federal learning module which are associated; the application and interaction layer is used for visualizing the analysis result and issuing a control instruction; wherein the causal analysis module is used for constructing a causal graph based on the multi-source heterogeneous data and performing causal inference. Through federal learning, on the premise of protecting data privacy of all parties, cross-organization and cross-region collaborative modeling and knowledge sharing are realized.

The invention relates to the technical field of medical information, and discloses an internet-based remote medical inquiry system, which comprises a data acquisition and quality assurance module for acquiring multi-source heterogeneous medical data and performing time sequence alignment, quality monitoring and intelligent repair interpolation; the feature extraction and fusion module is used for performing deep feature extraction, cross-modal semantic alignment and hierarchical attention fusion; the complication association reasoning module is used for obtaining a deep complication association reasoning result by adopting a graph attention network and multi-hop reasoning; the complication progress prediction module is used for constructing a complication progress prediction model and carrying out meta-learning enhancement and uncertainty quantification; the intelligent medication decision module is used for generating candidate schemes and screening a Pareto optimal scheme; the compliance management module is used for carrying out compliance causal inference and closed-loop optimization; the effect evaluation module is used for carrying out effect evaluation and dynamic optimization; according to the method, a compliance improvement mechanism is established through causal inference and reinforcement learning, and interpretable man-machine collaborative decision and closed-loop optimization management are realized.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention discloses a seismic geological disaster monitoring and early warning device, and the device comprises an intelligent sensing system which carries out the real-time collection of geological parameters, facility states and environmental factors through a multi-mode sensor array, and comprises the steps: capturing the strain of a pipeline through a distributed optical fiber sensor, monitoring the vibration through an MEMS accelerometer, and providing deformation data through an InSAR satellite; the analysis system constructs a complex network modeling engine, fuses the historical transition probability matrix and the real-time deformation rate parameter by using a directed weighted network model, and combines a two-channel causal inference engine which comprises a physical causal channel and a data causal channel; the physical causal channel is embedded into a coulomb fracture criterion to calculate a fault stress accumulation rate, the Darcy law simulates pore pressure propagation, and the data causal channel generates an anti-fact sample through a CaualGAN and extracts a real causal chain in combination with a time causal convolutional network; and the execution and feedback system is used for triggering graded early warning based on an analysis result of the analysis system.