1019 results about "Causal inference" patented technology

The invention provides a petrochemical production process anomaly diagnosis and optimization method and system integrated with a knowledge graph, and relates to the technical field of petrochemical production, and the method comprises the steps: collecting technological parameters and equipment state data, carrying out the preprocessing and feature extraction through adaptive wavelet threshold denoising and depth variational mode decomposition, and carrying out the feature extraction; a heterogeneous graph neural network is constructed, a causal relationship graph is constructed in combination with a neural causal discovery network, the causal relationship graph is optimized through knowledge distillation, missing relationships are complemented to obtain a knowledge graph, real-time features are compared with normal feature distribution of a pre-trained contrast diffusion model, and after anomaly detection is triggered, the real-time features are extracted. A causal inference engine is combined with a knowledge graph to analyze an anomaly propagation path, an anomaly root and a prediction trend are determined based on multi-agent reinforcement learning, a multi-objective optimization problem is constructed according to the knowledge graph, an optimization strategy is generated and dynamically adjusted, and an optimization result is evaluated by combining a knowledge distillation hybrid model. And finally, progressive optimization is realized and the knowledge graph is updated.

The invention relates to a multi-modal dynamic optimization educational resource recommendation system and method, and the system comprises the following modules: a multi-modal data collection module integrates video behaviors, answer tracks, physiological signals and other data through edge calculation, and constructs a learning feature map; the student portrait module adopts an LSTM-Attention network in combination with a graph neural network to dynamically model knowledge mastery and learning styles; the resource matching engine realizes multi-objective optimization of knowledge gain, cognitive load and interest matching based on reinforcement learning and knowledge graph analysis; the tag adaptive module dynamically adjusts resource weights through causal inference and comparative learning, the personalized recommendation module generates a dynamic learning path and pushes adaptive resources based on student portraits and real-time behavior data, and the learning progress tracking module monitors a learning state in real time and feeds back the learning state to the resource matching engine to optimize a recommendation strategy in a closed loop mode. The technical defects that resource recommendation of a traditional education platform is rigid and personalized adaptation is lacked are overcome.

The invention relates to a business credit evaluation and supervision method based on a multi-modal coevolution algorithm, and the method comprises the steps: collecting and cleaning multi-modal data, guaranteeing the data quality, uniformly storing the multi-modal data as a structural database, extracting features through cross-modal comparative learning, and enabling the features to be embedded into a dynamic multi-modal knowledge graph, a modal sharing network is used to extract and fuse modal sharing and specific features, consistency of modal feature distribution is optimized through adversarial learning, a global co-evolution credit scoring model is initialized, a dynamic multi-objective algorithm is used to optimize accuracy, robustness and interpretability of the model, and a credit scoring result is obtained. A causal reasoning model is introduced to analyze driving factors of enterprise credit changes, a causal relationship is established, a closed-loop feedback system is designed, a credit evaluation model is updated, adaptability is enhanced, an interpretable credit evaluation report is generated based on a generative adversarial network, and transparency and credibility are enhanced. According to the method, the accuracy and adaptability of credit assessment are improved, and the robustness and interpretability of an assessment result are ensured.

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 provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention relates to an intelligent power distribution room operation and maintenance method and system based on multi-source data fusion, and the method comprises the steps: collecting the environment and equipment data of a power distribution room through multi-source sensing equipment, and obtaining original multi-source data; performing standardization processing on the original multi-source data to obtain standardized heterogeneous data; performing space-time correlation modeling on the equipment vibration characteristics and the equipment power parameters through a space-time diagram convolutional network, and fusing the cross-modal depth characteristics extracted by the standardized heterogeneous data to obtain panoramic perception characteristics; performing anomaly detection through an isolated forest-long and short-term memory hybrid model according to the panoramic perception features, and deploying a causal inference engine to analyze a causal relationship among multiple variables to obtain a health state assessment result of the power distribution room; and performing operation and maintenance decision according to the health state evaluation result of the power distribution room, and performing operation and maintenance on the power distribution room. According to the invention, deep fusion of multi-source data is realized, equipment abnormity can be accurately detected, root causes can be analyzed, and the operation and maintenance efficiency and reliability of a power distribution room 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 provides an intelligent power grid relay protection reliability evaluation and lifting control method and system, and relates to the technical field of intelligent power grid relay protection, and the method comprises the steps: collecting the operation data of a relay protection device, carrying out the preprocessing and multi-domain feature extraction, and inputting a deep hybrid neural network to obtain a comprehensive feature vector; a multi-stage fault diagnosis model is utilized to identify a fault mode, a collaborative anomaly and a reliability level at a device level, an interval level and a station level respectively, and a fault propagation path and influence are predicted based on a causal reasoning network. And finally, constructing a hierarchical cooperative control strategy, and through multi-objective optimization, distributed game optimization and hierarchical rolling optimization, realizing coordinated optimization of a protection fixed value and improving the reliability of a relay protection device, thereby reducing the fault propagation probability and ensuring safe and stable operation of a power grid.

The invention relates to the technical field of greenhouse gas monitoring, in particular to a greenhouse gas collaborative monitoring and analysis platform. According to the technical scheme, the system comprises a cross-modal data fusion module, a causal reasoning analysis engine, a holographic dynamic visualization system, a distributed edge computing node and a self-adaptive decision optimization module, and the cross-modal data fusion module is used for integrating satellite remote sensing data, a ground sensor network, unmanned aerial vehicle mobile monitoring data and industrial Internet of Things emission source data in real time; a dynamic weight distribution algorithm is adopted, the multi-source data fusion weight is automatically adjusted according to environmental parameters, and a space-time continuous greenhouse gas concentration field is generated; a causal reasoning analysis engine is based on a hybrid architecture. According to the method, data privacy is guaranteed and delay is reduced through multi-source data fusion, causal reasoning analysis, holographic visualization and edge calculation, and comprehensive and accurate monitoring, scientific prediction analysis, efficient decision execution and risk prevention and control of greenhouse gas are realized based on decision optimization of dynamic games and block chain smart contracts.

The invention provides an embedded device online test and remote maintenance method and system, and relates to the technical field of device maintenance, and the method comprises the steps: collecting multi-source heterogeneous data, selecting a sampling period based on Shannon entropy, employing wavelet transform and fractal theory to extract features, inputting an anomaly detection model to generate an anomaly degree vector, and carrying out the detection of the anomaly degree vector; extracting features by using a capsule neural network and performing fault type probability distribution prediction; calculating system state indexes by combining a phase-space reconstruction technology; inputting a causal reasoning network to generate a fault diagnosis report; retrieving a maintenance scheme based on the fault diagnosis report; and the maintenance effect is evaluated through the twin neural network, and the maintenance instruction is iteratively optimized according to the effect until the maintenance effect reaches the standard.

The invention discloses an expressway lane control method based on real-time traffic flow. The expressway lane control method comprises the following steps of performing fusion sensing through a distributed optical fiber vibration sensor array and a millimeter wave radar; constructing a space-time diagram convolutional network model, and dynamically mapping the road topological structure into a lane connection diagram with variable weight; a layered reinforcement learning architecture is adopted, and an emergency lane dynamic management mode is triggered; and deploying a digital twin sand table to perform multi-resolution simulation verification, and when the actual traffic flow deviates from a predicted value by more than 15%, starting a causal inference engine to backtrack a decision link and update an agent strategy library. According to the method, global dynamic lane optimization is realized through a multi-source perception fusion and hierarchical reinforcement learning architecture, simulation data shows that the morning peak traffic volume is improved by 29.3%, and the accident scene traffic efficiency is improved by 28.6%; a digital twinning and causal inference engine is adopted, the strategy updating response time is shortened to 30 seconds, and the congestion propagation distance is reduced by 35%.

The invention discloses a medical adverse event risk prediction, prevention and control method based on causal inference. The method comprises the following steps: S1, collecting and preprocessing multi-modal medical data; s2, constructing a causal network through a time slicing technology; s3, introducing ethical factors to form an ethical causal network; s4, generating a causal enhancement vector; s5, constructing a dynamic risk prediction model, and outputting an evaluation result; s6, analyzing a path contribution degree, and screening high-risk paths and nodes; s7, generating a personalized prevention and control scheme; and S8, generating a transparent report, and displaying the transparent report to the medical personnel and the patient. According to the method, the dynamic causal relationship among the medical variables is accurately modeled through the dynamic causal network, the multi-modal data fusion and the ethical correction technology, the personalized prevention and control scheme is generated in combination with the optimization algorithm, comprehensive support is provided for medical decision making, and implementation of precise medical treatment is promoted.

The invention discloses a wind power plant equipment fault intelligent diagnosis method and system. The method comprises the following steps: acquiring multi-dimensional sensor data of wind power plant equipment; carrying out preliminary screening on the data by utilizing an edge analysis algorithm, and determining data needing to be further diagnosed; the screened data are input into a pre-constructed fault diagnosis model, the data are fused through a multi-modal deep learning model in the pre-constructed fault diagnosis model, and then a fault occurrence area is determined through transfer learning, domain adaptation and a multi-agent reinforcement learning framework; meanwhile, the causal inference engine and the knowledge graph enhanced neural symbol inference module are responsible for deep processing and fault diagnosis. And finally, inputting the determined fault area and the diagnosis result into a dynamic system health index calculation module to generate a final fault diagnosis result. According to the method, the diagnosis efficiency and accuracy of the equipment fault of the wind power plant are remarkably improved, and a powerful guarantee is provided for stable operation of the wind power plant.

The invention provides a multi-target collaborative optimization scheduling system based on artificial intelligence, relates to the technical field of intelligent scheduling, and realizes automatic task classification, task association relationship modeling, resource optimization distribution and dynamic adjustment of a scheduling strategy through task analysis, target association analysis, scheduling strategy generation, real-time monitoring and a dynamic scheduling module. The system utilizes natural language processing, machine learning, graph neural network, reinforcement learning and other technologies to extract features from task information, analyze association between tasks and generate a scheduling strategy according to resource requirements, a real-time monitoring module tracks a task execution state, a dynamic scheduling module evaluates deviation influence through a causal inference model, and a task scheduling strategy is generated. The system is suitable for the electronic equipment, the instructions are stored through the storage medium, and efficient task scheduling and management are achieved.

The invention relates to the field of nutrition and health management, and discloses a nutrition evaluation and prediction method and system based on big data, and the method comprises the following steps: firstly collecting the physiological data, diet record and activity data of a user, and carrying out the preprocessing of the data, and guaranteeing the data quality; dimensionality reduction is carried out through feature extraction and principal component analysis, and key features are extracted; the method comprises the following steps: evaluating daily nutritional requirements of a user by using a multivariable linear regression model, a Bayesian network and causal inference, predicting a nutritional requirement change trend of the user in a future period of time based on a long-short-term memory network, and optimizing daily diet selection of the user by using a Markov decision process; and finally, generating a personalized nutrition scheme by adopting linear programming and a genetic algorithm, and ensuring that the nutritional requirements and health targets of the user are met. According to the method, personalized and scientific diet suggestions can be provided, and the quality and effectiveness of individual nutrition management are improved.

A computer-implemented method includes receiving patient responses over a network from a recorded screening interview, utilizing a custom Large Language Model (LLM) to generate transcriptions and insights from the audio, performing video sentiment analysis to assess emotional states, and based on these data, generating an AI model to predict risk levels for various mental health conditions. The method further comprises presenting personalized questions based on previous screening insights, creating longitudinal summaries of patient histories, continuously improving the AI models through reinforcement learning, and integrating a recommendation engine to suggest targeted interventions. Additionally, the method includes tracking outcomes over time and enabling causal inference across multiple screenings, thereby providing a comprehensive platform for mental health assessment, intervention, and outcomes tracking.

The invention discloses a big data analysis-based early warning method for abnormal emission of pollution facilities. The method comprises the following steps of S1, collecting data and constructing a data set; s2, storing and preprocessing a data set; s3, constructing a space-time propagation network of the external events by using the graph neural network model; s4, modeling and predicting the time sequence features by using a mixed depth generation model; s5, analyzing the causal relationship between the external event and the facility emission behavior through a causal inference model; s6, adopting a Bayesian optimization algorithm based on uncertainty estimation of a strategy to dynamically adjust an emission early warning threshold value; s7, generating a new abnormal emission mode label by using a self-supervised learning model and a few-sample learning technology; and S8, adopting an interpretable artificial intelligence technology to generate an abnormal source tracing report, and providing coping suggestions and action schemes. According to the method, the early warning of the abnormal emission of the pollution facilities is realized by utilizing a cross-time-space event inference and self-supervised learning method.

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 provides a multi-modal intelligent irrigation decision algorithm based on causal inference, and the algorithm comprises the steps: carrying out the knowledge distillation of a pre-trained image feature extractor based on causal inference, and fully extracting the features of crop time sequence information and image information; according to the method, multi-modal data is considered, and heterogeneous data is deeply fused. Through knowledge distillation of an image feature extractor, the reasoning speed of the model is higher, the prediction accuracy is higher, end-to-end water shortage state prediction can be carried out based on greenhouse environment data, root system environment data and crop images, and a prediction result can be used for guiding crop irrigation and correcting the irrigation frequency and the irrigation amount of crops. And a more intelligent and comprehensive irrigation decision is realized.

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 big data aggregation analysis method and system based on a typical service scene. The method comprises the following steps: receiving multi-dimensional service data streams from a plurality of heterogeneous data sources; carrying out deep mining processing on time characteristics and causal relationships of data by utilizing the multi-dimensional service data flow and combining time sequence analysis and causal inference technologies to obtain a cross-domain data association network; based on the cross-domain data association network, performing deep learning processing on features of nodes and edges in the network by adopting a graph neural network algorithm to obtain key data nodes and a combination mode thereof; according to the key data nodes and the combination mode thereof, dynamically adjusting a selection strategy of the key data nodes through a Q-learning algorithm to obtain an optimized data aggregation analysis model; and performing comprehensive analysis processing on the service data based on the optimized data aggregation analysis model to generate a comprehensive service insight report. According to the technical scheme provided by the invention, the efficiency and accuracy of big data aggregation analysis can be remarkably improved.

The invention discloses a purchase demand prediction method and system based on big data analysis, and relates to the technical field of supply chain management and intelligent prediction, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the data fusion and preprocessing; based on a causal inference technology, key influence factors of purchase demand changes are analyzed, and a purchase demand prediction model is constructed; dynamically adjusting the prediction model by using real-time data, and generating a real-time or periodic purchase demand prediction result; carrying out data simulation and model optimization aiming at an extreme scene of a purchase demand; and optimizing a supply and demand matching strategy based on a prediction result, and carrying out iterative improvement on the prediction model through a feedback mechanism. Through multi-source heterogeneous data fusion, causal inference, dynamic adjustment and feedback optimization mechanisms, high-precision purchase demand prediction is realized, a supply and demand matching strategy is optimized, the adaptability of the model to market fluctuation and extreme scenes is enhanced, the purchase cost is effectively reduced, and the supply chain management efficiency and stability are 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 relates to the field of computers, in particular to a micro-service fault positioning method and device based on causal inference and a knowledge graph. The method comprises the following steps: firstly, collecting an original calling link, a multi-dimensional time sequence index and log data of a micro-service system, analyzing the correlation of micro-service indexes by using a partial correlation algorithm, then inferring the causal relationship of the indexes by using a structural causal model, and mapping the causal relationship of the indexes to specific service calling by using a principal component analysis method. And constructing a service dependency relationship. And then defining a knowledge graph ontology, constructing a fault dependency graph according to an index causal relationship and a service dependency relationship, and fusing the fault dependency graph with a calling link graph to form a complete fault dependency knowledge graph with multi-level influence. And finally, storing the graph in a graph database, taking exception as a service node starting point, and outputting a fault root cause candidate set through exception detection, random walk and node score calculation. According to the invention, the efficiency and accuracy of fault positioning are improved, and the stable operation of the micro-service system is ensured.

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 provides a chronic disease risk assessment and intervention strategy generation system based on data analysis. According to the system, multi-source heterogeneous information including clinical examination, behavior records, environment data and the like is collected, key features are extracted through a data fusion technology, and time and space features of data are enhanced through a space-time weighted tensor decomposition method. And in combination with a causal reasoning technology, the system can accurately evaluate the chronic disease risk of an individual, eliminate confounding factors and provide more reliable risk prediction. In addition, the system dynamically generates a personalized intervention strategy through a reinforcement learning algorithm, adjusts intervention measures according to real-time health data, and ensures accurate chronic disease management. The method has an efficient risk prediction capability and a personalized intervention scheme, and is helpful for improving the accuracy and effect of chronic disease management.