259 results about "Causal reasoning" 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 relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal causal reasoning and interpretation method, device, equipment and medium, and the method comprises the steps: obtaining original data streams of at least two different modals, and extracting modal features; a cross-modal attention mechanism is utilized to fuse modal features, and causal features are extracted through feature distillation; constructing a dynamic causal graph based on causal features, and updating an edge weight through a causal intensity function; identifying the causal relationship in the dynamic causal graph and performing anti-factual reasoning verification to evaluate the reliability of the causal relationship; and generating a causal interpretation result in combination with the dynamic causal graph and the causal relationship reliability. According to the method, the multi-modal data are fused, the causal features are extracted, and dynamic causal graph updating and anti-factual reasoning verification are combined, so that reliable modeling and explanation of the causal relationship in a complex scene are realized, the defects of single modal or simple fusion in the prior art are overcome, and the accuracy and interpretability of causal reasoning are improved.

The invention discloses a question and answer method and system based on a knowledge graph, and the method comprises the steps: outputting a structured query graph of which nodes comprise entities, relationships and semantic weights according to a natural language question input by a user; on the basis of the structured query graph, outputting candidate entity sub-graphs of which the ambiguity is eliminated; outputting a reasoning path set with the highest probability according to the candidate entity subgraph; based on the reasoning path set, outputting candidate answers which conform to logic and are coherent in grammar; and according to the topological consistency of the candidate answers and the knowledge graph, calculating an answer credibility score by using a causal reasoning model, and outputting a final answer and an interpretability verification report by analyzing a logic causal chain implied in the answers and verifying with the knowledge graph. By utilizing the embodiment of the invention, deep integration and value mining of park multi-source data can be realized, and powerful support is provided for fine management and intelligent decision-making of the smart park.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The embodiment of the invention provides a dynamic risk assessment method and system based on perception and causal reasoning, and belongs to the technical field of automatic monitoring. The method comprises the following steps: acquiring a video data stream in real time and analyzing the video data stream into a structured dynamic visual scene representation; through a neural network model used for executing cross-modal semantic alignment, mapping the dynamic visual scene representation into activation evidence of nodes in a pre-constructed causal knowledge graph in a multi-modal embedding space, the nodes of the graph being risk events or risk factors; and performing probabilistic reasoning on the map based on the activation evidence to calculate the posterior probability of the node corresponding to the to-be-predicted risk event. According to the method, the problems that visual perception and causal reasoning cannot be effectively integrated and the evaluation result lacks interpretability in the prior art are solved, obstacles between visual information and abstract causal concepts are overcome by constructing a perception-mapping-reasoning technology closed loop, and the accuracy, transparency and credibility of risk evaluation are remarkably improved.

The invention discloses a power plant secondary circuit fault tracing method based on causal reasoning. The method comprises the following steps: collecting and preprocessing multi-source operation data of a power plant secondary circuit system; based on the secondary circuit schematic diagram, the relay protection configuration table and the signal connection relation, establishing a topology constraint set, and generating an intervention node set; constructing an enhanced causal Bayesian network model based on the topology constraint set and the intervention node set; based on the topology constraint set, carrying out structure learning and parameter estimation on the enhanced causal Bayesian network model; inputting the standardized operation data set into the trained enhanced causal Bayesian network model, and executing reverse causal reasoning; and performing consistency verification on the fault root cause candidate set, the propagation path set, relay protection logic and interlocking rules, and generating a fault traceability report. According to the method, the enhanced causal Bayesian network is adopted, so that the power plant secondary circuit fault can be accurately traced.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The invention discloses an acute abdominal pain cause differential diagnosis system based on deep learning, and relates to the technical field of medical artificial intelligence, comprising: a data acquisition module acquires initial self-described text data of a patient; the standardization processing module generates a standardized symptom set through standardization processing; the cause reasoning module performs reasoning according to the standardized symptom set and the medical knowledge graph to obtain a suspected disease set; the contradiction detection module is used for detecting the contradiction between the symptom set and the self-contained text and calculating the contradiction intensity and disease cause relevancy; the priority calculation module ranks contradiction priorities according to contradiction intensity and disease cause relevancy; the contradiction processing module judges the contradiction significance score, if the contradiction significance score is lower than a threshold value, a standardized symptom set is output, and otherwise, an optimal clarification action is generated to solve the contradiction; an iteration updating module updates a symptom set according to clarification action feedback, and causes are inferred again until a termination condition is met; the method can ensure that the pathogenesis reasoning process gradually approaches the real pathogenesis, and improves the diagnosis reliability.

The invention relates to a multi-channel marketing effect evaluation method and system based on causal reasoning and anti-fact Shapley, and belongs to the technical field of digital marketing analysis, and the method comprises the steps: collecting the original behavior data of a user in a multi-channel marketing environment, and constructing a behavior sequence according to the original behavior data; performing feature extraction and engineering processing based on the behavior sequence to obtain structured features; respectively training a tendency scoring model and a result model by adopting a dual machine learning method based on the structured features, and obtaining an unbiased causal effect estimated value of each channel contact on the conversion target through cross fitting; calculating a causal contribution value and a causal contribution confidence interval of each channel contact through an anti-fact Shapley value calculation method; and performing budget optimization distribution according to the causal contribution value, and outputting a marketing strategy suggestion. The method has the effects of accurately quantifying the causal contribution of each marketing channel, eliminating the self-selection deviation of the user and providing a reliable statistical basis.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention discloses a multi-user-oriented smart home resource conflict negotiation and allocation method, which comprises the following steps of: detecting equipment use conflicts caused by at least two users by constructing a structural causal model for representing a causal relationship of a plurality of variables in a smart home environment, generating a candidate decision strategy set comprising resource isolation and alternative compensation, and allocating the candidate decision strategy set to the smart home environment; carrying out anti-fact inference by utilizing a causal model, quantifying the causal effect of each strategy on the user state, and selecting an optimal strategy for execution based on a minimum negative effect and a Pareto optimal principle; besides, the method ensures that the system dynamically adapts to user habit changes through online monitoring of prediction errors and correction of the causal model, and compared with the prior art, the method improves the decision accuracy through causal reasoning, realizes fair and personalized user resource allocation, and improves the user experience and long-term effectiveness of the smart home system.

The invention provides a circuit board electroplating process quality monitoring and early warning method, which comprises the following steps: carrying out real-time acquisition and standardization processing on process parameters (such as current density, bath solution temperature and pH value) of procedure nodes of acid pickling, activation, electroplating and the like by utilizing a distributed data acquisition system, and fusing historical causal relationships based on a knowledge graph structure to obtain a circuit board electroplating process quality monitoring and early warning result; according to the method, causal strength dynamic evaluation is realized by combining improved Granger test and a dynamic Pearson coefficient, long-range dependence between multi-hop graph attention network modeling parameters is adopted, an optimal abnormal attribution path is further screened through path credibility attenuation and a trend semantic encoder, and finally, an optimization suggestion template is automatically matched to output a process adjustment scheme, so that the accuracy of the abnormal attribution path is improved. According to the invention, the causal reasoning accuracy and the automatic optimization capability of the electroplating process anomaly analysis are improved, and the operation reliability and the intelligent level are improved.

The invention discloses a knowledge-enhanced multi-granularity causal fault diagnosis method, and the method comprises the steps: constructing a hierarchical bearing fault domain knowledge graph which comprises a structural layer, a fault layer and a feature layer, and systematically integrating multi-dimensional domain knowledge; designing a three-level causal chain structure covering a microscopic layer, a mesoscopic layer and a macroscopic layer, and realizing cross-scale reasoning from a microscopic signal feature to a macroscopic fault decision; a knowledge-causal dynamic fusion mechanism based on working condition perception is provided, and adaptive coupling of domain knowledge and causal reasoning is realized through dynamic weight adjustment; and finally, a diagnosis result containing fault types, positions and severity is output, so that the defects of an existing method in the aspects of knowledge fusion, cross-granularity reasoning and interpretability are effectively overcome, and the precision and credibility of bearing fault diagnosis under complex working conditions are improved.

The invention relates to the technical field of supply chain risk prediction based on causal reasoning, and discloses a supply chain risk prediction method and device based on causal reasoning, equipment and a medium. The method comprises the following steps: constructing a double-layer knowledge graph of a regulation layer knowledge graph and a logistics physical layer knowledge graph; real-time fusion and characterization of geographical regulation and logistics operation multi-source heterogeneous data are realized, the perception real-time performance and coverage dimension of risk events are improved, then a multi-level causal chain is automatically generated by introducing a large language model, a complex conduction path of risks in a supply chain network can be deeply inferred, and the risk prediction accuracy is improved. According to the method, the defect that hidden association mining is insufficient in a traditional method is overcome, reasoning of risk traceability is improved, and finally a risk prediction result is obtained by mapping causal chain nodes to a logistics physical layer knowledge graph and performing probabilistic calculation. The method has the beneficial effects that the risk prediction of the specified supply chain is realized, and the accuracy of the risk prediction result is improved.

The invention discloses a causal reasoning-based satellite-ground collaborative remote sensing image interpretation method and device, relates to the technical field of remote sensing image processing, and mainly aims to solve the problem that the inference requirement of environmental causes in a remote sensing image cannot be met in the prior art. Comprising the following steps: a ground end obtains initial interpretation features obtained by performing initial interpretation on multi-modal remote sensing data by a satellite-borne end, and interprets the initial interpretation features based on a first interpretation model of which model training is completed to obtain core interpretation features; determining causal variables based on the core interpretation features, and constructing a causal variable graph based on the causal variables; determining a cause label and an interpretation result of the label area according to the causal variable graph, and obtaining a verification result corresponding to the cause label and the interpretation result; and based on the knowledge distillation and the verification result, determining learning core features of the first interpretation model for updating training, so that a second interpretation model in the satellite-borne end coordinates the learning core features fed back based on the ground end.

The invention relates to the technical field of data analysis, can be applied to agricultural disaster monitoring, financial science and technology and other business scenes, and discloses a multi-source monitoring analysis and decision-making method, device, equipment and medium, and the method comprises the steps: obtaining multi-source monitoring data, and carrying out the time-space multi-scale fusion to generate time-space fusion data, extracting difference features of the target object before and after the event based on the fusion data to identify a first type of events, predicting a second type of events based on dynamic time sequence features by using a space-time diagram network model, and constructing a knowledge graph to perform causal reasoning on two types of event results to generate a reasoning enhancement result; and generating comprehensive abnormal analysis information and a response decision according to a reasoning enhancement result. According to the method, spatial-temporal features of multi-source data are fused, a causal reasoning mechanism is introduced, the identification and prediction results are subjected to correlation analysis, dynamic interpretation and response generation of complex events are achieved, and therefore the anomaly identification precision and the real-time performance and interpretability of risk decision making are improved.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

The invention discloses a multi-modal rumor detection method fusing texts, images and social propagation structures, and belongs to the technical field of natural language processing, computer vision and causal reasoning. Specifically, the invention provides a unified causal inference framework, and hybrid deviation in multi-modal data is effectively stripped by integrating text anti-fact causal inference and an image dot product causal intervention mechanism. Under the framework, social propagation structure features are further fused, and a multi-head collaborative attention mechanism is adopted, so that deep alignment and semantic enhancement in cross-modal features are realized. Adversarial samples are generated through projection gradient descent for adversarial training, and model parameters are optimized in combination with anti-fact loss, so that the classification accuracy and generalization ability of the model are improved. According to the rumor detection method, a causal reasoning normal form is introduced into a rumor detection task, the effectiveness of an anti-fact and intervention mechanism in a complex information scene is verified, and a new theoretical support and method path are provided for constructing a credible multi-modal information system.

The invention discloses a multi-modal quantifiable security threat reasoning method and system based on causal reasoning, and relates to the technical field of network security, and the method comprises the following steps: S1, data collection; s2, data preprocessing; s3, carrying out multi-modal fusion; s4, carrying out cross-modal causal association; s5, a causal inference engine; s6, real-time monitoring and response are carried out; and S7, performing CSA security proxy. According to the multi-modal quantifiable security threat inference method and system based on causal inference, comprehensive monitoring and response to network security threats are realized by fusing causal inference and a multi-modal dynamic sensing technology; the core problems that in traditional network security threat detection, the false alarm rate is high, attack root cause positioning is fuzzy, and cross-modal data relevance is weak are solved.

The invention belongs to the technical field of computer vision, and discloses a target identification tracking method based on a self-supervision mechanism, and the method comprises the steps: enhancing a self-supervision pre-training module through causality, constructing a causal sample pair through unlabeled video data, learning universal features through combining with comparison loss, and achieving the high-precision tracking without large-scale manual labeling. A multi-modal feature fusion and dynamic calibration mechanism further reduces dependence on annotated data, is especially suitable for industrial inspection, field monitoring and other scenes where data acquisition is difficult, significantly reduces time and labor costs in a data preparation stage, and broadens the application range of the technology in resource limited scenes; a causal reasoning and physical constraint mechanism is introduced, a dynamic relation between targets is modeled through a space-time causal graph, unreasonable tracks are filtered in combination with a physical rule, and complex conditions such as shielding, rapid movement and extreme weather are effectively dealt with; the dynamic feature calibration module corrects feature drift in real time, and ensures stable model performance in long-term tracking.

The invention provides a multi-modal fusion causal analysis method and platform for industrial safety production, and the method comprises the steps: carrying out the preprocessing of multi-modal key data from an industrial site, and generating a standardized time series data matrix; based on the standardized time sequence data matrix, combining industrial physical rules and operation process semantics to construct an industrial safety causal atlas; based on a self-supervised learning mechanism, optimizing the structure and edge weight of the industrial safety causal atlas by using the standardized time sequence data matrix to obtain an optimized causal reasoning model; and performing path-level risk activation judgment and accident causal traceability by using a real-time standardized time sequence data matrix constructed by the causal reasoning model and real-time inflow multi-modal key data to generate a risk alarm and traceability report. According to the method, the perspectiveness, the accuracy and the interpretability of industrial safety monitoring are remarkably improved.

The invention relates to the field of multi-modal neural network model compression, and particularly discloses a multi-modal model compression and distillation method and system based on causal reasoning, and the method comprises the steps: constructing a comprehensive causal discovery module, identifying a causal dependency relationship among the multi-modal features through information theory measurement, Granger causal analysis and intervention-based verification; executing an adaptive compression engine, and performing pruning, mixing precision quantification and low-rank decomposition based on a causal relationship; a cross-modal distiller is applied, and multiple loss function combinations are adopted to maintain the relationship between modals; and implementing a dynamic optimizer to carry out hardware perception and context-sensitive reasoning optimization. According to the method, the compression decision is guided through causal reasoning, the high compression rate is achieved while the key causal path is kept, and the deployment problem of the multi-modal model in the resource-constrained environment is effectively solved.

The invention provides an electric quantity prediction method and system fusing physical constraint factors, and relates to the technical field of electric quantity prediction. Historical load, weather, electricity price and calendar data are collected, and a key feature set is constructed through preprocessing and feature selection; a prediction model with the physical information neural network as the core is constructed, the prediction model comprises a recursion sub-module used for short-term prediction and a trend sub-module used for long-term prediction, and a physical constraint loss item based on a physical rule is introduced into model training so as to enhance the generalization ability; a multi-time granularity modeling framework is adopted, uncertainty quantization is achieved through a Monte Carlo Dropout or Bayesian neural network, and a confidence interval of a predicted value is output; and finally, causal reasoning is carried out through a Shapley value algorithm and anti-fact simulation, and key influence factors are identified. According to the method, the precision, stability and interpretability of electric quantity prediction are effectively improved, and reliable support is provided for power grid dispatching and decision making.

The invention discloses an Internet of Things monitoring management method and system based on deep learning, and relates to the related technical field of Internet of Things monitoring management, and the method comprises the steps: reading node multi-source data through an intelligent agent, constructing a deep causal structure field through cross-modal deep causal reasoning, and revealing the relationship between environmental inducements, state paths and risk propagation; constructing a space-time causal tensor representing dynamic risk distribution of a monitoring area, quantitatively evaluating monitoring income, energy consumption cost and risk exposure, executing game analysis under a multi-party game strategy, and establishing an intelligent agent behavior strategy; and updating the monitoring network topology, executing deep space-time-cause and effect joint reasoning, generating an exception explanation result, and performing exception report management. According to the invention, the technical problems of lack of causal interpretability, resource allocation rigidness and insufficient abnormal response capability of Internet of Things monitoring in the prior art are solved, and the technical effects of accurate abnormal management of Internet of Things nodes and improvement of resource utilization efficiency and risk decision interpretability are achieved.

The invention provides a multi-modal agricultural technology question and answer method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: extracting a text feature vector, an image feature vector, a time sequence feature vector and a spatio-temporal context feature vector according to a query text and a crop image; calculating an initial fusion feature according to the spatio-temporal context feature vector, the image feature vector and the time sequence feature vector; when the query text contains a semantic entity of a preset type, calculating to obtain a final fusion feature according to the text feature vector and the initial fusion feature; splicing the final fusion feature and the text feature vector, and mapping the spliced vector to a space-time knowledge graph for reasoning to obtain a causal reasoning path; and generating a question and answer result according to the causal reasoning path. According to the method, deep alignment of time and space and semantics is carried out on the multi-modal agricultural data, and causal reasoning is carried out in combination with the knowledge graph with time and space constraints, so that the accuracy of a question and answer result is remarkably improved.

The invention relates to the technical field of power consumption behavior analysis and intelligent auditing of power consumers, and discloses an auditing rule threshold dynamic recommendation method, system and device based on feature mapping and a medium, and the method comprises the steps: integrating multi-source data of a user, and generating a comprehensive feature vector through a multi-modal fusion network; then, performing group division on the users according to feature similarity under a federated learning framework; using a time sequence generative adversarial network to generate enhanced power consumption data for each user group so as to improve model robustness; training a threshold dynamic adjustment strategy network in combination with reinforcement learning and causal reasoning, and outputting an initial recommendation threshold; the optimal balance threshold set between the false alarm rate and the missing report rate is searched through multi-target Bayesian optimization, and diversified choices are provided for decision making. According to the method, the leap from automation to intellectualization and from single-point optimization to multi-target collaborative decision making is realized, and the accuracy of auditing efficiency and the scientificity of decision making are remarkably improved.

The invention discloses an intelligent sports event decision-making system based on causal-driven multi-modal fusion and spatio-temporal dynamic reasoning. The system comprises a multi-modal causal data acquisition and preprocessing module; a causal-oriented multi-modal knowledge graph construction and updating module, wherein the causal-oriented multi-modal knowledge graph construction and updating module is provided with a causal structure learning algorithm and an online learning framework; a space-time perception hybrid agent module; the causal constrained dynamic decision optimization module is provided with a deep reinforcement learning algorithm; and the interpretability analysis and visualization module is used for receiving the decision strategy sent by the space-time perception hybrid agent module, generating decision explanation according to the decision strategy and visually presenting the decision explanation. The objective of the invention is to construct an intelligent system capable of providing high-precision, interpretable and adaptive decision support by integrating causal reasoning, multi-modal learning and reinforcement learning, so as to significantly improve scientificity and effectiveness of sports event analysis and decision.

The invention provides a nuclear power plant safety system based on causal reasoning, and relates to the technical field of nuclear power plant safety monitoring and fault diagnosis. The nuclear power plant safety system comprises a multi-source sensing layer and a causal engine layer, the multi-source sensing layer is used for collecting operation parameters of a nuclear power plant, and the causal engine layer comprises a dynamic causal network construction module and an online reasoning module. The dynamic causal network construction module is used for constructing a dynamic causal network according to the operation parameters, a continuous causal edge generated based on an equipment physical equation and a discrete event edge generated based on a probabilistic safety evaluation event tree; and the online reasoning module is used for determining a key fault propagation path according to the dynamic causal network so as to locate a fault source. According to the method, the equipment physical equation is embedded into the dynamic causal network, so that the reasoning process is ensured to accord with thermal hydraulic and safety physical rules, the problem of high false alarm rate caused by correlation misjudgment in deep learning is avoided, and the safety of the nuclear power plant is improved.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention discloses an intelligent prediction method for a fracturing wellhead yield increase effect, and the method comprises the steps: carrying out the fusion collection, standardization and embedded dimension reduction of multi-source real-time and historical data, environment and geological parameters and expert experience, cooperatively extracting high-dimensional features, evaluating the uncertainty of the features through a Bayesian model, and carrying out the weighted input of a reinforcement learning architecture, thereby achieving the intelligent prediction of the yield increase effect of a fracturing wellhead. According to the method, causal reasoning and deviation correction are carried out in combination with physical and business indexes, high consistency of a strategy and a multi-target business index is realized by dynamically adjusting a reward function, and self-adaptive retraining and robust optimization of a model in extreme environments such as high noise and data missing are realized through field evaluation regression analysis of output. According to the method, the adaptability and prediction accuracy of the fracturing yield increase prediction and optimization system to complex working conditions are remarkably improved.