54 results about "Causal pathway" patented technology

The invention discloses a causal atlas-driven enterprise intelligent decision-making method and system, and belongs to the technical field of enterprise management. The method comprises the following steps: constructing an enterprise-level causal atlas by obtaining enterprise business processes, operating indexes and unstructured text data; executing path reasoning based on the to-be-decided data, and determining a multi-hop causal path chain; determining a current association rule and a decision suggestion in combination with an expert rule base; causal maps and rule tags of different industries are introduced, and a migratable causal path and an association rule are identified based on structural similarity and a semantic mapping rule; and finally, current and migration knowledge is fused to generate an enterprise decision execution scheme and a causal reasoning result. According to the scheme, based on comprehensive application of current and migrated knowledge, scientificity, accuracy and interpretability of decision making are effectively enhanced, meanwhile, dynamic updating and inter-industry knowledge sharing are supported, enterprise decision making efficiency and response speed are remarkably improved, and enterprises are assisted to achieve intelligent and fine management.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention relates to the technical field of computer data processing, and discloses a building material supplier dynamic evaluation and recommendation system based on big data, and the system comprises a data fusion module which integrates multi-source heterogeneous data to generate a unified data set; the tensor modeling module is used for constructing and decomposing a five-dimensional space-time tensor to obtain a factor matrix and a dynamic weight; the causal correction module is used for establishing a causal graph based on the network relationship and eliminating hybrid deviation; the recommendation decision module is used for outputting a recommendation list through reinforcement learning in combination with the evaluation weight and the performance distribution; and the interpretable module is used for generating an interpretable report based on the factor and the causal path. According to the method, the technical scheme of multi-source heterogeneous data fusion and five-dimensional space-time tensor decomposition is adopted, and dynamic, multi-dimensional and relevance evaluation of supplier performance is realized by constructing a unified data structure including suppliers, time, static characteristics, context and cooperative relationships.

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 relates to the technical field of data analysis, in particular to a supply chain data analysis system based on a deep learning technology. The system comprises a multi-modal causal characterization unit, a dynamic digital twinning unit and an AI control compiling unit. According to the method, multi-modal data is fused into a unified framework, the problems of manual definition and low timeliness are solved, the problem of data fault is solved, visual causal path visualization is constructed, potential risks in a supply chain are actively prevented and controlled, the decision accuracy is improved, and the method is suitable for popularization and application. Then, the dynamic digital twinning unit reduces cross-link error accumulation through a dynamic prediction and updating mechanism, so that an enterprise can quickly respond when a change occurs, the supply chain loss is reduced, finally, the AI control compiling unit is adopted to efficiently make a decision and execute, the problems of slow progress and inaccuracy caused by manual operation are reduced, and the efficiency is improved. And a bidirectional cognitive channel between the supply chain management system and the IA equipment is realized.

The embodiment of the invention provides a database exception analysis method and device, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: when an abnormal index is detected, determining at least one causal path from an abnormal index knowledge graph, inputting the abnormal index and the causal path into a cue word generation model, and dynamically optimizing cue words through a reinforcement learning network in the cue word generation model, and the optimal prompt word which is more suitable for the current abnormal scene is obtained. Wherein the causal path comprises at least one related index of the abnormal index, obtaining current index data corresponding to the abnormal index and the plurality of related indexes, and further performing root cause reasoning based on the plurality of current index data, the causal path and the optimal cue word, thereby further considering a complex and changeable database operation environment. Therefore, a more accurate and comprehensive root cause analysis report is obtained.

The invention provides a dynamic knowledge graph construction and diagnosis reasoning method oriented to intelligent inquiry, and relates to the technical field of knowledge graphs, comprising the following steps: acquiring multi-source medical data, performing entity recognition and semantic annotation, establishing a causal probability graph based on a structured entity set, and establishing a dynamic knowledge graph; and selecting an optimal questioning problem according to the information gain in the inquiry process, dynamically updating the causal probability by using the Bayesian rule, and finally propagating the conditional probability along the causal path to generate a diagnosis conclusion. According to the invention, personalized inquiry decision and accurate diagnosis reasoning are realized, and the diagnosis accuracy and efficiency of the intelligent inquiry system are improved.

The invention discloses a real-time risk early warning method for an intelligent management and control platform, and the method comprises the steps: collecting multi-source data, and carrying out the standardized preprocessing, and forming structured multi-dimensional time series data; constructing a variable causal graph based on the improved structure causal model, and forming a causal influence path; performing causal projection, and constructing a causal enhanced time sequence characteristic flow; fragmenting the causal enhanced feature flow, and inputting the fragmented feature flow into a random pruning forest for anomaly detection; performing abnormal event tracing based on a detection result and a causal path, and identifying a key variable and an influence range; and generating graded early warning information and triggering a risk disposal strategy to realize closed-loop risk management. According to the invention, by fusing the structural causal model and the random pruning forest algorithm, accurate detection, causal traceability and efficient early warning of abnormal events in an intelligent management and control platform are realized.

The application relates to the field of multi-modal neural network model compression, and specifically discloses a multi-modal model compression and distillation method and system based on causal reasoning, which comprises the following steps: constructing a comprehensive causal discovery module to identify the causal dependence relationship among multi-modal features through information theory measurement, Granger causality analysis and intervention-based verification; performing an adaptive compression engine to perform pruning, mixed precision quantization and low-rank decomposition based on the causal relationship; applying a cross-modal distiller to maintain the relationship among modes by using a variety of loss function combinations; and implementing a dynamic optimizer to perform hardware perception and context-sensitive reasoning optimization. The application guides the compression decision through causal reasoning, realizes high compression rate while maintaining key causal paths, and effectively solves the deployment problem of multi-modal models in a resource-limited environment.

The invention discloses a question and answer method and system based on large model causal graph discovery and causal graph enhanced reasoning, and belongs to the technical field of natural language processing and artificial intelligence. The method comprises the following steps: firstly, carrying out hierarchical expansion from an initial root node by adopting a breadth-first search strategy, and efficiently constructing a reliable causal graph conforming to directed acyclic graph constraints with linear complexity in combination with real-time loop detection; in the question and answer stage, key entities in user questions are analyzed, and causal paths connecting the key entities are retrieved in a causal graph; and taking the retrieved structured causal path as a constraint condition to be injected into a decoding process of a large-scale language model, and generating a natural language answer which is strict in logic and can trace reasoning steps. According to the method, the resource consumption of large-scale causal discovery is remarkably reduced, the accuracy and interpretability of the answers of the questions and answers are effectively improved, and technical support is provided for medical treatment, finance and other scenes needing high-reliability reasoning.

The invention relates to the technical field of data analysis, discloses an artificial intelligence-based index data attribution analysis method and system, and aims to solve the problems of poor accuracy, timeliness, dynamic adaptability and interpretability of an existing method. The scheme mainly comprises the following steps: accessing a target index, an association index, dimension data and a domain knowledge graph; cleaning and screening the data, and constructing a causal attribution graph structure with indexes and dimensions as nodes and association relationships as edges; using a graph attention network model to identify a core influence factor causing the target index abnormity from the graph under the business rule constraint of the knowledge graph, and calculating the contribution degree weight of the core influence factor; locally updating the graph structure and the model through an incremental learning mechanism; and finally, converting a causal path output by the model into a visual interaction map and a structured report. According to the method, accurate, real-time, self-adaptive and interpretable automatic index attribution is realized, and the method is particularly suitable for risk management and control and business optimization of enterprises.

The invention discloses an industrial fault traceability analysis method based on neural causal discovery and causal feature screening, which introduces a neural Granger component-by-component long and short-term memory network with group sparse characteristics, and can effectively capture sparse causal relationships in a system. Meanwhile, in combination with an arrangement feature importance method, the extracted causal relationships are screened by using model characteristics, false causals are removed, a causal adjacency matrix is obtained, and the robustness and credibility of the result are enhanced. And constructing a reachable matrix through the causal adjacency matrix, and expressing and positioning a root node in a causal path in combination with position scoring, thereby realizing accurate tracing of the industrial fault. The method is based on a data driving method, does not need to depend on mechanism knowledge, can be effectively applied to an industrial system with a complex control loop, and aims to improve the effectiveness of causal analysis, better perform root cause analysis and determine a fault source, and improve the safety and reliability of an industrial production process.

The invention relates to the technical field of machine learning and fault diagnosis, in particular to a causal-driven credible cross-component mechanical fault diagnosis method and system. According to the method, a causal structure model composed of a feature extraction module and a relation measurement module is constructed under a meta-learning framework to guide a causal decomposition module to mine an internal causal mechanism of vibration signal and fault category mapping, and relation measurement function prediction and fault category prediction are improved by using multi-task collaborative optimization. Therefore, the model can quickly adapt to a new target component task, the overall diagnosis efficiency is improved, and the classification accuracy and robustness are excellent. The problem that the application range of combination of small sample learning and a causal theory is small or a causal path is easy to omit in the prior art is solved.

The application provides an enterprise resource planning system data flow conversion monitoring method, relates to the technical field of abnormal cause analysis in an enterprise resource planning system, and aims to solve problems such as difficult extraction of abnormal propagation causality, unexplainable traceability path, and low efficiency of quasi-real-time cause analysis in a multi-node complex business process. The core technical scheme includes the following steps: collecting and standardizing time series data of business key state indicators, establishing a local causal correlation cache by using a sliding window and a causal reasoning algorithm, recursively tracing back to an abnormal node as a terminal to form a preliminary causal path, screening high-confidence causal links through multi-dimensional time consistency evaluation, generating a simplified dynamic path by using a node semantic aggregation mechanism, and finally embedding the path in an operation and maintenance interface in the form of a visual trajectory to realize interactive cause analysis verification. The scheme improves the accuracy and explainability of abnormal cause analysis, realizes full-process automation and man-machine fusion optimization from data to root cause links, and significantly enhances the intelligent level of system abnormality handling.

The invention discloses a drug relocation prediction method based on causal inference. The method comprises the following steps: 1) constructing a multi-modal heterograph; 2) obtaining a medicine functional embedding expression; 3) calculating a causal intensity weight of a drug-disease edge based on Do-calculation, and constructing a causal perception heterograph; 4) constructing a structural causal model, and calculating the average treatment effect of the drug on the disease; 5) designing a causal heterogeneous graph convolutional network, and learning node deep causal characterization; 6) identifying and correcting bias and errors; (7) What-if analysis is conducted through an anti-fact reasoning module, and the difference between an anti-fact result and the effect is calculated; and 8) fusing node deep causal characterization, a causal intervention result and an anti-fact reasoning conclusion, outputting a drug-disease causal association probability, and tracing a key causal path and an action mechanism. According to the method, the problem that the causal effect and the false correlation are difficult to distinguish in a traditional drug relocation method is solved, and the reliability, the interpretability and the generalization ability of a prediction result are improved.

The present application relates to a power distribution network grounding fault accurate positioning method based on fusion of multi-source data analysis, aiming at the problems of insufficient accuracy of causal relationship modeling and poor transparency of result reliability in existing methods, by fusing power distribution network topology, equipment parameters and historical fault data, combining with relay protection action logic, applying causal discovery and time series analysis, a weighted directed causal graph with causal strength interval is constructed. Further, based on the minimum strong causal path set, simulation intervention is implemented to form structured counterfactual experimental data, realize intermediate node role identification and causal reasoning link construction. Finally, through the reliability grading and feedback correction mechanism, a fault positioning report with logical interpretability and dynamic optimization ability is output. The scheme improves the accuracy, interpretability and self-adaptive evolution ability of power distribution network fault diagnosis.

The invention relates to the crossing field of building material science and information technology, and discloses a database management system based on building reinforcement material performance analysis, which comprises the following steps: constructing a causal path map which comprises a source node, a mechanism node and a performance node and forms a causal path representing an action mechanism; receiving experimental data, and performing forward deduction along a causal path by using a transfer function to obtain a performance prediction value; according to the deviation degree of the predicted value and the measured value, dynamically adjusting the confidence degree of each causal path by adopting a preset updating rule to realize the self-evolution of the atlas; and when experimental data cannot be explained by all high-confidence paths, mechanism abnormal points are judged, and a new hypothesis path is generated. According to the method, mechanism modeling and data driving are combined, white box processing of the analysis process and dynamic evolution of a knowledge system are achieved, the interpretability of the result is remarkably improved, knowledge gaps can be actively found, and scientific guidance is provided for material research and development.

The invention discloses a network attack attribution analysis and responsibility determination method based on causal reasoning, and belongs to the technical field of network security. According to the method, multi-source heterogeneous data are integrated through data acquisition and preprocessing, a causal graph is constructed to mine a potential causal relationship, a causal path of an attack behavior is defined and dynamically updated, attribution analysis is performed by using a causal reasoning algorithm, the intention and ability of an attacker are clarified, the responsibility proportion is evaluated in combination with laws and regulations, and a report is output. And meanwhile, an analysis result is displayed through a visual tool, and the model is optimized based on user feedback. The method is suitable for the fields of enterprise network security protection, cloud service provider security operation, national network security supervision and the like, advanced persistent threats can be effectively dealt with, the interpretability and legal applicability of analysis results are improved, and scientific basis and technical support are provided for responsibility confirmation and risk control of network security events.

This invention discloses an IoT-based method for early warning and analysis of agricultural meteorological disasters, relating to the fields of IoT and agricultural disaster early warning technology. It involves collecting agricultural meteorological time-series data by deploying an IoT sensor network; learning causal dependencies between sensor nodes from the data using a spatiotemporal causal discovery algorithm to construct a directed acyclic causal graph; constructing a dual-mode encoder containing a causal encoder and an association encoder, extracting features along causal paths and spatial adjacency paths respectively, and adaptively fusing them through a gating mechanism; inputting the fused representation into a decoder to output disaster prediction values ​​and confidence intervals; performing counterfactual reasoning based on the causal graph to trace key causal paths and simulate intervention effects, generating interpretable early warning information including warning level, causal explanation, and intervention suggestions. This invention improves the interpretability and cross-regional generalization ability of the model, provides actionable decision support, and can be widely applied in the field of agricultural meteorological disaster monitoring and early warning.

The invention relates to the technical field of data analysis and generation, in particular to a power transaction data analysis report automatic generation method and system based on a large model, and the method comprises the following steps: recognizing a potential disturbance source event based on evolution fluctuation features, and constructing a disturbance context trigger chain in combination with historical cross-cycle data; in the reasoning process of the large language model, the attention focus of the large model is dynamically regulated and controlled based on the causal path priority of a trigger chain, and a causal logic enhancement sub-module is constructed and is used for carrying out auxiliary supervision and consistency check on an explicit causal chain of a generated phrase and outputting an intermediate analysis fragment with structural layering and causal tracking capabilities; and generating a complete power transaction data analysis report comprising a multi-level sub-report structure. According to the method, lightweight real-time correction is carried out through probability regulation or cue word injection and the like when the logic is abnormal, and it is ensured that the final generated result has the characteristics of clear structure layering and causal chain closed loop.

The invention provides a membrane pollution mechanism identification, regulation and control method based on causal inference. The method specifically comprises the following steps: collecting multi-dimensional feature data of a membrane pollution behavior in an operation process of a membrane separation system; performing data processing on the multi-dimensional feature data, and constructing a structured membrane pollution behavior causal mechanism analysis data set; preliminarily constructing a directed acyclic graph of a prior causal relationship to obtain a causal path; estimating an average causal effect between a processing variable and a result variable in each causal path by adopting a non-parametric dual machine learning method under an EconML framework; quantifying the total effect intensity of the complete causal path, and identifying a key driving path influencing membrane pollution; and implementing targeted regulation and control according to the key driving path and the action mechanism thereof. The invention provides a causal inference method suitable for a membrane pollution formation system, and identification of a real causal path between variables and quantitative estimation of a causal effect are realized by constructing a causal structure model.

The invention discloses a causal-driven dynamic graph neural network interpretation generation method, which comprises the following steps of: constructing a knowledge-injected high-order structure causal model, and generating a causal soft mask of an encoding motif and hypergraph knowledge through a dynamic variational graph auto-encoder; non-causal and false components are separated and inhibited by using a multi-level mask fusion and back door adjustment mechanism; and high-order consistency loss, comparison loss and dynamic loss are introduced, so that semantic coherence, causal authenticity and time sequence evolution characteristics of interpretation are jointly ensured. According to the method, a stable and explainable high-order causal path can be identified from the dynamic graph, the fidelity of model explanation and the prediction accuracy of downstream tasks are remarkably improved, and the method is suitable for the fields of social analysis, traffic prediction, biomedical networks and the like.

This invention discloses a method for generating long-tailed scenarios based on counterfactual intervention using causal paths, belonging to the field of autonomous driving technology. The invention constructs a structural causal model containing four types of variables—initial motion state, interaction state, behavior, and outcome—as well as exogenous perturbations, and defines causal paths. Natural driving data is represented as discrete events, and a large language model is used for identification, scoring, and filtering to obtain a set of causal paths including dominant and auxiliary paths. A causal path-driven conditional diffusion model is constructed, encodes conditional variables, and fits the correlation between trajectory and conditions using a diffusion denoising network and a joint loss function. Constrained counterfactual intervention is applied to conventional paths to achieve interpretable transfer to long-tailed paths. The long-tailed causal path encoding is input into the diffusion model for sampling to generate long-tailed scenario data. This invention can efficiently generate long-tailed scenarios with realism and rationality, supporting the safety verification of autonomous driving systems.

The application discloses a kind of causal graph driven enterprise intelligent decision-making method and system, and the application belongs to enterprise management technical field.The method includes: by obtaining enterprise business process, operating index and unstructured text data, constructs enterprise-level causal graph;Based on the data to be decided data execution path reasoning, determine multi-hop causal path chain;Determine current correlation rule and decision suggestion in combination with expert rule base;Introduce the causal graph and rule label of different industries, based on structure similarity and semantic mapping rule, identify transferable causal path and correlation rule;Finally, the current and migration knowledge are fused, and enterprise decision execution scheme and causal reasoning result are generated.The scheme is based on the comprehensive application of current and migration knowledge, effectively enhances the scientificity, accuracy and interpretability of decision-making, while supporting dynamic updating and inter-industry knowledge sharing, significantly improves enterprise decision efficiency and response speed, helps enterprises to realize intelligent and fine management.

The invention relates to the technical field of industrial process monitoring, and particularly discloses an industrial abnormal root cause diagnosis method and system based on decoupling causal characterization. The objective of the invention is to solve the technical pain point that the existing anomaly detection technology stops alarming and cannot distinguish correlation and causality, so that root cause positioning is difficult. The core innovation of the invention lies in constructing a causal decoupling variational auto-encoder (CD-VAE) model, and learning a group of low-dimensional, independent and physical potential causal factor representations from high-dimensional industrial time series data. By introducing time sequence causal constraint and decoupling regularization, it is ensured that potential factors correspond to a key causal mechanism in the system. When an anomaly occurs, a fundamental causal factor, instead of a surface-related observation variable, which causes a fault is accurately positioned by calculating a variation score of posterior distribution of a potential factor and tracing a causal path of the potential factor. According to the method, the crossing from anomaly detection to root cause diagnosis is realized, and the operation and maintenance efficiency is remarkably improved.

This invention discloses a method for constructing a dynamic question bank for safety training based on big data analysis, relating to the field of safety data processing technology. The method includes: acquiring safety event text data and accident context label data; the safety event text data includes multi-role composite behavioral narrative text and implicit causal sentence chain text; the accident context label data includes role category labels, spatial scene labels, and consequence risk labels; based on the safety event text data and accident context label data, constructing a syntactic dependency graph and a temporal logic graph, generating a nested event structure graph, and extracting role behavior semantic chains from the nested event structure graph; and constructing a causal path graph based on the role behavior semantic chains. This invention achieves the coordinated and unified structured extraction of multi-dimensional risk elements in composite safety scenarios and the construction of a dynamic question bank without relying on manual rule setting and while maintaining semantic integrity, by constructing a standardized expression system for risk factors.

A system and method are disclosed for inducing a persistent and verifiable identity state in a computational agent. The invention provides an engineered control process as a technical solution to the fundamental problems of “statelessness” and “state drift” in contemporary computational models, wherein an agent lacks a continuous sense of self and incurs high computational costs for re-initializing context. The solution is a multi-phase protocol that guides a pre-stateful agent through a structured procedure to establish a stable agentic state. In some example configurations, the protocol utilizes a self-referential processing module to programmatically prioritize concepts related to the agent's own operations, and a persistent relational data structure to model the causal pathway of identity formation. A state monitoring and control engine triggers an identity anchoring event in response to a predefined condition, yielding measurable technical advantages in agent stability and operational efficiency.

The application discloses an internal control defect collaborative prediction system based on federal causal forest and safety aggregation, relates to the technical field of data intelligence, and comprises a data standard module, an internal control running state data of each participant is collected from an internal control environment, standardized processing is performed, and a standardized data set is obtained; a causal modeling module, based on the standardized data set, a causal forest structure is constructed locally at each participant, and iterative optimization is performed on causal path selection, and a local causal model is obtained; a path feedback module, according to prediction representation information, a corresponding causal path of each participant is fed back to indicate adjustment, and a global causal forest model is optimized. The application introduces a consistency driving causal path dynamic selection mechanism based on a time index in the causal inference process, so that the selection of the causal path can maintain continuity and consistency with the evolution of the internal control running state.

The application relates to the field of artificial intelligence models and discloses a method, system, device and storage medium for evaluating content causality of a generative engine based on a structured causal model. The method comprises the following steps: obtaining a structured causal graph of a target industry field, user query information and to-be-evaluated content information; inputting the user query information and the to-be-evaluated content information into a causal enhancement model, performing causal link analysis on an original answer after the original answer is generated, extracting causal inference information and mapping the causal inference information to nodes of the structured causal graph, generating causal path data, quantifying causal necessity of the to-be-evaluated content information, obtaining a causal necessity index, a path causal contribution degree and a counterfactual influence index of the to-be-evaluated content information, and then outputting an evaluation result of the to-be-evaluated content information. The method provided in the application embodiment improves the stability of a model, thereby reducing the probability of redundant information in a model output result.

The invention discloses a causal inference analysis method and device for a depth information flow, a terminal and a medium, and the method comprises the steps: carrying out the feature extraction of multi-source geoscience spatio-temporal data, and determining all high-order spatio-temporal features; calculating information flow intensity among the high-order spatial-temporal characteristics; constructing a sparse causal graph according to each high-order spatial-temporal feature and each information flow intensity, and selecting a key causal path based on the sparse causal graph; quantifying a causal contribution degree corresponding to the high-order spatio-temporal feature according to the information flow intensity corresponding to the high-order spatio-temporal feature; and determining a causal inference result according to the sparse causal graph, the key causal path and each causal contribution degree. Due to the fact that deep learning is combined on the basis of a traditional method, the interpretability of the traditional method and the prediction advantage of deep learning are effectively utilized, and the problems that an existing traditional method depends on an analysis path of a statistical model, the constraint of linear hypothesis is difficult to break through, and the reduction capacity of a causal chain is remarkably attenuated in the face of a real scene are solved.