334 results about "Causal model" patented technology

The invention discloses an auditing decision support system and method based on a dynamic knowledge graph, relates to the technical field of computers, and aims to solve the problems that auditing data are heterogeneous and complex, risk identification is not timely and causal interpretation is lacked. According to the system, multi-modal audit data is collected in real time through a streaming event processing framework, and a dynamic audit knowledge graph with timeliness weight is constructed. Based on a graph calculation engine and cross-domain rule mining, identifying a high-frequency risk mode, and generating a risk conduction path graph; further fusing a multi-modal graph attention network, identifying and positioning abnormal entities, and outputting abnormal nodes and risk links thereof; and finally, the abnormal node embedding representation is dynamically updated through the time sequence diagram attention network, an interpretable audit causal map is generated in combination with a structural causal model, and closed-loop support from data acquisition and risk identification to interpretive audit decision is realized. The intellectualization and transparency of audit decision making are improved, and an efficient and traceable decision making basis is provided for a complex audit scene.

The invention discloses a multi-modal AI-based power equipment anomaly detection method and system, and the method comprises the steps: synchronously collecting electrical, mechanical and thermal modal data of power equipment through an edge computing node, carrying out the load adaptive dynamic preprocessing, and uploading the data to a cloud end; the cloud constructs a multi-modal feature extraction network based on a structural causal model, analyzes a causal path between modals through a Bayesian network and performs weighted fusion on feature vectors; capturing device state mutation by using a gating attention mechanism, and updating the feature vector; executing time-space consistency verification of the equipment group to identify regional group abnormality and suppress single-point misinformation; generating an interpretable report containing an abnormal root cause analysis and priority ranking maintenance strategy; and establishing a closed-loop feedback mechanism to correct the cause and effect probability distribution of the Bayesian network model. The system comprises a multi-modal sensor array, an edge computing node and a cloud analysis platform, wherein the cloud analysis platform is integrated with a causal reasoning engine, a space-time consistency verification module and the like. According to the invention, by analyzing the multi-modal deep causal association, the method adapts to the dynamic change of the equipment, reduces the false alarm rate, generates an interpretable report, supports closed-loop self-optimization, and improves the anomaly detection accuracy and operation and maintenance decision efficiency of the power equipment.

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 discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention discloses a target behavior prediction method and related equipment, and relates to the technical field of target prediction, and the method comprises the steps: obtaining multi-modal historical behavior data and real-time environment semantic information of a target object; determining key causal event nodes based on the multi-modal historical behavior data and a space-time causal model; based on the key causal event nodes and the real-time environment semantic information, determining a dynamic causal graph; determining a future potential event type and a corresponding event occurrence probability based on the dynamic cause and effect graph; determining an event triggering constraint condition based on the event occurrence probability; determining node weight updating parameters of the dynamic causal graph based on an incremental learning framework and multi-modal historical behavior data input in real time; and determining a predicted behavior track of the target object based on the event triggering constraint condition and the updated dynamic causal graph. According to the invention, by fusing the multi-modal data and space-time causal reasoning, the accuracy and environmental adaptability of target behavior prediction are improved.

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 discloses a hydrological data management method and system based on machine learning. The method comprises the following steps: S1, generating a hydrological monitoring data set with a unified temporal-spatial resolution; s2, constructing a fuzzy entropy matrix of the hydrological monitoring data; s3, generating a hydrological monitoring data causal model expressed in a directed acyclic graph form; s4, identifying the abnormal hydrological monitoring data in real time by dynamically adjusting the abnormal detection threshold value; s5, generating an abnormal traceability candidate path, and forming an abnormal hydrological monitoring data causal chain; and S6, performing fuzzy entropy optimization processing on the abnormal traceability candidate path, screening out a key abnormal causal path according to the fuzzy entropy value of the hydrological variable in each causal chain and the influence weight of the hydrological variable in the causal chain, and further positioning the root cause of the abnormal hydrological monitoring data. The invention provides a more efficient and intelligent technical scheme for water resource management, disaster prevention early warning and environment monitoring.

The invention discloses an intelligent rehabilitation guidance system based on machine learning, and relates to the technical field of virtual rehabilitation guidance, and the system comprises an integrated multi-source sensor, collects the physiological and motion data of a user in real time, and constructs health data; fusing multi-modal data, adjusting the resistance difficulty according to the condition of the patient through virtual reality, and calculating a comprehensive health score and an action offset early warning index; setting a rehabilitation cooperation particle swarm algorithm, combining the comprehensive health score and the risk early warning index, combining the resistance difficulty and the muscle-joint cooperation coefficient to compensate action compensation, optimizing rehabilitation action parameters, and returning to the upper layer; and analyzing an abnormal root based on a structural causal model, and generating targeted rehabilitation suggestions. The problem that the risk of secondary injury is increased due to lack of dynamic regulation and control of training intensity and muscle compensation behaviors of a patient in traditional rehabilitation training is solved.

The invention belongs to the technical field of data analysis, and provides a multi-source operation and maintenance data anomaly analysis method and device, equipment, a medium and a product, the method comprises the steps that multi-source operation and maintenance data is acquired, and the multi-source operation and maintenance data comprises at least two of index time sequence data, application logs, call link tracking data, configuration change records, alarm events and work orders; carrying out joint anomaly modeling on the preprocessed multi-source operation and maintenance data based on a multi-model fusion architecture to identify an abnormal event in the multi-source operation and maintenance data; based on the operation and maintenance knowledge graph and the structured causal model, fault influence path tracing and root cause positioning are carried out on the abnormal event, a root cause analysis result is obtained, and the root cause analysis result is used for indicating a fault root cause node and a fault propagation path in the abnormal event. Therefore, the accuracy of anomaly analysis of the multi-source operation and maintenance data is remarkably improved.

The invention relates to the technical field of engineering cost management, in particular to an artificial intelligence-based cost whole-process management method and system, and the method comprises the steps: collecting environment data, market data and project data, and carrying out the feature extraction; calculating the weight of each modal data through a self-attention mechanism, and obtaining a fused feature vector; the knowledge graph, the graph convolutional network and TransE are combined to generate a knowledge graph feature vector, a self-adaptive cost prediction model is constructed, and a prediction cost is output and implemented; fusing a graph attention network, a structure causal model and an improved fuzzy comprehensive evaluation method, constructing a risk assessment model, outputting a risk grade score, and automatically generating and adjusting an optimal risk coping strategy; constructing a low-code intelligent collaboration platform; and based on an evaluation index result, optimizing model parameters and platform configuration by using a genetic algorithm. According to the invention, the risk factors are accurately analyzed through the risk assessment model based on fusion of multiple technologies, and the assessment accuracy is improved.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

The invention provides a method, a device and equipment for extracting polymerizable causal information of a medical image and a medium. Relates to the technical field of representation learning, causal inference and depth generation models. The method comprises the following steps: constructing a causal representation learning framework; the causal representation learning framework comprises an encoder, a structural causal model, a decoder and a discriminator, the encoder is used for encoding an input medical image into a low-dimensional exogenous variable, the structural causal model takes the low-dimensional exogenous variable as input and generates a causal representation based on the low-dimensional exogenous variable, the decoder is used for intervening and reconstructing the causal representation, and the discriminator is used for discriminating the causal representation. The discriminator is used for adversarial training; and establishing a model training loss function, training the causal representation learning framework based on the model training loss function, and extracting the polymerizable causal information in the medical image by using the trained causal representation learning framework. The causal graph identified by the method can clearly describe the causal relationship among different features, and more transparent decision support is provided for clinicians.

The invention discloses an aquaculture environment intelligent regulation and control method and system based on an AI model, and relates to the technical field of aquaculture management and control. The AI model-based aquaculture environment intelligent regulation and control method comprises the steps of S1, collecting water quality state data, feeding behavior data and meteorological data in an aquaculture environment, preprocessing and storing the data, and then constructing an aquaculture regulation and control management database; s2, constructing a water body state time sequence causal model based on the multi-source time sequence data, and outputting a causal connection structure; s3, performing prediction and risk assessment on water quality evolution under different feeding schemes through multivariable time series and causal coupling; s4, analyzing a feeding scheme target based on the feeding decision amount, the water quality risk and the growth income; and S5, continuously correcting the causal relationship and the feeding strategy through feeding execution feedback. The problems that the bidirectional coupling relation between the feeding behavior and the water quality feedback is not clear, and the feeding decision and the water quality risk are difficult to cooperatively regulate and control are solved.

The embodiment of the invention discloses a mine gas regulation and control method and device based on multi-modal data and a medium, and relates to the technical field of gas regulation and control, and the method comprises the steps that multi-source real-time sensing data, roadway point cloud data and CFD airflow simulation grid data in a target mine roadway are obtained, space-time alignment is carried out to determine a real-time roadway data cube, and the real-time roadway data cube is obtained; determining gas concentration field distribution and causal contribution popularity data in a preset future time window through a multi-mode depth causal model and the real-time roadway data cube; generating a risk field gradient feature vector based on gas concentration field distribution and causal contribution popularity data, and matching a decision path according to the risk field gradient feature vector; and when the decision path is a causal decision path, a fan / air door adjusting target point is determined, the fan / air door adjusting target point is simulated to determine a dynamic safety constraint boundary, a preset optimization objective function is solved to generate a fan rotating speed adjusting instruction and an air door opening degree control instruction, and fan / air door control is achieved.

The invention relates to the technical field of automatic driving, in particular to a causal reinforcement learning system for a vehicle in a safety key scene with causal confusion, and the system comprises a strategy generation network module and a causal graph module. The causal graph module is composed of a causal model; the causal model comprises a state causal model and a reward causal model; the reward causal model is used for redistributing rewards according to the causal relationship between the rewards and other features, and the state causal model identifies the real causal relationship to prevent the algorithm from being misguided by causal confusion features; the causal model is a combination of a causal graph and a full connection layer; the causal model is constructed by adopting a gradient-based causal discovery algorithm; the causal graph is represented by a directed binary adjacency matrix, and the matrix comprises a reward causal graph and a state causal graph at the same time. The invention provides a causal reinforcement learning system for a vehicle in a safety-critical scene with causal confusion, so as to enhance the robustness of an automatic driving vehicle in the safety-critical scene with causal confusion.

The invention relates to the technical field of artificial intelligence and nutrition health management, and discloses an intelligent nutrition correction tracking early warning method and system.The intelligent nutrition correction tracking early warning method comprises the steps that a hierarchical heterogeneity causal effect model is constructed, and individual features are mapped to a potential type space; representing a causal relationship between nutrition intervention and result variables by using a structural causal model, and evaluating an intervention effect by using a dual robust estimator; personalized grouping is realized by identifying individual subgroups; a reinforcement learning algorithm based on context awareness is combined with an anti-fact reward function, and a personalized nutrition intervention strategy is optimized; key feature combinations are extracted through a differentiable decision tree and SHAP value analysis, and multi-level causal interpretation is generated. According to the method, anti-factual reasoning, reinforcement learning and causal relationship extraction technologies are fused, accurate prediction, evaluation and early warning of the individual nutrition intervention effect are realized, and the personalized precision of nutrition intervention and the user compliance are remarkably improved.

The invention discloses a management decision-making method and system based on a knowledge base construction technology. The method comprises the following steps: performing sequential relationship extraction on multi-source financial data to obtain a sequential relationship set related to query content; constructing an event-entity incidence matrix corresponding to the time sequence relation set; according to the time sequence relation set and the event-entity incidence matrix, constructing a dynamic knowledge graph; determining causal effect parameters in the causal graph structure by adopting a dual machine learning model; constructing a structural causal model according to the causal graph structure and the causal effect parameters; and generating an anti-fact prediction result by using the structural causal model, and generating a decision scheme corresponding to the query content based on the anti-fact prediction result. The technical problem that decision information including accurate causal basis and prospective simulation information cannot be generated due to the fact that the causal relationship between financial data is difficult to determine and the intervention effect cannot be dynamically deduced in a related management decision method is solved.

The invention discloses a face feature decoupling representation method and device with causal effect transmission and a medium, and relates to the technical field of causal discovery, image generation and decoupling representation learning. The method comprises the following steps: establishing a variational auto-encoder, and in response to input face data, learning from a potential space based on the variational auto-encoder to obtain potential feature distribution; integrating a structural causal model into the variational auto-encoder, and modeling a causal effect to extract causal features from the potential feature distribution; establishing a graph attention network, inputting the causal features into the graph attention network, and transmitting a causal effect during decoupling; and designing a loss function, and training the graph attention network by using a discriminator. The method can effectively mine and transmit the causal relationship between the face features, improves the explanatory and transferability of feature representation, has high application potential, and is especially suitable for tasks such as face recognition and expression analysis.

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 discloses a bamboo industry-oriented AI intelligent production service management cloud platform and system, and the platform comprises the following steps: a data collection module which is used for collecting multi-source heterogeneous data, and constructing a process behavior sequence and a state variable sequence; the causal modeling module is used for executing a causal migration focusing algorithm to construct a causal influence map and identifying a key variable sequence; the time sequence modeling module is used for jointly inputting the process behavior sequence and the key variable sequence into a Jamba model and outputting key control parameters and process control path suggestions; the causal evaluation module is used for outputting a path causal credibility score; the optimization rerouting module is used for triggering an expert sub-network rerouting mechanism and generating an optimized process control path suggestion; and the process execution control module is used for suggesting to execute the processing operation of each process link of the bamboo according to the optimized process control path. According to the method, causal modeling and the Jamba model are combined, so that accurate control and production service management of the bamboo wood process are realized.

The invention relates to the technical field of artificial intelligence, and discloses a reinforcement learning decision optimization method, system and device based on a causal large language model, and the method comprises the following steps: initializing an intelligent agent and a strategy network thereof; obtaining track information of a historical sequence decision generated by interaction; extracting causal variables from the trajectory information by adopting a large language model, and constructing a structural causal model; obtaining an agent strategy-driven causal intervention mechanism, and dynamically correcting a causal relationship in the structural causal model; according to a task-related causal chain extracted from the corrected structural causal model, generating a semantic sub-target corresponding to a causal relationship; designing a multi-modal reward function fused with semantic similarity; and updating the strategy network by adopting the obtained sub-targets and rewards. According to the method, the problems of low learning efficiency, insufficient adaptability and lack of effective reasoning ability of the reinforcement learning agent in a complex environment in the prior art are solved, and the method has the characteristic of high decision-making efficiency in a dynamic environment.

The invention discloses a substation operation and maintenance task risk digital assessment method and system, and belongs to the technical field of task risk digital assessment, and the method comprises the steps: carrying out the hidden risk dominant modeling based on the structural data of a data lake, generating a personnel and equipment dynamic coupling risk coefficient, and carrying out the hidden risk dominant modeling; the space-time diagram neural network captures a personnel-equipment-environment coupling relationship by fusing spatial topology and time sequence features, the model can be associated with a historical fault mode of adjacent equipment, potential risks are identified in advance, and the causal model can identify the potential risks in advance by calculating causal strength between nodes and revealing hidden risk driving factors. The attention weight mechanism dynamically adjusts the fusion proportion of the space and time features, the method adapts to the complex scene of the transformer substation, the dynamic coupling risk coefficient is combined with the frequency and similarity of the historical accident library, the risk weight is quantified, and the probability of artificial misjudgment is reduced.

The invention relates to the technical field of equipment fault diagnosis, and discloses an intelligent fault detection method for a speed reducer, and the method comprises the steps: extracting fault feature knowledge from a source domain based on a domain adaptive transfer learning algorithm, migrating the fault feature knowledge to a target domain, and generating a synthetic fault sample based on physical model constraints; constructing interaction influence among the multi-subject causal capture equipment; executing causal intervention and anti-factual reasoning, and determining a fault root cause; constructing a fault knowledge graph and continuously optimizing the fault knowledge graph; and constructing a preventive maintenance decision system to generate an optimal maintenance strategy. According to the method, high-accuracy fault diagnosis can be realized under the condition of sample scarcity, interaction influence among multiple devices is analyzed, fault root causes are traced, and reliable preventive maintenance decision support is provided.

Methods and systems for identifying poisoned training data used for training artificial intelligence (AI) models are disclosed. To identify poisoned training data in a proposed training dataset, a causal model may be obtained. The causal model may include relationships relating data elements. The proposed training dataset may be identified as poisoned when data elements within the proposed training dataset do not satisfy the relationships set forth by the causal model. When the identification of poisoned training data is made, the AI model may not be updated using the proposed training dataset and the proposed training dataset may be discarded. If poisoned training data is not identified prior to training an AI model, methods and systems are disclosed for the remediation of the poisoned training dataset and subsequent tainted AI models. By doing so, the effect of poisoned training data may be prevented and / or efficiently computationally mitigated.

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 relates to the technical field of fault prediction and computer big data processing, in particular to a generalized zero sample composite fault diagnosis method, device and system based on anti-fact reasoning. According to the generalized zero sample composite fault diagnosis method based on the anti-fact reasoning, a two-stage generalized zero sample composite fault diagnosis model based on the anti-fact reasoning is constructed. According to the model, internal causal components of fault data are pointed out from the angle of causal theory, and then a structural causal model is constructed to describe decoupling and generation of fault features under the guidance of anti-factual reasoning. On the basis, a generative model is improved through a reinforced discriminator in the first stage so as to realize binary classification of a single fault and a composite fault. In the second stage, a single fault category is predicted through supervised training of a classifier, and meanwhile, a traditional zero sample learning method is designed to classify composite faults. According to the method, the diagnosis precision of the model is greatly improved, and the problem of deviation of model diagnosis on visible classes and invisible classes is solved.

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

The invention discloses a causal control VLA end-to-end system fusing perception and a world model, and belongs to the technical field of automatic driving. The system comprises a perception and world modeling subsystem; a reasoning and generating subsystem; a verification and collaborative learning subsystem; moreover, the perception and world modeling subsystem is composed of an environment perception module and a world model training module, the reasoning and generation subsystem is composed of a causal modeling module and a diffusion trajectory generation module, and the verification and collaborative learning subsystem is composed of a form verification module and a federal collaborative distillation module. According to the VLA end-to-end system, traditional correlation modeling of a world model is enhanced through causal modeling, so that the world model becomes more accurate; meanwhile, on the basis of traditional verification and updating of the world model, the verification and updating process of the world model generation trajectory is further enhanced through the verification and collaborative learning subsystem, so that the generated trajectory is safer and more reliable.

The invention provides a pollutant traceability method, system and device based on multiple media and multiple links and a medium, and belongs to the technical field of target area pollution abatement, and the method comprises the steps: preprocessing pollutant monitoring data to obtain a standardized data set; the pollutant monitoring data comprises data of pollutants in a plurality of links and a plurality of media in a target area, and the standardized data set comprises a plurality of standardized data; based on the standardized data set, main characteristic components representing pollutant distribution and evolution rules are extracted; constructing a transfer coupling relation model based on the main characteristic components, and obtaining a transfer path and a coupling strength coefficient of the pollutants according to the transfer coupling relation model; based on the transmission path and the coupling strength coefficient, constructing a causal model and carrying out joint verification; and when the verification is passed, outputting a pollution traceability result. According to the invention, the scientificity, the accuracy and the decision support reliability of mine pollution traceability are obviously improved.

The invention relates to the technical field of ecological remote sensing and image processing, and particularly discloses a method and system for monitoring the ecological restoration state of thinopyrum intermediary grassland in an alpine region. The method comprises the following steps: acquiring a multi-temporal remote sensing image and ground ecological parameters, and constructing a remote sensing-ground combined observation data set; utilizing a semantic segmentation model of a multi-scale attention mechanism to extract a thinopyrum intermedium block mask; vegetation indexes, texture features and ecological factors are extracted from the mask region to construct a multi-dimensional feature sequence; inputting the feature sequence and manual intervention information into a training factor interaction model in a graph neural network and causal modeling architecture, and outputting a repair level and evolution data according to the training factor interaction model; and generating a visual layer in combination with historical data, and updating a remote sensing feature extraction strategy based on model feedback. The alpine grassland ecological restoration state monitoring system realizes intelligent monitoring and dynamic feedback of the alpine grassland ecological restoration state, has strong timeliness, clear causality and adaptive optimization capability, and is suitable for continuous monitoring and management of a complex ecological system.