280 results about "Causal model" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of medical data analysis and business intelligence, and discloses a hospital pharmacy competitiveness evaluation method and system based on multi-source data fusion, and the method comprises the following steps: collecting and processing multi-source data, and constructing a market entity feature vector; constructing an initial market causal atlas based on the feature vectors; initializing a digital twinning environment, and setting a dynamic behavior rule of a simulation entity; running simulation to obtain a reference competitiveness evolution trajectory; real data is periodically acquired to calculate simulation deviation; and if the deviation is greater than a preset threshold value, correcting the causal atlas and updating the simulation entity rule. The system comprises a data processing and feature construction module; a market causal relationship graph construction module; a market digital twin environment initialization module; a competitiveness evolution simulation module; a simulation reality deviation calculation module; and a causal atlas correction module. According to the method, the dynamic causal model is constructed by fusing multi-source data, so that accurate and prospective competitiveness evaluation is realized.

The invention provides an AI-based trachinotus ovatus juvenile fish culture water parasite prediction and prevention method and system, and the method comprises the steps: collecting multi-source data from a culture water body through a sensor array, including a nucleic acid signal, a fish school behavior track and an environment parameter sequence, and obtaining an original data set; unifying the data format and the acquisition frequency according to the original data set by adopting a time sequence alignment method, and obtaining an aligned feature flow; aiming at the aligned feature flow, applying a convolutional neural network to extract a space-time pattern of a nucleic acid signal and an abnormal index of a fish school behavior, and determining an extracted feature set; if the abnormal index in the extracted feature set exceeds a preset threshold value, a causal association between environmental parameters and parasitic risks is classified through a support vector machine, and a preliminary risk level is judged; simulating a water body change scene according to the comprehensive causal model to obtain a predicted trajectory of parasite propagation; and determining a quantitative score of the parasite risk and generating a prevention and control instruction sequence by comparing the predicted trajectory with the real-time monitoring data.

The invention provides a titanium tetrachloride boiling chlorination process optimization method based on a causal model and electronic equipment, and the method comprises the steps: constructing a generation mechanism between structural equation model expression variables based on the causal relationship between boiling chlorination key process parameters and result variables; the effect of external intervention on the TiCl4 yield and unit cost is simulated through causal inference, and a multi-target optimization model with the minimization of the unit product manufacturing cost and the maximization of the TiCl4 yield as targets is constructed in combination with the structural model and Monte Carlo sampling estimation expectation; and iteratively solving the optimization model by adopting a causal-based non-dominated sorting genetic algorithm, and obtaining a Pareto optimal solution set meeting variable constraint conditions through operations such as causal variable classification, dynamic penalty weighting, non-dominated sorting and sensitive driving disturbance. According to the method, a causal modeling and optimization integrated framework suitable for the boiling chlorination process is constructed, personalized process strategy generation and production operation decision making are supported, and the raw material utilization rate and the process operation economy are improved.

The invention relates to a weight loss intervention method and device for chronic renal failure and obesity patients based on an individualized prescription. The method comprises the following steps: constructing a multi-modal dynamic time sequence feature vector of a patient; identifying and quantifying potential causal effects of different weight loss intervention measures on physiological outcomes of renal functions, electrolyte balance, anemia states, bone mineral metabolism and weight indexes of the CKD patient according to a dual robust estimator; dynamically constructing a weight loss intervention measure causal model according to the potential causal effect of the physiological outcome of each patient and the physiological and pathological background; and inputting the multi-modal dynamic time sequence feature vector of the patient into a weight loss intervention measure causal model, and generating a daily diet scheme, an exercise scheme and corresponding high-confidence risk early warning and causal explanation of the patient. The method overcomes the limitation that the traditional method only pays attention to correlation and cannot clearly determine the intervention effect, ensures that the causal atlas and the intervention scheme are optimized according to the real-time change state of the patient, and realizes a real dynamic prescription.

The invention relates to the technical field of intelligent wharfs, in particular to a wharf container truck dynamic optimization scheduling method and system combining machine learning and path planning. Comprising a behavior data acquisition and feature coupling unit; a learnable incentive and behavior guide unit; a scheduling demand prediction unit; and a path planning and scheduling unit. According to the method, on the basis of the coupling characteristics, the excitation coefficient is optimized through reinforcement learning, the excitation instruction is dynamically pushed, and targeted guidance of the non-operation staying behavior of the container truck is achieved; according to the method, based on standardized time series data, an association rule of a historical staying period and a working condition is learned through an LSTM model, a prediction result is optimized in combination with real-time data, a prospective constraint basis is provided for scheduling, and meanwhile, a time, space, resource and priority multi-dimensional path constraint system is constructed through a structural causal model; container truck-berth matching and dynamic path planning are completed by matching with an improved A * algorithm fused with dynamic weights, and scheduling conflicts are effectively avoided.

The invention belongs to the technical field of network scheduling, discloses an intelligent network scheduling method and system based on hybrid reinforcement learning, designs a multi-head attention strategy network based on Transform, can execute discrete protocol selection and continuous rate adjustment at the same time, and breaks through the limitation of a traditional scheduling model on action dimension division. Therefore, the scheduler can flexibly select the optimal strategy when facing a complex task, and the refinement and adaptability of the decision are remarkably improved. In order to overcome the problem of value over-estimation in reinforcement learning, a double-Q-Critic network structure is constructed, and double evaluation paths are introduced. In the training process, through minimizing the estimation value deviation of the two Critic values, the overhigh estimation is effectively inhibited, so that the convergence stability and the estimation value precision of the strategy are improved. In the reward mechanism, a structural causal model (SCM) is introduced to generate an anti-factual reward signal, and a real reward and an anti-factual reward are fused by constructing a causal intervention path for a specific action.

Example embodiments described herein provide a two-stage approach for training, on a dataset of samples received as input, a structural causal model (SCM). In a first stage of the example two-stage approach, a trained causal ordering predictor is used to infer a causal order of variables from the dataset. In a second stage, the SCM is trained on the same dataset using the predicted causal ordering from the first stage. Once trained, the SCM may be used to predict a causal effect of an action on a target system.

The invention discloses a bicycle flow prediction method based on an improved space-time neural structure causal model. Constructing a causal graph, and carrying out systematic analysis on influence factors from a causal perspective; performing causal intervention on the subitems by applying a front door criterion, setting an input door module, fusing a context environment and observation traffic, and eliminating hybrid deviation; a multi-head attention mechanism and an anti-fact representation reasoning module are embedded in a time sequence encoder and a decoder, long and short term dependence among factors is dynamically captured, and a future anti-fact state is deduced; a spatial-temporal evolution diagram convolutional network is proposed, three weighted adjacency matrixes of a geographic distance diagram, a transition probability diagram and a dynamic causal diagram are combined, modeling is carried out on a geographic topological structure of a parking point and time-varying causal association of the parking point through three-layer diagram convolution, and the sensitivity to spatial distribution is enhanced; and residual networks are embedded between the sub-modules and in deep residual connection positions, so that cross-layer feature fusion and gradient stable transmission are realized, and the problem of deep network degradation is effectively relieved.

The invention relates to the technical field of medical informatics, in particular to a method for determining a drug code, which comprises the following steps of: acquiring a specification text, a chemical structure, an active component and target information, and integrating into task input; respectively extracting a molecular topological vector and a semantic topological vector based on topological coherence, injecting the molecular topological vector and the semantic topological vector into a sparse high-dimensional super-vector, and mapping the sparse high-dimensional super-vector into a continuous vector through binding transformation; generating a preliminary code by using a generative neural network combining diffusion denoising and a converter, and calculating a super-dimensional residual error; constructing a causal model in the pharmacological knowledge graph, and outputting a correction code after residual error calibration; the joint probability variance is evaluated through Monte Carlo discarding, and automatic confirmation, manual recheck or anti-fact search are realized through two-stage threshold values; and writing the combined super vector, the final code and the variance into an experience library, and optimizing the generation network and the causal model at the same time by using a hierarchical Bayesian near-end strategy. According to the invention, the extrapolation capability and interpretability of new drugs are considered, the coding accuracy is obviously improved, and the labor cost is reduced.

The invention discloses a big-data-based silicon carbide polishing effect analysis method and system, and relates to the technical field of silicon carbide polishing effect analysis. Polishing equipment parameters, wafer attribute information and post-polishing image data are collected, formats and structures are unified for modeling, a multi-dimensional wafer data set is constructed, a data structure is associated, image defects are identified and quantified, and the polishing effect of silicon carbide is analyzed. The method comprises the following steps: fusing polishing parameters, wafer attributes and image data, extracting defect parameters to generate defect fingerprint vectors, establishing a prediction model between the parameters and defects, mining key process variables and causal paths, clustering batches, constructing a causal model, positioning abnormal root causes and quantifying parameter deviation. According to the method, mapping and causal paths between process parameters and defects are established, batch clustering and abnormal root cause positioning are realized, traceable optimization parameter suggestions are generated, a polishing quality intelligent closed-loop regulation and control system is formed, and wafer defect identification precision and process adjustment efficiency are improved.

The invention relates to the technical field of fault prediction and health management, and discloses a pump station equipment risk intelligent prediction method based on big data analysis, and the method comprises the steps: collecting equipment operation and environment data; a hybrid intelligent causal analysis model for automatic screening is constructed to obtain correlation characteristics among equipment parameters; constructing a risk analysis model which combines graph topology analysis and time sequence characteristics and is calibrated by environmental data, and outputting a systematic operation risk index and a dynamic critical value; and constructing a long-time-sequence prediction model, carrying out long-term trend pre-judgment on the risk index, calling a causal model to carry out root cause tracing when the predicted risk reaches a critical value, and generating a prevention management scheme containing a clear early warning level and a specific resource demand. According to the method, the whole-process intelligentization from unified risk assessment and accurate prediction to closed-loop decision support is realized.