153 results about "Causal inference" patented technology

The application discloses an industry data management method based on big data, and belongs to the technical field of industry data management, and specifically comprises the following steps: constructing an after-sales data warehouse based on historical after-sales maintenance records; extracting the correlation relationship of spare parts and restoring the level of finished parts from the after-sales data warehouse to construct a coupling correlation topology; screening abnormal samples deviating from the expected failure mode, extracting corresponding supplier production process parameters and batch information, and forming a process parameter set; deconstructing the coupling correlation topology and combining the process parameter set to establish an attribution mapping parameter set; constructing a causal inference model between the attribution mapping parameter set and the failure mode to obtain the attribution weight of each supplier batch to the failure mode; and marking the supplier batch with an attribution weight exceeding a preset threshold as a target traceability batch and outputting the same. The application realizes accurate attribution from after-sales failure data to a supplier batch, and provides a quantitative basis for supply chain quality control.

The application provides a cognitive diagnosis method and system based on causal inference to eliminate problem difficulty bias, relates to the technical field of educational data mining, and specifically includes the following steps: preprocessing the historical record of a student's answer to a problem to obtain a training set composed of a student, a problem, problem difficulty and a score; introducing a problem difficulty variable on the basis of an existing diagnosis model, eliminating bias by using causal inference, constructing a cognitive diagnosis model, and training the cognitive diagnosis model by using the training set; inputting a student to be diagnosed, a problem and problem difficulty into the trained cognitive diagnosis model to obtain a cognitive diagnosis result of the student output by the diagnosis model; the application provides a general cognitive diagnosis framework, creates a cognitive diagnosis model based on an existing diagnosis model, eliminates the adverse effects of problem difficulty bias by using causal inference technology, and performs unbiased training on the model to diagnose the cognitive level of the student, thereby improving the accuracy and efficiency of cognitive diagnosis.

This invention relates to the field of industrial equipment fault diagnosis technology, specifically to a knowledge graph-embedded method and system for causal tracing of industrial equipment events. The method includes the following steps: collecting multi-source heterogeneous event data during the operation of industrial equipment; preprocessing and extracting features from the collected multi-source heterogeneous event data to obtain event feature vectors. This invention constructs a knowledge graph in the industrial equipment domain, structurally representing the complex mapping relationships between entities such as equipment type, fault type, fault cause, and fault result, providing rich semantic prior knowledge for subsequent causal inference. Compared to traditional data-driven fault diagnosis methods, the knowledge graph introduced in this invention can encode domain experts' in-depth understanding of equipment fault mechanisms, making the tracing results more accurate and interpretable.

The application provides a multi-source Parkinson's disease database integration method based on LEDD-UPDRS composite index alignment, and is applied to the technical field of data processing. In view of the heterogeneous problems of multi-source Parkinson's disease databases, the application integrates heterogeneous data of PPMI, PDBP and local queues, disassembles characteristics according to four modes of heredity and clinic, introduces a standardized term library and a BERT-BiLSTM-CRF model unified term adjusted in combination with medical corpus, generates a cross-library feature table; a storage architecture is constructed based on FlashROM mode to realize data storage and retrieval; a composite observation system is built with LEDD and UPDRS-III, consistency is verified by multiple methods, data is standardized, missing data is filled in combination with multiple models to generate an integrated data set; a double prediction model is built relying on the data set, and the advantages of the data are realized by a Cohen's d value verification method; finally, cross-library joint analysis is carried out, phenotype-genotype correlation modeling and causal inference are completed, and data support is provided for Parkinson's disease research and clinical prediction.

The application is a federated learning out-of-distribution generalization method based on causal inference, and belongs to the technical field of edge intelligent devices. The method comprises the following steps: constructing a federated learning framework to train a task model, in each round of training, a server sends a global task model parameter and a global prototype library to a client participating in training; the client trains a local model by using a local sample, obtains individualized features and global features of the sample by a decoupling representation learning module, and obtains causal features after removing pseudo-association by a mixed factor removing training module; the client sends updated model parameters and various local prototypes to the server; the server updates the global prototype library and the global model parameter by using a hierarchical update strategy module; the training is repeated until a convergence condition is met, and the obtained global task model is deployed to a new device for use. The application improves the generalization ability of the task model when facing invisible distribution data, and ensures the usability and applicability of the task model.

This invention belongs to the field of ecological and environmental protection technology. It discloses a regional ecological protection intelligent decision support and dynamic management system based on big data. The system acquires multi-temporal ecological monitoring data, constructs an ecological factor correlation network through time-delay mutual information analysis, uses virtual intervention experiments and counterfactual comparisons to perform causal inference, and identifies real ecological action paths. It calculates lag propagation time and signal attenuation rate to establish a multi-scale time anchor sequence; identifies feedback loops and calculates loop gain and stability domain boundary parameters; constructs a dynamic evolution model containing a system of time-delay differential equations, and simulates and generates a hierarchical set of decision schemes; and performs residual analysis through real-time monitoring of ecological responses to trigger adaptive correction of model parameters and dynamic adjustment of schemes. This invention achieves a leap from correlation analysis to causal identification, and from static planning to dynamic management, improving the scientific rigor and accuracy of ecological protection decision-making.

This invention discloses an incremental federated causal structure learning method in dynamic scenarios, involving the interdisciplinary field of computer causal inference and federated learning. The method includes the following steps: S1: Historical federated causal structure learning; S2: Adaptive selection of new clients; S3: Weighted federated causal structure learning. A weighted federated causal structure learning mechanism is designed to achieve efficient collaborative incremental learning between the server and high-quality, non-redundant new clients. This invention uses an improved clustering method and a multi-dimensional quality assessment strategy to select high-quality nodes from new clients. Among these high-quality clients, redundant new clients with historical causal structures similar to those on the server are identified to reduce redundant computation. Weighted federated aggregation learning is then performed on the selected new clients based on the historical causal structure, eliminating the need for full learning of both historical and new clients, effectively reducing communication overhead while maintaining learning accuracy.

The present application relates to the technical field of anesthesia state evaluation, in particular to an anesthesia state evaluation method and system based on electroencephalogram data analysis, comprising: obtaining original electroencephalogram signals through an electroencephalogram acquisition device and preprocessing to obtain pure electroencephalogram data; extracting characteristic indexes reflecting electroencephalogram oscillation patterns and synchronization characteristics; analyzing dynamic causal relationships between the characteristic indexes based on a causal inference model, constructing a brain function network representing anesthesia depth evolution; calculating quantitative parameters of network global integration efficiency and local isolation degree; introducing baseline electroencephalogram data before anesthesia induction to calibrate the quantitative parameters; combining an anesthesia depth determination model to determine the anesthesia state grade, and generating an evaluation report by fusing network structure characteristics. The method improves the accuracy and consistency of anesthesia state evaluation and is suitable for clinical anesthesia monitoring scenarios.

Various embodiments of the present disclosure provide computer forecasting techniques for initiating presentation of an interactive user interface. The techniques may include receiving one or more candidate prediction-based actions and generating a plurality of causal risk-based impact scores with respect to a candidate prediction-based action. The techniques include generating a plurality of causal quality-based impact scores and an action sequence for a plurality of evaluation entities and generating a causal net impact score based on (i) an aggregation of the plurality of causal risk-based impact scores and the plurality of causal quality-based impact scores and (ii) a sequence impact metric corresponding to the action sequence. The techniques include generating a sequence ranking for the action sequence and initiating a presentation of an interactive user interface reflective of the action sequence and the sequence ranking.

This invention discloses a verifiable application attack detection method based on artificial intelligence causal inference, including data collection and structuring, causal graph construction, intervention effect calculation, abnormal causal determination, verifiable evidence chain generation, detection result output, and false positive adaptive correction steps. By constructing a structured causal graph model between attack behavior and abnormal system states, this invention can effectively distinguish between "spurious correlations" and "true causality," significantly reducing the false positive rate caused by business fluctuations or system changes. The verifiable mechanism designed in this invention can generate a complete and tamper-proof causal evidence chain from the original attack payload to the final abnormal state for each detection conclusion, making the detection results not only accurate but also transparent, auditable, and reproducible. This greatly improves the judgment efficiency of security operations teams and provides a reliable technical foundation for security forensics scenarios based on blockchain or third-party arbitration.

The application provides a bearing temperature and vibration signal fault evolution path intelligent identification method and system, relates to the technical field of fault identification, and comprises the following steps: collecting and time-aligning bearing temperature, vibration signals and working condition parameters to construct a multi-source heterogeneous data matrix; extracting multi-dimensional features after adaptive noise reduction processing; constructing a fault evolution mechanism causal correlation network by using a causal inference model; performing time series clustering to divide health state clusters based on the network and establishing a state transition model by using a hidden Markov model; and finally applying a Viterbi algorithm to decoding to obtain an optimal fault evolution path. The method can effectively identify the bearing fault evolution law and improve the prediction accuracy.

The present application belongs to the field of abnormal root cause analysis, and relates to a system performance bottleneck detection method based on reinforcement learning. The system performance bottleneck detection method is as follows: first, extract system performance index data; second, find the first abnormal time period and abnormal dimension using threshold method; third, perform causal inference on abnormal data and further root cause analysis. The present application can effectively solve the system performance bottleneck detection problem in a high-load environment, help system administrators identify and solve problems faster, reduce the risk of system crashes, and improve the stability and reliability of the system, so that the system can respond to user requests faster and improve user satisfaction. The present application can be applied to a wider range of root cause analysis problems, effectively helping operation and maintenance personnel solve system performance bottleneck detection problems using artificial intelligence methods, and has good applicability and robustness.

This invention discloses a machine vision-based safety early warning method for deep foundation pit construction, belonging to the field of computer vision and safety monitoring technology. It includes acquiring multi-source synchronous monitoring data; extracting vibration spectrum features from the surface of the support structure; delineating high-risk areas; extracting regional risk vibration features; using vibration spectrum features as physical constraints to guide the reconstruction of three-dimensional structural features and generate fused features; and solving the causal risk chain based on a causal inference graph model and outputting hierarchical early warning information. This invention employs dynamic behavior-guided vibration analysis focusing and causal logic tracing, enabling accurate identification and root cause tracing of early signs of instability in deep foundation pit structures, filtering out construction background interference and reducing false alarm rates, thereby improving the intelligence level and response efficiency of deep foundation pit construction safety management.

The application discloses an intelligent health monitoring system based on electroencephalogram signals, and relates to the technical field of intelligent medical treatment, comprising the following steps: constructing a user personalized neural baseline model through small sample calibration and meta-learning; carrying out daily monitoring based on the model, and dynamically updating the model by using an online Bayesian algorithm to track long-term physiological drift of the individual; when significant neural baseline drift is detected, automatically matching and triggering intervention measures; constructing an anti-fact causal inference model to quantify the net effect of the intervention measures on the neural baseline; generating a personalized health insight report based on the net effect; and driving the iterative evolution of the neural baseline model through reinforcement learning by using closed-loop data containing drift, intervention measures and net effect, so that the application realizes intelligent health management with long-term self-adaptation, accurate quantitative intervention effect and self-evolution ability.

This invention belongs to the fields of computer technology and on-demand delivery technology, and provides a method, device, and electronic device for analyzing rider learning curves based on causal inference. The method includes: sending a data request carrying rider identification to an on-demand delivery platform via a data interface, and receiving multiple completed order data for riders corresponding to the rider identification; constructing a theoretical rider learning curve model, wherein the theoretical rider learning curve model uses the total delivery time in the completed order data as the dependent variable, the cumulative number of orders as the independent variable, and at least one of seven factors—delivery distance, order price, restaurant waiting time, temperature, humidity, visibility, and wind speed—as control variables; solving for the model parameters of the theoretical rider learning curve model using multiple completed order data and storing them in a database in association with the rider identification; calculating and outputting the rider's learning rate based on the model parameters. The introduction of a learning curve effectively depicts the growth trajectory of the rider's learning process.

A medical image diagnostic apparatus, an inference method, an inference apparatus, and a storage medium according to an embodiment include acquiring attribute information for a target patient to be subjected to inference, and estimating a causal relationship between the attribute information for the target patient and a treatment effect for the target patient using a causal inference model including a decision tree model optimized based on knowledge information defining rules for determining a treatment to be applied to other patient and clinical information indicating relationship between attribute information for the other patient and other treatment effect observed after the treatment.

The application belongs to the technical field of data processing, and particularly relates to an intelligent prediction and evaluation system based on wire and cable insulation aging. The system comprises: a data acquisition preprocessing unit, which is used for mapping a line topology into graph structure data; a physical priori knowledge base management unit, which converts an aging mechanism into a logical constraint item; a double-flow feature extraction processor, which utilizes a graph neural network and a long short-term memory network to capture space-time features and combines the logical constraint to perform physical reduction; an intelligent prediction core execution mechanism, which performs transfer learning through meta-learning and eliminates environmental interference by using causal inference; and a multi-dimensional health degree fusion evaluation output unit, which adaptively adjusts feature weights and generates a health degree curve and a residual life distribution. The application improves prediction robustness and interpretability, and provides precise support for cable life cycle management.

The application discloses a power system anomaly treatment method and system based on causal inference, and relates to the technical field of power anomaly diagnosis. The method comprises the following steps: obtaining operation state data and power grid topology data of a target power grid area; based on the operation state data, calculating at least one abnormal index value representing power quality, and comparing the abnormal index value with a preset index threshold to determine whether there is an abnormal power quality; if it is determined that there is an abnormality, determining at least one candidate cause leading to the abnormality based on the operation state data and the power grid topology data; for each candidate cause, calculating a comprehensive scheduling cost required for intervention according to a predefined evaluation rule; selecting a cause subset from a preselected set constituted by the candidate causes; and outputting a treatment action suggestion corresponding to the cause subset. The application automatically selects a treatment measure combination with the optimal comprehensive scheduling cost and generates a treatment suggestion, thereby improving treatment efficiency and reducing resource invalid occupation.

The application discloses a kind of state evaluation method and device of electric power internet gateway, belong to electric power equipment intelligent gateway technical field.The application is by the real-time monitoring data of four levels of communication protocol processing layer, data forwarding path, security protection layer and system resource management layer respectively collected by pre-deployed probe, to construct real-time feature set;Using the historical feature set of the corresponding level of the gateway to be evaluated, unsupervised training is carried out on a kind of support vector machine model, and a trained dynamic health baseline model is obtained;Real-time feature set is input into the trained dynamic health baseline model, and the corresponding feature anomaly score is obtained;Characteristic anomaly score is input into the pre-constructed bayesian network model to carry out causal inference, determine target root cause node, and determine the health level inside the gateway to be evaluated in combination with feature anomaly score.Thereby realize the health evaluation and active decision of electric power internet gateway operating state.

This invention provides a method for safety alignment assessment of large industrial models, belonging to the technical field of large industrial models. This invention collects multi-dimensional parameter time-series data of industrial systems and establishes a physical constraint rule base. It combines Gaussian process regression and Bayesian optimization to search for a dynamic safety boundary point set, establishes a structural causal model, performs do-of-fact calculus and counterfactual reasoning to extract causal inference chains, and inputs the boundary robustness index and causal inference chains into a spiral progressive network structure safety boundary alignment model to output an alignment score. Through temporal continuity analysis, alignment spikes are detected, and a reverse alignment correction process is initiated to adjust the physical constraint weight coefficients. This solves the technical problem of unstable alignment assessment in large industrial models due to the lack of physical mechanism constraints and causal reasoning capabilities during safety boundary identification.

This invention discloses a real-time monitoring system and method for automotive parts production data, belonging to the field of industrial intelligent monitoring technology. The method includes mapping a coupled dataset of parts production to a quantum state space to obtain a compressed quantum feature vector; applying an environmental compensation factor to dynamically adjust the compressed quantum feature vector using a rotating door mechanism, outputting an environmentally adaptive quantum feature vector; inputting the environmentally adaptive quantum feature vector into a risk causal inference model; performing quantum state probability transformation calculations in a quantum probability calculation layer; and quantifying feature interactions and causal effects in a causal inference layer, outputting a heatmap of parts perforation offset. This invention effectively processes and fuses data from different sources by mapping the coupled dataset of parts production to a quantum state space and using a quantum variational encoder for quantum amplitude compression. Simultaneously, it utilizes a risk causal inference model to deeply analyze and quantify the root causes of quality problems during the production process.

This invention discloses a method, system, device, and medium for energy efficiency assessment based on digital twins, specifically relating to the fields of digital twins and energy efficiency assessment technology. The method includes: collecting multi-source data from energy-consuming systems, generating time-series datasets, and constructing equipment topology diagrams and operating condition labels; constructing a hybrid twin model pool comprising high-fidelity mechanisms, lightweight data-driven models, and reduced-order models; generating a fusion model and confidence sequence through weighted fusion using a dynamic scheduler; performing root cause diagnosis based on causal graphs and counterfactual reasoning; encapsulating the fusion model into a reinforcement learning environment, offline training a policy network to output the optimal action sequence and predicted energy efficiency gain curve; calculating a comprehensive energy efficiency assessment index to determine whether to issue control commands, and judging whether to update the model based on confidence and prediction errors. This invention achieves high-precision energy efficiency assessment, root cause diagnosis, and autonomous optimization control of energy-consuming systems through dynamic fusion of multiple models and causal inference.

This invention relates to a fault tracing and analysis method for garment production line equipment. Addressing the challenges of processing multi-source heterogeneous sensor data and the interpretability of causal models in garment production lines, it proposes a fault identification and causal attribution method based on operational condition semantics and disturbance analysis. This method includes multi-source sensor data normalization and operational condition semantic mask construction, generation of physically constrained causal disturbance samples, dual-channel feature encoding to decouple causal features, counterfactual reasoning to measure the contribution of the causal subspace to the diagnostic results, and finally, generation of a structured fault report containing a causal inference chain. Furthermore, it can dynamically optimize the model feature space partitioning based on new fault modes. This scheme achieves efficient alignment and utilization of data diversity and dynamism in complex industrial environments, improves the accuracy of fault diagnosis and the transparency of decision support, and significantly enhances the interpretability and adaptability of the model.

The present application relates to a data product pricing trend prediction method based on transaction behavior clustering analysis, aiming to solve the technical problems of complex transaction behavior causal relationship identification, causal mechanism dynamic evolution and trend prediction accuracy improvement. The core scheme is: through transaction behavior clustering and multi-dimensional unsupervised correlation measurement to generate a coarse-grained causal graph, combined with causal stability evaluation, conditional independence test and graph fusion to realize dynamic updating of causal relationship; further using semantic annotation path set, path distillation encoder and overall trend prediction link to form an end-to-end price interval prediction and model dynamic closed-loop optimization. This technology can effectively mine the causal structure of transaction behavior, enhance the stability of causal inference, improve the accuracy of trend prediction, and realize adaptive parameter optimization, thereby improving the intelligent level of market trend analysis.

This invention discloses an intelligent control method and system for a horizontal lathe based on digital twins. The invention relates to the technical field of digital twins. Based on the dynamic identification of the working state map, the transient changes of the horizontal lathe during operation are determined to identify the feature mapping result from a high-dimensional physical space to a low-dimensional potential feature space. This feature mapping result is input into the digital twin space, and a long short-term memory network is introduced into the digital twin space to improve the accuracy of the horizontal lathe's working evolution trajectory in future time periods. Dynamic compensation data and the current working state of the horizontal lathe are input into a causal inference network to identify multiple key factors causing abnormalities in the horizontal lathe. Dynamic inference is performed on these key factors and the current working history of the horizontal lathe. Combined with the working constraints of the horizontal lathe, an intelligent control strategy for the horizontal lathe is determined, improving the accuracy of the intelligent control strategy.

This application relates to the field of computer vision technology, and provides a method and system for sports action recognition and judgment based on computer vision. The processing module realizes the parallel output of action intention category and initial skeleton points based on a multi-task learning network, generates a standard trajectory based on the action intention category, and constructs individual ability boundaries based on historical motion data. The matching module uses the standard trajectory as prior information, the initial skeleton points as observation information, and the individual ability boundaries as constraints to complete the iterative optimization of skeleton point positions and generate a skeleton point sequence through a state estimation algorithm. The inference module generates residual features by comparing the skeleton point sequence with the standard trajectory, combines user physiological data and historical motion data to determine the root cause of deviation through causal inference, and outputs the sports injury level. The adjustment module generates error correction instructions according to the root cause of deviation, and dynamically adjusts the intervention intensity according to the sports injury level to achieve the adaptation of intervention intensity with deviation type and injury risk.

This invention discloses a method, device, equipment, and medium for monitoring and early warning of agricultural food-water-energy-carbon emissions, belonging to the field of smart agriculture technology. The method includes: acquiring production big data of a target area; performing spatial registration based on the production big data and corresponding units in a preset static basic spatial database to construct a performance-environmental condition paired dataset; inputting the performance-environmental condition paired dataset into a causal inference engine to identify direct environmental factors that have a direct causal relationship with crop yield, and eliminating interference factors that only have a correlation relationship, generating a set of sensitive factors for triggering early warning; based on the set of sensitive factors, using a threshold search algorithm to automatically identify the optimal early warning threshold for each key factor affecting crop yield; and monitoring energy crops in the target area based on the set of sensitive factors and the corresponding optimal early warning threshold to obtain monitoring results. This application realizes the monitoring and early warning of land, food, energy, and carbon emissions.

This invention relates to the field of computer vision and discloses a facial expression recognition method based on enhanced action units-guided causal inference. The method includes extracting high-dimensional semantic feature maps from the original image; performing reparameterized sampling to output a sequence of local visual feature blocks; calculating the geometric relationship between the feature sequence and facial key points to generate a position-enhanced feature sequence with superimposed embeddings; predicting the activation intensity and uncertainty of action units, and combining a static prior association matrix to perform causal intervention to generate a corrected feature sequence and a counterfactual feature sequence; and aggregating the corrected sequences to output the expression classification result. By dynamically adjusting the sampling distribution, the method focuses on high-discriminative regions and reduces noise; combines spatial structure and uncertainty estimation to suppress low-confidence features; and utilizes prior knowledge to perform causal inference to remove spurious correlations, verify causal sufficiency, and improve the model's generalization performance in complex scenarios.

The application discloses a kind of robust causal relationship learning method, system, equipment and storage medium, large-scale variable set can be effectively handled, and when variable set scale is larger, the problem is decomposed into multiple small-scale problems and is handled, improve the efficiency of processing large-scale variable set;Meanwhile, the application has good scalability, can be used with any causal algorithm, so that the application can adapt to a variety of different application scenarios and needs;And, the application provides a new way to solve complex causal inference problem, the idea of divide and conquer and the concept of causal cut provide a new perspective and tool for subsequent causal inference research;In addition, the directed causal graph finally obtained by the application has high accuracy, and can improve the effect of the application field.