53 results about "Causal information" patented technology

The invention discloses a plug-in inductor winding process control method based on production parameter dynamic compensation, and provides a closed-loop compensation control strategy taking a physical field evolution model as a core aiming at the problems of unstable sensing quantity and non-uniform structure in the traditional process. The method comprises the following steps: firstly, constructing an ideal physical field reference model according to target inductance parameters and a structure, and describing an evolution law of characteristics such as a magnetic field, an electric field or impedance in an ideal state; in the actual winding process, a physical field signal is collected through a sensor, production parameters such as wire tension and main shaft rotating speed are obtained, and a physical field distribution snapshot of the current winding state is generated. And the system compares the extracted actual features with the reference model, identifies a dominant disturbance source causing deviation in combination with real-time production parameters, and outputs a disturbance type label carrying causal information. And according to a label and a production parameter change trend, a compensation instruction is dynamically generated and executed, fine adjustment of tension, a winding displacement track or density is realized, and consistency and precision of an inductor finished product are ensured.

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 a logistics management system based on artificial intelligence, and the system is used for improving the efficiency and intelligent level of logistics scheduling and resource configuration, and comprises the following steps: building a unified data set through collecting and positioning, sensor and operation log and other types of logistics data; a modeling method is used for fusion to form a data structure containing time-space and transportation state information, the system adopts a causal analysis model to identify key influence relations between logistics events, a cross-modal feature extraction technology is combined to generate representation vectors such as transportation tracks, loads and states, and the logistics events are subjected to self-adaptive analysis through a self-adaptive strategy. And the causal information and the multi-modal features are fused to generate a decision basis, and the fusion proportion is dynamically adjusted according to actual operation feedback, so that transportation path optimization, distribution task planning and reasonable resource allocation are realized. The system has a continuous learning capability, and can keep the accuracy and flexibility of scheduling decisions in a continuously updated data environment.

The invention relates to the technical field of intelligent building heat load prediction and heat balance regulation and control, and discloses an intelligent building heat balance dynamic regulation and control method and system based on load prediction, and the method comprises the steps: constructing a time-frequency causal double-flow analysis network, mutual enhancement of the time-frequency characteristics and the causal relationship is realized; designing a hierarchical causal discovery algorithm, and mining a multilevel causal structure; developing a causal enhanced time-frequency representation learning method, and fusing time-frequency and causal information; establishing an intervention decision framework based on anti-fact analysis, and evaluating an intervention effect; an abnormal mode self-evolution recognition system is realized, and new abnormal modes are continuously learned; an interpretable abnormity diagnosis mechanism is developed, and a clear diagnosis report is provided; according to the method, the thermal load prediction accuracy is improved, the abnormal early warning capability is enhanced, the intervention efficiency is improved, the system interpretability is enhanced, and the optimization of the energy utilization efficiency is realized.

The invention relates to the field of intelligent early warning of guaranteed power supply, and provides an abnormal early warning method and system for a guaranteed power supply system, and the method comprises the steps: obtaining the topological structure, real-time operation data and equipment state data of each node in a guaranteed power supply network, employing the conditional entropy and instantaneous causal entropy calculation technology, precisely evaluating the causal intensity between variables, and achieving the early warning of the abnormal state of the guaranteed power supply system. And a causal adjacency matrix dynamically changing along with time is constructed to reflect a complex causal relationship in the network. Then, in combination with an equipment topological structure and dynamic causal information, multi-scale time sequence features are extracted through feature updating and expansion causal convolution, and a comprehensive feature vector is formed; after the vector is input into the Bayesian neural network, a plurality of prediction value samples are generated through sampling weight distribution, a mean value is used as a prediction result, and the prediction uncertainty is measured through variance. Accurate prediction and timely early warning of potential anomalies of the power supply system are realized, and the stability and safety of the system are effectively improved.

The invention relates to the technical field of artificial intelligence operation and maintenance, in particular to an AIOps multi-task scheduling method and system cooperatively driven by large and small models. The method comprises the steps of obtaining a heterogeneous operation and maintenance time sequence data set, performing data consistency calibration on the heterogeneous operation and maintenance time sequence data set, and determining a multi-source standardized operation and maintenance data stream; analyzing the multi-source standardized operation and maintenance data flow, determining operation and maintenance index dynamic causal information, and determining optimized target event information according to the operation and maintenance index dynamic causal information; determining a real-time operation and maintenance task chain according to the optimized target event information, and determining a large and small model collaborative scheduling strategy according to the real-time operation and maintenance task chain; and performing real-time operation and maintenance adjustment on the system state according to the large and small model collaborative scheduling strategy, and determining and outputting a collaborative operation and maintenance report. According to the method, the problem of over-optimization traps existing in the operation and maintenance process by using the AIOps technology is effectively solved, and the operation and maintenance process is highly matched with the high dynamic response requirement of the system.

The invention provides a septal tumor accurate diagnosis system based on artificial intelligence, a multi-scale attention fusion module receives multi-modal data, focuses a septal region, dynamically adjusts the weight and outputs a key feature vector, an adversarial self-supervision pre-training module digs tumor morphological features based on the vector, robustness and generalization ability are enhanced, and the accuracy of diagnosis is improved. And the causal intervention diagnosis decision-making module constructs a decision-making tree by using enhanced features to perform preliminary diagnosis and transmits causal information to the meta-learning enhanced diagnosis module, and the meta-learning enhanced diagnosis module adjusts parameters by using a small number of samples for rare subtypes, outputs a final diagnosis result and feeds back the final diagnosis result. According to the system, multi-mode and dynamic attention, cooperative confrontation self-supervision pre-training and causal reasoning are fused, rare tumor diagnosis is optimized through meta-learning, a closed-loop feedback and shared knowledge graph is formed, and the diagnosis precision, interpretability and rare case diagnosis capacity are integrally improved.

The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method for causal characterization decoupling and causal strength embedding between data. The method comprises the following steps: firstly, acquiring multi-source-domain bearing historical fault data acquired by a plurality of data acquisition units; thirdly, performing causal characterization decoupling processing on the historical fault data of the bearing, and decoupling causal features and non-causal features of each data collector; then, performing causal intensity embedding processing by using all the causal features to obtain comprehensive causal features; and finally, fault prediction is carried out on the comprehensive causal features, and a corresponding bearing fault type is predicted. Therefore, the cross-domain bearing fault diagnosis method can effectively improve the accuracy of cross-domain bearing fault diagnosis by separating the causal features related to the fault from the non-causal features of environmental interference and fusing the causal information of the multiple data collectors.

The invention discloses an oil pipeline monitoring method and system based on spatio-temporal modeling and multi-dimensional analysis, and belongs to the technical field of oil and gas pipeline safety monitoring, data processing and intelligent prediction.The method comprises the steps that multi-source spatio-temporal data is collected to construct a probability generation model; performing inversion on the model by using the observation value to reconstruct four-dimensional posterior probability distribution; performing causal inference on the posterior probability distribution to generate a dynamic causal information flow map; and analyzing topological evolution of the atlas to generate a stability monitoring report. According to the method, a technical path of combining probability modeling based on an information field theory and causal dynamics inversion is adopted, and accurate prediction of a systematic risk critical transition precursor can be realized by reconstructing a pipeline holographic state field and analyzing topological evolution of a causal network of the pipeline holographic state field; and the operation safety and the intelligent monitoring level of the long-distance oil pipeline are obviously improved.

The invention discloses a soil moisture content prediction method and device based on deep learning, and the method comprises the steps: carrying out the seasonal trend decomposition of historical soil moisture content time sequence data, and obtaining a trend term, a seasonal term and a residual term; secondly, analyzing a causal relationship between each decomposition item and a meteorological variable in combination with a plurality of causal test methods, and constructing a dynamic causal adjacency matrix to obtain a causal tree structure; for the trend item, the season item and the residual item, designing a causal LSTM sub-model according to the fruit tree structure, inputting a historical decomposition sequence and the screened causal meteorological variables, and carrying out independent prediction; and finally, fusing prediction results of the sub-models through a full connection layer, and outputting a final prediction value of the soil moisture content in a future period. The innovation point of the method is that causal information and time dependence characteristics of decomposition items are fused, and the interpretability of the model and the response capability of the model to long-term trend and short-term mutation are enhanced.

The invention discloses a causal relationship knowledge graph construction method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting input data which comprises topological structure data, performance index data, event log data and external environment data of a network; constructing a causal relationship knowledge graph between network entities based on the input data; generating potential causal relationship candidate pairs of the causal relationship knowledge graph by using a causal discovery algorithm; and optimizing the causal relationship knowledge graph on the basis of the causal relationship candidates to obtain an optimal causal relationship knowledge graph, thereby being beneficial to solving the problem that in the prior art, in large-scale wireless network diagnosis, the causal relationship between network entities is difficult to accurately and efficiently excavate, so that the causal information cannot be effectively utilized to perform network fault diagnosis.

The invention relates to the technical field of artificial intelligence model interpretation, in particular to a multivariable time sequence prediction interpretation method and system based on an information theory and causal reasoning. The method comprises the following steps: acquiring original multivariable time sequence data; performing data preprocessing on the obtained original multivariable time sequence data; performing causal information reasoning on the preprocessed multivariable time sequence data; based on causal information reasoning, calculating a dynamic feature weight of the multivariable time sequence data; performing local fitting and interpretation generation based on the dynamic feature weight; and a time-feature two-dimensional thermodynamic diagram is obtained. Through multi-dimensional feature relationship mining, an optimized causal relationship reasoning algorithm, dynamic weight calculation and an intuitive time-feature two-dimensional thermodynamic diagram generation mechanism, time continuity is effectively considered, prediction interpretation is presented in an intuitive and understandable mode, and a user can quickly understand an artificial intelligence model decision process.

The application discloses a kind of teaching and learning behavior data support under causally driven dynamic organization method and system of teaching resources, and the method comprises the following steps: obtaining online teaching and learning behavior data, the online teaching and learning behavior data include teaching activity data, student learning data and online education resource information;According to the online teaching and learning behavior data, resource access dynamic time sequence causality is modeled, and the causality information of online education resource access is determined;According to the causality information, the online education resource access heat is predicted by neural network;According to the online education resource access heat obtained by prediction, the file level teaching and learning behavior data support under causally driven dynamic organization process of teaching resources is constructed, to realize the causally driven dynamic organization of teaching resources under teaching and learning behavior data support.The application can effectively improve the education user data access efficiency, and can be widely applied in the field of computer technology.

This application relates to the fields of autonomous driving scene representation and artificial intelligence technology, and particularly to a method for causal saliency mining and anti-interference semantic representation in autonomous driving scenarios. The method includes the following steps: encoding multimodal scene elements separately and concatenating the encoding results to obtain a unified scene representation; inputting the unified scene representation into a contrastive representation module, and performing global importance ranking based on the internal weights of a self-attention mechanism; classifying scene elements into different categories and filtering the categories to construct category-aware automated positive and negative samples; performing context alignment on the category-aware automated positive and negative samples based on a recoding mechanism; and constructing a contrastive loss function to enable the model to distinguish key causal information from interference noise in the feature space. This application, through an attention-guided automated sampling mechanism and recoding strategy, filters out key features with causal relationships from high-dimensional heterogeneous driving scenarios and suppresses spurious correlation noise in the environment.

The present application relates to the technical field of machine vision, in particular to an experimental process intelligent error correction method and device. The method comprises: dynamically evaluating the importance of each channel of multi-channel time series data and performing dimension reordering; inputting the reordered data into an encoder to obtain first and second latent vectors of adjacent time steps, and constructing a spliced latent vector that fuses time series and causal information; inputting the spliced vector into a forward and backward decoder to calculate a reconstruction inconsistency score; when the score exceeds a threshold value, locking the first latent vector, optimizing the gradient of the second latent vector to minimize inconsistency, obtaining a correction vector and generating error correction data. The scheme of the present application improves the recognition accuracy of complex anomalies, enhances the ability to distinguish between normal changes and errors, and improves the reliability of error correction results.

The invention discloses a pathological full-slice interpretable analysis method and system based on dynamic causal discovery, and the method comprises the steps: segmenting an input full-slice image into a plurality of image blocks, carrying out the spatial clustering of the image blocks, generating an anchor point, obtaining the representation of an initial region through the correlation between a learnable kernel and the anchor point, and obtaining the representation of the initial region; splicing the initial region representations into a learnable global token to obtain region kernel features; the global token only receives causal information of all regional nodes through a self-attention mechanism; modeling a causal relationship between the pathological areas based on a directed acyclic graph to obtain a causal graph between the pathological areas; propagating a causal relationship in the causal graph through a graph convolution operation, and performing N-renn iterative optimization to obtain an iteratively optimized region kernel feature and a final causal graph; the features of the global token in the final causal graph serve as final global representation, and an interpretable causal chain is formed. The objective of the invention is to model a causal relationship between pathological areas and diagnosis decision dependence, and improve the fine granularity and interpretability of tumor analysis.

The present invention discloses a soil moisture prediction method and device based on deep learning. First, historical soil moisture time series data is decomposed into seasonal trends to obtain trend terms, seasonal terms, and residual terms. Second, a variety of causal testing methods are combined to analyze the causal relationship between each decomposition term and meteorological variables, construct a dynamic causal adjacency matrix, and obtain a causal tree structure. For the trend term, seasonal term, and residual term, causal LSTM sub-models are designed based on the causal tree structure, and the historical decomposition sequence and the filtered causal meteorological variables are input for independent prediction. Finally, the prediction results of each sub-model are fused through a fully connected layer to output the final predicted value of soil moisture in the future period. The innovation of the present invention lies in the fusion of the causal information of the decomposition terms and the time-dependent characteristics, which enhances the interpretability of the model and its ability to respond to long-term trends and short-term mutations.

The present disclosure relates to the technical field of anomaly detection in electromechanical systems, and in particular to a method and device for anomaly detection in complex electromechanical systems based on a domain-adaptive causal decoupling network. The method comprises: obtaining a training data set and causal information of the electromechanical system, the training data set comprising system monitoring data and corresponding system fault label data, the causal information being used to indicate the functional relationship between multiple components of the electromechanical system; and training an anomaly detection model based on a domain-adaptive causal decoupling network based on the training data set and the causal information, the domain-adaptive causal decoupling network being used to indicate the corresponding relationship between system monitoring data and component-level health status representation, and the anomaly detection model being used to perform anomaly detection on the electromechanical system. The embodiment of the present disclosure combines domain adaptation and causal decoupling technologies to train a domain-adaptive anomaly detection model, so that the model can adapt to a changing system operating environment.

The application discloses a plug-in inductance winding process control method based on production parameter dynamic compensation, aiming at the problems of unstable inductance and uneven structure in the traditional process, and proposes a closed-loop compensation control strategy with a physical field evolution model as the core. The method first constructs an ideal physical field benchmark model according to target inductance parameters and structure, describes the evolution law of characteristics such as magnetic field, electric field or impedance in the ideal state. In the actual winding process, the physical field signal is collected through the sensor, and the wire tension, spindle speed and other production parameters are obtained to generate a physical field distribution snapshot of the current winding state. The system compares the extracted actual characteristics with the benchmark model, identifies the dominant disturbance source causing the deviation in combination with real-time production parameters, and outputs a disturbance type label carrying causal information. According to the label and the production parameter change trend, the compensation instruction is dynamically generated and executed to fine-tune the tension, wire arrangement track or density, and ensure the consistency and accuracy of the inductance finished product.

The application discloses a medical knowledge enhanced question answering and auxiliary diagnosis and treatment method for biliary surgery, comprising the following steps: recognizing biliary surgery corpus through an entity recognition model to obtain a plurality of biliary surgery entities and extracting the causal relationship between the biliary surgery entities; constructing a biliary surgery knowledge graph based on the biliary surgery entities and the causal relationship, summarizing the information in each biliary community to obtain a plurality of community abstracts; segmenting the community abstracts to obtain a plurality of abstract blocks, calculating the scores of the abstract blocks, screening the abstract blocks according to the scores, obtaining high-relevance abstract blocks, inputting the query text of a user, a structured generation template of a causal chain and the high-relevance abstract blocks into a large language model to obtain a clinical answer text. The application accurately matches clinical causal information through a causal perception mechanism, and outputs answers conforming to a clinical thinking process based on the structured generation of a causal chain, thereby improving the accuracy and interpretability of diagnosis and treatment assistance.

The present application relates to the technical field of artificial intelligence model explanation, in particular to a multivariate time series prediction explanation method and system based on information theory and causal reasoning. The method comprises the following steps: acquiring original multivariate time series data; performing data preprocessing on the acquired original multivariate time series data; performing causal information reasoning on the preprocessed multivariate time series data; calculating the dynamic feature weight of the multivariate time series data based on the causal information reasoning; performing local fitting and explanation generation based on the dynamic feature weight; and obtaining a time-feature two-dimensional heat map. Through the multidimensional feature relationship mining, the optimized causal relationship reasoning algorithm, the dynamic weight calculation and the intuitive time-feature two-dimensional heat map generation mechanism, the present application effectively considers the time continuity, presents the prediction explanation in an intuitive and easy-to-understand manner, and enables the user to quickly understand the artificial intelligence model decision-making process.

The invention provides a fault diagnosis method and device based on causal state space prompt, a medium and electronic equipment, and relates to the field of data processing, and the method comprises the steps: mapping each IT operation and maintenance event into an event feature vector; inputting the online state space model according to a time sequence, and outputting a potential hidden state corresponding to each time step; in response to the received IT operation and maintenance diagnosis request, mapping the potential hidden state corresponding to the current time step into a plurality of discrete state lexical elements; and splicing the problem description corresponding to the IT operation and maintenance service interruption, the IT operation and maintenance event occurring in the target time window and the plurality of discrete state lexical elements into a target prompt, and inputting the target prompt into the frozen large language model to obtain a fault diagnosis result. The method not only has the advantage of natural language reasoning of the large language model, but also avoids the illusion problem caused by limited context windows and missing of key causal information, and meanwhile, the use mode of freezing the large language model greatly reduces the training cost and the computing power consumption.

This application provides a vehicle collision recognition method and a vehicle, capable of jointly extracting features from acquired raw acoustic and vibration signals to obtain a topological embedding feature vector; performing temporal causal modeling based on the topological embedding feature vector to obtain causal information; and performing multi-agent collaborative discrimination based on the causal information and the topological embedding feature vector to obtain an anomaly score vector; performing multi-source information fusion decision-making based on the anomaly score vector, causal information, and the topological embedding feature vector to obtain fusion evaluation data; determining a dynamic alarm threshold based on the real-time vehicle status; and determining the collision recognition result based on the dynamic alarm threshold and the fusion evaluation data. By introducing a joint modeling mechanism for acoustic and vibration signals, this method addresses the current problem of insensitivity to low-amplitude undercarriage impacts on vehicles.

The application relates to the technical field of fault diagnosis, in particular to a fault diagnosis method for decoupling and embedding of causal representation and causal strength between data. First, bearing historical fault data of a multi-source domain collected by multiple data collectors is acquired; then, the bearing historical fault data is subjected to decoupling processing of causal representation, and causal features and acausal features of each data collector are decoupled; then, all the causal features are used for embedding processing of causal strength to obtain comprehensive causal features; finally, the comprehensive causal features are subjected to fault prediction to predict corresponding bearing fault types. Thus, the application can effectively improve the precision of cross-domain bearing fault diagnosis by separating the causal features related to faults from the acausal features of environmental interference and fusing the causal information of multiple data collectors.

The present application relates to the technical field of evaporation waveguide path loss prediction, and discloses a kind of electromagnetic wave path loss time sequence prediction method under evaporation waveguide environment, comprising: obtaining the time series data of electromagnetic wave historical path loss, and inputting it into path loss prediction model;By bidirectional perception layer, the historical dependence relationship and future context information of time series data are captured using bidirectional long short-term memory network;By semantic alignment layer, non-causal information of future context information is filtered out using gated recurrent unit, and then causal alignment is carried out;Through attention reconstruction layer, the influence degree of each feature vector in the aligned feature sequence on electromagnetic wave signal fading is used to weight feature vector using self-attention mechanism;Through time sequence decoding layer, the time sequence logic of each feature vector in the weighted feature sequence is obtained using post-gated recurrent unit, to predict future path loss according to the weighted feature sequence and the time sequence logic of each feature vector therein.

The present invention discloses an artificial intelligence-based logistics management system for improving the efficiency and intelligence level of logistics scheduling and resource allocation, which includes the following steps: By collecting various types of logistics data such as positioning, sensors, and operation logs, a unified data set is established, and a data structure containing spatio-temporal and transportation status information is formed through a modeling method. The system uses a causal analysis model to identify the key impact relationships between logistics events, and combines cross-modal feature extraction technology to generate representation vectors such as transportation trajectories, loads, and statuses. Through an adaptive strategy, causal information and multi-modal features are fused to generate decision-making bases, and the fusion ratio is dynamically adjusted according to actual operation feedback to achieve optimized transportation routes, distribution task planning, and reasonable resource allocation. This system has the ability of continuous learning and can maintain the accuracy and flexibility of scheduling decisions in an ever-updating data environment.

The invention provides an event causal identification method and device based on dynamic relation graph convolution, equipment and a medium. The method comprises the following steps: constructing an encoder to extract overall representation characteristics of each event in an event text; splicing the features of the event pair to obtain a spliced feature, and obtaining a preliminary causal probability value of the event pair through a causal probability preliminary prediction module; based on the constructed dynamic relation graph convolutional network, event pair interaction features are obtained; obtaining explicit causal features of the event based on an explicit causal mining module; utilizing a multi-granularity implicit causal matching mechanism to obtain implicit causal features; and finally, event causal identification is realized through a full-connection network, and the causal relationship and the causal probability value of each event pair in the event text are obtained. According to the method, the interaction relationship between the event pairs can be fully extracted, the causal information is mined and displayed, and the accuracy of event causal identification is improved by utilizing multi-granularity implicit causal knowledge.

The present invention discloses a method and system for constructing an interpretable visual question answering model based on a factual scenario, which includes obtaining a first data set and a second data set; pre-training a visual question answering model to obtain an image feature extraction network and a text feature extraction network; processing the image feature extraction network by a weight backpropagation method to obtain image counterfactual samples; processing the text feature extraction network by an open-source machine learning library to obtain text counterfactual samples; introducing adversarial semi-factual samples to iteratively update the visual question answering model to obtain a visual question answering prediction model; extracting feature data and validating the visual question answering prediction model through the feature data to obtain an interpretable visual question answering model; the beneficial effect of the present invention is to solve the problem of weak model interpretability in current visual question answering research, enabling the model to preserve key causal information to enhance the model's reasoning ability and capture image features and text features in a more fine-grained manner.

The invention provides a causal analysis method and system for ocean heat wave events and chlorophyll changes, which are applied to the technical field of ocean environment monitoring, and are characterized in that a multi-dimensional causal feature matrix is constructed through multi-source long-time sequence environmental data in combination with time-space identifiers and abnormal change sequences of the heat wave events, and the causal analysis result of the heat wave events and chlorophyll changes is obtained through joint probability distribution expression. And performing decomposition calculation on the causal information of the heat wave accumulated intensity characteristic and the dynamic environment element characteristic relative to the chlorophyll abnormal change to obtain multiple types of causal information components, judging the dominant action type of the chlorophyll change according to the proportion of the multiple types of causal information components, and outputting a corresponding causal attribution result. According to the causal analysis method and system for the ocean heat wave event and the chlorophyll change, the causal relationship of the ocean heat wave event to the chlorophyll concentration change of the phytoplankton is analyzed through construction of a multi-dimensional causal feature matrix, joint probability distribution mapping and causal information decoupling.

A learning system is disclosed that leverages intention-driven causality to enhance skill learning. The learning system enables an author to easily develop mixed reality (MR) tutorial content for performing a task that advantageously captures causal relationships between steps and, thus, enables such causality to be conveyed to the novice user when learning how to perform the task. To this end, the learning system leverages a novel hierarchical representation of causality and intention alongside a systematic workflow suitable for designing skill learning content. By preserving and presenting causal information to the novice user, the user can better understand not only the steps required to perform a task, but also why each step is performed.