52 results about "Interactive modeling" patented technology

The embodiment of the invention provides a digital human interaction system and method based on multi-modal emotion recognition, and belongs to the technical field of digital human interaction. The system comprises a multi-modal sensing module used for collecting multi-modal data and preprocessing the multi-modal data to generate a standardized data stream; the cross-modal fusion and emotion recognition module is used for carrying out interactive modeling on the multi-modal features and outputting a current emotion label and emotion intensity; the reaction planning module is used for generating a composite reaction strategy; and the digital human rendering module is used for mapping the composite reaction strategy into control signals corresponding to the voice, the facial expression and the action respectively, and driving a digital human to execute corresponding voice output, facial expression change and limb action through the control signals so as to realize interaction. According to the method, multi-modal data are deeply fused through the cross-modal graph neural network and comparative learning, the weight is dynamically adjusted in combination with the modal confidence, and the emotion recognition accuracy and robustness are improved.

The invention relates to the field of intelligent dialogues, and particularly discloses an AI multi-modal dialogue system based on multi-modal recognition, which comprises the following steps of: firstly, respectively extracting text word granularity and image local features, secondly, constructing dynamic association between a text and an image at a feature level through a bidirectional cross attention mechanism, and then, constructing a dynamic association between the text and the image at a feature level; dynamic association of cross-modal high-level semantics is captured through multi-head attention, a hierarchical processing mechanism enables the system to be adaptive to multi-modal information value weights of different task stages, the problem of modal gaps caused by simple splicing in a traditional method is solved, cross-modal space-time association is captured through a dynamic attention mechanism, and the multi-modal information value weights of different task stages are obtained. And finally, realizing accurate response through intention recognition and large model generation. In this way, the limitation of the prior art in the aspects of cross-modal feature extraction and dynamic interactive modeling is broken through, and the accuracy and response correlation of multi-modal intention understanding are remarkably improved.

The invention relates to an implicit voxel Boolean subtraction optimization method in five-axis machining simulation. According to the method, an implicit tool geometric expression model based on a symbolic distance function is constructed, and a dynamic voxel modeling technology with a layered structure is combined, so that efficient interactive modeling of a material removal process between a tool and a workpiece is realized. The whole process of the method comprises four key stages of spatial positioning, vertex interference detection, projection correction and grid reconstruction, and can cope with geometric modeling challenges caused by continuous motion of a multi-degree-of-freedom and multi-attitude cutter. A dynamic block loading mechanism and a local state synchronization strategy are introduced, so that the real-time response capability of Boolean subtraction operation under complex working conditions is effectively improved, and meanwhile, the continuity and integrity of machining geometric boundaries are kept. The method has good expandability and robustness, obviously reduces space-time complexity of calculation on the basis of keeping geometric surface continuity and topological correctness, realizes millisecond-level response time in an experiment, and has a good application prospect. The method is widely applied to virtual machining simulation and intelligent process verification systems of complex free-form surface parts such as aero-engine blades and automobile structural parts.

The invention discloses a multi-modal image fusion model construction method, and particularly relates to the technical field of image fusion model construction. Interactive modeling of a modal contribution imbalance coefficient and a confidence coefficient estimation anomaly coefficient is introduced in a multi-modal image fusion process; a causal association between confidence prediction fluctuation and modal weight extreme is converted into a quantifiable cross-modal complementarity degeneration index, and the cross-modal complementarity degeneration index is used as a core adjustment mechanism to dynamically optimize a fusion weight and training regularization, so that the problem that a single mode is excessively amplified or mistakenly weakened is effectively inhibited on a fusion strategy level; according to the method, the structure retention capability of the model in edge details and texture regions is remarkably improved, and artifacts and information loss caused by modal complementarity degradation are avoided.

The invention provides a knowledge graph complementing method based on deformable convolutional feature reordering, and relates to the technical field of knowledge graphs. Unknown entity embedding is predicted by using an embedding vector trained by a TransE model, and feature positions are dynamically adjusted by introducing a deformable convolutional network, so that feature reordering is realized; and enabling the input features to explicitly contain the target entity information. And further utilizing relation embedding as a convolution kernel, enhancing the interactive modeling capability of the features, and finally completing prediction of knowledge graph missing information. According to the method, the accuracy and universality of the model in a link prediction task are improved, the adaptability of a knowledge graph completion technology to a data complex scene is remarkably enhanced, and powerful technical support is provided for application in the fields of intelligent search, recommendation systems, question and answer systems and the like.

The invention belongs to the technical field of intelligent emotion recognition, and discloses an aspect-level multi-modal emotion analysis method, system and device and a medium, and the method comprises the steps: respectively obtaining image features and text features based on an image and a text, and selecting aspect word features through a pooling operation; modeling the image-text words and the image-aspect words through an image convolutional neural network to obtain image-text interaction features and image-aspect interaction features; image-text interaction features are used as query vectors, image-aspect interaction features are used as key vectors and value vectors, residual connection and normalization are introduced, interaction features are obtained and fused with aspect word features, and sentiment classification is performed based on the fused features. According to the method, from multi-modal feature extraction, interactive modeling to fusion optimization, a series of operations are progressive layer by layer, comprehensive, accurate and high-quality feature support is provided for sentiment classification, and finally, the accuracy and reliability of aspect-level multi-modal sentiment classification are effectively improved.

The invention discloses a pedestrian trajectory prediction method based on GCN interactive modeling, and the method comprises the steps: defining each pedestrian as a node, and building a pedestrian space diagram according to the relative position of the pedestrian, so as to capture the space relation between the pedestrians; in the pedestrian space diagram, speed information is added for pedestrians, and a speed adjacency matrix is constructed; in the pedestrian space diagram, the categories of objects around pedestrians are used as labels to be embedded, and a label adjacent matrix is constructed; combining the speed adjacency matrix with the label adjacency matrix to generate a semantically guided adjacency graph; inputting the adjacent graph into a space-time graph convolution network, carrying out space-time convolution operation on the graph representation of the pedestrian trajectory, and respectively extracting a time feature and a space feature; and inputting the obtained features into an improved time extrapolation pyramid convolutional neural network, and jointly estimating two-dimensional Gaussian distribution parameters of a future trajectory through multi-layer prediction features so as to obtain a pedestrian trajectory. According to the method, the influence degrees of different objects are considered, different label classifications are designed for different objects, and the social attributes are fully utilized to calculate the weight of social interaction, so that a better pedestrian prediction result is realized.

The invention discloses an interactive modeling language and classification scheme for interactive systems and applications. In various examples, an interactive agent platform hosting development and / or deployment of an interactive agent may provide an interpreter or compiler for interpreting or executing code written in an interactive modeling language, and a designer may provide customized code written in the interactive modeling language for execution by the interpreter. The interaction modeling language may be used to define an interaction flow that indicates what action or event is generated by the interpreter in response to the detected and / or executed man-machine interaction sequence. An interaction classification scheme may classify interactions according to standardized interaction modalities and / or corresponding standardized action categories. Thus, a stream may be used to model agent intent or inferred user intent, which may be used by a designer to construct a more complex interaction mode with an interactive agent.

The invention relates to a BIM-based three-dimensional modeling interaction method and system, an electronic device and a storage medium, and belongs to the technical field of three-dimensional modeling. Comprising the following steps: starting interactive modeling, dividing a key modeling area according to a business scene, and defining a plurality of user coordinate systems based on the key modeling area; wherein the user coordinate system is allowed to be newly added, deleted, modified, subjected to explicit-implicit control and switched; in the interactive modeling process, a related user coordinate system is dynamically switched as a current user coordinate system according to needs; based on the current user coordinate system, dynamically setting a temporary reference plane as required; based on the temporary reference plane, a capture position is determined through tentative capture, and model elements are created, placed and edited at the capture position for three-dimensional accurate drawing. According to the method, based on a temporary reference plane, three-dimensional accurate drawing is carried out through tentative capture, and the problems that an existing modeling interaction mechanism based on a working plane is poor in universality and low in efficiency are solved.

The invention discloses a multi-person collaborative human factor risk monitoring method and device based on multi-modal perception, and relates to the technical field of human factor engineering, intelligent perception and group decision behavior modeling. The cognitive state, emotion change and physiological load of participants are sensed in real time by fusing EEG, motion state, positioning track, heart rate, blood pressure and other multi-source information, and the collaborative decision behavior and cognitive consistency of a group in a high-pressure task are evaluated through interactive modeling. The method is suitable for multi-personnel, multi-role and task-intensive cooperation scenes such as emergency management, military drilling, industrial safety, complex traffic guidance and the like.

The invention relates to an interactive modeling method based on a large model, and the method comprises the steps: receiving a natural language instruction inputted by a user, carrying out the analysis of the natural language instruction through a natural language model, and determining the modeling demands of the user; according to the modeling demand, determining a model establishment scheme, and generating a model establishment instruction corresponding to the model establishment scheme; sending a model building instruction corresponding to each modeling step in the model building scheme to a corresponding data processing module in sequence, so that each data processing module carries out model building according to the model building instruction and modeling data; generating a model effect index according to the model establishment result of each data processing module; and feeding back the model establishment result of each data processing module and the model effect to the user.

The invention discloses a staring estimation method based on multi-modal three-dimensional feature fusion. The method comprises the following steps: firstly, extracting multi-modal three-dimensional features such as human body three-dimensional posture features, view direction priori and scene object three-dimensional position features from an RGB image and a depth image; then the extracted three-dimensional features are converted from a camera coordinate system to a self-center space with the head as the original point; then, direction and distance separation coding is carried out on the transformed three-dimensional features; and finally, interactive modeling is carried out on the coded three-dimensional features of the human body and the object through a Transform network, so that a final three-dimensional staring direction is predicted. According to the method, multi-modal three-dimensional information is fused and normalized interactive modeling is carried out, so that the dependence of a traditional method on a clear and close-range facial image is effectively overcome, and the robustness and accuracy of three-dimensional staring estimation in unconstrained scenes such as long-distance and shielding scenes are remarkably improved.

The invention provides a task unloading decision-making method based on multi-agent reinforcement learning. The method comprises an execution stage and a training stage. In the training stage, the centralized critic network can obtain global information of all agents so as to guide optimization of strategies of all the agents. In the execution stage, each agent only needs to realize the distributed unloading decision of each wireless device through a local actor network according to the local observation of the agent. According to the method, the global state and the joint action are evaluated by adopting the centralized value evaluation network, and the resource competition and coupling relationship between the intelligent agents is explicitly learned through the interactive modeling structure, so that the value estimation deviation caused by a simple splicing structure is reduced, the joint value evaluation precision is improved, and a more accurate updating direction is provided for each actor.

The invention relates to a VR-based multi-scene learning state modeling and learning effect evaluation method and system, and the method comprises the steps: defining a student learning state as a dynamic interaction modeling object and a static modeling object according to the VR modeling requirements and the demands of multi-scene learning effect evaluation for modeling, and carrying out the decomposition; drawing a multi-scene learning effect evaluation modeling network diagram; a teaching scene modeling topological structure is extracted from the network diagram, multi-scene learning state modeling parameter quantification is carried out, and a student multi-scene learning state model is constructed through a virtual reality modeling technology; and based on a multi-scene learning target, constructing a learning effect comprehensive evaluation parameter, carrying out multi-scene learning effect comprehensive evaluation and each scene process learning effect evaluation, automatically generating an evaluation report, and synchronizing the evaluation report to a teacher end and student multi-scene learning state model. According to the method, scene coverage is more comprehensive, data association is tighter, evaluation precision is improved, unified modeling of multi-scene data is achieved, and the visualization effect is better.

The invention discloses a Web service causal depolarization recommendation method and a Web service causal depolarization recommendation system based on attention enhancement contrast learning. The method comprises the following steps: 1) randomly sampling a negative sample for each positive sample in user-project interaction, and dividing into two mutually exclusive training sets, namely, a more popular positive sample set and a more popular positive sample set, according to project popularity; 2) based on the two mutually exclusive training sets, executing a graph convolutional network and an Attention attention mechanism, and generating an interest representation matrix and a consistency representation matrix; 3) performing regularization constraint on the embedding space of the user and the project by introducing a triple loss function on the basis of a comparative learning representation distribution optimization framework; 4) performing joint optimization on a plurality of losses of interest modeling, conformity modeling, interactive modeling, independence constraint and comparative learning, and optimizing the model through a curriculum-type training strategy for gradually tightening constraint boundaries, and 5) calculating a comprehensive preference score based on the final interest representation matrix and the consistency representation matrix, and outputting a debiased recommendation result.

The invention discloses an interactive modeling system based on a large model, and the system comprises a data preprocessing module which is used for carrying out the preprocessing of modeling data based on an input first interaction instruction, and obtaining the preprocessed modeling data; the feature engineering module is used for performing feature engineering processing on the preprocessed modeling data based on an input second interaction instruction to obtain modeling data after feature engineering processing; the model construction module is used for performing model construction on the modeling data subjected to feature engineering processing based on an input third interaction instruction to obtain a model corresponding to the modeling data; the intelligent agent is used for receiving and analyzing an input modeling starting instruction, obtaining a modeling step according to input modeling data and feeding back the modeling step to a user; and respectively calling the data preprocessing module, the feature engineering module and the model building module in sequence based on the modeling step. Through the above mode, conversational interactive modeling can be realized, the modeling threshold is reduced, and the modeling efficiency and accuracy are improved through intelligent recommendation.

The invention discloses an interactive generation method for a three-dimensional scene, and the method comprises the steps: carrying out the interactive image segmentation based on SAM: extracting a key primitive taking a mountain as a main part in a picture, and generating a segmentation silhouette of the corresponding primitive; performing three-distance perspective analysis on the landscape painting image based on LLM: outputting probability distribution and interpretable analysis results of high-distance, deep-distance and flat-distance perspective modes, and recommending an optimal perspective mode; according to the three-dimensional scene generation based on three-distance perspective, SAM-based primitive segmentation, LLM-based perspective analysis and a three-dimensional perspective conversion algorithm can be fused into a whole, and a'primitive segmentation-perspective analysis-three-dimensional generation 'three-stage interactive modeling system is constructed. Through interpretable three-distance perspective recommendation and real-time slider parameter adjustment, the system helps a user to quickly grasp three perspective layout points of high distance, deep distance and flat distance, and solves the pain points of difficult spatial restoration and long adjustment time under landscape painting scatter perspective.

The invention is applied to the technical field of new energy power generation prediction, and discloses a wind power prediction method based on non-overlapping multi-scale division and interactive modeling, and the method comprises the specific steps: firstly, carrying out the space-time embedding of historical wind power data, and obtaining the space information; secondly, a multi-scale token length set is set, non-overlapping multi-scale partitioning is carried out on the embedded sequence, and tokens of different scales are generated so as to reduce redundancy caused by overlapping; then, constructing a hierarchical interactive encoder, extracting features layer by layer from a small scale to a large scale, and embedding the small-scale potential representation of the previous layer into the input of the current layer in the large-scale encoding process to realize the guidance of the large scale by the small-scale features; and finally, fusing and mapping each scale feature output to obtain a future wind power prediction value, and training and optimizing the model by adopting mean square error loss. Verification on actual wind power plant data shows that the method can effectively improve the accuracy of wind power prediction.

The present invention relates to the technical field of image detection, and particularly to a three-dimensional human pose estimation method based on deep learning, including generating multi-level initial features for the input pose sequence; capturing the temporal dependencies of multiple initial features in the time domain; establishing a feature refinement module to enhance the initial representation; performing interactive modeling on the refined multiple pose features; and aggregating multi-frame pose information into a single-frame representation through strided convolution to complete pose estimation. The present invention can compress the computational amount of the original Transformer model and at the same time ensure the position accuracy of human pose estimation.

This application relates to the interdisciplinary field of artificial intelligence and reliability systems engineering, and provides a method and apparatus for intelligent generation of equipment fault behavior logic models based on LLM (Limited Linear Modeling). This method combines a vector database to achieve enhanced retrieval, constructs an agent-based framework for generating equipment fault behavior logic models, and then generates structural elements in the agent for each business component involved in the equipment fault behavior logic. Based on the model structure, the results are integrated and merged, ultimately achieving a complete and runnable equipment fault behavior logic simulation model. This method integrates the interactive modeling capabilities of agents with the semantic understanding and logical reasoning capabilities of large language models, enabling intelligent generation of fault behavior models for complex equipment and effectively improving the generation efficiency of equipment fault behavior models.

The invention relates to the technical field of natural language processing, and particularly discloses a few-sample relation classification method and system based on multi-scale hierarchical prototype learning, and the method comprises the steps: constructing a few-sample relation classification task, and generating a support set and a query set; encoding the support set sample, the query set sample and the relationship description information by using a pre-training language model to generate semantic vector representation; multi-scale hierarchical prototype representation is constructed through three levels of cross-set collaboration, intra-class collaboration and inter-class collaboration; calculating the distance between the query sample and each type of prototype, and outputting the classification probability through softmax; the cross entropy loss and the distribution alignment loss are jointly optimized, the contribution of each support sample to prototype construction is dynamically adjustable by introducing an intra-class collaborative modeling mechanism, the recognition ability of the model to the fine-grained relationship is improved by introducing inter-class interactive modeling and relationship description information, and the recognition ability of the model to the fine-grained relationship is improved by introducing a distribution alignment module between a support set and a query set. And the spatial offset between the prototype and the to-be-classified sample is obviously reduced.

The invention discloses a method and a system for enhancing multi-scale and multi-modal intelligent interactive modeling, and a medium. The method comprises the following steps: generating vertexes and 3D human body grids by using an SMPL model; performing feature extraction on the sketch by using a double-branch encoder, wherein the double-branch encoder comprises a segmentation encoder of a pre-trained human body sketch and a sketch local feature encoder of a sketch based on ResNet34; enhancing the ability of the model to capture a key region in the feature map by using a spatial feature enhancement module; using a multi-scale context fusion module to extract a multi-scale feature map from an intermediate layer of the double-branch encoder; and the output is converted into a 3D human body grid through a multi-branch regression device and an SMPL model. According to the invention, key spatial details are reserved through the multi-scale context fusion module, so that the model can focus key information on feature maps of different levels and scales. According to the invention, higher precision can be realized in 3D human body reconstruction, and optimal performance can be achieved.

The present invention relates to a 3D modeling interaction method, system, electronic device and storage medium based on BIM, belonging to the technical field of 3D modeling. It includes: starting interactive modeling, dividing key modeling areas according to business scenarios, and defining a number of user coordinate systems based on the key modeling areas; wherein, adding, deleting, modifying, showing / hiding control and switching of the user coordinate systems are allowed; during the interactive modeling process, dynamically switch relevant user coordinate systems as the current user coordinate system according to needs; based on the current user coordinate system, dynamically set a temporary reference plane according to needs; based on the temporary reference plane, use tentative capture to determine the capture position, and create, place and edit model elements at the capture position for 3D precise drawing. This method is based on a temporary reference plane and uses tentative capture for 3D precise drawing, solving the problems of poor versatility and low efficiency of the current modeling interaction mechanism based on the working plane.

The invention discloses a common preference enhanced collaborative recommendation method and device, and the method comprises the steps: obtaining user implicit interaction data in a target domain and a source domain, and constructing corresponding user features and commodity feature representations; carrying out fusion coding on the target domain user features, the source domain user features and the target domain commodity features, and generating cross-domain migration features facing target domain recommendation tasks; performing fusion iteration de-noising processing on the cross-domain migration features, gradually inhibiting source domain noise interference and strengthening migratable collaborative preference information through multiple rounds of feature fusion and weight screening operation, and generating de-noised cross-domain user feature representation; after denoising is completed, user structure constraint is introduced, and consistency constraint is carried out on the similarity relation between the target domain users before and after feature enhancement, so that the overall distribution stability of the target domain user interest structure is kept; and finally, performing interactive modeling on the final user representation and the target domain commodity features to obtain a matching relationship between the user and the commodity. The device comprises a processor and a memory. Through a collaborative modeling mode of fusing iterative denoising and structural constraint, the problems of noise interference in cross-domain recommendation and user similarity structure change before and after feature migration can be relieved, and the stability and accuracy of a recommendation result are improved.

The present invention discloses an interactive modeling and optimization platform and method for electromagnetic devices, comprising a view layer, a control layer, and a model layer. The platform and method can display options corresponding to modeling and optimization algorithms through a visual interactive interface, obtain the modeling and optimization algorithms selected by the user, receive observation variables and targets set by the user, execute parameterized modeling and optimization processes based on the modeling and optimization algorithms selected by the user and the set observation variables and targets, and display the execution results of the parameterized modeling and optimization processes in the visual interactive interface. The present invention can provide users with a visual interactive interface, through which users can select the modeling and optimization algorithms used in the modeling and optimization processes, thereby reducing repetitive programming and algorithm debugging work and improving the efficiency of electromagnetic device modeling and optimization. The present invention has broad application in the field of electromagnetic device modeling and optimization technology.

An interactive intelligent troubleshooting guiding method, system, device and medium relate to the technical field of data processing, and comprise the following steps: collecting equipment fault record data, carrying out structured processing, extracting troubleshooting knowledge, carrying out knowledge fusion and disambiguation, and storing in a fault knowledge base; providing a visual interactive modeling tool, constructing a fault tree, editing a troubleshooting guide strategy of a fault event, and automatically generating an interactive troubleshooting guide model; and constructing an intelligent troubleshooting recommendation engine, connecting fault data, and automatically recommending an optimal troubleshooting strategy and the like from fault knowledge, an interactive electronic technology manual IETM and a troubleshooting guide model. Through the intelligent troubleshooting recommendation engine, the fault knowledge base, the troubleshooting guide model and the troubleshooting strategy in the IETM are autonomously optimized and recommended, an interactive and intelligent troubleshooting guide means is adopted, and the troubleshooting efficiency and accuracy are improved.

The invention relates to the technical field of trajectory prediction and planning, in particular to a parallel iterative joint prediction planning method considering bidirectional interaction characteristics, and the method comprises the steps: constructing a bidirectional interaction modeling mechanism considering future trajectory interaction; iteratively executing interactive trajectory prediction and interactive trajectory planning based on a bidirectional interactive modeling mechanism in parallel until a preset condition is met, so as to respectively obtain a final planning code and a final prediction code; and decoding the final planning code and the final prediction code to obtain a multi-modal prediction trajectory and a planning trajectory of a planning time domain, and obtaining a final prediction trajectory and a planning trajectory according to the confidence coefficient. Therefore, the problems that the coupling relation between prediction and planning is difficult to fully reflect and the vehicle track cannot be timely adjusted according to the traffic environment change due to the fact that only the one-way influence of prediction on planning is established in the related technology are solved; therefore, the problems of conservative planning, influence on road traffic efficiency, even traffic accidents and the like easily occur in a complex dynamic scene.

An interactive modeling system based on experimentally validated computational fluid dynamics (CFD) simulations of particle transport inside anatomic reconstructions of human respiratory cavities has been disclosed. The system includes an interactive interface that displays CFD output and the projected drug delivery within anatomical respiratory airways using an interpolation / extrapolation algorithm. The projected drug delivery is displayed for different regions of the respiratory airway based on specific drug features, e.g., particle sizes, spray plume geometry, particle speeds, formulation density, and viscosity.

The invention discloses a technology debt elimination opportunity prediction method and system based on multi-modal data fusion. The method comprises the following steps: firstly, extracting technical debt features including numerical features, text semantic features and files related to Git submission records, and constructing a file covariant graph based on the submission records; secondly, performing multi-modal feature coding on the numerical features, the text semantic features and the graph structure features based on the file covariant graph; then, based on a cross-modal attention mechanism, interactive modeling is carried out on the coded numerical value features, text semantic features and graph structure features, and three paths of interactive representations are generated respectively and spliced into a joint representation; and finally, inputting the joint representation into a multi-layer perceptron regression network, and predicting a numerical value of the residual life of the technology debt through layer-by-layer calculation. According to the method, static code measurement, text semantic information and graph structure features can be fused, key factors are dynamically weighted through attention, and by means of a deep classification model, high-precision prediction of the technical debt remaining life is achieved.

The invention discloses an interactive perception trajectory prediction method for heterogeneous agents in a shared space, and the method comprises the steps: S1, encoder-historical trajectory and interactive feature extraction, applying Transform-based spatial modeling at each time step, processing historical trajectory data, and extracting and fusing the motion features of each agent and the spatial interactive features of a historical period; and S2, decoder-future trajectory iterative prediction and interactive modeling: iteratively predicting a future trajectory based on an encoder output state, and dynamically and sparsely updating interaction between the intelligent agents in the prediction process to simulate future interaction evolution. According to the method, heterogeneous characteristics can be accurately perceived, future interaction can be foreseen, and the calculation efficiency is high.