167 results about "Interaction model" patented technology

The invention discloses a power electronic networking equipment stability analysis method, system, equipment and medium, and the method comprises the following steps: building a full-system state space model, carrying out the cross validation of the full-system state space model through employing a modal analysis method, an impedance analysis method and a complex torque analysis method, and obtaining the stability of the full-system state space model; obtaining a multi-mode coupling stability analysis result; according to the multi-mode coupling stability analysis result, constructing a power grid s domain node admittance matrix; establishing a virtual power angle dynamic equation of the network-forming converter, and optimizing control parameters according to an evaluation result; an inertia center reference system is adopted, a dynamic interaction model of the synchronous machine and the network-forming converter is established, and dynamic coupling characteristics between devices are analyzed through a relative motion equation. According to the method, the comprehensiveness, the precision and the engineering applicability of dynamic stability analysis of the novel power system are improved through fusion of a multi-dimensional collaborative evaluation framework and an innovative technical system.

The invention provides a self-organizing flight planning method based on behavior parameter dynamic adjustment, and belongs to the technical field of unmanned aerial vehicle flight planning. Each unmanned aerial vehicle obtains and updates own state and environmental threat information through a sensor, and establishes a cluster member level interaction model to interact neighbor motion states; comprehensively considering the interaction between members in the cluster and the interaction between the members and the external environment, respectively calculating an internal interaction effect and an external interaction effect, and further obtaining an overall interaction effect; on the basis of an asynchronous optimization criterion triggered by an event, online optimization adjustment is carried out on a behavior parameter set of the unmanned aerial vehicle in combination with an optimization guard interval, a flight state is predicted through a motion model, an optimal parameter is determined by means of a local performance evaluation function, a final control input driving state is generated and updated, and autonomous flight planning is realized. According to the method, the online cooperative flight requirement of the unmanned aerial vehicle cluster in a dynamic unknown environment is met.

The invention discloses a multi-modal interaction method and system, electronic equipment and a storage medium, and relates to the technical field of image understanding, and the method comprises the steps: obtaining a language instruction input by a user and a target image, and extracting an interaction content region pointed by the language instruction from the target image; inputting the language instruction, the target image and the interaction content area into a multi-modal interaction model for multi-modal information fusion processing to obtain an interaction function identifier and a basic interaction parameter; generating complete interaction parameters according to the basic interaction parameters; and calling an interaction function module corresponding to the interaction function identifier to generate an answer to the language instruction according to the complete interaction parameter, and returning the answer to the user. According to the method, the image-text and interaction content area is identified through the multi-modal interaction model, and the text is complemented through light-weight OCR, so that the time delay can be effectively reduced, and the real-time efficient response requirement of a learning scene is met.

The invention provides an entity enhancement and context-aware paragraph retrieval method for an RAG system for solving the problems of fuzzy query intention and insufficient paragraph context modeling in RAG retrieval. The method comprises the following steps: firstly, identifying and extracting a key entity by using a named entity, carrying out weighted fusion on the key entity and question representation obtained by a pre-training model to form an enhanced query vector, and accurately describing a core semantic intention of the question; secondly, performing semantic modeling on paragraphs in a document library, mining a potential semantic association relationship between the paragraphs, and constructing a context interaction model between the paragraphs based on a graph neural network and a gating loop unit mechanism, so as to obtain paragraph vector representation with complete semantics and clear hierarchy; and finally, calculating the similarity between the enhanced query and the paragraph vector, and completing high-precision paragraph-level retrieval. According to the method, the correlation and the recall rate are remarkably improved, insufficient entity utilization and weak context modeling are relieved, and good expansibility and cross-domain applicability are achieved.

The invention provides a building equipment monitoring method and system based on artificial intelligence, and relates to the technical field of building intelligent management, and the method comprises the steps: firstly obtaining a multi-source monitoring information set of target equipment in a building, including real-time operation parameters, surrounding environment perception and historical operation record information; then scene feature embedding modeling is carried out to obtain an equipment operation state scenarized interaction model; calling a pre-trained artificial intelligence scene evaluation model to carry out time sequence evolution and effect pre-judgment, and generating an equipment operation state evolution trajectory and a regulation and control effect pre-judgment result; determining an optimal regulation and control demand according to the equipment operation state evolution trajectory and the regulation and control effect pre-judgment result, and generating an equipment dynamic regulation and control instruction set; and finally, an equipment dynamic regulation and control instruction set is sent to an equipment execution module, feedback information is received, and evaluation model parameters are iteratively updated, so that intelligentization, precision and dynamic monitoring of building equipment are realized.

The invention discloses an ancient building VR education interaction system and method based on gesture control and physical feedback, and relates to the technical field of VR intelligent education. Multi-source data of a target ancient building are collected and processed through a data processing method, and then a corresponding target ancient building three-dimensional model is generated based on the processed multi-source data; establishing a VR education scene through a VR engine, establishing a target ancient building education interaction model, formulating an interaction logic rule between a user gesture and the target ancient building education interaction model, analyzing user interaction data, recording corresponding operation through the VR education scene, and setting a physical feedback signal according to the interaction data of the user; finally, interaction data of the user is recorded in real time, the learning progress, interest points and knowledge mastering conditions of the user are evaluated, the presentation mode of VR education content is dynamically adjusted, personalized learning suggestions and guidance are provided, and powerful support is provided for efficient inheritance and high-quality education of historic building culture.

The invention provides a digital human interaction action generation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: inputting multi-modal data of interaction between a target user and a digital human into a feature extraction module of an interaction model, and obtaining multi-modal features; wherein the multi-modal features comprise a visual focus feature, a voice feature and a text emotion feature corresponding to the voice feature; the multi-modal features are input into a fusion module of the interaction model, the fusion module adjusts the initial attention score of the fusion module based on the time sequence change features of the visual focus features, and the adjusted attention score is obtained; and the fusion module performs fusion processing on the multi-modal features according to the adjusted attention score to obtain multi-modal fusion features, and generates control parameters for driving at least one body part of the digital human to move according to the multi-modal fusion features.

The invention discloses an interaction model training method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a training sample set, the training sample set comprises a plurality of training samples, the training samples at least comprise first training data, second training data, and third training data, the first training data is used for representing a user emotion corresponding to a user state of the user, the second training data is used for representing a role behavior executed by a virtual role corresponding to the user under the user emotion, and the third training data is used for representing a user behavior executed by the user under the user emotion; inputting the first training data and the second training data into a to-be-trained interaction model, and obtaining a predicted user behavior output by the to-be-trained interaction model; and training a to-be-trained interaction model according to the predicted user behavior and the third training data to obtain a trained interaction model. According to the interaction model, the virtual character can provide more personalized interaction experience for the user.

The invention provides an emotional voice interaction method and device based on a humanoid robot, and relates to the field of robot interaction. The method comprises the following steps: acquiring voice data of a target user through a humanoid robot, and extracting acoustic features and semantic features in the voice data; inputting the acoustic features and the semantic features into a cross-modal fusion emotion classification model, and outputting an emotion recognition result corresponding to the target user; determining an emotion recognition misjudgment risk, and correcting an emotion recognition result based on the emotion recognition misjudgment risk; inputting the corrected emotion recognition result and the semantic feature into an emotion voice understanding interaction model, and outputting an emotion voice interaction result; and controlling the humanoid robot to execute a corresponding multi-modal interaction response based on the emotion voice interaction result. The problem that the voice interaction experience is greatly reduced due to the fact that the voice interaction content output by an existing humanoid robot is difficult to fit the real emotional state of the elderly user in multiple aspects such as text expression and voice emotion is solved.

The invention belongs to the field of artificial intelligence, and particularly relates to a voice interactive intelligent terminal emotion adaptation system for teenager users. According to the system, accurate recognition, dynamic modeling and active guidance of adolescent emotions are realized through five units, namely, multi-modal emotional feature perception, developmental stage psychological modeling, nonlinear emotional evolution prediction, adaptive interaction strategy decision and feedback closed-loop regulation. According to the system, a development psychological theory is fused, acoustic, semantic and non-verbal features are combined, a personalized emotion interaction model is constructed, real-time and low-delay flexible emotion response is completed on the premise that privacy is guaranteed, and the affinity of human-computer interaction and the mental health supporting capacity are effectively improved.

This invention provides an active flow control method and system based on intelligent algorithms. The method includes: determining the number of inlet parameter sets for a typical flight state point, constrained by simulation economy and sampling efficiency; performing numerical simulations according to the number of inlet parameter sets to obtain dataset 1, with inlet parameters as independent variables and flow field characteristics as dependent variables; obtaining dataset 2, with flight state as independent variables and flow field characteristics as dependent variables, through numerical simulation for a certain set of inlet parameters; constructing a proxy model of inlet flow field characteristics for a typical flight state point using deep learning based on dataset 1; constructing a proxy model of inlet flow field characteristics for a wide-speed-domain flight state based on the proxy model of inlet flow field characteristics for the flight state point, using a transfer learning algorithm and dataset 2; constructing an environment-agent interaction model based on a deep reinforcement learning algorithm, using the proxy model of inlet flow field characteristics for a wide-speed-domain flight state as the environment model for deep reinforcement learning; setting optimization termination conditions based on the environment-agent interaction model, and obtaining a control sequence with continuously optimal inlet performance under different flight states through deep reinforcement learning. This invention can stably adapt to flight scenarios with a wide speed range and a large airspace, and achieve real-time optimal control of the air intake flow field under a wide speed range, solving the problem of poor generalization ability of traditional methods under different operating conditions.

The invention relates to a complex mechanical control instruction interaction method based on natural language processing, and the method comprises the steps: carrying out the construction of a knowledge base through the combination of information classification and automatic extraction, constructing a large-scale pre-training model based on the knowledge base, carrying out the fine adjustment of a model based on the control instruction classification and entity node relation information, and carrying out the recognition of the model. Then, an operator control instruction is obtained, the number of matched vocabularies and the importance degree of the vocabularies in the operator control instruction are counted and analyzed through the interaction models, an optimal execution scheme is determined, a control instruction is generated, and the mechanical execution process is controlled. The invention further relates to a complex mechanical control instruction interaction system based on natural language processing. Accurate component operation instructions can be automatically generated according to inaccurate language text information given by operators, and the operators can conveniently issue new instructions through natural languages to perform task re-programming of control programs.

The invention relates to an interactive training data generation method and device, vehicle-mounted equipment, a storage medium and a program product. According to the method, historical dialogue data of a vehicle user is obtained, a virtual user model corresponding to the vehicle user is constructed according to the historical dialogue data, and then the virtual user model is utilized to initiate multiple rounds of dialogues to an initial intelligent interaction model. And after each round of conversation, adjusting an emotion label of the virtual user model according to reply information of the initial intelligent interaction model, generating conversation content of the next round based on the adjusted emotion label, and then generating interactive training data according to conversation data and corresponding emotion labels in the multi-round conversation process, the interactive training data is used for training the initial intelligent interactive model. According to the method, massive interactive training data with emotion tags can be generated, and thus the intelligence of an intelligent interaction model trained through the interactive training data can be improved.

The application relates to an unmanned vehicle adaptive path planning method based on a dynamic window method and a proximal strategy. First, an agent-environment interaction model for an unmanned vehicle is constructed, a proximal strategy optimization learning (PPO) model based on an actor-critic framework is established, a reward function is defined according to the principle of a dynamic window method algorithm (DWA) and main evaluation elements, model parameters in the input layer, the output layer size, the number of hidden layers and the number of neurons are determined, and a DWA-PPO deep reinforcement learning model is constructed. Then, the established DWA-PPO deep reinforcement learning model is used for iterative training, and finally, a network model capable of representing the potential relationship between surrounding environment information and evaluation function weight parameters is converged, and the construction of the adaptive PPO-ADWA algorithm is completed. Finally, the feasibility and effectiveness of the unmanned vehicle adaptive path planning strategy based on the PPO-ADWA are verified through simulation comparison experiments.

The present application relates to the field of OTN resource matching, and more particularly to an OTN resource intelligent matching method based on artificial intelligence, comprising: inputting the preprocessed service demand, available wavelength resource, real-time bandwidth utilization and support OTU level into a feature extraction model based on a residual multi-layer perception architecture to generate a prediction base feature; inputting the prediction base feature into a feature interaction model based on a compressed interaction network architecture to generate a service comprehensive interaction feature; and inputting the service comprehensive interaction feature into an OTN resource matching model based on an expert gate network architecture to generate a recommended OTU level. Through the hierarchical intelligent model processing of the residual multi-layer perception architecture, the compressed interaction network architecture and the expert gate network architecture, the present application realizes the significant improvement in the accuracy, efficiency, adaptability and network operation value of OTN resource matching.

A computer-implemented method for transforming security advisories into targeted actionable mitigation strategies is provided. The method includes receiving a security advisory describing a vulnerability, processing the security advisory using a first language learning model (LLM) to extract vulnerability characteristics, generating an interaction model of sub-systems within a target system based on a topology of the target system, generating at least one actionable mitigation strategy for the vulnerability using a fine-tuned second LLM based on the extracted vulnerability characteristics and the interaction model, and outputting the actionable mitigation strategy as a security enhancement implementable on the target system. The method enables customized security enhancements for systems lacking official patches or manufacturer support.

This invention discloses a human-computer interaction method based on intelligent data analysis, belonging to the field of human-computer interaction technology. This invention collects user interaction actions on various interactive content, obtains multiple interaction records, constructs a user behavior dataset, adjusts the decay of interaction record data based on the regularity of the interaction content, and obtains the retention weight of interaction records based on the frequency of interaction actions, updating the user behavior dataset and the human-computer interaction model. The aim of this invention is to improve the efficiency of user interaction with the human-computer interaction model by updating the user behavior dataset and the human-computer interaction model, enabling the user behavior dataset to more accurately reflect user interaction preferences.

The embodiment of the invention provides an information processing method and device, and the method comprises the steps: obtaining a logistics information processing request, and determining a processing type corresponding to the logistics information processing request; calling a target interface corresponding to the processing type, obtaining logistics information associated with the logistics information processing request, inputting the logistics information and the logistics information processing request into an interaction model for processing, and obtaining multiple pieces of candidate feedback information; according to user feedback information associated with the logistics information processing request, target feedback information is determined in the multiple pieces of candidate feedback information, and the interaction model is optimized into a target interaction model according to the target feedback information. The external information and the user query are input into the model together, so that the model can be combined with the external information for model prediction, the model prediction accuracy is improved, the model is optimized online through the feedback information, the model has the self-updating capability, and the model adaptability is improved.

The invention provides an autonomous ship test scene optimization generation method based on multi-agent game interaction, and the method comprises the steps: recognizing a navigation scene from ship navigation historical data based on ship navigation scene features, and extracting ship manipulation behaviors, so as to correspondingly obtain a navigation scene database and a ship behavior database; constructing a ship game interaction model based on a preset initial test state of surrounding ships, and solving the ship game interaction model through a Nash equilibrium strategy to obtain manipulation behaviors of the surrounding ships; performing strategy optimization on the manipulation behaviors of the surrounding ships by using a multi-agent depth deterministic strategy gradient model; and on the basis of a pre-constructed ship navigation test scene optimization model, closed-loop optimization is carried out on the optimized manipulation behavior through the environment state transfer function so as to iteratively form a continuous navigation test scene. According to the invention, diversity, complexity and efficiency of autonomous ship navigation decision-making capability can be effectively improved.

The application discloses an import and export commodity intelligent classification method based on knowledge graph metadata topology, relates to the technical field of reinforcement learning, and comprises the following steps: inputting initial data packets into a dynamic interaction model, performing explicit association mining in a semantic enhancement layer, optimizing rule matching paths in a rule evolution layer, and constructing a dynamic commodity knowledge graph; performing topology structure derivation on the dynamic commodity knowledge graph, generating a graph topology analysis report and a metadata list, integrating knowledge reasoning on the graph topology analysis report and the metadata list, and generating an intelligent navigation engine; calling the intelligent navigation engine to perform multi-path semantic query and rule verification on the dynamic knowledge graph, generating a candidate classification scheme set; performing multi-objective collaborative optimization on the candidate classification scheme set, generating a sorting scheme sequence, performing traceability packaging on the sorting scheme sequence, and outputting an intelligent classification scheme. The application improves the rule adaptation efficiency in complex scenarios through the dynamic interaction model and multi-objective collaborative optimization.

The invention relates to the technical field of data processing, and discloses an interaction optimization method and device based on expectation confirmation, equipment and a medium, which can be applied to the financial and medical fields, and the method comprises the following steps: carrying out vectorization modeling processing and user satisfaction evolution modeling processing on user historical interaction data to obtain a satisfaction evolution object; performing recognition correction processing on the satisfaction evolution object to obtain a preference correction object; and performing expectation confirmation feedback processing and preference optimization processing on the preference correction object to update interaction model parameters, and generating an interaction result object. In the invention, aiming at the problem that the existing large language model cannot dynamically capture the trend of the user satisfaction, preference optimization processing can be carried out on the calculated expected confirmation feedback object to update the interaction model parameters, so that the corresponding interaction result object is generated, and therefore, the user satisfaction degree can be dynamically captured. Therefore, the big language model can dynamically capture the trend of user satisfaction, and the model output efficiency is improved.

This invention relates to a virtual reality landscaping simulation method and system integrating dynamic navigation. The method first constructs a digital twin model of a campus ecosystem, including various ecological entities and their quantified state parameters; establishes an ecological interaction model, defining and quantifying the type and intensity of interaction relationships between any two entities through mathematical functions; and generates a visualized ecological relationship network diagram based on this model. Users intervene in the model within the VR environment by editing commands on the network diagram, and the system translates these commands into modifications to the interaction model. Subsequently, the modified model is driven to perform dynamic ecological simulation calculations, obtaining the process of ecosystem state changes triggered by the editing operations. Finally, a dynamic visualization of this change process is generated and displayed in the VR environment. This invention achieves quantitative simulation and intuitive dynamic display of the ecological effects of greening schemes, enhancing the ecological intelligence and decision support capabilities of VR design tools.

This invention relates to the field of data analysis technology, specifically a cross-platform marketing data fusion and analysis system, comprising: an interaction modeling unit configured to acquire cross-platform marketing data of user groups under authorized status; and a heterogeneous interaction graph constructed based on the cross-platform marketing data. The heterogeneous interaction graph uses user nodes, interest tag nodes, product nodes, and loyalty level nodes as node types, and user-interest association, user-product association, user-loyalty level association, and user-user social association as edge types. Nodes carry attribute features corresponding to their type, and edges carry interaction strength attributes. This invention utilizes a heterogeneous graph neural network to propagate messages along meta-paths and dynamically weights type-aware attention to generate multimodal embedding features for each user. This fully leverages the multidimensional information of interest, purchase, loyalty, and social aspects in the cross-platform data, significantly improving the expressive power and semantic richness of user representations.

This application discloses a method and system for interpreting sparse interaction utility modeled by a black-box artificial intelligence model, relating to the field of machine learning technology. The method and system can automatically analyze the interaction distribution modeled by the model. Implementation of the method and system includes the steps of providing data that is essential for evaluation; predicting the data using a black-box model and obtaining the model's prediction results; modeling the interaction effects between sample input units based on the output of the black-box model, calculating the interaction intensity of the combinations formed between the input units, representing the black-box model as "and additive relationships" and "or additive relationships" between combinations of input units; and optimizing the "and additive relationships" and "or additive relationships" to make them more sparse. The advantage of the present invention is that it provides a quantification method for interpreting interactions modeled by a black-box artificial intelligence model, enabling a more sparse and concise interpretation of interactions compared to conventional studies.

The application discloses a kind of training method of multi-agent interaction model, multi-agent interaction method and equipment, wherein training method includes: when the target agent of being navigated needs to obtain next hop path, the position of target agent is positioned according to the object feature observed by target agent using the positioning network of model by navigation agent;And using the path planning network of model, the shortest path between the position and target position is determined;Navigation network of model is used to navigate target agent, including: the path navigation language of next hop path is generated for target agent according to the shortest path and is sent to target agent, to trigger target agent to predict next hop moving path according to path navigation language;After reaching target position, based on the output result of corresponding module in positioning network and navigation network of navigation agent and target agent, loss function value is calculated and is used to optimize model. Efficiently complete navigation task using the application and low cost, strong generalization.

The invention relates to the technical field of voice interaction, and discloses a voice interaction model optimization method and device, equipment and a medium, and the method comprises the steps: receiving a privacy enhancement parameter set from a plurality of terminal equipment; wherein the parameters in the privacy enhancement parameter set are obtained by sequentially performing screening and privacy protection processing on model parameters in the voice interaction model by the terminal equipment; performing cross-terminal knowledge fusion on the privacy enhancement parameter set to obtain a global teacher model; performing forward calculation on the preset reference data set by using the global teacher model to generate a soft label containing probability distribution information; and issuing the soft label to a terminal device, thereby enabling the terminal device to perform distillation optimization on the voice interaction model based on the soft label, and obtaining an optimized voice interaction model. The method can be applied to financial science and technology or medical care service program systems, and can improve the understanding accuracy, scene adaptability and privacy protection intensity of the intelligent voice interaction model.

The application provides a water quality analysis method and system for a receiving lake in a diversion project combined with multi-source data fusion, and relates to the technical field of water resource management and analysis. Firstly, field basic data is collected and a water quality correlation field is constructed. Then, interaction relationship modeling operation is performed on the field units to obtain a field unit interaction model. Based on the field unit interaction model, dynamic coupling fusion processing is performed on the field basic data to generate coupled water quality data. Then, the water quality correlation field change trend data and the coupled water quality data are combined to deduce the water quality trend evolution path of the receiving lake. Finally, the core interaction unit is identified, and a dynamic regulation and control scheme for the water quality of the diversion project is generated in combination with the trend evolution path. The application can comprehensively analyze the water quality of the receiving lake, realize accurate dynamic regulation and control, and ensure the stability of the water quality.

The application relates to the technical field of electroencephalogram monitoring and data processing, in particular to an interactive model training method and device based on extended reality and sensing analysis, which comprises the following steps: constructing a dynamic virtual world according to virtual world generation parameters; acquiring electroencephalogram data of a user when the user is performing interactive training in the dynamic virtual world, and at least one of physiological sensing data and behavior sensing data; and determining the interactive training effect of the user; in a cyclic iteration process, when the interactive training effect does not satisfy a convergence condition, adjusting the virtual world generation parameters according to a preset prediction model, and continuously constructing a new dynamic virtual world according to the adjusted virtual world generation parameters until the interactive training effect satisfies the convergence condition, ending the cyclic iteration process, and obtaining an interactive model through training. The interactive training scheme based on virtual reality and electroencephalogram can trigger the interactive response of a user in an immersive and personalized environment, so that the effect of 'virtual adjustment' is achieved.

An online system configures one or more system AI agent instances that interact with user AI agents and performs one or more tasks on behalf of the online system. Thus, responsive to detecting the presence of a user AI agent representing a particular user, the online system directs the session for the user to communicate and interact with a system AI agent.

The disclosure provides a digital human generation method based on a multi-modal large model, comprising: constructing a digital human basic model; generating a structured training set; generating a question and answer model supporting multi-channel interaction; outputting a semantic answer to a user question, extracting a text emotional tendency of the semantic answer, and outputting an emotional intensity parameter; generating facial muscle movement trajectory data, and performing real-time rendering on the digital human basic model according to the facial muscle movement trajectory data to output a digital human three-dimensional image with emotional expression. Through cross-modal alignment of text, image and audio data, and by using a joint training method to optimize a multi-modal large model containing a visual, speech and knowledge model, a more natural and smooth multi-channel interaction experience is realized. In addition, by introducing an emotional recognition model and a facial interaction model, the emotional tendency implied in the semantic answer can be more accurately captured and reflected, thereby outputting a digital human three-dimensional image with real emotional expression.