50 results about "Multimodal communication" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an anomaly detection emergency processing system and method based on artificial intelligence, and the system comprises a data collection module; a data preprocessing module; an anomaly detection module; an emergency decision module; an emergency execution module; a real-time monitoring and state feedback module; a multi-mode communication and coordination module; a man-machine interaction and visualization module; and a knowledge updating and model iteration module. The method is reasonable in design, the accuracy and timeliness of anomaly detection are remarkably improved through a multi-source heterogeneous data fusion and dynamic threshold adjustment technology, and the model robustness is enhanced in combination with incremental learning and an adversarial training mechanism; the intelligent decision-making module realizes multi-objective optimization processing by relying on a knowledge graph and a digital twinborn pre-judgment risk; redundant fault-tolerant execution and distributed consistency guarantee ensure high reliability of the system, and a man-machine cooperation mechanism considers both automation efficiency and manual intervention accuracy.

The present invention introduces a universal AI-powered pet management platform that establishes an entirely new category of technology, transcending conventional pet training systems. This comprehensive system integrates a modular wearable pet device with interchangeable sensors, sophisticated AI processing capabilities, and diverse output modules to create a unified ecosystem for holistic pet care. Unlike traditional training devices focused solely on behavior modification, this platform simultaneously manages multiple domains including real-time health monitoring, environmental safety assessment, emotional well-being analysis, autonomous training, emergency response, and seamless integration with external systems. The platform's universal architecture enables dynamic adaptation across diverse applications from companion animals to service animals, wildlife monitoring, and specialized deployments. By leveraging advanced artificial intelligence models, multimodal communication pathways, and a universal API for third-party integration, the system creates an interconnected technological framework that fundamentally transforms the relationship between pets, technology, and human interaction, rendering isolated pet devices obsolete.

An adaptive modular multimodal communication and computing panel includes a multilayer stack with a protective layer transmissive in selected bands, a reconfigurable communication layer operable in phased array, reflectarray, hybrid phased reflector, free space optical, or quantum modes, and electronics with heterogeneous processors. A multi scale interconnect and input and output fabric couples electrical, radio frequency, guided optical, and free space optical domains through interfaces including electro optic transduction and RF or baseband conversion. The fabric may implement programmable true time delay, resonators, and comb referenced timing. A management system coordinates beamforming, sensing, routing, calibration, workload placement, and security. Panels tessellate and connect by electrical, radio frequency, and fiber optic interfaces, supporting hot swappable modules, blind mate connectors, robotic servicing, and anti tamper features. Power and thermal subsystems harvest, store, regulate, and dissipate energy. The architecture scales from chip level modules to vehicle, airborne, maritime, orbital, and deployable systems.

The invention relates to the field of intelligent control, in particular to an intelligent washing control method and system based on multi-modal data, and the method comprises the steps: carrying out the time sequence alignment and feature fusion of a running state parameter set and a communication demand parameter set, and obtaining a multi-modal communication feature matrix; analyzing the coupling relationship between the dehydration stage identifier and the real-time energy consumption value of the washing equipment through a preset conflict prediction model, dynamically calculating the channel preemption priority of each washing equipment according to an instruction execution time window, and generating a clustering type communication resource scheduling sequence in combination with the priority of a control instruction; obtaining the number of instruction conflicts identified by each washing device in the dehydration stage, and constructing a load balancing optimization function with the instantaneous circuit load; and when the output value of the load balancing optimization function is minimized, obtaining a dehydration peak shifting control instruction. The problems of communication quality reduction and control delay during cooperative control of a plurality of washing devices are solved, and the timeliness and safety of control of the washing devices are improved.

The embodiment of the invention provides a multi-modal general-purpose model collaborative reasoning method based on a dynamic routing mechanism, and the method comprises the steps: receiving a task instruction and multi-modal data corresponding to the task instruction, determining a task type corresponding to the task instruction, and extracting the data features of the multi-modal data, fusing the data features through a task general model to generate general fusion features, matching a task special model of the current task type, a modal processing flow and a fusion feature weight according to a preset processing mapping relation, and transmitting the data features and the general fusion features to the task special model through a routing node, and enabling the task special model to process the data features and the general fusion features according to the modal processing flow and the fusion feature weight to obtain special fusion features, and processing the special fusion features through a task execution component to obtain a task result corresponding to the task instruction. According to the method, the defects of lack of model flexibility and the like during multi-modal data processing can be effectively solved, and the convenience degree of semantic fusion is remarkably improved.

The invention provides a multi-modal communication edge aggregation gateway system for a virtual power plant, and the system comprises a smart energy unit which serves as edge gateway equipment and is used for carrying out the bidirectional communication with a user branch load, a distributed energy source and a flexible load; the measurement monitoring unit is arranged on a user branch load loop or a user side equipment end and is used for acquiring power utilization data of a branch line and an equipment operation condition; a multi-mode communication module; and the self-adaptive protocol switching unit is used for dynamically selecting communication protocols and realizing switching among the protocols according to the communication environment parameters, the equipment types and the data transmission requirements. The invention thoroughly solves the problem that the traditional communication system cannot adapt to different application scenes and equipment type communication requirements by adopting a single communication protocol, realizes the goal of dynamically adjusting the communication strategy according to the network environment change, and effectively avoids the phenomenon that the communication quality is reduced or even interrupted.

The invention discloses a multi-mode communication and perception channel twinborn AI data set generation method. The method comprises the following steps: establishing a high-precision three-dimensional indoor scene model and an outdoor scene model; configuring parameters of a signal receiving and transmitting end and the RIS, determining dielectric constants and magnetic conductivities of different object surfaces in a scene according to a signal center frequency, and determining reflection, refraction, scattering and diffraction effects of electromagnetic waves in different media based on a geometric optical theory and a geometric diffraction theory; calculating channel parameters of each ray, generating a channel matrix and a channel intensity thermodynamic diagram based on the channel parameters, and generating a complete data set in combination with scene information; and aligning the coordinates of the scene and the ray, loading and rendering ray data and a channel intensity thermodynamic diagram based on a moving path selected by a user, and obtaining a visual file of the multi-modal channel twin AI data set. According to the invention, refined materials and electromagnetic properties are given to objects in a scene, and efficient simulation and second speed visualization of electromagnetic wave propagation are realized.

The invention provides a coal mine underground MR safety training system, which comprises an intrinsically safe MR head display device, a multi-mode communication center module, a roadway environment sensing unit, a virtual-real fusion training engine and an intelligent evaluation system, and is characterized in that the intrinsically safe MR head display device comprises an annular distributed antenna array and a wide bandgap semiconductor circuit; the antenna is embedded in an anti-collision beam of the mining helmet, and intrinsic safety is achieved through dynamic power control; the multi-mode communication central module forms a dual-channel redundant architecture by an industrial Ethernet interface and a mining wireless unit, is internally provided with a dynamic routing chip, and automatically switches communication modes according to methane concentration and dust detection values to ensure low-delay and high-reliability transmission; the roadway environment sensing unit integrates a geological radar and a micro-seismic monitoring device, constructs a three-dimensional dynamic geological model in real time, and continuously updates roof displacement data.

The invention provides an emergency response terminal system with image data return capability, which relates to the technical field of emergency response terminals and comprises an emergency response terminal management subsystem, an emergency response terminal, a multi-mode communication network, an image data return channel and an equipment state monitoring system. The emergency call terminal management subsystem is deployed on an emergency management platform and is used for generating and auditing an early warning task, dynamically selecting a communication link, interacting with a terminal through a satellite network and receiving a field image and equipment state data returned by the terminal; through the satellite, Beidou and 4 / 5G multi-link redundancy design, it is ensured that early warning information can be reached under the three-disconnection scene of network disconnection, power failure and disconnection, the problem of communication failure of a traditional system is solved, low-delay and wide-coverage transmission of the early warning information is achieved, the encrypted image return function is adopted for providing real-time disaster situation pictures for a command center, the rescue decision accuracy is improved, and the rescue cost is reduced. And the decision support capability is improved.

The invention relates to the technical field of substation equipment state monitoring, in particular to a substation switch cabinet state online monitoring device and method based on multi-source data fusion, and the monitoring device comprises a multi-dimensional sensing acquisition system, an edge intelligent processing unit, a multi-mode communication system and an intelligent early warning and decision-making system. The multi-dimensional sensing acquisition system acquires various types of operation parameters, the edge intelligent processing unit processes the parameters, the multi-modal communication system transmits data, and the intelligent early warning and decision-making system performs early warning and generates a maintenance scheme; according to the monitoring method, sensor deployment and calibration, multi-source data acquisition and synchronization, data preprocessing and feature extraction, state diagnosis and health assessment, data transmission and cloud collaboration, and early warning triggering and maintenance decision making are carried out in sequence. The switch cabinet state can be comprehensively monitored, the fault diagnosis accuracy is improved, and safe and stable operation of a power grid is guaranteed.

The invention discloses a convergence media manuscript propagation effect prediction method based on an AI technology, and the method comprises the steps: reading a to-be-predicted manuscript, carrying out the classification and processing of the to-be-predicted manuscript according to a text manuscript, a video manuscript and an audio manuscript, and carrying out the normalization processing of the feature data obtained through the processing, and obtaining a structural feature vector; according to the structured feature vector, retrieving the same type of manuscripts based on theme similarity in a knowledge base, and performing analysis to obtain a spatial-temporal feature tensor; constructing a four-dimensional propagation element incidence matrix, establishing a feature cross validation mechanism, and generating a propagation potential comprehensive evaluation vector; and by constructing a dynamic prediction model, analyzing the structured feature vector, the spatial-temporal feature tensor and the propagation potential comprehensive evaluation vector to obtain a prediction result, and generating a structured propagation value analysis report based on the prediction result. According to the method, a multi-modal propagation value prediction method combining an AI kernel and a knowledge base is used, a multi-dimensional propagation effect evaluation system is constructed, and propagation value prediction analysis is effectively provided.

The invention relates to the field of fire risk assessment, in particular to a public place fire risk assessment method and system, and the method comprises the steps: building a communication channel attenuation model through high-temperature smoke interference parameters of a target place, and calculating comprehensive signal attenuation coefficients of different spatial positions of the target place; performing dynamic interference analysis on the multi-mode communication network of the target place according to the comprehensive signal attenuation coefficient to obtain a communication system stability coefficient; and analyzing the stability coefficient of the communication system in combination with historical fire data to obtain a communication topology vulnerability map of the target place under high-temperature smoke interference, and obtaining a risk assessment result of the target place according to the communication topology vulnerability map. The risk assessment accuracy of the fire-fighting communication system under the interference of high-temperature smoke in public places is effectively improved, and the fire-fighting safety guarantee level in public places is improved.

The embodiment of the invention provides a multi-modal universal model self-evolution method and system based on incremental learning, and the method comprises the steps: receiving a current target task and multi-modal data corresponding to the current target task, and determining a corresponding trained target universal model; extracting data features of the multi-modal data to obtain multi-modal data features, and processing the multi-modal data features and the multi-modal data through a target special model to obtain a target result; historical multi-modal data features corresponding to the historical multi-modal data are obtained, and general-purpose model parameters of the trained target general-purpose model are updated; and monitoring the performance index of the trained target general-purpose model according to a preset monitoring period, and if the performance index corresponding to the current preset monitoring period does not meet the preset performance index, adjusting the trained target general-purpose model according to a preset adjustment mode. According to the method, the defect of insufficient collaboration between general knowledge and special knowledge is effectively overcome, and the collaboration effect and the updating effect of updating according to new knowledge are remarkably improved.

The invention relates to the technical field of wheelchair dynamic obstacle avoidance path planning, in particular to a wheelchair dynamic obstacle avoidance path planning method and system, and the method comprises the steps: collecting the original perception information of a pedestrian, extracting the limited representation of the action capability corresponding to the pedestrian according to the original perception information of the pedestrian, and obtaining the dynamic obstacle avoidance path of the wheelchair according to the limited representation of the action capability. Determining a corresponding preset action capability limitation level, wherein action capability limitation characterization reflects that a characteristic index influencing normal walking of the pedestrian exists; according to the action capability limited level, calling a corresponding differential response strategy from a preset response strategy library, the differential response strategy comprising an avoidance action sequence and a multi-mode communication instruction; and continuously monitoring the original sensing information of the pedestrian, and stopping executing the differential response strategy when judging that the pedestrian leaves the preset passing route of the wheelchair. The method has the advantages that differentiated avoidance and communication strategies can be provided, and the safety and efficiency of interaction between the wheelchair and pedestrians in a complex environment are improved.

The invention discloses a multi-mode communication networking method and system for an emergency medical command system, and the method comprises the steps: obtaining communication channel state data, determining a quantitative index of link stability through the analysis of the communication channel state data, and generating the probability distribution of a signal interruption risk. And according to the probability distribution of the signal interruption risk, judging the real-time guarantee degree of a link by adopting a preset threshold value, extracting a standby channel from a multi-mode communication resource pool, and generating a priority channel set. And for the priority channel set, acquiring a data transmission demand, and determining a bandwidth resource allocation scheme by calculating a data priority weight. Therefore, according to the multi-mode communication networking method of the emergency medical command system, the real-time guarantee and the complex terrain adaptability of the communication system in a disaster environment are realized, and the reliability and the effectiveness of emergency communication are improved.

A contact center server provides as part of a voice conversation with a customer device a voice prompt indicating the unavailability of any voice agent the agent devices and an option to route the voice conversation to any available chat agent at one of the agent devices. The contact center server receives a confirmation from the customer device to route the voice conversation to any available chat agent. In response to the confirmation, a multimodal communication session between the customer device and an available chat agent is established. Subsequently, the contact center server orchestrates a multimodal conversation in the multimodal communication session by speech-to-text conversion of customer messages received from the customer device, and text-to-speech conversion of agent messages received from the available chat agent with whom the multimodal communication session is established.

Systems, computer program products, and methods are described herein for integrative analysis of multimodal communication features for misappropriation detection. The present invention is configured to acquire data across multiple communication modalities including spoken communication, written text, and non-verbal cues. The invention standardizes and preprocesses this diverse data, extracting key features indicative of communication patterns. Utilizing a sophisticated machine learning model, the system analyzes these patterns to identify deviations from established norms, potentially signaling attempts at misappropriation.

The invention discloses a multi-mode communication energy storage converter cooperative control system and method, and relates to the technical field of energy storage converter cooperative control. The system comprises a signal processing module, a multi-mode communication optimization module, a multi-mode communication module, a data processing and analysis module, a control strategy module, an execution and feedback module and an interactive display module. The signal processing module collects physical signals, and characteristic parameters are extracted through preprocessing and dynamic threshold calibration; the multi-modal communication optimization module dynamically evaluates the link quality, distributes data priorities, selects an optimal communication mode and optimizes parameters; the multi-mode communication module dynamically switches a transmission path through a wired / wireless network; the data processing module analyzes the data to generate an equipment state report; the control strategy module generates an optimization instruction and issues the optimization instruction after verification; the execution module drives the power unit and feeds back data to form a closed loop. According to the invention, the problems of communication delay and low reliability of a traditional system are solved, and the multi-device cooperation efficiency and stability are improved.

This invention provides a multi-agent cooperative underwater logistics system and method based on reinforcement learning, applicable to the fields of underwater robot cooperative control and intelligent logistics technology. The system integrates six modules: task management, multimodal communication, cooperative localization, path planning, energy management, and fault handling. Through a dynamic switching mechanism in multimodal communication, it adaptively selects between acoustic, optical, and magnetic induction modes to ensure communication reliability in underwater environments. A distributed cooperative localization method based on dynamic weights is employed, optimizing weight allocation by considering communication quality, battery power, and computing power to achieve cooperative localization. A multi-agent reinforcement learning controller with a multi-branch attention mechanism is introduced to achieve real-time obstacle avoidance, formation maintenance, and task-oriented cooperative path planning. An intelligent charging strategy based on multi-level battery thresholds and task urgency supports continuous system operation. A sliding window fault detection and adaptive task reassignment mechanism enhance the system's fault tolerance and robustness.

The invention provides a multi-mode communication control method for an intelligent driving automobile, which comprises the following steps of: acquiring key vehicle dynamic parameters, including steering, braking and power system state parameters, through a vehicle-mounted sensing terminal; the parameters are transmitted to a control center and are stored and analyzed by using a cloud database. The control center processes the data by using a machine learning algorithm and calculates a real-time risk coefficient Rc. And the safety threshold Rth is determined through a steering dynamic coefficient, a braking dynamic coefficient, a power system risk coefficient and a scene adaptive weight. When the risk coefficient calculated in real time exceeds a safety threshold value, the system triggers risk early warning, gives an alarm to a driver through the HUD module, and meanwhile generates a vehicle control instruction to be displayed in the HUD module so as to reduce the risk. According to the method, the safety and reliability of the intelligent automobile under various driving conditions are improved.

System and Method for Contextual Relationship-Analytics A computer-implemented relationship-analytics system ingests time-stamped multimodal communication between a first and second human subject together with public-web persona data and first-subject annotations. Natural-language processing transforms the combined corpus into (i) an attachment-style vector that classifies anxiety-avoidance orientation and (ii) an affinity-language vector that identifies a dominant affection modality. Both vectors are stored in typed fields of an Emotional Digital Fingerprint (EDF) record. Concurrently, a wearable sensor supplies a biosignal, which is digitized to compute a normalized Balance-Load Index (BLI) tagged with a precise time-stamp. The BLI is correlated with co-occurring interaction data within a configurable window; correlation weights update the stored vectors, and a link identifier tying the BLI to its interaction subset is persisted in the EDF. An inference engine reads the updated EDF to output probabilities of long-term compatibility and incompatibility, which are transmitted to a client device for user guidance.

This invention relates to a smart device connectivity maintenance method for automatic switching of multimodal communication links, aiming to solve the problems of inconsistent state data, context drift, and limited real-time adaptability of end-side models in multi-source heterogeneous links. The technical solution uses a graph neural network model to perform multimodal standardization, dynamic topology mapping, and deep extraction of spatiotemporal features on link state data, including signal quality, service load, device movement trajectory, and environmental interference information, generating a multi-candidate link switching strategy distribution. On the end side, a lightweight temporal graph neural network is used, along with cloud-based knowledge distillation and single-step strategy alignment mechanisms, to calibrate local inference model parameters in real time, improving the accuracy and robustness of switching decisions. This solution significantly improves the real-time performance, stability, and service continuity of communication link switching, and can adaptively adjust the end-side knowledge synchronization frequency to achieve optimal communication experience under end-cloud collaboration.

Multimodal communication system for everyday use, consisting of - a physical access unit in the form of a QR code, an NFC tag or an RFID transponder, - a digital communication area uniquely assigned to the physical access unit, characterized by that a PÜ4 authentication unit is assigned to the digital communication area and that the digital communication area is accessible exclusively via this PIN authentication unit, wherein the physical access unit is connected to an access trigger unit which enables PIN-protected access to the digital communication area.

A threat monitoring system for a head-mounted wearable device is disclosed. The system includes a controller, an Internet of Things (IoT) cloud server with an IoT database generating virtual data feeds, and a plurality of physical sensors configured to sense and generate visual and radar feeds. An environment monitoring unit receives and integrates data from the sensors and virtual data feeds to generate a real-time, multi-dimensional map of the environment. An awareness unit analyzes the user's position and context to identify threat indicators. The controller analyzes user profile data, sensor feeds, environmental context, and virtual data to predict threats. Warnings are delivered to the user via a multimodal communication interface, providing directional and intensity cues through audio, visual, or haptic modalities, adaptively tailored to the user's context. The system enables proactive safety alerts for users in various activities, enhancing protection against visible and invisible threats.

The invention relates to a fraud number recognition method and system, and the method comprises the steps: carrying out the voice abnormality recognition, text abnormality recognition and behavior abnormality recognition of the obtained multi-mode communication data of each number; weighted dynamic scoring is carried out on the voice abnormity recognition result, the text abnormity recognition result and the behavior abnormity recognition result obtained by each number through a weighted fusion mechanism, so that current case-related numbers are obtained, and each case-related number is used as a core node. Constructing a case-related association network based on a core node according to the multi-mode communication data of each case-related number, performing semantic learning on the case-related association network by adopting a GAT map attention network, and generating a fraud prediction model based on the captured fraud mode and the active period of the core node; and carrying out identification and early warning on future case-related numbers through the fraud prediction model. Therefore, according to the method and the system, the fraud number is accurately identified, and meanwhile, early warning can be performed on the occurrence of future fraud behaviors.

Techniques are described for using multiplexed data flows in multimodal communication services and / or applications, e.g., emerging immersive technologies like extended reality (XR), which have seen an increase in both content applications and headset technology. An example wireless communication method includes transmitting, by a core network to a base station, information related to a multimodal service associated with multiple quality-of-service (QoS) flows. Another example wireless communication method includes receiving, by a base station from a core network, information related to a multimodal service associated with multiple QoS flows, and performing, based on the information, a subsequent communication. In these example methods, at least two of the multiple QoS flows are configured with different parameters.

This invention relates to a multimodal intelligent review method for communication documents, specifically in the field of artificial intelligence. By performing deep fusion analysis of visual and linguistic elements on images of communication bidding documents, a document knowledge network rich in semantic associations is constructed. It then simulates expert thinking for automated reasoning and verification, accurately understanding complex content such as mixed text and images, and stamped annotations. This transforms unstructured information into a computable knowledge graph and supports rule-based and vector-based intelligent retrieval, significantly improving the objectivity, accuracy, and efficiency of the review process. It achieves automatic extraction of review elements, verification of logical consistency, and deep semantic querying, providing reliable and interpretable intelligent decision support for communication project review.

The invention discloses a Polar code resource allocation optimization method and system in a multi-user scene, and relates to the technical field of channel coding resource optimization, and the method comprises the steps: generating a plurality of user channels; performing multi-mode communication accident feature deduction to obtain a communication accident portrait group; performing a Polar code resource adjustment decision to obtain a Polar code resource adjustment domain; performing guarantee priority calculation to obtain a communication guarantee priority matrix; establishing a multi-level resource adjustment optimizing framework; and executing multi-dimensional evolution joint optimization, and carrying out Polar code resource allocation optimization. The technical problems that in the prior art, Polar code resource allocation in a multi-user scene lacks pertinence and is single in risk management and control, so that communication quality is unstable, and multi-user differentiation requirements are difficult to adapt are solved, and the technical effects that Polar code resource allocation optimization in the multi-user scene is achieved, the communication quality is improved, and the multi-user requirements are adapted are achieved.

The invention provides an object-attribute-relation driven multi-modal semantic communication system, method, equipment and medium, and relates to the technical field of semantic communication, OAR processing is performed on a video frame and a corresponding description text at a coding end, and the processing results of the video frame and the corresponding description text are fused to obtain multi-modal OAR and then transmitted to a decoding end, so that the OAR is more accurate. A graph structure is constructed at a decoding end based on object actions, categories, directions and relationships among objects, and video frame reconstruction is realized by using a graph neural network and text feature fusion, so that the transmission data volume and complexity can be reduced, the communication bandwidth pressure is effectively relieved, the transmission delay is shortened, and the transmission efficiency is improved. And the robustness and the reconstruction effect in a complex scene are improved, so that the multi-mode communication requirement for 6G network application is better met.