980 results about "Information exchange" patented technology

A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.

A scalable platform for orchestrating networks of collaborative AI agents utilizing modular hybrid computing architecture. The platform integrates classical, quantum, and neuromorphic computing paradigms through hardware-accelerated translation layers and cross-paradigm coordination mechanisms. A central orchestration engine manages interactions between domain-specific AI agents, dynamically distributing workloads across heterogeneous computing cores based on task complexity, computational requirements, and resource availability. The platform employs hardware-accelerated translation between paradigms, enabling efficient cross-paradigm information exchange while maintaining semantic consistency and computation integrity across different architectures. Specialized monitoring and optimization systems continuously adjust resource allocation and fine-tune performance across computing paradigms. Advanced cache management and fault tolerance mechanisms ensure reliable operation, while privacy-preservation techniques enable secure collaboration. The platform's modular architecture supports integration of different computational approaches, enabling complex multi-domain problem solving that leverages the unique advantages of each paradigm while maintaining system-wide efficiency, scalability, and coherence.

A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.

The invention relates to a dynamic cooperative path planning and autonomous landing control method for an unmanned aerial vehicle and an unmanned ship, and the method comprises the following steps: collecting the state data of the unmanned ship and the unmanned aerial vehicle and the high sea condition environment data, and carrying out the data fusion processing; an improved fast extended random tree algorithm is adopted to plan a safe navigation path for the unmanned ship, the generated path of the unmanned ship is optimized based on an unmanned ship motion model considering environmental interference, and a model prediction control algorithm is adopted to plan a path for the unmanned ship to fly to a predicted landing point of the unmanned ship. Dynamic cooperation between the unmanned ship and the unmanned aerial vehicle is realized through information interaction between the unmanned ship and the unmanned aerial vehicle; the unmanned aerial vehicle recognizes an unmanned ship landing identifier through a visual servo guide system, calculates the position and attitude deviation relative to a landing point, adjusts flight control parameters according to interference conditions by using an adaptive control algorithm, and performs trajectory prediction compensation in combination with an unmanned ship motion model to realize autonomous landing. Compared with the prior art, safe landing of the unmanned aerial vehicle on the unmanned ship under the high sea condition is achieved.

The invention belongs to the technical field of wind power prediction, and particularly relates to a wind power prediction method and system based on multi-source data fusion. According to the method, a cascade prediction architecture composed of a wind speed prediction model and a power prediction model is constructed, and the wind speed prediction module extracts cross-variable association features of multi-dimensional meteorological parameters from numerical weather forecast data by adopting a variable-level attention mechanism and a patch-level attention mechanism; capturing a long and short time dependence mode from the actually measured historical operation data; the power prediction model decomposes a predicted wind speed sequence through one-dimensional average pooling, and constructs a nonlinear power mapping model. Besides, an attention fusion module is provided, a learnable global variable is introduced as an information exchange bridge, information interaction between meteorological data and actually measured data is realized, and the collaborative utilization efficiency of multi-source data is effectively improved. Experiments show that the method is superior to an existing prediction model based on deep learning.

Disclosed in the present invention is a general-purpose intelligent agent. The general-purpose intelligent agent comprises: an input module, which is configured to acquire a preprocessed input signal; a consciousness module, which is configured to perform perception and cognitive operations in a transparent and interpretable abstract thinking form; a self-awareness module, which is configured for perception and cognition of an intelligent agent body; a subconsciousness module, which is configured to use a data-driven model to implement perception and cognitive functions at a subconscious level; an information exchange module, which is configured to perform mutual exchange between perception, cognition and other information generated by the consciousness module, the self-awareness module and the subconsciousness module, thereby enabling integration and utilization; and an output module, which is configured to output a processing result on demand. In a machine intelligent agent of the present invention, a machine uses a general-purpose language as an underlying language for thinking and interaction, a thinking process and result are completely transparent, and thus autonomous decision-making, autonomous learning and continuous evolution are realized under fully controllable conditions. Further disclosed is a method for constructing a general-purpose intelligent agent and a general-purpose language. Compared with the prior art in which a natural language is used, the machine intelligent agent based on the general-purpose language has wide versatility in application.

The invention relates to the technical field of energy storage systems, and provides a self-adaptive power distribution method and system for multiple energy storage units of an island microgrid. According to the invention, a discrete consistency algorithm is adopted to realize coordination control of multiple units, each unit only exchanges information with adjacent units, and power distribution targets of all the units are converged to a consistent value through iterative computation, so that the communication bottleneck and the single-point fault risk of centralized control are avoided; according to the invention, a complete fault-tolerant processing mechanism is designed, the operation state of each energy storage unit is monitored in real time, topology updating and power redistribution are automatically carried out when a unit fault is detected, and the self-healing operation of the system is realized. Experiments show that compared with a traditional method, the method has remarkable advantages in key performance indexes such as capacity balance among energy storage units, system operation efficiency, communication overhead and fault recovery time, and the operation efficiency and reliability of the island microgrid system are remarkably improved.

The invention discloses a double-branch pyramid attention time sequence prediction method, which belongs to the field of time sequence prediction, and comprises the following steps of: supplementing and standardizing input sequence deletion, and embedding and aligning time covariables; a horizontal branch coding time domain and a vertical branch coding variable are dynamically fused to obtain a unified feature; multi-scale representation is formed through multi-layer convolution down-sampling, and information interaction is completed through local-cross-scale mask attention. The result is subjected to pyramid residual normalization and feedforward network updating, a global context is generated through multilayer stacking, and a linear head outputs future multi-step prediction. The complexity of the method is O (L log L), long-range dependence and a fine-grained mode are captured at the same time, the method is remarkably superior to thirteen existing models on five data sets including ECL, Exchange and Trafic, and the method has the advantages of being high in precision, high in speed and easy to expand.

The invention relates to the technical field of signal processing, in particular to an intelligent blind equalization method and device for large-scale carrier frequency deviation, and the method comprises the steps: carrying out the down-conversion of an original received signal of a preset modulation type, so as to sample an original complex sampling point sequence with large-scale carrier frequency deviation; performing local information exchange, parameter correction and splicing on the original complex sampling point sequence to obtain a compensated complex sampling point sequence; performing pre-extraction and channel amplification on the compensated complex sampling point sequence to obtain pre-extraction features containing implicit channel state information, and further extracting spatial information and time sequence dependence in the pre-extraction features to balance multipath fading of the original complex sampling point sequence and obtain balanced output; and demodulating the original bit information of the original complex sampling point sequence according to the balanced output. Therefore, the problem that a blind equalization method in the related technology cannot effectively adapt to a time-varying multipath channel and a scene with large-scale carrier frequency deviation is solved.

The invention discloses a multi-intersection traffic signal cooperative control method driven by a cross attention neural network, and the method employs a local cooperative Transform architecture, integrates a decision converter and a shared memory mechanism, and achieves the efficient modeling of a space-time dependence relation of a multi-intersection traffic state. The method comprises the following steps: firstly, through a memory head module, extracting a hidden state of each agent in a sequence modeling process, and updating global shared memory for supporting information interaction and strategy collaboration among multiple agents; and then, a cross attention module is adopted to carry out cross calculation on the local representation and the shared memory of each agent, so that dynamic perception and efficient modeling of the global state of the traffic system are realized. A backbone network of the model is based on Transform, and the understanding ability of time and space traffic characteristics is enhanced through position coding, self-attention and cross attention mechanisms. In the fine tuning stage, only the inserted Adapter module and the output layer are subjected to parameter updating.

In response to a programmatic request, configuration information representing a multi-network-segment gateway established on behalf of a customer is stored at a networking service. In response to another programmatic request, a communication session is established between a route signaling node of the gateway and a routing information source located at a customer premise. In response to additional programmatic input, the networking service stores an indication that the gateway is to be used to transfer packets between a cloud-side virtual network and a customer-side virtual network. The routing information exchanged in the session pertains to the cloud-side and customer-side virtual network, and is used to transfer data packets between the two virtual networks.

The invention discloses a prediction method based on a channel information exchange space-time diagram convolutional network, which comprises the following steps of: respectively constructing a static diagram based on priori knowledge and a dynamic diagram based on node embedding, fusing a time and space embedding matrix with self-adaptive learning capability with original data, and endowing dynamic space-time semantic information to the data. A dual-channel space-time diagram convolution module is constructed, two channels respectively use static and dynamic diagram convolution circulation unit feature extraction models, and comprehensive modeling of a data space-time dependency relationship is realized in combination with adjacent matrixes corresponding to static and dynamic diagrams. And constructing a channel information exchange module based on channel attention and a gating mechanism for controlling flow and fusion of static and dynamic information between channels, and outputting a final prediction result by using a convolutional layer. By introducing the dynamic graph of the learnable parameters and the channel attention mechanism, the prediction precision can be effectively improved, and the method is suitable for prediction of a complex topology network.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle cluster formation cooperative control method, system and device and a medium. The method comprises the following steps: establishing a dynamic model of each unmanned aerial vehicle in an unmanned aerial vehicle cluster and a dynamic model of an obstacle; establishing an information exchange topology model for describing a data interaction path between the unmanned aerial vehicles in the unmanned aerial vehicle cluster; according to each established model, designing a time-varying formation tracking protocol and a collision avoidance controller based on an artificial potential field method; all the unmanned aerial vehicles in the unmanned aerial vehicle cluster are controlled to form a preset formation which dynamically changes according to task requirements in the flight process through a time-varying formation tracking protocol, and a preset flight path which dynamically changes according to the task requirements is tracked; all the unmanned aerial vehicles are attracted to move towards a preset target point through a gravitational field of the collision avoidance controller, and all the unmanned aerial vehicles are repelled to move towards obstacles and each unmanned aerial vehicle is repelled to move towards other unmanned aerial vehicles through a repulsive force field of the collision avoidance controller, so that control over the unmanned aerial vehicle cluster is achieved.

The invention provides a multi-unmanned aerial vehicle airborne cooperative control method, electronic equipment and a readable storage medium, the multi-unmanned aerial vehicle airborne cooperative control method comprises the following steps: S1, dispersing control logic to each unmanned aerial vehicle through a distributed control architecture, and establishing communication connection between the unmanned aerial vehicles; s2, adopting a real-time communication mechanism to realize information exchange and collaborative decision-making among the unmanned aerial vehicles on the communication connection among the unmanned aerial vehicles; s3, according to the task demand, the information exchange result and the collaborative decision result, generating a collaborative control scheme and executing the task; s4, adjusting the structure of the unmanned aerial vehicle system through a dynamic reconstruction mechanism and the distributed execution task; and S5, state monitoring is carried out on the adjusted unmanned aerial vehicle system structure, and abnormal conditions are processed. According to the invention, the distributed control architecture and real-time communication capability of the ROS2 are utilized to realize efficient, reliable and extensible multi-unmanned aerial vehicle cooperative control.

The invention relates to a battery database access system and method based on Dify and a large model question and answer mechanism, and the system comprises a front end interaction module which is responsible for carrying out information interaction with a user, receiving a natural language question of the user, and transmitting the question to a rear end for processing; the Dify agent management module is responsible for task distribution and user intention recognition; the Prompt generation and scheduling module is responsible for converting the user problem into a query request which can be understood and processed by the system; the database interface module is responsible for being connected with a new energy battery database; the large model generation module is used for generating natural language answers of the user questions through a large language model; and the context management module is responsible for storing and managing context information in multiple rounds of conversations. A user can intelligently interact with the new energy battery database through a natural language, and can easily and efficiently access information in the real-time database.

The invention relates to a multi-modal image fusion method based on hierarchical semantic richness, and belongs to the technical field of multi-modal image fusion. According to the method, global information exchange is balanced by adopting multi-scale feature aggregation and redistribution, meanwhile, fusion and segmentation tasks are dynamically bridged, a progressive semantic dense injection strategy is introduced, and global semantics are injected into highly consistent infrared features through dense connection, so that the global semantic fusion is realized. And then the semantic-infrared mixed features are transmitted to visible light features. Secondly, two types of feature fusion modules are introduced, one is to use a cross-modal attention mechanism to carry out more comprehensive feature fusion, the other is to use semantic features as third input to enhance semantic representation of image fusion, and feature fusion is realized in a complex scene by dynamically balancing global semantic consistency and fine-grained local detail representation. According to the method, layering and enriching of semantic information are achieved through strategies such as semantic collection, distribution and injection, and the fusion visual effect and downstream perception performance are enhanced.

The technology disclosed relates to Artificial Intelligence techniques for sharing and managing information across organizational boundaries. Specific embodiments of the technology disclosed include the integration of LLM / LMM-based information comparison to enable alignment on a ground truth (or an agreed upon data standard) across multiple documents related to a community's data, and communicating that alignment to the users in the community. As a result, the users can update data and make amendments to documents affecting related actions among multiple community members. in order to resolve any misalignment between data and physical reality. Information exchange can be securable through verifiable credential technology.

The invention discloses a three-dimensional packaging modeling method based on NeRF and a diffusion model, and the method comprises the steps: firstly receiving demand description information, fusing packaging physical constraint conditions for joint coding, and forming a comprehensive condition vector; secondly, a cross-path dynamic information exchange mechanism is established through a geometric generation path and a texture optimization path, an initial three-dimensional packaging model is generated, physical consistency constraints are added, and NeRF is used for conducting volume rendering reconstruction on the initial three-dimensional packaging model; and then introducing an observation distance adaptive dynamic scale perception neural radiation field optimization mechanism, and generating a multi-scale NeRF model after scale perception optimization. And finally, establishing mapping of physical attributes and optical characteristics of the packaging materials, and generating a final three-dimensional packaging model through independent optical calculation and physical synthesis processes of the multi-layer materials. According to the method, the semantic understanding deviation of CLIP coding in vertical field application is solved, and the material reality representation of industrial-grade three-dimensional packaging modeling is realized.

The invention discloses a vehicle scheduling method and system based on Beidou navigation. According to the vehicle scheduling method and system based on Beidou navigation, the high-precision positioning and short message communication functions of the Beidou navigation system are utilized, the accuracy of vehicle position information and the reliability of communication are ensured, information interaction between the vehicle and the server can be achieved even in an area with poor communication signals, and the vehicle scheduling efficiency is improved. A solid data basis is provided for vehicle scheduling, scheduling optimization distribution is performed on vehicles through an intelligent optimization algorithm, reasonable configuration of vehicle resources is realized, transportation efficiency is improved, operation cost is reduced, the vehicles are monitored in real time, a scheduling scheme can be adjusted in time when emergencies occur, smooth completion of transportation tasks is ensured, and the transportation efficiency is improved. The service quality and customer satisfaction are improved, statistical analysis is carried out on transportation task data, data support is provided for subsequent vehicle scheduling optimization, scheduling strategies can be optimized continuously, and the overall operation management level is improved.

The invention discloses a multi-mode emotion recognition method and system based on prompt enhancement, a medium and equipment, and belongs to the technical field of emotion analysis, and the multi-mode emotion recognition method based on prompt enhancement comprises the steps: obtaining input data of text, visual and audio modes; the modal-based prompt encoder performs prompt template-based feature enhancement on each modal input data to generate prompt enhanced feature representation; establishing an inter-modal information exchange channel by adopting a cross-modal adaptive alignment mechanism based on prompt enhanced feature representation, and generating alignment dominant features; based on the aligned dominant features, dynamically fusing the high-quality features by adopting a multi-level quality evaluation mechanism to generate enhanced dominant features; and performing multi-modal fusion on the enhanced dominant feature and the other two modal features, and outputting an emotion prediction result. According to the method, different data missing modes are effectively dealt with, the limitation of a rigid interaction mode of a traditional method is solved, the quality and reliability differences of different modes are considered, and robust multi-mode sentiment analysis is achieved.

The present invention relates to a communication system and method designed for patients in clinical settings who face challenges using standard communication devices. This system includes a mobile application and associated devices that establish a secure, asynchronous communication channel between clinicians, patients, and their support networks. Authorized users can record and send voice messages and other media, which are automatically played on the patient's device without requiring active operation. The system is tailored for ease of use, accommodating patients with severe physical or cognitive limitations through hands-free operation and simple commands. It adapts to the patient's condition, pausing messages during rest or medical procedures and resuming when appropriate. The system also features cross-linguistic messaging, translating and synthesizing voice messages across languages while maintaining the speaker's voice. Additional features include support for rich media, automatic transcription, and AI-driven content analysis. Secure, encrypted channels ensure privacy, complying with healthcare regulations. This invention enhances emotional connection and support for isolated patients, acting as a virtual visitation tool.

The invention discloses a multi-modal face living body detection method and device based on text enhancement, and the method comprises the steps: inputting a multi-modal face image into an image block embedding module and a visual prompt generation module, and carrying out the encoding through an image encoder, thereby obtaining an image feature and a visual prompt; inputting the dichotomy text description into a text embedding module, randomly initializing a text prompt vector, and encoding through a text encoder to obtain text features and text prompts; respectively enhancing image features and visual prompts by using a hybrid expert module and a bypass prompt enhancement module; performing information exchange on the visual prompt and the text prompt by using a text enhancement module to obtain an enhanced text prompt, and performing information exchange on the image feature and the text feature by using an image mask module to obtain an enhanced image feature; and finally calculating the similarity between the image features and the text features, and taking the category corresponding to the highest similarity as a detection result. According to the invention, the method can effectively enhance the discrimination capability of human face features, and improves the detection accuracy and generalization.

The invention relates to a security data isolation and information exchange method and system based on a lightweight protocol, belongs to the technical field of industrial automation control system security, and solves the technical problems of low transmission efficiency, high analysis overhead, high security risk and high resource consumption of an existing method. Comprising the following steps: collecting and preprocessing original plaintext data at an industrial control network side; an external unit identifier, a timestamp and a pre-shared key on the industrial control network side are used as encryption factors, lightweight stream cipher encryption and packaging are carried out on the preprocessed data, and a lightweight protocol data packet is generated; through a special physical isolation device, the light-weight protocol data packet subjected to deep detection is unidirectionally isolated and ferried from an industrial control network side to an internal network side; performing post-processing on the received lightweight protocol data packet at the intranet side to restore original data; and delivering the restored original plaintext data to an intranet service application. And safe and efficient information data exchange is realized.

Aspects of this disclosure relate to an emergency alert system for reducing risk during a crisis while providing emergency services with information related to the crisis. Other aspects relate to a portable electronic device for assisting a user through a crisis or emergency. The device may be a cell or mobile phone using any form of wired or wireless communication for augmenting information exchange with relevant authorities.

The invention discloses a multi-intelligent body-assisted growth defect and rare disease intelligent analysis system based on a large language model, and relates to the technical field of artificial intelligence and medical treatment. The objective of the invention is to solve the problem of reasoning deviation of a large language model in medical diagnosis analysis. According to the system, a medical analysis process is decomposed into five steps of inquiry, potential common disease judgment, potential rare disease judgment, disease symptom query and comprehensive analysis, and the five steps are respectively and cooperatively completed by a chief complaint agent, a common disease agent, a rare disease agent, a disease background agent and an analysis agent. All agents are trained in a specific field, and through information interaction and cooperation, full-process processing from patient complaint to standardized medical language conversion, common / rare disease screening, disease background information generation and comprehensive analysis suggestion is achieved. Through intelligent agent division and cooperation, the accuracy and reliability of medical diagnosis analysis are remarkably improved, and the reasoning deviation of a single model is reduced.

The invention discloses a power plant node impedance matrix collaborative correction method based on distributed parallel computing, relates to the technical field of power plant node correction, and aims to solve the problem of poor correction effect caused by inaccurate node analysis in a power plant. Regions are divided in a hierarchical clustering mode based on topological relevance and other characteristics, dynamic optimization is carried out in combination with load balancing and other constraints, node resource accurate allocation tasks are matched, regional topological integrity is guaranteed, calculation loads are balanced, resource waste is reduced, key parameters are synchronized in time through a standardized boundary information exchange mechanism, and the reliability of the system is improved. Coupling errors caused by region segmentation are eliminated, local autonomy and boundary collaboration are achieved, local calculation pertinence is reserved, consistency of electrical characteristics among regions is ensured, global deviation is corrected by combining layered optimization, it is ensured that a matrix conforms to an electrical law, multi-reference verification and four-dimensional index evaluation are conducted, and it is ensured that results are accurate, stable and practical.

The invention relates to the technical field of printing equipment, and provides a developing agent storage box, a developing device and image forming equipment, the developing agent storage box is used for being assembled with a photosensitive assembly, and the developing agent storage box comprises a box body; a developing roller; the chip component is provided with an electric connection end; the acting component comprises a first elastic component and a second elastic component, and the first elastic component comprises a first acting part which is movably arranged on the box body and can move along a first track relative to the box body; the second elastic component comprises a second acting part which is movably arranged on the first acting part and can move along a second track relative to the first acting part, the supporting frame is provided with a limiting structure, and the limiting structure comprises a first limiting part located on the first track and a second limiting part located on the second track. According to the invention, the problem of unstable information exchange between the chip and the interior of the equipment in the existing split type developing equipment can be solved.

The invention provides a vehicle trajectory prediction system embedded with a spatial-temporal feature fusion strategy and a control method thereof. The system comprises a data time sequence decomposition processing module; a future feature reasoning module; a cross-time motion mixer module; a future interactive attention predictor module; time sequence decomposition is carried out on vehicle trajectory data through time sequence coding, short-term dynamic features and long-term trend features in the vehicle trajectory data are obtained, obtained combined feature information provides input for historical and future trajectory feature interactive modeling and trajectory prediction, and meanwhile a model is helped to achieve long-term prediction of future trajectories; the interaction of complex spaces is considered, a dynamic shuffling mechanism and a channel attention mechanism are combined, the channel attention mechanism helps a model pay attention to important interaction features, the dynamic shuffling mechanism enhances information exchange between channels, local space features and global space features are fused through deep convolution and global pooling, and the dynamic shuffling mechanism and the channel attention mechanism are combined. The generated future interaction features help the model to accurately simulate future vehicle behaviors.

A system can facilitate broadband cellular communications with user equipment via a group of antennas of the system, wherein the group of antennas comprises multiple-input multiple-output antennas. The system can determine channel state information of user equipment for which broadband cellular communications are enabled via the group of antennas. The system can purpose an antenna element of the group of antennas for energy harvesting based on the channel state information failing to satisfy an information exchange criterion. The system can harvest, via the antenna element, non-contributory interference plus noise signals as electrical energy. The system can direct the electrical energy to a processing circuit involved in an information transmission task.

The technology disclosed herein provides a digital health privacy platform (Platform) and digital health passport (Passport) that together equip consumers to avoid unauthorized surveillance as they engage with their many authorized and variously located healthcare providers in information exchange and transactions for healthcare goods and services across episodes of care for diverse health problems while managing health-related self-maintenance, legal and financial matters.