1214 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 discloses a power grid visual diagnosis system based on multi-source data fusion, and relates to the field of power grid diagnosis systems. The system comprises an information acquisition module, an electrical fault alarm module, a non-electrical quantity fault alarm module, a fault discrimination module, a communication module and a fault recording module. Firstly, information of a power grid is collected through the information collection module, then the information is transmitted to the electrical fault alarm module and the non-electrical fault alarm module for monitoring, corresponding actions are made when abnormity occurs, then the information is transmitted to the fault judgment module, input information is analyzed, and the fault type is obtained; meanwhile, the fault recording module records and broadcasts the power grid; the communication module collects information of other modules in the system and exchanges information with the monitoring system. According to the method, the abnormal information of the power grid is monitored and alarmed in combination with the multi-source data, and the fault type is judged through the data model, so that the omnibearing monitoring of the power grid is realized, and data support is provided for a subsequent maintenance plan.

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 discloses a transformer substation intelligent monitoring system and method based on digital spasm, and relates to the technical field of digital spasm, the transformer substation intelligent monitoring system comprises a transformer substation information exchange hub, the transformer substation information exchange hub is in communication connection with an operation parameter monitoring module, a fault early warning module and a data visualization module; the parameter monitoring module collects temperature and current data of a transformer substation, the fault early warning module combines a simulation model and historical and real-time monitoring data, an LSTM model is used for early warning a fault, the data visualization module displays performance, trend and alarm, an LSTM is called to predict a load, and an algorithm is used for optimizing energy consumption, cost and load distribution. Intelligent monitoring of the transformer substation is achieved through the digital spasm technology, the accuracy and the real-time performance of monitoring are improved, the fault early warning precision and the load prediction capacity are improved through the LSTM model, meanwhile, the energy consumption, the cost and the load distribution are optimized through the algorithm, and the operation efficiency and the economic benefits of the transformer substation are effectively improved.

The invention discloses a power monitoring system host security micro-isolation method and system, particularly relates to the technical field of power terminal security isolation, and aims to monitor electromagnetic interference and quantum state changes in real time and improve the stability and security of a quantum key distribution process in combination with quantum error correction and dynamic prediction technologies. A high-confidentiality encryption key is generated through the randomness of a quantum key generator, the interference level is monitored in real time in a high electromagnetic interference environment, a security protocol is automatically triggered, and parameters of a quantum system are adjusted through an active feedback mechanism; a dynamic Bayesian network is utilized to predict entanglement attenuation of a quantum state, an information exchange behavior of a quantum system is analyzed through quantum entanglement entropy, the attenuation degree and interaction intensity of the quantum state are comprehensively analyzed, and a dynamic micro-isolation strategy is implemented according to an analysis result; the reliability of quantum key distribution is improved, and the anti-interference capability of a power monitoring system in a complex electromagnetic environment is enhanced.

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.

The invention discloses an intelligent dialogue system for time sequence memory management and task distribution, which is based on CrewAI multi-agent collaboration, combines time sequence memory updating and multi-agent collaboration work, introduces an independent memory unit in order to solve the problem of low memory efficiency management, and uses an undirected graph structure to store learned information. Storing each piece of information as a node in the graph; meanwhile, memory is increased, modified and forgotten according to a time sequence, so that the problem that important information is forgotten is relieved to a certain extent; in order to solve the problem of insufficient task interaction, a multi-agent cooperation mechanism is introduced, user demand understanding, task execution and task verification are respectively submitted to three different agents for independent work, and through independent work and complete information exchange among the three agents, information synchronization and state sharing among modules are realized, information islands are reduced, and the task interaction efficiency is improved. And decision accuracy is improved.

Disclosed is an information exchange method. In the information exchange method, an electronic device is configured to display a first interface in a first display region of a display. After obtaining recommendation information, the electronic device may reduce the first display region, and display the recommendation information in a second display region. Subsequently, when detecting a first gesture operation of a user, the electronic device may open, in response to the first gesture operation, an interface corresponding to the recommendation information. According to the information exchange method, the user can conveniently view the recommendation information and directly open, based on the first gesture operation, the interface corresponding to the recommendation information.

The invention discloses a cross-scale spatio-temporal feature interactive fusion traffic flow prediction method, which comprises the following steps of: firstly, extracting short-term, periodic and long-term trend features by a spatio-temporal data feature extraction module through multi-level processing on historical time series data, accurately capturing a time trend and spatial dependence in traffic flow data, and extracting a time trend and a spatial dependence in the traffic flow data; and high-quality feature input is provided for subsequent modules. Then, a cross-scale attention mechanism is introduced into the cross-scale feature information exchange module, information interaction between different time scales and hierarchical features is achieved, the expression ability of global features can be enhanced, capturing of local detail information is also considered, and the model can efficiently process a complex space-time dependent structure; and finally, a multi-scale feature fusion module adopts an attention-based progressive fusion strategy to gradually integrate spatial-temporal features of each layer, highlight key features and generate more comprehensive and accurate feature representation, so that a more accurate prediction result is obtained.

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 discloses a multi-dimensional battle field interactive digital simulation engine construction method, which belongs to the technical field of physical simulation, and comprises the following steps: constructing a distributed engine; designing an intelligent agent distributed dynamic deployment mechanism; designing battle field partition management; designing a distributed interaction mechanism; the battle domain is a medium for information exchange and state updating among different intelligent agents. According to the method for constructing the interactive digital simulation engine in the multi-dimensional battle domain, the problem of collaboration between tens of thousands of agents and complex physical field battle domain calculation is solved, calculation precision and real-time performance are both considered, intelligent scheduling of calculation resources and dynamic agent load balancing are achieved, the resource utilization efficiency is improved, and the application prospect is wide. And the accuracy and continuity of the simulation process are ensured.

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.

A wireless power transmission method executed by a power transmitter comprising multi-coils, according to one embodiment of the present invention, comprises the steps of: detecting a second power receiver while transmitting power to a first power receiver; determining at least one primary coil adequate for power transmission; by using the determined at least one primary coil, determining whether the second power receiver supports a shared mode protocol; and if the second power receiver supports the shared mode protocol, transmitting power to the first and second power receivers according to the shared mode protocol, wherein the shared mode protocol may be a protocol for simultaneously managing information exchanges between the power transmitter and multiple power receivers.

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.

A wireless power transmission method executed by a power transmitter comprising multi-coils, according to one embodiment of the present invention, comprises the steps of: detecting a second power receiver while transmitting power to a first power receiver; determining at least one primary coil adequate for power transmission; by using the determined at least one primary coil, determining whether the second power receiver supports a shared mode protocol; and if the second power receiver supports the shared mode protocol, transmitting power to the first and second power receivers according to the shared mode protocol, wherein the shared mode protocol may be a protocol for simultaneously managing information exchanges between the power transmitter and multiple power receivers.

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 discloses a sensor coverage optimization method based on deep reinforcement learning and domain adaptive multi-task optimization. The method comprises the following steps: constructing a target function by taking the maximum sensor coverage rate, the minimum sensor radius and the minimum sensor number as targets; and solving the target function by adopting an improved multi-task algorithm to obtain the optimal coverage rate, radius and number of the sensors. According to the method, an appropriate task is selected as a source task to perform information exchange with a target task through deep reinforcement learning, so that the possibility of negative migration in the process is reduced; when knowledge migration occurs, individuals in a population are adjusted through a domain self-adaption mechanism, so that a source task is converted into a target task, and the domain self-adaption method effectively reduces the distribution difference between tasks, reduces the negative migration risk and improves the optimization efficiency. Therefore, efficient optimization of sensor network coverage is realized through dynamic task selection, feature distribution alignment and shape similarity adjustment, and the coverage efficiency of the sensor network is remarkably improved.

The exchange of resource allocation assistance information between a requester User Equipment (UE) and an assistant UE allows the requester to make better resource allocation decisions. The assistance information may include a blacklist, a whitelist, resource allocation information, a candidate resource set, and measurement information, for example. Both UEs may have an assistance configuration to assist in the exchange of resource allocation assistance information, that may be obtained from a third apparatus, such as a base station. The configuration may include information related to when to trigger assistance, identities of UEs, and types of assistance information, for example. The requester may, based on the assistance information received, modify a candidate resource set at a Medium Access Control (MAC) layer. The requester may, at the MAC layer, determine a configured sidelink grant for a selected destination using assistance information targeting this destination.

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.

A base station and wireless communication methods of inter-cell coordination signaling reduction for cross link interference (CLI) mitigation in dynamic time division duplex (TDD) are disclosed. The wireless communication method performed by the base station includes exchanging an assistance information with a plurality of neighbor / adjacent base stations based on a master-slave base station model, an inter-cell cluster information exchange, an information exchange above a threshold value, or an offline information exchange, wherein the assistance information comprises a downlink (DL) / uplink (UL) configuration information, a scheduling information, a traffic information, a CLI measurement report, and / or a sounding reference signal (SRS) measurement.

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.

Methods and devices utilizing artificial intelligence (AI) or machine learning (ML) for customization of a device specific air interface configuration in a wireless communication network are provided. An over the air information exchange to facilitate the training of one or more AI / ML modules involves the exchange of AI / ML capability information identifying whether a device supports AI / ML for optimization of the air interface.