66 results about "Distributed collaboration" patented technology

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

The invention belongs to the technical field of urban power grid safety control, and particularly relates to a source-load-storage modeling method and a power grid voltage control method and system. The method comprises the following steps: carrying out fine modeling of photovoltaic, energy storage and classified loads, collecting equipment operation state data and identifying parameters; building a centralized coordination-distributed coordination double-layer control architecture, deploying a centralized coordinator and a distributed coordinator, and defining functions; measuring topology, source load real-time and historical data of the power distribution network under the control architecture, and executing dynamic cluster division; based on initialization of a historical optimal solution database, an ADMM distributed optimization algorithm is adopted to calculate the cluster total power adjustment amount of each cluster; and based on the cluster total power adjustment amount, the voltage deviation factor and the resource margin factor, power distribution of the photovoltaic reactive adjustment amount and the energy storage active adjustment amount is carried out on equipment connected with the nodes in combination with the model and the model parameters. And due to refined modeling, a basis is provided for voltage control, and the accuracy and reliability of control can be improved.

This invention provides a method for reconstructing actuator faults in a heterogeneous multi-agent integrated energy system, relating to the field of fault diagnosis. The invention establishes a unified dynamic model for the agents; defines intermediate variables and constructs their dynamic equations; builds a global error dynamic system for distributed intermediate observers and agents; derives the stability criterion for the global error system based on Lyapunov stability theory, obtaining the state observer gain matrix for state estimation; loads the state observer gain matrix and the gain matrix for state estimation into each agent; and during the real-time operation of the agents, obtains the current state vector estimate and intermediate variable estimate, and calculates the reconstructed actuator fault value. This invention utilizes an output space coupling mechanism, enabling distributed collaboration between different energy networks without requiring a unified model dimension, thus breaking down the physical barriers to actuator fault reconstruction in integrated energy systems.

The application relates to a communication-optimized distributed cooperation method, and belongs to the field of machine learning. Firstly, the relative entropy is calculated based on the difference between the global model of the last round and the local model of the current round, and whether the current model layer is transmitted is judged according to the result of the relative entropy; then, different quantization levels are specified by the server under different rounds to realize time-adaptive quantization, and different quantization levels are continuously specified for different clients under the same round to realize client-adaptive quantization, so that the problem of excessively high communication cost in a bandwidth-limited environment is solved. In addition, on the basis of normal local training of the client, the situation of frequent accidental interruption in the transmission process is considered, and when the geographical position of the client continuously changes, the federated learning can still be normally trained.

This invention discloses a hardware-in-the-loop (HIL) simulation platform for load cluster tracking control. The master station simulation module simulates the issuance of load cluster control aggregation commands. Any smart energy unit module decomposes the load cluster control aggregation commands according to a distributed collaborative control algorithm, achieving distributed collaboration among each smart energy unit. Any adjustable load in each adjustable load cluster decomposes the load cluster control commands according to the distributed collaborative control algorithm, achieving distributed clustering of each adjustable load in the adjustable load cluster. Each adjustable load calculates its current adjustment amount based on the received load control commands and adjusts its operating state in real time according to this adjustment amount. This invention employs a distributed collaborative control strategy on the HIL platform and uses a synchronization mechanism to ensure the continuity of the distributed strategy, improving the system's stability and robustness.

This invention discloses a distributed intelligent lighting control method and system based on edge computing, belonging to the field of intelligent lighting technology. The system includes a distributed lighting node module, an edge computing node module, a cloud collaboration module, a multimodal sensing module, a communication module, and a user interaction module. These modules are deployed in a distributed manner, achieving an organic combination of localized control and cloud-based collaborative management. The multimodal sensing module collects multi-dimensional data such as illumination, personnel, temperature, and humidity within the area. The edge computing node module completes lighting control decisions through localized computation. This invention also discloses the corresponding control method, including steps such as initialization, data acquisition and preprocessing, localized control, distributed collaboration, and cloud optimization. This invention solves the shortcomings of existing systems, such as high response latency, poor fault tolerance, low control accuracy, and high energy consumption, achieving precise, rapid, and energy-saving lighting control, improving system stability and scalability, and adapting to various application scenarios.

The application provides a smart logistics and warehouse control processing method and system. The method detects the power grid and communication state in real time through an independent monitoring device, and immediately broadcasts an emergency activation signal once an anomaly occurs; an edge computing node receiving the signal switches to emergency power supply and activates a low-power communication module to form a regionalized emergency collaborative subnetwork; each AGV reports its own state to the subnetwork master node, and the master node generates a candidate set containing multiple emergency tasks for each AGV based on global information; and the AGV selects and executes the optimal task according to the preset decision model and its own state. The method realizes seamless switching from centralized control to distributed collaboration, ensures that the system can still operate in an orderly manner under sudden failure, and avoids system-wide paralysis; through regional networking, the state sharing and task collaboration among AGVs are realized, and the emergency efficiency is improved.

The invention discloses a phase synchronization method and system for distributed cooperative nodes, and relates to the technical field of wireless communication. The method comprises the following steps: each distributed cooperative node generates a transmitting phase weight of a current time slot and cooperatively transmits a signal to a target node, wherein the transmitting phase weight is only composed of a reference phase and a disturbance phase; when each distributed cooperative node generates a next time slot disturbance phase, a deterministic inversion mechanism based on a reverse state mark is introduced, a phase disturbance step length is adaptively and dynamically adjusted by using a hysteresis type double counter, iteration is carried out until each node phase converges, and the next time slot disturbance phase is generated; the problems of reverse response delay, high calculation overhead, poor step length stability and the like in the prior art are solved. The reverse operation can be triggered without waiting for continuous failure accumulation, the phase calculation architecture is simplified, the node storage and calculation burden is reduced, meanwhile, the stability and convergence precision of phase disturbance step length adjustment are improved, and the phase synchronization requirement of the distributed cooperative beam forming system can be effectively met.

The embodiment of the invention provides a personnel resource allocation method and system based on real-time data driving, and belongs to the technical field of scheduling optimization. The method comprises the following steps: acquiring original behavior data of each person based on a butted process data source, and generating a real-time load vector for representing a person load state based on the original behavior data of each person; constructing a task dependence directed graph based on task management data of a to-be-executed task, and establishing a mapping relation from task nodes to personnel nodes based on the task dependence directed graph; predicting cross-regional communication delay among the personnel, and inputting the communication delay, the real-time load vector and the task dependence directed graph into a deployment model to generate at least one candidate deployment scheme; and evaluating the efficiency score of each candidate allocation scheme by adopting a heuristic search algorithm, and outputting the allocation scheme with the highest score to an execution interface. According to the invention, the real-time performance and effectiveness of personnel resource allocation in a distributed cooperation scene are effectively improved.

The invention discloses a lightweight batch authentication method based on long-term and short-term historical information fusion, and the method comprises the steps: collecting and recording long-term and short-term historical information with cooperation time parameters in a process that edge equipment executes a distributed cooperation task; establishing a memory bank dynamic updating mechanism based on comprehensive storage scores, and performing self-adaptive management on collaboration historical data; selecting historical information fragments which are uniformly distributed in time and are difficult to eavesdrop, predict and analyze by a third party from the memory bank by adopting a time hierarchical selection strategy as authentication information; performing linear transformation on local parameters in the authentication information by utilizing dynamic linear transformation to generate behavior tokens, and broadcasting the behavior tokens to group members; and after receiving all the tokens, the edge device reversely restores the local parameters locally, reconstructs the global parameters, and determines whether the authentication is passed by analyzing whether the global parameters are consistent with the global parameters in the authentication information. According to the invention, the calculation and communication overhead of edge cooperation authentication is effectively reduced, and the security and efficiency of authentication are improved.

The invention discloses a tea garden early warning management system based on big data analysis, which relates to the technical field of tea garden early warning management and comprises a data acquisition module, a feature set generation module and a distributed analysis module. According to the method, a distributed collaborative learning framework is constructed, each tea garden locally trains a model and only uploads encryption parameters to the cloud, and a universal knowledge base is formed through fusion, so that a single tea garden can use experience of multiple regions for reference, and the early warning capability for novel diseases and insect pests and complex water stress is remarkably improved; the system realizes strategic transformation from passive response to active prevention and control by combining a self-evolution risk assessment rule and a near-end strategy optimization algorithm based on multi-source data acquisition and a spatio-temporal joint perception model, can generate future risk prediction, provides decision support for preventive management, forms a closed-loop optimization mechanism through strategy execution feedback, and improves the risk assessment efficiency. The early warning precision and decision effectiveness are continuously improved, and the intelligent level and the anti-risk capability of tea garden management are comprehensively enhanced.

In various examples, override-based data change propagation for distributed collaborative content creation platforms is provided. An override layer may be generated that comprises a representation of a sequence of override primitives that may be applied against a baseline universal scene description (USD) scene definition layer to render an updated / current version of the scene. A digital content creation application may record edits as modifications (overrides) to scene definition primitives are applied. A CCA may propagate the edits to other CCAs at other client nodes subscribed to the CCCP by sending a new layer message to the server of the CCCP, instructing the server to add the override layer to the scene graph of a root USD file. Other CCAs subscribed to the root USD file receive a message from the server that indicates that the new override layer has been added, and may selectively apply propagated override primitives.

This application discloses a message processing method, execution entity, system, medium, and product. The message processing method is applied to any execution entity in an execution entity pool, including: for a first OSPF message sent by a neighboring device and distributed by a forwarding device, determining whether to inject the first OSPF message into the kernel protocol stack through an OSPF proxy deployed in the execution entity; for a second OSPF message sent externally by an OSPF application, determining whether to send the second OSPF message externally through a forwarding device through an OSPF proxy deployed in the execution entity. The above technical solution adopts a peer-to-peer architecture and a distributed collaboration mechanism, deploying an OSPF proxy in each execution entity, enabling any execution entity to have message processing capabilities, achieving fault redundancy between nodes, ensuring that the failure of a single node will not affect the overall OSPF service continuity, and improving the reliability and availability of the OSPF protocol stack in a heterogeneous execution entity environment.

This invention discloses a method, system, and medium for street light leakage current monitoring and protection based on distributed collaboration. The method includes: synchronously collecting raw leakage current data through a multi-circuit detection device and a distributed detection terminal, and preprocessing it to obtain dual-end preprocessed data; an edge computing unit performing local fusion, anti-interference filtering, and key feature extraction on the dual-end preprocessed data; when the collaborative verification trigger condition is met, dual-end collaborative verification is executed: a cloud platform performs a risk pre-assessment based on the dual-end preprocessed data and environmental factors, generating a pre-assessment result; when the edge computing unit determines that a local high-risk threshold has been reached, it calls the pre-assessment result and cross-verifies it with locally cached historical data from the light pole side to generate a collaborative verification result; the edge computing unit executes circuit breaker protection based on the collaborative verification result, or performs graded early warning based on key features. This invention significantly improves the safety and operation and maintenance efficiency of street light power supply systems.

The application discloses a kind of photovoltaic external sending system resource mutual aid optimization method and system to extreme low power, method includes: obtaining sending end photovoltaic output data, judge whether to satisfy extreme low power scene criterion;Flexible resource model and receiving end multivariate load aggregator model are constructed;The double-layer collaborative response architecture of benchmark layer and mutual aid layer is constructed, benchmark layer is guided by time-of-use electricity price Load cross-period transfer forms benchmark plan, mutual aid layer with dynamic shadow price drives each load aggregator to carry out secondary mutual aid adjustment;With dynamic shadow price as coordination signal, establish distributed solution framework of alternate direction multiplier method and carry out iterative solution, each load aggregator independently solves local subproblem and only reports power decision, until supply and demand residual error converges;The scheduling scheme of each flexible resource and mutual aid power allocation scheme are output.The optimal cross-zone configuration of energy in extreme scenario is realized by scarce pricing and distributed collaboration, improve system supply capacity and protect the privacy of each subject.

The invention discloses a distributed filling scheduling control method and system based on a knowledge graph, and the system comprises a knowledge graph construction module which is used for mapping a filling production object into an entity and a relation, and writing the entity and the relation into a graph rule; the edge data acquisition and processing module is used for forming a time sequence observation sequence and synchronously updating the knowledge graph fact layer; the change point detection module is used for executing Bayesian online change point detection to obtain a change point event; the map reasoning verification module is used for generating a feasible action set and an executable parameter set; the scheduling generation module is used for constructing an improved GlowNet model and outputting a scheduling scheme; and the instruction issuing and rescheduling module is used for issuing the instruction to an edge node for execution and triggering incremental rescheduling on an unexecuted part. According to the method, the incremental rescheduling closed-loop control of an executable scheduling scheme and an unexecuted part is realized through Bayesian online change point detection and improved GlowNet distributed coordination.

This invention belongs to the interdisciplinary field of machine learning and data center thermal management, specifically disclosing a machine learning-based intelligent control method for data center cooling systems. This method configures each cooling unit as an intelligent agent with perception and decision-making capabilities, achieving distributed collaboration through a digital pheromone communication mechanism. It autonomously adjusts the opening of air and water valves based on pheromone concentration gradients and optimizes energy consumption and temperature deviation using a local reinforcement learning model. In the event of a fault, neighboring agents can automatically identify the anomaly and take over the cooling task. Through the above technical solution, this invention achieves sub-second real-time control, highly reliable self-healing operation, and a significant improvement in cooling energy efficiency.

The present disclosure belongs to the technical field of urban power grid safety control, and particularly relates to a source-load-storage modeling method, a power grid voltage control method and system. The method comprises: carrying out fine modeling of photovoltaic, energy storage and type-specific load, collecting equipment operation state data and identifying parameters; building a double-layer control architecture of centralized coordination-distributed collaboration, deploying centralized coordinators and distributed collaborators and defining functions; under the control architecture, measuring power distribution network topology, source and load real-time and historical data, and performing dynamic cluster division; based on historical optimal solution database initialization, using ADMM distributed optimization algorithm to calculate cluster total power adjustment of each cluster; based on cluster total power adjustment, voltage deviation factor and resource margin factor, combining the model and model parameters to perform power distribution of photovoltaic reactive power adjustment and energy storage active power adjustment on the equipment connected to the node. Due to fine modeling, the voltage control is provided with a basis, and the accuracy and reliability of the control can be improved.

This invention relates to an interactive AI sound effect generation system based on a diffusion model and its multi-user collaborative method, belonging to the field of artificial intelligence audio generation technology. This method breaks through the real-time bottleneck of AI sound effect generation, realizes a seamless multi-user collaborative experience, provides broadcast-grade audio production quality, and forms a complete end-to-end solution integrating AI generation, distributed collaboration and high-fidelity audio processing. This invention is applicable to scenarios such as game development, film and television production, and efficient sound effect design and team collaboration.

This invention discloses a heterogeneous edge distributed collaborative parallel inference method for a large language model, relating to the field of distributed edge computing technology. The method includes: determining the calculation formulas for the parameter quantity and inference time of the self-attention layer allocated to each edge device; determining the calculation formulas for the parameter quantity and inference time of the multilayer perceptron allocated to each edge device; obtaining the total memory constraint; optimizing the minimum computation time of the self-attention layer and the multilayer perceptron under the total memory constraint to obtain the attention head allocation ratio of the self-attention layer and the parameter matrix partitioning ratio of the multilayer perceptron; distributing the parameters of the large language model to each edge device according to the allocation and partitioning ratios, and performing parallel inference of the large language model through each edge device. This method improves the segmentation accuracy of the large language model parameters on edge devices.

The invention discloses a multi-agent reinforcement learning multi-stability control measure distributed cooperation frequency stabilization system. The system comprises an initialization module, an interaction module, a decision module, a learning module, a fusion system frequency response model, a DC power support and load shedding agent only observes local information, and distributed cooperation is realized without interaction. Through optimization of offline training, an experience playback pool, Gaussian noise and the like, network updating is guided by a reward function containing frequency deviation, power support and load shedding cost. According to the method, the problems of traditional single control, insufficient collaboration, complex calculation and the like are solved, a control instruction can be quickly and accurately given, the control cost is reduced while the system frequency stability is ensured, and the decision accuracy and timeliness are improved.

The application provides an airport data intelligent processing method and device and electronic equipment. The method comprises the following steps: obtaining a question text for airport data; performing intention recognition on the question text by an intention recognition agent in a multi-agent collaborative processing system to obtain a target intention category; arranging a plurality of agents in the multi-agent collaborative processing system according to the target intention category by a general control planning agent in the multi-agent collaborative processing system to obtain the plurality of agents after arrangement; and processing the airport data according to the question text by the plurality of agents after arrangement to obtain a processing result of the airport data. The multi-agent collaborative processing system can dynamically assign the intention category of a complex question text to a plurality of agents capable of processing structured subtasks. The plurality of agents can realize accurate analysis of cross-domain data association through task cascading and distributed cooperation, thereby significantly improving the processing capability of complex composite intentions.

The invention belongs to the technical field of machine learning and data center heat management crossing, and particularly discloses a data center cooling system intelligent regulation and control method based on machine learning. According to the method, each cooling unit is configured as an intelligent agent with sensing and decision-making capabilities, distributed coordination is realized through a digital Fischer-Mongolian communication mechanism, the opening degrees of an air valve and a water valve are automatically adjusted according to a Fischer-Mongolian concentration gradient, and energy consumption and temperature deviation are optimized in combination with a local reinforcement learning model; when a fault occurs, a neighboring agent may automatically identify an anomaly and take over a cooling task. According to the technical scheme, sub-second-level real-time regulation and control are achieved, high-reliability self-healing operation is achieved, and the cooling energy efficiency ratio is remarkably improved.

The application discloses a hidden danger link type full-cycle safety management method and system based on distributed cooperation, S1, a hidden danger comprehensive database is constructed, and the hidden danger comprehensive database is partitioned; S2, historical hidden danger data and real-time hidden danger information are collected and uploaded to the hidden danger comprehensive database; S3, a server calls real-time hidden danger information, and historical hidden danger data according to the type of the real-time hidden danger information, and data analysis is carried out, and the analysis data are visually displayed; S4, the analysis data are manually audited and classified, and the real-time hidden danger information is stored in the database according to the classification result; S5, the hidden danger information is pushed to maintenance personnel according to the work task of the maintenance personnel and the hidden danger degree of the real-time hidden danger information; multiple data types and processing permissions are designed, complete closed loops of full-cycle processing of hidden dangers are formed, research and analysis are carried out on three main nodes of discovery, review and rectification of hidden dangers, and better division of work and cooperation can be achieved.

This invention discloses an intelligent microgrid energy management system based on wind, solar, and energy storage integration, relating to the field of microgrid energy management technology. It includes an energy management center, which is communicatively connected to the following modules: a collaborative current stabilization module, used to construct a distributed collaborative control network with energy storage modules as intelligent agents. This invention achieves rapid voltage self-stabilization under decentralized scheduling by constructing a distributed collaborative control network with energy storage modules as intelligent agents, combined with consensus algorithms and digital twin simulation. Each energy storage module intelligent agent calculates adjustment commands in real time based on its local state and responds collaboratively with adjacent nodes, effectively suppressing DC bus voltage oscillations. Even in the event of communication delays or local equipment failures, voltage stability can still be maintained through distributed collaboration, significantly enhancing dynamic robustness and operational continuity under both off-grid and grid-connected conditions.

This invention discloses a low-latency, low-communication-overhead navigation method for resisting sudden link interruptions, relating to the fields of wireless communication, cooperative positioning, and graph signal processing. The invention constructs a momentum-accelerated graph filter in a distributed network, accelerating convergence by introducing an inertial momentum term into node state updates, and adaptively adjusting the polynomial truncation order using residual energy detection to reduce latency and communication overhead. Simultaneously, a spatiotemporal two-dimensional trust region model is constructed, combining temporal consistency and spatial geometric verification to perform real-time trust scoring of neighboring node links. Finally, based on the trust score, a robust M-estimator based on the Huber kernel function is used to update the graph displacement operator. This invention solves the problems of slow convergence and high communication overhead in traditional algorithms, and can effectively cope with sudden link interruptions and measurement drift in highly dynamic scenarios, achieving high-precision, highly robust distributed cooperative navigation.

Several examples demonstrate override-based data change propagation for distributed collaborative content creation platforms. An override layer can be created, comprising a representation of a sequence of override primitives that can be applied to a universal baseline scene description / scene definition (USD) layer to render an updated / current version of the scene. A digital content creation application can record edits while modifications (overrides) are applied to scene definition primitives. A CCA can propagate the edits to other CCAs on other client nodes subscribed to the CCCP by sending a new-layer message to the CCCP server, instructing the server to add the override layer to the scene graph of a USD root file.Other CCAs that subscribe to the USD root file receive a message from the server indicating that the new override layer has been added and can selectively apply propagated override primitives.

This application provides a dynamic intelligent dialogue system and method based on workflow orchestration, relating to the fields of artificial intelligence and workflow orchestration technology. The dynamic intelligent dialogue system based on workflow orchestration includes: a dynamic workflow construction engine, a hybrid execution model engine, a multi-Pod distributed collaboration module, a three-level variable management module, and an interruptible streaming output module. By integrating the dynamic workflow construction engine, hybrid execution model engine, multi-Pod distributed collaboration module, three-level variable management module, and interruptible streaming output module, the system achieves flexible and configurable workflow orchestration, high-performance hybrid execution, highly available distributed collaboration, powerful variable management, and a natural dialogue experience, thereby solving the problems of lack of dynamic configuration capabilities, low execution efficiency, insufficient high availability guarantees, and weak variable management capabilities in existing technologies.