99 results about "Distributed collaboration" patented technology

The invention relates to the technical field of lithium battery safety management, and discloses a lithium battery fault diagnosis method based on a BMS, and the method comprises the steps: obtaining multi-dimensional battery operation data and real-time data, firstly extracting a local feature vector, generating a preliminary fault signal indication, then extracting an abnormal feature vector according to the preliminary fault signal indication, and carrying out the fault diagnosis according to the abnormal feature vector; and if the preset threshold is exceeded, compressing and transmitting to the adjacent management unit to form a shared data packet. According to the local feature vector and the shared data packet, updating a diagnosis model parameter to obtain an optimized fault recognition model for analyzing real-time data and calculating a fault matching degree, and determining a potential fault type if a threshold value is exceeded; and generating a collaborative query request to the distributed network to obtain a historical fault empirical data set, integrating the historical fault empirical data set, refining parameters to obtain an accurate fault probability, activating an alarm and recording a log if a warning threshold is exceeded, and finally updating the global shared knowledge base. According to the method, the problem of insufficient lithium battery fault diagnosis accuracy in a distributed scene is solved, and collaborative optimization of diagnosis accuracy and distributed collaboration is realized.

The invention discloses a power equipment monitoring and early warning system based on intelligent patrol, and relates to the technical field of power system intelligent monitoring and safety early warning, the system fuses visible light, infrared, ultraviolet, acoustics, vibration and other multi-source signals, innovatively encodes multi-modal features in a complex manner, realizes high-speed and high-dimensional feature extraction through an optical convolution chip, and realizes high-precision and high-dimensional feature extraction. And the early perception capability of hidden dangers is greatly improved. The adaptive complex neural network is introduced, the model structure and parameter dynamic optimization, self-learning and self-evolution functions are achieved, the fault recognition accuracy is remarkably improved, and false alarm and missing alarm are reduced. The system supports cloud side end distributed collaboration, data real-time convergence and multi-scene visualization, has hierarchical deployment and online adaptive capabilities, and adapts to intelligent operation and maintenance requirements of multiple types of power stations. The system has the advantages of high performance, high intelligence, easiness in integration, expandability and the like, is prominent in innovativeness, and can be widely applied to intelligent monitoring and operation and maintenance in electric power and related fields.

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 present invention relates to the field of avionics equipment. Provided is an avionics fault knowledge fusion method oriented to distributed collaboration. The method comprises the following steps: S1, acquiring multi-source fault knowledge data of avionics equipment, and performing analysis processing and screening on the acquired multi-source fault knowledge data of the avionics equipment; S2, for the problem of knowledge conflicts across different timelines, comprehensively calculating machining fault knowledge on the basis of attribute similarity, in order to complete fault knowledge fusion; S3, for the problem of fault knowledge conflicts from different sources, completing fault knowledge fusion by means of fault element annotation and identification; and S4, constructing a fault knowledge graph, and storing fused fault knowledge. The present invention can mitigate knowledge conflicts during both the initial construction of a fault knowledge model and the expansion of existing fault knowledge models, thereby improving the accuracy, reliability and integrity of knowledge model construction for avionics information equipment, and thus providing better support for fault localization and maintenance of the avionics equipment.

The invention discloses a multi-agent cooperative electric power inspection method and system, which realizes intelligent and efficient inspection task distribution and operation inspection task processing by introducing a task coordinator middleware to optimize a task distribution process of a contract network protocol and combining a priority task queue and a dynamic task scheduling strategy. The method provides help for distributed cooperation and efficient operation inspection application of a panoramic inspection intelligent agent and an equipment operation inspection intelligent agent of an electric power station, and solves the problems of low inspection efficiency of an existing electric power industrial station and low task allocation and resource utilization rate in a multi-agent electric power inspection method.

The invention discloses a heterogeneous federated learning method based on attention guidance aggregation and prototype enhancement, and relates to a heterogeneous federated learning method. The invention aims to solve the problem that the existing heterogeneous federal learning method cannot improve the granularity of client feature representation learning and cannot solve class imbalance in global head training. The method comprises the following steps: step 1, constructing a feature aggregation mechanism based on attention guidance; 2, designing a contrast learning objective function, and explicitly enhancing inter-class separability and intra-class compactness of feature representation in a local training process; 3, designing a self-adaptive prototype enhancement strategy, and relieving a class imbalance problem; and 4, constructing a collaborative optimization framework of the global model and the local model. The invention belongs to the technical field of distributed collaborative learning.

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.

The invention discloses an intelligent MIMO signal transmission method and system based on distributed cooperation, and the detection method comprises the steps: continuously employing different distributed units in an MIMO system to execute preset detection steps until the number of iterations reaches a preset number-of-iterations threshold L; sending the estimation vector subjected to the L times of iteration to a central processing unit, and quantizing the estimation vector by the central processing unit to obtain a final detection signal; the preset detection step comprises the following steps: processing a received radio frequency signal into a baseband signal; performing channel estimation by using the pilot signal to obtain a local channel matrix; based on a multi-step conditional random block Kaczmarz algorithm, updating an estimation vector by using a baseband signal and a local channel matrix, and selecting a next distributed unit based on a multi-step conditional sampling probability; and sending the updated estimation vector to the selected next distributed unit based on the topological structure of the MIMO system. According to the invention, not only can higher performance and lower complexity be realized, but also the required data transmission bandwidth is smaller.

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 an intelligent summary automatic processing method for bad asset related cases, and relates to the technical field of data processing, which comprises the following steps: Sp1: collecting original case records of bad asset related cases, wherein the original case records comprise judicial ruling texts, financial reconciliation data and protocol scan images; Sp2: constructing a distributed collaborative analysis unit and receiving the original case records; in the distributed collaborative analysis unit, the original case records are converted into a weighted characteristic matrix through a case characteristic clustering algorithm, wherein the weighted characteristic matrix comprises a core characteristic vector, an auxiliary characteristic vector and a stability factor vector; by introducing the distributed collaborative analysis unit and the k-means clustering algorithm, the technical solution realizes automatic characteristic extraction and clustering processing of large-scale case data. Compared with the inefficiency of manual review, the present application can quickly process hundreds to thousands of cases, significantly shortening the processing time.

The invention relates to a multi-hydrogen energy industrial park hierarchical game operation method, which belongs to the field of hydrogen energy industrial park comprehensive energy system optimization scheduling, and comprises the following steps: firstly, combining a carbon transaction mechanism to construct an energy aggregator economic operation model based on energy hub optimization and a user energy consumption cost model based on demand response; a price-demand response collaborative optimization operation strategy of a single hydrogen energy industrial park based on a master-slave game is proposed by taking the maximum net income of an energy aggregator and the minimum energy consumption cost of a user as targets, and a multi-EA collaborative operation model based on hydrogen energy transaction is established based on a cooperative game and considering the operation privacy of multiple HIPs. Distributed optimization with the goal of minimizing alliance cost is realized by adopting an alternating direction multiplier method, and a benefit distribution mechanism is designed by utilizing a Shapley value. According to the method, modeling is carried out on interests and interaction mechanisms of different subjects, a scheduling method based on distributed collaboration is adopted, the collaboration and game problems of the subjects in a multi-HIP operation scene are solved, and multi-HIP economical and low-carbon operation is achieved.

The invention provides a power supply bus monitoring method and device, and relates to the technical field of power supply bus monitoring. The method comprises the following steps: carrying out standardization processing on collected operation data to obtain local storage data in a unified format; performing real-time monitoring and state judgment on the corresponding bus system based on the local storage data, and generating capacity information of the corresponding bus system; the edge data processing servers communicate with each other through a lightweight communication protocol to share respective capacity information; generating a load transfer instruction based on a preset distributed capacity coordination rule set; and the edge data processing server sends a load transfer instruction to the bus system of the edge data processing server and the opposite bus system. According to the method, the limitations of data islands, data isomerism, communication limitation and difficulty in efficient utilization of dispersed historical data are broken through, a light-weight and high-robustness distributed coordination mechanism is constructed, and dynamic and rapid mutual assistance and distribution of cross-regional bus capacity based on a real-time state are realized.

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 present invention discloses a method for collecting and locating traveling wave characteristics of distributed cable faults, which relates to the field of cable fault technology. The method includes collecting traveling wave characteristic information through branch point sensing units and sharing the collected traveling wave characteristic information through a communication network; constructing a dynamic topology perception and network reconstruction framework, wherein weighted averaging includes assigning weights to data sources based on signal-to-noise ratio, topological distance, and time consistency; establishing a propagation path model of traveling wave signals in distributed cables, and using hyperbolic positioning rules in combination with the propagation path model to define the cable fault location coordinates. The present invention enhances the distributed collaboration capability of the system. While improving positioning accuracy, the overall solution has good scalability and adaptability. It can be widely used in fault monitoring and management of large-scale distributed cable networks, effectively reducing troubleshooting time and cost, and providing strong support for the intelligent operation and maintenance of power systems.

The present invention relates to the field of power system monitoring and protection technology, and discloses an FPGA-based real-time monitoring fault intelligent processing system for a strong power system, the system comprising: a pulse encoding module for converting the monitored voltage or current signal into a time-domain pulse sequence; a hardware pulse neural network module for processing the pulse sequence to generate a local warning state vector; a distributed consensus engine module for combining the local vectors of multiple nodes to generate a global warning state vector through consensus; a parameter adaptive adjustment module for adjusting the response characteristics of the neural network according to global vector feedback; a collaborative decision-making and intelligent control module for executing a collaborative strategy to handle potential faults when the global warning reaches a threshold. The present invention can detect weak fault precursors with extremely low latency and high sensitivity, and through distributed collaboration, improves the global reliability and intelligent processing capabilities for coping with systemic risks.

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 invention discloses a calculation unloading method based on deep reinforcement learning in a cellular-free large-scale MIMO system, and the method comprises the following steps: inputting environment information into a task transmission MADDPG network in a time slot t, and outputting a current access point connection strategy through minimizing the task transmission energy consumption of a user; for the kth user side, all access points connected with the kth user side are clustered according to the access point connection strategy of the kth user side, a plurality of access point clusters are obtained, each access point cluster unloads the MADDPG network through calculation, energy consumption is calculated through minimization of tasks, and the access point cluster unloads the MADDPG network through calculation; selecting a main access point and allocating computing resources to perform computing unloading on a task of the kth user side, returning computing result data to the kth user side after computing unloading is completed, and ending the time slot; repeating the above process to realize calculation unloading; according to the method, the distributed cooperation characteristic of the access points in the non-cellular large-scale MIMO is fully utilized, and the total energy consumption and algorithm complexity of the system are reduced while the communication reliability requirement is met.

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.