101 results about "Distributed algorithm" patented technology

The invention belongs to the technical field of lean manufacturing monitoring, and particularly relates to an anomaly detection and processing method for a production process of an intelligent factory, and the method comprises the steps: constructing a dual monitoring node chain and a layered anomaly association evaluation node chain based on a preset production process flow chain, and collecting the operation state and process parameter data of equipment in real time; the hierarchical anomaly association assessment node chain analyzes the equipment health degree through the operation state assessment sub-chain, mines a cross-process anomaly propagation relationship through the whole-process technology anomaly assessment sub-chain, generates a whole-process association anomaly assessment space, fuses real-time data and historical anomaly strategy index information based on a distributed algorithm, and finally performs the whole-process association anomaly assessment on the basis of the real-time data and the historical anomaly strategy index information. Dynamically constructing a difference exception processing strategy library, and realizing processing strategy synchronous feedback of positioning exception; according to the invention, through a dual monitoring architecture and hierarchical association evaluation analysis, the problems of fuzzy abnormal propagation path and response lag are solved, and the real-time performance and processing accuracy of abnormal detection are improved.

The invention relates to a heterogeneous unmanned ship task dynamic optimization method based on intelligent distributed CBBA, and relates to the technical field of unmanned autonomous cooperative control, the method comprises the following steps: firstly, using an intelligent distributed CBBA algorithm to realize task initial allocation, and constructing a cost function integrating distance cost, time cost and time window default penalty; and a task sequence is adjusted through a greedy strategy so as to meet strict time window constraints. And secondly, in the task execution process, a real-time monitoring and dynamic re-planning mechanism is established, and emergencies can be rapidly dealt with. A redistribution mechanism is introduced, the weight of a cost function is dynamically adjusted through a genetic algorithm, and loss and scheme optimization benefits caused by task execution subject change are balanced. Compared with the prior art, the task execution efficiency, reliability and adaptability of the heterogeneous unmanned ship cluster in a complex dynamic environment are effectively improved, and the method is suitable for complex application scenes such as maritime search and rescue, material delivery, patrol monitoring and the like.

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

The invention discloses a distribution line load prediction and optimal scheduling method and system, and relates to the technical field of intelligent scheduling of power systems, and the method comprises the steps: generating a time-aligned multi-source fusion input data set; constructing a mixed time sequence load prediction model, and introducing a weighted quantile loss function in a model training process; constructing a joint probability distribution model of the renewable energy output and demand response participation rate, and sampling joint probability distribution; constructing a rolling time domain power distribution network optimization scheduling model; a two-layer mixed strategy is adopted to deal with uncertainty, an optimization problem is decomposed into a plurality of sub-problems, and an alternating direction multiplier method with adaptive penalty parameters is used for distributed solution. According to the method, a multi-objective optimization scheduling model is established in a rolling time domain, and dynamic closed-loop optimization is realized; and by introducing a two-layer hybrid solving strategy and an ADMM distributed algorithm with an adaptive penalty parameter, the calculation efficiency and expandability are remarkably improved while the global consistency is ensured.

The invention discloses a power distribution network robust optimization scheduling method considering a user side, and the method comprises the steps: S1, building an optimization scheduling model of a power distribution network layer, comprehensively considering the power generation cost of each distributed power supply and a micro-grid in a power distribution network, and providing a flexible adjustment service for the power distribution network through excitation of the micro-grid, so as to minimize the operation cost; s2, establishing an optimal scheduling model of a micro-grid layer, comprehensively considering the power generation cost of each distributed power supply of the micro-grid, and flexibly adjusting services by responding to the power distribution network amount so as to minimize the operation cost; s3, establishing a multi-microgrid power distribution system robust optimization model considering the source-load uncertainty, and establishing the robust optimization model to cope with the influence of output fluctuation on the model by considering the uncertainty of source-load output and demand; and S4, establishing a distributed algorithm based on Benders decomposition in combination with CCG to solve the model, and determining the output between the distribution network and the multiple microgrids.

The invention discloses a multi-agent-based virtual power plant layered voltage coordination control system and method, and relates to the technical field of coordination control, and the method comprises the steps: obtaining the original operation data of a distributed energy unit in a virtual power plant, and constructing a dynamic mapping model representing the relation between node voltage and control input; decomposing the dynamic mapping model into a fast dynamic sub-model and a slow dynamic sub-model; generating a fast loop control law based on the fast dynamic sub-model, and performing residual calculation and online correction on the slow dynamic sub-model according to the fast loop control law; establishing a global optimization objective function according to the corrected slow dynamic sub-model, and solving a reference value of each node through a distributed algorithm; issuing the node reference value to a local inverter, and generating a control input signal in combination with a fast loop control law; according to the method, the problems of voltage oscillation, tracking errors and slow convergence caused by virtual power plant fast and slow control conflicts are solved through fast and slow dynamic layering cooperative control.

The invention discloses a fire-fighting spatial-temporal trajectory data processing and off-line data warehouse construction method based on a stream batch fusion architecture, and relates to the field of digital fire fighting, and the core steps of the method are as follows: after multi-source data are collected by an edge gateway, high throughput distribution and storage are realized by Kafka; real-time optimization such as track cross repair and offset correction is completed through Flink streaming calculation; performing offline batch processing on the historical data by the Spark to generate an analysis result; generating an optimal rescue path through a distributed Dijkstra algorithm in combination with road topology, real-time traffic and fire fighting truck parameters; the method comprises the following steps: realizing mixed storage by adopting ElasticSearch secondary index and HBase total storage; based on hot and cold data hierarchical management, intelligent applications such as fire-fighting commanding and dispatching, battle comment and review and the like are supported. Practice verifies that the method can shorten the fire control time of the main urban area by 60%, significantly improves the fire emergency response capability, and is suitable for large-scale fire track data scenes.

The invention belongs to the technical field of edge computing, particularly relates to a water conservancy gate remote control system and a method thereof, and aims to solve the problem that a centralized cloud center architecture adopted by existing traditional water conservancy gate remote control cannot realize effective consideration and unified optimization in the aspects of real-time performance, reliability, economical efficiency, intelligent cooperation and the like. According to the scheme, the system comprises a multi-source data acquisition module; the collaborative decision-making module is connected with an edge calculation module, the edge calculation module is connected with a data processing module, and the data processing module is connected with the multi-source data acquisition module; according to the method, a cloud, edge and end collaborative multi-stage edge computing architecture is constructed, and a distributed algorithm based on a market mechanism is introduced, so that unified optimization and fundamental improvement on multi-dimensional targets such as low-delay real-time control, network interruption autonomy, computing resource elastic sharing and AI model safety evolution are realized.

The invention discloses an emergency multi-unmanned aerial vehicle distributed service quality assurance method and system based on private flow sensing integrated design. The method comprises the following steps: establishing an equivalent Nakagami-channel model for a physical channel of a base station-unmanned aerial vehicle link in an emergency scene, establishing a signal model based on rate division multiple access, and calculating effective capacity; establishing a sensing signal model, and constructing an extended effective capacity of the fusion communication and sensing performance indexes; constructing a joint optimization problem; designing a dynamic weighted federated learning (DW-FL) algorithm, and solving a joint optimization problem; according to the method, the private flow of rate segmentation multiple access is designed into the sensing dual-function signal, and the federated learning DW-FL distributed algorithm for dynamic weighting based on the performance contribution degree is combined, so that the sensing resources of the multi-unmanned aerial vehicle network can be subjected to high-time-efficiency collaborative optimization and distribution in an emergency scene with limited resources, and the resource utilization rate of the multi-unmanned aerial vehicle network is improved. And the total effective capacity of the system is maximized while the dual service quality of communication delay and perception probability is ensured.

The invention relates to the technical field of data processing, and discloses a log data batch updating method and device, equipment and a medium, and the method comprises the steps: obtaining a full-amount historical log in a preset database in real time, converting the full-amount historical log into an event stream, and writing the event stream into preset message middleware, obtaining a partition ID of the total historical log in the message middleware, generating a global unique identifier bound with a partition according to the partition ID to obtain a distributed ID, generating a consumer ID associated with the user identity information according to the distributed ID by calling a pre-designed distributed algorithm, and sending the consumer ID to the message middleware. And carrying out lock-free parallel updating on the database according to the consumer ID based on connection pool preheating and asynchronous submission to obtain an updating result, carrying out consistency verification on the updating result to obtain a verification result, and confirming the updating result according to the verification result. And the batch updating efficiency of the log data is improved.

The invention discloses an unmanned aerial vehicle cluster elastic formation control method and system and computer equipment, and the method comprises the steps: embedding a local auction mechanism in each unmanned aerial vehicle, calculating a safe neighbor set, and updating bidding through employing a maximum consensus mechanism, achieves the decentralized task distribution between unmanned aerial vehicles, only needs the local communication between neighbors in each auction, and improves the auction efficiency. Global coordination is not needed, and the communication overhead is greatly reduced. On the basis of keeping a communication topology, an artificial potential field method based on a Leader-Follower formation theory is introduced, a small number of leaders guide the whole formation to move through a predetermined track or a task target, and other followers dynamically keep and adjust the formation by using attraction and repulsive force in the artificial potential field. The method is especially suitable for unmanned aerial vehicle cluster formation performance, high-altitude flight tasks and other scenes. The lightweight distributed algorithm developed by using the characteristics of the unmanned cluster provides technical support for a subsequent unmanned cluster formation method.

The invention relates to the technical field of power distribution networks of power systems, in particular to a power distribution network elasticity improvement method based on distributed dynamic recovery, which comprises the following steps: step 1, acquiring operation data of a power distribution network; 2, establishing a topology reconstruction and rolling optimization dual-stage recovery model, taking maximized load recovery as a target, considering network constraints and resource operation constraints, and converting a probability model into a deterministic mixed integer linear programming problem through opportunity constraint conversion; 3, decomposing the model into distributed sub-problems by adopting an alternating direction multiplier method, introducing a boundary variable compensation mechanism to enhance the robustness under communication interruption, and realizing a multi-period dynamic recovery decision based on rolling optimization; step 4; an integer variable processing method combining projection and relaxation iteration is designed, and an induced acceleration objective function is constructed, so that the solving efficiency of a mixed integer sub-problem is improved, and the convergence and calculation speed of a distributed algorithm are ensured.

The invention discloses a distributed photovoltaic-containing power distribution network planning layer multi-objective optimization method, which comprises the following steps of: constructing a multi-objective optimization model taking investment cost, network loss and voltage deviation as sub-objectives, and integrating the sub-objectives into a unified optimization function through a weight coefficient method; multi-dimensional constraint conditions such as node voltage safety, power balance, capacity upper limit, branch current and whole network permeability are established in combination with load flow calculation, photovoltaic optimal point distribution and capacity configuration are solved by adopting an improved flower pollination algorithm, and the convergence speed and optimization precision are improved. The improved dynamic projection primal-dual distributed algorithm is adopted, and local information exchange and a self-adaptive step length mechanism are utilized, so that the real-time network loss minimization and voltage stability control of the high photovoltaic penetration power distribution network are realized, and the safety and economy of the system are ensured.

The invention discloses a communication sensing time slot allocation method and system based on a potential game. According to the invention, frequency spectrum hardware sharing of radar sensing and wireless communication is realized through system initialization, a time allocation mode is dynamically selected based on a 5G NR frame structure, and the time allocation mode comprises a segmentation mode and an aggregation mode, so that time slot resources are flexibly divided; constructing a non-cooperative potential game model, taking a vehicle as a participant, defining a utility function which takes mutual information maximization of a radar system as a target and is fused into communication capacity constraint, and ensuring collaborative optimization of perception and communication performance; and in combination with greedy initialization and a random search strategy, distributed algorithm iteration is executed, so that the calculation complexity is remarkably reduced, and the real-time response capability is improved.

The application belongs to the technical field of target tracking and fusion, and relates to a nonlinear filtering distributed target tracking method based on variational inference. Measurement noise parameters of a conventional nonlinear filtering distributed algorithm are assumed to be known, and the application extends the nonlinear filtering distributed algorithm based on variational inference to a single-target tracking scene under unknown measurement noise parameters. Under the distributed algorithm, weight likelihood parameters of each sensor to the target state tend to be consistent, so that the algorithm has strong robustness, small calculation amount, high flexibility and timeliness, and in combination with the variational inference method, the algorithm can achieve high-precision target tracking in a distributed multi-sensor scene under unknown measurement noise variance. The algorithm has close tracking precision to a distributed particle filtering algorithm with known measurement noise variance, and enhances practicability.

The invention discloses an offshore wind power plant distributed frequency supporting method and device based on a communication link, and the method comprises the steps: obtaining a grid-connected topological structure of a target offshore wind power plant, dividing wind turbine generators on the same feeder line into a group of wind turbine generator units, and building a rapid communication link between a DC converter station and the wind turbine generators; determining a frequency supporting capability coefficient of the wind power plant according to the rotor rotating speed of the wind turbine generator; when the frequency deviation exceeds a threshold value, the active power of the wind turbine generator in the frequency supporting stage is determined according to the frequency supporting capacity coefficient, the frequency deviation of the power grid frequency and a consistency control algorithm; and determining whether the wind power plant exits frequency support according to the wind power plant output power and the system frequency, and controlling the wind turbine generator to stably recover the rotating speed. Based on the communication link and the distributed algorithm, the direct control wind turbine generator of the flexible direct current converter station collaboratively participates in frequency support, the frequency support effect is effectively improved, the response speed is increased, and safe and stable operation of the wind turbine generator is guaranteed.

The application discloses a kind of multi-robot task allocation methods based on distributed optimization, it is related to control and information technology field.The application combines saddle point dynamics with optimistic gradient ascent descent algorithm or super-gradient algorithm to establish the distributed task allocation algorithm of multi-robot system, can solve non-convex task allocation problem, and the application range of algorithm is wider.And the distributed task allocation algorithm established by the application is a kind of completely distributed algorithm, and it is not related to the solution of sub-optimization problem, and only through simple algebraic operation to update state, with lower computational complexity, it can guarantee that the system state of all robots converges to the optimal integer solution of task allocation problem quickly.

This invention belongs to the field of signal processing, and particularly relates to direction-of-arrival (DOA) estimation for electromagnetic and sonar signals. Specifically, it is a subspace-based DOA estimation method based on a distributed algorithm, which can be used for passive localization and target detection. The method includes the following steps: S1, establishing a distributed array model at the receiver; S2, calculating the signal subspace of the received signal; S3, calculating the weight w(θ); S4, calculating the spatial spectrum and performing a spatial domain search to find the DOA value. The proposed method achieves distributed solution of the algorithm, thus fundamentally avoiding the shortcomings of centralized algorithms. Simultaneously, it maintains good estimation performance, similar to that of centralized algorithms, and outperforms distributed MUSIC methods at low signal-to-noise ratios.

The application discloses a kind of film thickness intelligent control methods in electromagnetic shielding film preparation, it is related to shielding film preparation technical field, comprising the following steps: setting multiple film thickness sensors;Electrostatic neutralization is applied in coating area;Microclimate control device is set in the periphery of coating platform;Non-contact deformation detection system is set, and the deformation time series data of each coating head unit is collected;Real-time value of film thickness and deformation is predicted;Compensation value is introduced to compensate film thickness deviation and deformation deviation, and control module executes displacement or pressure control according to compensation value to each coating head, and the input information of prediction model is updated synchronously;Cloud platform uses distributed algorithm to coordinate each control node, and is fused by asynchronous scheduling and prediction model;The application effectively inhibits the edge thickening / thinning phenomenon caused by coating head deformation, tension gradient.

The invention discloses a shared energy storage interactive configuration and lease pricing method and equipment oriented to multiple energy-using main bodies in a park. The method comprises the following steps: establishing a shared battery energy storage configuration and park operation collaborative optimization model and an in-park energy consumption main body energy management model; adding power interaction constraint between the park operator and the energy consumption main body based on the constructed model, establishing a shared energy storage interactive configuration model, and performing iterative solution based on a distributed algorithm to obtain the optimal energy storage configuration capacity of the park operator and the energy storage capacity and duration rented by each energy consumption main body; and taking the product of the lease capacity and the lease duration of each subject of the park as a lease settlement amount, establishing a differential pricing constraint based on a quantity discount mechanism according to the settlement amount of each subject, constructing a price interaction negotiation model between the park operator and the energy consumption subject, and performing alternate iteration solution to obtain a final lease pricing. According to the invention, reasonable configuration of energy storage resources and leasing strategy optimization are realized, the energy consumption efficiency is improved, and the energy consumption cost is reduced.

The invention provides a multi-power-supply group control system and method based on a distributed algorithm, and the system comprises power supply modules, a distributed communication module, a local control unit, a coordination control unit, a monitoring unit, and a redundancy switching module. The local control unit executes local control strategies of the corresponding power supply modules, the coordination control unit carries out optimization scheduling on operation parameters of the power supply modules by means of a distributed algorithm, and the monitoring unit is responsible for collecting operation state data of the power supply modules. The method effectively avoids the single-point fault risk existing in centralized control, improves the reliability and response speed of the system, achieves the efficient cooperative operation of multiple power supplies through the dynamic weight factor, and is suitable for the multi-power-supply cooperative scenes of a micro-grid, distributed power generation and the like.

The invention provides a hybrid distributed decentralized control direct current micro-grid secondary control method and system, and the method comprises the steps: constructing a direct current micro-grid system which comprises a plurality of converters, and each converter communicates with a group of neighbor converters through a low-bandwidth communication network; detecting the output voltage, current and power of each power converter and the voltage of a common direct current bus; calculating a droop coefficient correction term by using a distributed algorithm; calculating a voltage correction term by using a distributed algorithm; power sharing and voltage recovery. According to the invention, the technical problems of poor voltage and power regulation of the micro-grid, low system stability, low response speed and low precision are solved.

The application discloses a distributed decoupling optimization method for a comprehensive energy system considering carbon trading, and comprises the following steps: establishing a centralized optimization framework of an electrically coupled system, initializing global data and information of a power system and a gas system; constructing a centralized model of a regional comprehensive energy system, wherein an objective function of the model comprises the power system, the gas system and carbon emission cost, and corresponding operation constraints are added to the objective function; converting an original non-convex problem into a mixed integer second-order cone programming based on SOCP relaxation, and performing reactive power optimization by using an ADMM consistent distributed algorithm; introducing consensus variables to represent the synergistic effect between the power and natural gas networks based on the ADMM method of consistent variables, so as to solve a distributed optimization problem of the regional comprehensive energy system; and finally, a case analysis of a regional electric and gas comprehensive energy integrated system is performed, and the distributed decoupling optimization of the system is realized by using the ADMM method of consistency. The application also comprises a distributed decoupling optimization system for a comprehensive energy system considering carbon trading.

The invention discloses an equipment access intelligent management and control method and system based on a distributed algorithm, and the method comprises the steps: when to-be-accessed equipment initiates an access request, the to-be-accessed equipment is verified by adjacent edge nodes, and after the verification is passed, each edge node cooperatively elects a task coordination node, the task coordination node carries out capability comprehensive scoring on the multi-dimensional capability index of each node according to the node capability evaluation model, and determines a candidate access node set according to the score; the task coordination node obtains feedback information when each candidate access node actually executes a task, and performs adaptive adjustment on the importance weight of each capability index of each candidate access node according to the feedback information, so as to update the comprehensive capability score; and according to the updated capability comprehensive score and the load condition of each candidate access node, the task coordination node determines a target node which finally bears the access of the to-be-accessed device from the candidate access node set.

A digital marketing method, system, program product and storage medium. In the method, after receiving a new task instruction of a user, relevant information is extracted, and a material script is put into a preset marketing channel to start marketing; during the marketing, every time the local time of the system reaches a synchronization node, the task quantity value is obtained, the local and external time are synchronized by means of a distributed algorithm, an encrypted time stamp is generated, and it is verified whether the quantity reaches the standard; if it reaches the standard, it continues to be put into; if it does not reach the standard, it is marked as a potential fault node and continues. The technical scheme provided by the application can execute marketing according to the preset time and quantity, effectively reduce the marketing failure caused by time deviation or data anomaly, improve the accuracy and stability of the execution of the digital marketing task, and thus improve the overall marketing effect.

The application discloses an engine model parameter dynamic calibration optimization method, which comprises the following steps: obtaining a target parameter to be optimized, and determining a corresponding dynamic optimization problem according to the target parameter to be optimized and a working scene; calling an algorithm portfolio according to the dynamic optimization problem, and updating optimal solutions of each algorithm in the algorithm portfolio through optimization information interaction and sharing strategies among algorithms; determining optimization promotion corresponding to each algorithm according to the optimal solutions obtained by each algorithm, and distributing corresponding calculation resources to each algorithm according to the determined optimization promotion degree; optimizing the target parameter to be optimized through the distributed algorithm portfolio, and calibrating the optimized target parameter as a parameter meeting a performance index according to an actual output value and an expected output value of an aero-engine simulation model. The application applies the algorithm portfolio to the dynamic calibration problem of the engine parameter, and improves the robustness and stability of the parameter calibration.

The invention provides an emergency demand response load side flexibility resource scheduling optimization method and system. The method comprises the following steps: constructing a resource scheduling control architecture based on regional flexibility resource adjustment capability; the resource scheduling control architecture comprises a cloud server, a control terminal and a region control unit RCU which is in communication connection with the cloud server and the control terminal. Establishing an emergency demand response scheduling model which comprehensively considers timeliness, economic benefits and user satisfaction; and calculating an emergency demand response scheduling decision under the resource scheduling control architecture by using an RAFT distributed algorithm based on the emergency demand response scheduling model. According to the emergency demand response architecture and scheduling decision optimization method based on the distributed algorithm provided by the invention, rapid decision and benefit optimization are taken into consideration, the dynamic change of flexible resources is adapted, various complex power grid operation environments can be coped, and dynamic evaluation and optimal distribution for flexible resource adjustment potential are realized.

The application discloses a power distribution network optimization partitioning method considering three-phase power imbalance, relates to the technical field of large-scale power distribution network partitioning, and comprises the following steps: according to local sensitivity calculation, combining line admittance parameters of a power distribution network, calculating a voltage sensitivity matrix between nodes, and deducing electrical distance; then taking the electrical distance as an edge weight of a power distribution network topology, combining the concept of modularity, and using a Louvain algorithm to obtain a preliminary partitioning result; then according to a partitioning number constraint, selecting a flow method or a hierarchical flow method to perform sub-region merging, and obtaining a non-overlapping partitioning result; finally, according to a distributed algorithm requirement, the non-overlapping partitioning result can be overlapped based on a maximum information interaction principle. The application not only solves the influence of three-phase imbalance on partitioning, but also speeds up the partitioning algorithm, and has more advantages in partitioning effect and partitioning stability.

Systems and methods are disclosed for operating a wireless audio network including a plurality of wireless microphone units (e.g., wireless delegate units) and a central access point having a mixer. The wireless microphone units may perform voice detection and level sensing, and make a preliminary gating decision. The central access point may make a final gating decision, determine the granting of wireless communications channels, and generate a final mixed audio output signal.

The application provides a distributed unmanned aerial vehicle cluster parameter estimation method based on adaptive data selection, a cross matching mechanism is established based on fusion data transmitted at the last time, and then a new data selection factor lambda is obtained k (t) generating a strategy and determining whether unmanned aerial vehicle k exchanges data with neighbor unmanned aerial vehicles based on the new lambda k (t) The application improves the estimation accuracy while reducing the calculation cost and communication cost and saving the energy consumption of the unmanned aerial vehicle cluster. The distributed ADS-DLMS algorithm is designed based on the cross matching mechanism, which not only improves the parameter estimation accuracy of the distributed unmanned aerial vehicle cluster, but also reduces the calculation cost and communication cost of the unmanned aerial vehicle node, and provides a new information fusion technical scheme for the distributed unmanned aerial vehicle cluster.