189 results about "Distributed algorithm" patented technology

The invention discloses an unmanned aerial vehicle cluster intelligent cooperative control method, and the method comprises the steps: optimizing a network topology through heterogeneous unmanned aerial vehicle cluster dynamic networking and a dynamic clustering algorithm, and guaranteeing the reliability of a communication link; a layered hybrid decision architecture is designed to improve the task allocation rationality and the dynamic adaptability of the unmanned aerial vehicle cluster; a distributed control strategy network is trained by using a multi-agent near-end strategy optimization MA-PPO algorithm, and unmanned aerial vehicle cluster behavior collaboration is ensured in combination with space-time consistency constraint; an asynchronous incremental consensus protocol AICP is provided, the data transmission amount is reduced, and topology reconstruction is accelerated; real-time three-dimensional environment reconstruction and dynamic threat prediction are realized based on a neural radiation field NeRF technology; a lightweight anti-interference communication middleware is developed, and the instruction transmission stability is enhanced by adopting a space-time coding diversity technology. The method solves the problems of high delay of centralized control of the unmanned aerial vehicle cluster, poor convergence of a distributed algorithm and the like, and is suitable for high-dynamic task scenes such as urban street battle and complex terrain search.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

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 provides an elevator energy recovery application method and system based on a super capacitor, and relates to the technical field of energy recovery, and the method comprises the steps: collecting elevator operation data and super capacitor state data, constructing a state space and an action space, and constructing a reward function according to peak-valley electricity price economic benefits, super capacitor life loss and energy utilization efficiency; training a deep reinforcement learning model based on the double-Q network to obtain an energy recovery optimization strategy; when the elevator is braked, regenerative braking energy is stored in the super capacitor; when the elevator is driven, the stored energy is released for traction; the optimal storage allocation scheme of the multiple elevator rooms is calculated through the virtual energy storage unit, hierarchical control is implemented based on an optimization strategy and the allocation scheme, the upper layer adopts fuzzy self-adaptive weight to determine the energy storage priority, and the lower layer adopts a distributed algorithm to calculate the energy allocation coefficient and the target power; and the charge-discharge rate is updated according to the health state of the supercapacitor.

The invention is suitable for the technical field of unmanned aerial vehicle route planning, and provides an unmanned aerial vehicle route planning method for low-altitude airspace unmanned aerial vehicle operation, which comprises the following steps of: constructing dynamic route networks at different moments based on three-dimensional GIS (Geographic Information System) gridding; evaluating the comprehensive risk of any grid at different moments based on the multi-source heterogeneous data; multiple uncertainties in a flight environment are considered, and a double-layer planning robust optimization model of unmanned aerial vehicle route planning is established according to multiple origin-destination points and time windows of unmanned aerial vehicle tasks; and designing a two-stage distributed algorithm of the bilayer planning robust optimization model for solving the route planning of the unmanned aerial vehicle. On the basis of three-dimensional GIS gridding, factors such as obstacle distribution, wind shear, a dynamic restriction area, radio interference and unmanned aerial vehicle performance are comprehensively considered, targets such as safety, efficiency and green are considered according to origin-destination points and time windows of a plurality of unmanned aerial vehicle tasks, a plurality of unmanned aerial vehicle paths are generated, and the safe and efficient level of low-altitude airspace operation is assisted.

The invention discloses an expert network automatic hybrid distributed algorithm based on cloud side end cross-domain data, and belongs to the field of large model distributed training. According to the method, an innovative method is introduced in the field of large model distributed training, an OSDP is used for training an MOE model in a distributed mode, distribution of different experts in a GPU cluster is optimized, and higher parallel computing efficiency is ensured. The combined motivation is used for improving the training efficiency under the condition that the model performance is not sacrificed. Through hybrid parallel accelerated training and hardware resource saving, the training cost of a large-scale model is reduced, and training of a super-large-scale deep learning model becomes more economical and feasible. And an automatic hybrid distributed algorithm based on an expert network is introduced, so that cloud, edge and end collaborative cross-domain heterogeneous data fusion is realized, and the complex requirements of the industrial intelligent field in a multi-base and multi-data-source environment can be met.

The invention discloses a dynamic water quality monitoring system based on an unmanned ship and a data acquisition and distribution algorithm. Comprising an unmanned ship platform module, an initial navigation path setting module, a water quality data acquisition module, a dynamic adjustment and optimization module, a control interaction module and a dynamic adjustment and optimization module, wherein the dynamic adjustment and optimization module dynamically adjusts the navigation strategy and sampling frequency of the unmanned ship in a target to-be-detected water area through a second branch of a first neural network model; the unmanned ship can sail autonomously, continuous and global water quality monitoring is achieved, through an efficient data processing algorithm, the accuracy and reliability of data are improved, a scientific basis is provided for water quality management, a distributed algorithm model is achieved to conduct predictive analysis on each specific target sampling point, the accurate predictability of unmanned ship monitoring is improved, and the water quality monitoring efficiency is improved. The monitoring strategy is dynamically adjusted according to actual requirements, optimal configuration of monitoring resources is achieved, the monitoring cost is reduced, the water quality change trend is found in advance through prediction analysis of the neural network model, and powerful support is provided for early warning and emergency response.

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, which relates to the technical field of radar anti-interference algorithm optimization, discloses a radar anti-interference algorithm optimization platform comprising an environment sensing module, an interference classification module, a dynamic filtering module, a waveform optimization module, an array processing module and an efficiency feedback module. The environment sensing module is used for carrying out multi-dimensional sampling on radar receiving signals by adopting a broadband digital channelization technology to obtain original electromagnetic environment data; the interference classification module is used for analyzing the original electromagnetic environment data by adopting a deep convolutional neural network to obtain a feature parameter set; the dynamic filtering module is used for carrying out adaptive processing on the characteristic parameter set by adopting an improved RLS-NLMS hybrid algorithm to obtain a baseband signal; the waveform optimization module is used for carrying out waveform parameter inversion on the baseband signal by adopting a genetic algorithm to obtain a waveform code; and the array processing module is used for performing spatial domain synthesis on the waveform codes by adopting a distributed MVDR algorithm to obtain an anti-interference beam forming weight.

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 discloses an unmanned aerial vehicle assisted emergency communication network performance optimization method and system. The method covers communication link optimization based on environment perception, information is collected through multiple sensors, an environment model is established by applying a machine learning algorithm, and the flight attitude and the communication frequency are dynamically adjusted according to the environment model. The distributed resource allocation utilizes a distributed algorithm and a game theory, takes a utility function Ui = w1Qi + w2Ri-w3Ei as a decision basis, and reaches Nash equilibrium through an iterative game; efficient energy management improves endurance by means of devices such as a solar panel and a wind energy collector in combination with an intelligent algorithm and an energy sharing mechanism. The system comprises a plurality of unmanned aerial vehicles equipped with various modules and corresponding control modules. The stability, the resource utilization rate and the cruising ability of the emergency communication network are effectively improved, and powerful communication guarantee is provided for emergency rescue.

The invention discloses a multi-base-station cooperative wireless resource allocation method based on queue sensing, relates to the technical field of electronic communication, and provides a low-complexity distributed resource optimization solution aiming at the problem of random arrival of URLLC service in a multi-cell cellular system. The resource management problem of dynamic URLLC task multi-time-slot downloading under dense cellular network deployment is researched, and the defect that queue stability is neglected in an existing scheme is overcome; a multi-dimensional time coupling amount is decoupled by converting an original problem into a series of online sub-problems with the maximum rate, and then a distributed algorithm with multiple parallel base stations is provided to decompose global calculation tasks to the respective base stations, so that the calculation complexity is reduced; the method can effectively adjust the compromise between the throughput and the queue cache, and has obvious advantages in the aspect of improving the long-term performance of the system.

The invention provides a concentrator system data remote backup and recovery method, which comprises the following steps: loading all concentrators in the same network as nodes into a distributed network based on a distributed framework, each concentrator having a unique identity label; during the operation period of each concentrator, compressing and packaging key data of the concentrator; each node concentrator makes a decision by using a distributed algorithm, and distributes at least part of key data to at least part of nodes to realize redundant backup; when any concentrator in the distributed network is damaged or needs to be replaced, the replacement device asks for backup data belonging to the concentrator to be replaced from other concentrator nodes in the network, unpacking and decompressing are carried out, and data recovery is completed. According to the invention, even if part of concentrators are completely damaged, data recovery can still be carried out, and extra special equipment does not need to be additionally arranged to carry out remote backup of data.

The invention discloses a virtual power plant distributed power generation instruction distribution method and system for a power distribution network, and the method comprises the steps: firstly carrying out the linearization of a power distribution network AC power flow model, carrying out the modeling of the line active power and node voltage in the power distribution network, and representing the node voltage and the line active power as the mapping of the node power variation; setting constraint conditions including a power balance constraint, a node voltage constraint, a line capacity constraint, an adjustable unit power adjustment upper and lower limit constraint and a climbing rate constraint of each adjustable unit, and constructing a power generation instruction scheduling optimization model taking the highest frequency modulation performance and the lowest carbon emission as double optimization targets; and finally, solving the power generation instruction scheduling optimization model in each control period by adopting a Nesterov momentum acceleration-based distributed algorithm to realize rapid and accurate solving of the instruction, and setting a communication condition to reduce the communication pressure during distributed information exchange and improve the convergence speed of the model. The method is high in convergence speed and high in precision.

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 a multi-agent collaborative optimization control method and system for regional energy interconnection, and belongs to the technical field of multi-agent collaborative optimization control. According to the method, through a constructed Markov game model, each single agent is regarded as a node in a GNN graph neural network graph; capturing a coupling relationship among a plurality of single agents through a GNN graph neural network; introducing a federated learning framework, training the PPO algorithm of each single agent, and obtaining PPO algorithm training model parameters of each single agent; and according to the coupling relationship between the single agents, aggregating and updating the obtained PPO algorithm training model parameters of the single agents, constructing a distributed PPO algorithm training model, updating the system parameters of the single agents of the energy interconnection system, and obtaining a multi-agent collaborative optimization control strategy. According to the method, cooperation among the intelligent agents of the multi-region energy interconnection system can be optimized, and efficient, stable and flexible system operation is achieved.

The invention discloses a traffic jam relieving method and system based on the Internet of Vehicles, a medium and equipment. The method comprises the following steps: an Internet of Vehicles communication step: carrying out real-time communication between vehicles, between a vehicle and an infrastructure, and between a vehicle and a cloud; a global traffic state sensing step: collecting position, speed and direction information of vehicles in a region and state data of traffic infrastructures through a multi-sensor fusion technology, and constructing a real-time traffic state diagram; according to the cooperative path planning step, path optimization is carried out on the vehicles in the area based on a centralized or distributed algorithm, and an efficient, flexible and safe intelligent traffic jam relieving scheme is provided by integrating the Internet of Vehicles technology, the multi-sensor fusion technology, the machine learning algorithm and the cooperative path planning method. According to the scheme, the traffic flow efficiency can be remarkably improved, reliable path planning and cooperative driving support can be provided for the automatic driving vehicle, and important social benefits and economic values are achieved.

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 relates to the technical field of digital filters, and provides an improved FIR filter structure based on a distributed algorithm (DA). The structure comprises a DA table, a partial product selection module and a shift accumulator. Wherein the DA table generates all possible partial products according to input; the partial product selection module selects an actual partial product according to the address sequence; the shift accumulator realizes accumulation and summation of partial products. Compared with the traditional DA-FIR filter, the method disclosed by the invention has the advantage that the consumed lookup table (LUT) and register (FF) resources are obviously reduced.

The invention discloses a multi-robot task allocation method based on distributed optimization, and relates to the technical field of control and information. According to the method, the saddle point dynamics and the optimistic gradient ascending and descending algorithm or the super-gradient algorithm are combined to establish the distributed task allocation algorithm of the multi-robot system, the non-convex task allocation problem can be solved, and the application range of the algorithm is wider. The distributed task allocation algorithm established by the invention is a completely distributed algorithm, the solution of a sub-optimization problem is not involved, the state is updated only through simple algebraic operation, the calculation complexity is relatively low, and the system states of all robots can be ensured to be quickly converged to the optimal integer solution of the task allocation problem.

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 discloses a multi-objective inter-domain collaborative distributed optimization control method for a distribution network, which mainly comprises the following steps: step 1, for a distribution network connected to multiple microgrids, dynamically partitioning the distribution network according to node voltage sensitivity; step 2, establishing an upper optimization model based on the partitioning, taking active network loss and voltage quality in each region as objective functions, and taking power flow equation constraints, safe operation constraints, photovoltaic output constraints in the distribution network, and power constraints of a public interconnection line between the distribution network and the microgrid as constraint conditions; step 3, solving the upper optimization model by adopting a fully distributed ADMM algorithm based on improved penalty factor modulation to obtain the power of the public interconnection line between the distribution network and the microgrid; step 4, constructing a lower optimization model, taking the operation cost of the microgrid as the objective function, taking the power balance constraints of the internal power of the microgrid and the power of the public interconnection line between the distribution network and the microgrid, MT operation constraints, DE operation constraints, and BESS charging / discharging constraints as constraint conditions, and solving the lower optimization model.

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 relates to a virtual power plant scheduling method and system under Byzantine attack, and the method comprises the following steps: collecting the real-time power of each distributed power generation node in a virtual power plant and the connection topology information between the distributed power generation nodes, and obtaining an adjacent matrix of the distributed power generation nodes according to the connection topology information; taking the minimum power generation cost of all distributed power generation nodes as a target, simulating abnormal distributed power generation nodes by Byzantine nodes, updating the real-time power of the distributed power generation nodes by adopting a Lagrange duality method on the premise of referring to the positions of all elements in a constraint set, and determining the real-time power of the distributed power generation nodes on the basis of a communication review mechanism. And the communication cost is reduced while scheduling control is performed on the virtual power plant. The method is effective and reliable, the Byzantine attack is judged through a distributed algorithm, power scheduling participated by all power generation nodes is provided on the premise of overcoming risks, and an optimal potential scheduling mode capable of saving communication cost is provided for a virtual power plant.

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.