567 results about "Operation scheduling" patented technology

The invention relates to the technical field of steel production plan optimization based on multi-dimensional constraint dynamic coupling, in particular to a molten iron operation plan arrangement method based on dynamic constraint modeling hybrid optimization, which comprises the following steps: acquiring order data, equipment state data and constraint rule data to generate structured input parameters comprising order priority labels; constructing a multi-objective optimization model based on the molten iron distribution constraint and the maintainable time length of the equipment, extracting the maintenance conflict relationship between the equipment groups, constructing a maintenance rule knowledge graph, and generating a dynamic constraint condition set; generating an initial molten iron distribution and maintenance plan through global search by adopting a genetic algorithm, and outputting a molten iron distribution path and an equipment maintenance time sequence through local optimization by combining a linear programming algorithm; according to the method, dynamic collaborative optimization of molten iron distribution and equipment maintenance is achieved, and the resource utilization rate, the order delivery rate and the production system stability are improved.

The invention belongs to the field of civil aviation, provides a civil aircraft field service operation scheduling control system, and aims to solve the problem of low cooperative efficiency of multiple links of ground service scheduling. File management is based on an Ali cloud OSS development interface, supports multi-format files, and realizes functions of permission access, encryption and the like; communication adopts an MQ / MQTT architecture to construct an IM system, and supports multi-type message transmission and http network communication; data processing packages a multi-language database interface, and internal and external network secure intercommunication is realized through a gatekeeper agent. Flight functions cover and guarantee node management and control, dynamic updating, cross-department communication, real-time flight data butt joint, task distribution of unified operation according to work types, and support of multi-terminal such as a web terminal and a handheld terminal and nailing access. Ground service multi-scene requirements can be met, and the operation efficiency and the management level are improved.

The invention discloses a power transmission line fault diagnosis and operation and maintenance scheduling method and system based on networking learning, and relates to the technical field of power transmission line fault diagnosis operation and maintenance scheduling. Related data is extracted to construct a high-risk equipment area and a visual high-risk area thermodynamic diagram, a visual risk grading diagram is constructed in combination with electrical quantity data, and meanwhile, an intelligent recognition storage network and a fault type classification recognition model are constructed in combination with a convolutional neural network-long and short-term memory network hybrid model; the model is optimized through networking learning and an attention mechanism, maintenance teams and resources are autonomously allocated in combination with an operation and maintenance management system, then autonomous optimization and closed-loop operation are achieved, full-process coverage of fault sensing, intelligent decision making and efficient response is achieved, the response time after a line fault occurs is remarkably shortened, and the maintenance efficiency is improved. And the fault handling and operation maintenance capabilities of the power grid system are comprehensively enhanced.

The invention discloses a multi-park cross-space-time comprehensive energy consumption multi-objective optimization method and system based on deep learning, and the method comprises the steps: carrying out the feature extraction of data in the operation of a multi-park energy system through an extended long-short term memory network model, and constructing a multi-park multivariate energy efficiency evaluation index model according to the extracted multi-dimensional energy consumption features; evaluating the correlation degree of the operation process of the multi-park integrated energy system, and constructing an energy complementary relation matrix; according to the multivariate energy efficiency evaluation index model and the energy complementary relation matrix, constructing a multi-park system multi-target optimization model; solving the optimal solution of the optimization model under the operation constraint through a Transform-based multi-target particle swarm optimization algorithm, and obtaining an energy operation scheduling strategy of the integrated energy system of the plurality of parks; user interaction feedback is adopted in optimization, so that the result better meets the actual demand and target of the user; according to the method, cross-space-time comprehensive energy multi-target optimization of multiple parks can be realized, and the comprehensive energy utilization efficiency of the parks is improved.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention relates to the technical field of collaborative construction, in particular to a collaborative construction method and system based on BIM, and the method comprises the following steps: obtaining component information and a construction path, calculating the center distance, the number of intersection lines and the blocking ratio, evaluating the density and the staggering degree, generating an urgent distribution set and a task list, extracting spatial parameters, and calculating the overlapping level. And judging the compression feasibility of the time window, and generating a non-parallel task list. According to the method, through quantitative evaluation of blocking risks between construction nodes, a dynamic sorting list generated based on urgency degree values and construction plan binding tasks, conflict levels between component pairs are divided through an operation space overlapping degree index, compression judgment is performed on an adjustable time window, and a minimum interference construction time period is allocated; strong interference task pairs are calibrated to avoid parallel construction, the sorting rationality of construction tasks and the space decoupling efficiency of job scheduling are effectively improved, and fine matching and efficient cooperation of component scheduling sorting and resource allocation in a dynamic scene are achieved.

The invention discloses an island micro-grid operation scheduling method and device. According to the scheme, the method comprises the steps that power generation characteristic modeling is conducted on a distributed power source and an energy storage system of a diesel storage island micro-grid; constructing a function taking the minimum daily total operation cost as a target, wherein the function covers the operation cost of a diesel generator and an energy storage system and the pollutant treatment cost; establishing a power impact type load mathematical model including power characteristics, system dynamic frequency response characteristics, micro-grid dynamic frequency safety constraints and diesel generator frequency control constraints; establishing a micro-grid operation constraint model which has constraints such as power balance, Distflow power flow, energy storage, charging, discharging and capacity, diesel generator output and climbing rate; and solving the model by using a particle swarm optimization algorithm, generating a diesel generator output, energy storage system charging and discharging and frequency modulation reserve capacity distribution strategy, and obtaining an island microgrid optimal operation scheduling decision.

The invention provides a multi-AMR robot collaborative operation scheduling optimization method and system and a medium. The method comprises the steps that a collaborative operation scheduling map is constructed based on environment information; allocating a corresponding first target task based on the first state information of the AMR robot and the first task information of each to-be-executed task; planning an initial operation route of the AMR robot based on the collaborative operation scheduling map in combination with the second state information of the AMR robot and the second task information of the first target task; performing path conflict detection optimization based on the initial operation path of the AMR robot to obtain a first target operation path; and based on the real-time state information of the AMR robot in the process of executing the first target task, optimizing the first target operation route and the first target task of the AMR robot, and determining a second target operation route and a second target task of the AMR robot. According to the method, the efficiency and the stability of collaborative operation of the multiple AMR robots are improved.

The invention relates to the technical field of energy storage battery equalization adjustment, in particular to an energy storage power station intelligent operation scheduling management system which comprises a battery parameter estimation unit, a dynamic equalization threshold generation unit, an equalization circuit control unit, a data fusion prediction unit and a scheduling strategy generation unit. The battery parameter estimation unit calculates key parameters of a single battery, the dynamic equalization threshold generation unit generates a dynamic equalization threshold through a fuzzy logic algorithm in combination with a battery aging stage, multiple parameters and temperature factors, and the equalization circuit control unit determines an energy transfer strategy according to a model prediction control algorithm. A switch tube is controlled through an adaptive phase shift angle adjustment technology, a fault diagnosis and fault-tolerant control mechanism is provided, the data fusion prediction unit predicts key indexes of the energy storage power station, the scheduling strategy generation unit formulates an optimal operation scheduling strategy, and the ability of the energy storage power station to participate in power grid auxiliary service is improved.

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 passenger ship operation scheduling method and system based on big data analysis, and the method comprises the steps: building a port passenger ship operation digital twin model, synchronizing the ship positioning data, facility operation states and passenger dynamic distribution data of a port in real time through Internet of Things equipment, and generating a virtual simulation scene; calling a parallel simulation engine of the digital twin model, performing space-time conflict detection and resource load rehearsal on the candidate shift scheduling schemes, and screening initial scheduling schemes meeting conditions; boarding bridge distribution, waiting area capacity matching and ship berthing time sequence optimization are simulated in the digital twin model, and an operation scheduling plan is generated; and establishing a scheduling evaluation model, performing multi-target iterative optimization on the passenger retention risk, the facility vacancy rate and the carbon emission intensity of the operation scheduling plan according to a dynamic weight adjustment algorithm, and outputting an optimal operation scheduling plan. According to the method, the smoothness of the port operation process is improved, the boarding time of passengers and wait area resource waste are reduced, and the ship berthing efficiency is improved.

The invention discloses a cluster job planning method and system based on a dual time window mechanism. The method comprises the steps that jobs are divided into a multi-level structure with an execution sequence, and a directed acyclic graph is constructed to form a task topological structure; constructing a dual time window mechanism; designing a multi-objective optimization function; modeling cluster job scheduling into a multi-agent Markov decision process, setting a hierarchical action space, and designing a reward function reflecting scheduling balance; the method comprises the following steps: constructing a hybrid neural network architecture, extracting a time sequence correlation feature of a task, encoding a heterogeneous feature between the task and a server by using a heterogeneous graph neural network, and generating an intelligent matching score between the task and the server; a multi-agent near-end strategy optimization algorithm is applied, and agent collaborative optimization is achieved through a collaborative mechanism updated by a centralized value function and a distributed strategy. According to the method, fine modeling and state dynamic perception of complex operation topology are realized, and scientificity and controllability of scheduling decision are improved.

The invention discloses an AI auxiliary operation scheduling and compliance identification method for operation under a high-voltage line. The method comprises the following steps: firstly, according to an actual layout path, a voltage grade and a protection boundary of a high-voltage line, constructing a three-dimensional coordinate system matched with the terrain for delimiting a dangerous grade area; in the operation process, the spatial position and attitude parameters of the operation machine and the position point location information of an operator are continuously collected and converted into a track input data set. Through construction of a continuous track sequence and spatial difference analysis, operation behaviors exceeding a safety boundary or approaching a critical height are identified, and key space-time and equipment information is extracted. And performing risk classification on the abnormal behaviors in combination with the construction plan and the field progress, and generating a dynamic scheduling suggestion according to the current operator density and the equipment arrangement condition. The suggestion can be synchronously pushed to a field terminal, and is linked with a voice broadcast and warning device to carry out early warning, so that active intervention and scheduling optimization of high-risk operation behaviors are realized.

The invention relates to the technical field of data processing, and particularly discloses a cross-platform and cross-cluster hybrid operation method and system. By constructing the initial mixed environment adaptation model, pre-adjustment of the job scheduling strategy can be realized according to the resource feature parameters and job attributes of the physical machine cluster and the cloud environment cluster, so that resources in different environments can be fully coordinated, the flexibility and efficiency of job scheduling are improved, and the job scheduling efficiency is improved. Seamless sharing and circulation of job data and states between a physical machine and a cloud environment are achieved by dynamically adjusting the execution sequence and the resource allocation quantity of jobs and utilizing a cross-environment data sharing space, optimization factors are generated by collecting the operation states and resource change data of sub-job units in real time, and the optimization efficiency is improved. The real-time performance and the accuracy of job scheduling are ensured, an initial mixed environment adaptation model can be continuously optimized to form closed-loop optimization by means of a mixed environment job execution evaluation report feedback mechanism, and the stability and the high efficiency of job execution are ensured.

The invention discloses a distributed job scheduling method and device, a storage medium and computer equipment, relates to the technical field of distributed task scheduling, is suitable for the field of financial and medical services, and mainly aims at solving the problem that an existing distributed job scheduling system lacks task dependence management and dynamic scheduling capability. Comprising the following steps: performing task modeling processing on each to-be-executed task by adopting a binary tree structure to obtain a task binary tree corresponding to each to-be-executed task; performing serialization processing on the task binary tree by adopting a job arrangement engine to obtain a corresponding task sequence; issuing the task sequence to an event flow bus; after the event flow bus is started, tasks to be executed are allocated to different distributed actuators for execution based on the central control scheduler; and the distributed executor returns an execution result to the event flow bus in real time, so that the event flow bus triggers the downstream task after the upstream task is completed until the task sequence is completely executed.

The invention discloses an automatic shipping operation scheduling method for a container wharf, and the method is realized through the following steps: determining a current workable candidate container list based on a preset on-board position dependence relation and a storage yard real-time state through feasibility screening; performing execution cost evaluation and opportunity utility evaluation on the candidate jobs respectively, quantifying the costs of equipment movement, overturning and ramming operation and the like by the execution cost evaluation, and actively and quantitatively unlocking the key jobs by the opportunity utility evaluation, thereby forming earnings brought by double-box collaboration or matching of wharf resources; and calculating a final decision score through a weighting algorithm fusing utility and cost, and selecting an optimal job generation instruction according to the final decision score. According to the method, a complex scheduling problem is decomposed into a plurality of evaluation levels, refined and intelligent management of ship loading operation decisions is realized, and the overall efficiency, balance and economical efficiency of automatic wharf ship loading operation can be effectively improved.

The invention discloses a multi-robot collaborative job scheduling optimization method based on a graph neural network. The method comprises the following steps: S1, constructing a collaborative scheduling graph comprising robot nodes, task nodes and meeting point nodes; s2, initializing a feature vector of each node; s3, node feature aggregation and scheduling representation vector generation are executed through the graph neural network; s4, an improved Co-DPSIPP algorithm is adopted to calculate a handover score of a meeting point node, and optimization is carried out based on task completion time, energy consumption change and communication load; s5, selecting an optimal handover mode according to the handover score, and updating the scheduling graph; s6, executing task redistribution and path planning based on the updated scheduling graph; and S7, repeatedly executing reasoning and scheduling control until all tasks are completed, and outputting a scheduling scheme and a path sequence. The scheduling efficiency and path planning of the multi-robot system are remarkably improved, and the method is widely applied to automatic production and robot cooperation tasks.

The invention discloses a micro-grid capacity optimization configuration method based on an improved grey wolf algorithm. The method comprises the following steps: S1, constructing a capacity optimization configuration model of a micro-grid based on each distributed power supply of the micro-grid; s2, setting a micro-grid operation scheduling strategy; s3, under the microgrid operation scheduling strategy, solving the capacity optimization configuration model through an improved grey wolf algorithm to obtain an optimal configuration scheme; wherein the solving process comprises the following steps of: initializing a population by adopting a good point set, and confirming an initial configuration scheme; self-adaptive weight coefficients in different advancing directions are determined according to gray wolf positions, and population updating is carried out in combination with convergence factors; using a lens reverse learning strategy to update an optimal individual, and introducing a greedy strategy to carry out individual screening; the optimal configuration scheme is obtained through iteration; the method has higher optimization precision and better optimization stability, and can effectively reduce the annual value comprehensive cost and the power deviation rate of the micro-grid.

The invention discloses a surgical instrument automatic scheduling method, system and terminal based on pre-operation scheduling, and the method comprises the steps: obtaining pre-operation scheduling information which comprises an operation style, an operator, a time period and inter-operation scheduling; a standard instrument set template is obtained according to the operation mode, historical instrument use behavior data of the operator is obtained, and the historical use behavior data is used for dynamically correcting the standard instrument set template to obtain a personalized template; acquiring instrument inventory information in the time period, and intelligently scoring the standard instrument package template and the personalized template according to the instrument inventory information and the recommendation model to obtain a target template; obtaining a scheduling task list according to the target template, the operation style, the time period and the inter-operation arrangement; and inputting the scheduling task list into an automatic transfer system, generating a route task package, and packaging, delivering and delivering the instruments according to the route task package and the scheduling instruction. According to the method, the instrument set is automatically generated and optimized, and the surgical instrument configuration efficiency is remarkably improved.

The invention relates to the field of gypsum library scheduling, and discloses an intelligent gypsum library job scheduling control system, which is provided with a monitoring module, a task scheduling module, a constraint reforming module, a grading module, a collision avoidance regulation and control module and a task scheduling module, simulates different operation task allocation conditions based on a driving scheduling model, and screens an optimal task allocation scheme; the constraint reforming module draws a driving speed change diagram according to the instruction parameters of the driving grading instruction based on the speed constraint, and draws a walking route map according to the speed change diagram; the grading module determines the collision avoidance priority of driving based on the task remaining time; the collision avoidance regulation and control module controls the travelling crane with the low collision avoidance priority to start a collision avoidance program when the distance difference between the two travelling cranes tends to and is equal to the maximum safety distance. According to the invention, on the basis of considering the speed constraint, task allocation optimization is realized, the speed change diagram of driving is updated by taking the speed constraint as a reference, and the driving collision avoidance effect is optimized on the basis of considering the driving speed constraint.

The invention provides a multi-type micro-grid cross-layer collaborative scheduling method based on multi-agent reinforcement learning, and relates to the technical field of power system and micro-grid scheduling, and the method comprises the steps: firstly constructing a multi-type micro-grid layered collaborative architecture, a multi-agent reinforcement learning scheduling model, and a centralized coordination layer where global agents are deployed in a platform layer; and the local agents are deployed at each distributed micro-grid node of the platform layer, a cross-layer cooperative training mode is adopted to train the model, and finally, cooperative decision results of the global agents and the local agents are integrated to generate a global-local cooperative scheduling scheme. According to the method, global optimization and local flexibility can be considered, and the hierarchical multi-agent architecture adopts a multi-type micro-grid hierarchical collaborative architecture, so that the method can be suitable for multi-type micro-grid operation scheduling of a novel power system, and multi-type micro-grid collaborative scheduling considering global optimization and local flexibility is realized.

The invention provides a data decoupling centralized query method and system based on MQ message triggering, and relates to the technical field of data query, and the method comprises the steps of receiving a message queue data change message, constructing a field, dividing check nodes by depending on a directed acyclic graph, carrying out data consistency verification, and pushing a transaction identifier to carry out concurrent operation scheduling. And collecting hotspot information by adopting a Bloom filter to adjust data fragmentation and cache distribution, generating a distributed query routing table, and distributing query requests according to the routing table. According to the method, the data consistency is improved, and the query performance is optimized.

The invention relates to a hybrid computing resource optimization model. Network optimization for arranging computing subtasks in a hybrid computing environment is provided. The method includes receiving an input of a network of nodes and edges representing computational processes and composition information thereof, wherein the nodes are grouped depending on whether they use classical computing resources or quantum computing resources. The method generates workflow constraints, scheduling constraints, and computing resource allocation constraints. The method generates an objective function. And solving the optimization problem according to the objective function and all the constraints. The solution determines the best computational goal achieved, computational workflow through selection of nodes, computational job scheduling, and allocation of computational processes between classic and quantum computational resources. The computing workflow is then executed to achieve the best computing goal according to the computed job schedule and the allocation of the computing process between the classical computing resources and the quantum computing resources.

The invention belongs to the technical field of data processing. The invention discloses a matrix multiplication calculation task processing system, method and device, a storage medium, a program product and a chip, and the system comprises a storage module which is used for storing a preset weight matrix in advance; the linear transformation control module comprises a finite-state machine used for matrix multiplication operation scheduling and is used for generating an input request signal so that the matrix multiplication calculation task processing system can receive an input matrix; the operation module is used for executing matrix multiplication operation on the input matrix and a preset weight matrix to obtain a matrix multiplication operation result and generate an output matrix; and the data flow control module is used for controlling a plurality of groups of data flows formed by the input data in the input matrix according to rows to sequentially enter the operation module according to the input request signal until all the data in the input matrix execute and complete the matrix multiplication operation. The problems that in the prior art, a matrix multiplication accelerator lacks flexibility in a linear layer implementation task of artificial intelligence hardware, the data migration cost is high, and the energy efficiency ratio is low are solved, and the method is suitable for self-defined extension based on an RISC-V instruction set architecture, can be used as a coprocessor of an RISC-V processor, and can be used as a coprocessor of the RISC-V processor. And linear layer calculation in the neural network is accelerated, especially in an artificial intelligence task, the calculation efficiency can be remarkably improved, the power consumption can be reduced, and the real-time processing requirement can be met.

The invention belongs to the technical field of power grid optimization, and particularly relates to a GAN driven learning-based power grid planning equalization optimization method and device, and the method comprises the steps: constructing a physical constraint-based space-time generative adversarial network PI-ST-GAN, building a composite risk index, and screening out a high-risk scene from a generated source-load probabilistic time sequence scene; constructing a risk-cost balanced double-layer optimization model; reconstructing the double-layer optimization model into a single-layer mixed integer programming (MIP) model based on a strong dual theory, and setting constraint conditions of the single-layer mixed integer programming (MIP) model; based on a single-layer mixed integer programming MIP model, a solver is utilized to carry out joint solution on a line investment scheme and operation scheduling variables, PI-ST-GAN is dynamically guided to enhance high-risk scene generation according to a solution result, an optimization-generation-feedback closed-loop collaborative iteration solution process is realized, and finally an optimal power grid planning scheme is output. The method can improve the toughness and economical efficiency of a power grid under the high permeation of renewable energy sources.

The invention discloses a new energy low-carbon operation scheduling method and system for an island microgrid, and the method comprises the steps: generating a day-ahead scheduling plan with the minimization of the weighted average carbon emission intensity of a system as a core target through an upper-layer day-ahead optimization model based on load prediction data and renewable energy power prediction data; according to the day-ahead scheduling plan, generating an adjustment instruction of output of each unit through a lower-layer intra-day rolling optimization model; based on the adjustment instruction, calculating the wind and light abandoning power of the renewable energy source, and optimizing the renewable energy source consumption through a cost punishment mechanism; according to the optimized unit output and renewable energy consumption scheme, coordinating a charging and discharging strategy of an energy storage system to obtain an optimized scheduling instruction; and executing the scheduling instruction, and updating the system state through a closed-loop feedback mechanism. According to the embodiment of the invention, power balance and low-carbon operation of the island microgrid under a complex fluctuation working condition can be realized, and the utilization rate of renewable energy sources and the overall environmental protection property of the system are improved.

The invention discloses a multi-ship optimization method for cooperative operation and maintenance scheduling of an offshore wind plant, and belongs to the field of power system scheduling. According to the method, a heuristic algorithm and an accurate algorithm are combined, so that the problem of large-scale multi-ship collaborative maintenance scheduling of the offshore wind plant is solved; the method comprises the steps that a maintenance task constraint decomposition heuristic method is adopted to decompose a collaborative optimization scheduling problem into single-ship scheduling sub-problems, and it is ensured that constraint conditions of an original problem are met; performing path planning on each sub-problem by using an A * algorithm and a DATPF algorithm, and quickly and sequentially solving the sub-problems by using a CP-SAT solver; combining the solutions of single ship scheduling to form a comprehensive solution of a multi-ship collaborative maintenance scheduling problem; finally, a calculation result is obtained, and the effectiveness and robustness of the method are verified.

The invention discloses a wind, light and fire storage operation scheduling optimization method, device and equipment, a medium and a product, and relates to the field of comprehensive energy optimization, and the method comprises the steps: constructing an operation model and an operation scheduling optimization model of a to-be-adjusted wind, light and fire storage system; according to the value of the judgment parameter, determining the adjustment requirement of the fire and energy storage combined output at the current moment, and based on the adjustment requirement and a preset priority adjustment mode, determining an operation constraint for limiting the adjustment priority of the thermal power element and the energy storage element; solving the mixed integer model to obtain an operation scheduling scheme of the wind-solar-thermal storage system to be adjusted; the mixed integer model comprises an operation constraint and an operation model and an operation scheduling optimization model of the to-be-adjusted wind-light-fire storage system, the cleanliness and reliability of the wind-light-fire storage system can be considered, the stability of an electric power system is ensured, meanwhile, consumption of traditional energy such as fire coal is reduced, carbon emission is reduced, and the service life of the wind-light-fire storage system is prolonged. And the comprehensive utilization maximization of energy is realized.

The invention discloses a network change operation scheduling method and device. The method comprises the steps that a conflict rule set and a to-be-executed network change operation set are acquired, and the conflict rule set is used for determining conflict reasons, conflict types and conflict eliminating rules between different network change operations; according to the conflict rule set, generating a conflict dependency graph of the to-be-executed network change operation set, the conflict dependency graph being used for representing a dependency relationship and a conflict relationship between operations in the to-be-executed network change operation set; and determining an execution sequence of all network change operations in the to-be-executed network change operation set according to the conflict dependency graph. The method provided by the invention at least solves the technical problem that the fault risk is relatively high after the network change operation is executed due to the fact that conflicts between the network change operations are not considered when the network change operation is executed in related technologies.

A method for generating an operation scheduling scheme of a hydrogen-photovoltaic-storage-charging integrated energy station includes the following steps: setting operating parameters of a hydrogen-photovoltaic-storage-charging integrated energy station; establishing an operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station according to the operating parameters; and finally, iteratively solving the operation scheduling model of the hydrogen-photovoltaic-storage-charging integrated energy station by means of an improved grey wolf optimization algorithm to obtain individual position with a maximum predation benefit in a current wolf population, and outputting the individual position as an optimal scheduling scheme of the hydrogen-photovoltaic-storage-charging integrated energy station. The method stablishes a novel operation scheduling model under the condition that the correlation between multiple types of energy in the hydrogen-photovoltaic-storage-charging integrated energy station and uncertain factors in operation of the hydrogen-photovoltaic-storage-charging integrated energy station are taken into account.