686 results about "Coscheduling" patented technology

The invention provides a perception collaborative decision-making method and system based on multi-modal heterogeneous data fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting a global environment situation perception graph into a pre-trained multi-target collaborative decision-making model; the multi-target collaborative decision-making model forms a multi-target decision-making feature set by analyzing the resource entities and the incidence relation in the graph; based on the multi-target decision feature set, decision optimization is carried out to obtain a comprehensive collaborative scheduling scheme; performing instruction analysis and packaging on the comprehensive collaborative scheduling scheme to obtain an executable instruction sequence; and issuing the executable instruction sequence to a corresponding decision node and a control terminal in parallel through a distributed communication architecture to complete real-time scheduling of resources and collaborative issuing of control instructions. According to the invention, by constructing a linkage mechanism of multi-modal data fusion, dynamic environment perception and collaborative decision execution, intelligent perception and quick response to a complex environment are realized.

The invention discloses a digital twinning-based flow machine operation intelligent collaborative management system, and the system comprises an operation data collection module which is used for obtaining multi-source data in a target scene and carrying out the preprocessing of the multi-source data; the twinning modeling module is used for constructing a flow machine operation digital twinning model and generating a twinning operation initial situation; the space-time path analysis module is used for constructing a time hierarchical path network and generating a path conflict cone set and a feasible space-time path set; the operation load calculation and scheduling module is used for establishing task link representation, generating a storage yard congestion potential field and forming a multi-stream-machine cooperative scheduling candidate scheme; and the virtual-real correction module is used for issuing execution and collecting operation feedback, correcting the twin model and generating an optimized target collaborative scheduling scheme. According to the method, the space-time-behavior-link coupled digital twinborn model is constructed to realize flow machine operation predictive collaborative scheduling, and the method has the advantages of conflict recognition in advance, congestion trend suppression and remarkable improvement of scheduling efficiency.

The invention discloses a heterogeneous computing power resource dynamic cooperative scheduling method and system and a computer device. The method comprises the steps of obtaining multi-dimensional feature information of a to-be-scheduled task, current state information of each node in a heterogeneous computing power resource pool and scheduling environment state information; training a time sequence model by adopting historical associated data, inputting the multi-dimensional feature information, the current state information and the scheduling environment state information into the trained model to obtain associated trend information in a future preset time window, and then combining the multi-dimensional feature information and the current state information of all the nodes to obtain the scheduling environment state information. A multi-objective optimization function fusing task delay, cost consumption and energy consumption is constructed, the function is optimized based on a preset strategy, a target decision strategy is obtained, a scheduling instruction set is generated after analysis, allocation information and target nodes are determined according to the scheduling instruction set, and tasks are issued. According to the method, dynamic collaborative scheduling of heterogeneous computing power resources can be realized, and the overall utilization efficiency of the resources is accurately, efficiently and effectively improved.

The invention relates to the technical field of production logistics scheduling management, and discloses a digital twin workshop production logistics real-time scheduling management method and system, and the method comprises the steps: 1, building a unified state data and standard parameter baseline, calculating a consistency deviation value, and setting a two-stage deviation judgment threshold; 2, receiving a field event, and determining a scheduling time window; 3, determining task priorities, and generating a production to-be-executed list; 4, generating a carrying task set according to the production to-be-executed list, and determining carrying equipment and task allocation under constraint; step 5, performing channel conflict-free path planning, and generating a space-time occupation table; step 6, performing cross consistency check, and executing issuing or local recalculation according to a threshold; and step 7, executing field operation and updating unified state data, and executing consistency recovery when the deviation exceeds the limit. According to the invention, real-time collaborative scheduling and high-reliability operation of workshop production and logistics tasks are realized.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention discloses a cooperative scheduling method for a zero-carbon park complementary energy storage system, and the method comprises the steps: enabling an electric energy quality index to be explicitly incorporated into an optimization target through multi-source resource dynamic modeling and scene prediction, and building a strong coupling relation between a physical constraint and a scheduling decision; the hierarchical execution mechanism gives consideration to global optimization and local quick response, realizes undisturbed switching under abnormal working conditions, forms a prediction-optimization-execution-feedback closed-loop control system, and can accurately describe physical connection and electrical characteristics of a park power grid by establishing a power distribution network equivalent model and acquiring topological parameters, thereby realizing the optimal control of the park power grid. And basic network data is provided for subsequent optimization. By determining the controllable resource set and completing topological mapping, the position and the regulation and control range of each device in the power grid can be determined, and mistaken sending or conflict of instructions can be avoided. An apparent power upper limit constraint and SOC dynamic model is established, overload operation of equipment can be avoided, the energy storage charging and discharging capacity can be accurately represented, and the performability of a scheduling scheme is ensured.

The invention discloses a multi-agent collaborative scheduling method and system oriented to science and technology intelligence analysis, and relates to the technical field of science and technology intelligence analysis, and the method comprises the steps that a task issuing layer outputs a standardized task description by executing task demand element analysis; after the intelligent coordinated scheduling layer receives the standardized task description, closed-loop collaborative decision making is carried out, and a task distribution instruction is output; and the professional agent execution layer drives the data acquisition agent group to acquire science and technology information data through a data access adapter of the data resource layer, executes a science and technology information analysis task, outputs a structured processing result, and visually displays the structured processing result to a user through the task release layer. The technical problem that in the prior art, a science and technology information processing system lacks intelligent task allocation, and consequently the information processing efficiency is low is solved, and the technical effects that four-layer architecture closed-loop cooperation of science and technology information analysis tasks and efficient dispatching of specialized intelligent agent groups are achieved, and the task allocation accuracy and the information processing efficiency are improved are achieved.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

The invention provides a data center calculation, electricity and heat collaborative optimization scheduling method and system, and relates to the technical field of comprehensive energy scheduling. According to the scheduling method and system, a collaborative scheduling model of computing power, electric power and thermal power is constructed, and a user side thermal demand response mechanism is introduced, so that mismatching of waste heat supply of a data center and user thermal demand in time and space is dynamically relieved, and the waste heat utilization rate and the overall energy efficiency of the system are remarkably improved; by establishing a joint optimization framework, a complex coupling relationship among computing power, electric power and heating power is accurately described and coordinated, so that the comprehensive operation cost of the data center is minimized on the premise of ensuring the service quality of the workload of the data center; and a large language model-assisted deep learning algorithm is further adopted to solve the scheduling model so as to cope with multiple challenges of workload fluctuation, electricity price change and heat demand uncertainty, and a self-adaptive, intelligent and interpretable scheduling decision is realized.

The invention discloses an inference task collaborative scheduling system and method oriented to multiple terminals of a swan gap. The inference task collaborative scheduling system comprises a communication module Broker, a system monitoring module SystemProfilter, a scheduling module Scheduler and an inference task execution module Worker. According to the method, a structured resource state vector is constructed based on an NDK native interface so as to comprehensively represent the real-time availability of equipment; a lightweight MQTT protocol is adopted to support low-overhead and high-robustness cross-terminal state synchronization and task distribution; a comprehensive load scoring mechanism fusing task priorities and dynamic weights is designed, a multi-objective optimization problem based on a non-dominated sorting genetic algorithm (NSGA-II) is introduced, task delay is minimized in a combined mode, terminal loads are balanced, and energy consumption is controlled; meanwhile, the execution time delay is efficiently estimated in combination with a proxy model based on linear regression, and the scheduling overhead caused by real reasoning and calling is avoided; the whole architecture is deeply adaptive to a swan-mong system, and is compatible with an Android platform through modular packaging and unified communication interface design, and efficient, self-adaptive and cross-platform collaborative scheduling oriented to a swan-mong multi-terminal reasoning task is realized for the first time.

The invention relates to a side cloud collaborative adaptive workflow scheduling method and system, and belongs to the technical field of distributed computing and artificial intelligence. The method comprises the following steps of: firstly, in a macroscopic candidate screening stage, reducing problem granularity through task clustering, and obtaining balance between utilization and exploration based on a weighted distance probabilistic preferential strategy; then, in a collaborative scheduling decision-making stage, a global network state diagram is constructed through a graph neural network, deep spatial features of nodes and neighborhoods of the nodes are extracted, context-aware state representation is formed, and a reinforcement learning agent makes an optimal collaborative decision in multiple options such as local execution, edge migration or cloud unloading according to the state representation; and finally, in a local adaptive optimization stage, performing fine-grained optimization after the task is issued, dynamically adjusting a scheduling frequency and a multi-target weight through an online learning mechanism, realizing balance between a task deadline and a resource utilization rate, and ensuring efficient and robust execution of a node level.

The invention provides a power distribution network multi-source cooperative scheduling method and system based on big data, relates to the field of power distribution network scheduling, and solves the technical problem that an optimization strategy is not flexible enough in the prior art. The method comprises the following steps: acquiring multi-source real-time data of a power distribution network through an Internet of Things terminal and an intelligent electric meter; based on the multi-source real-time data, predicting a behavior mode of the electric vehicle and an output value of renewable energy by adopting a machine learning algorithm, and estimating a charging state of the electric vehicle by using a probability SOC prediction model; the behavior pattern is used for representing a travel rule of the user; generating a multi-time-scale scheduling strategy based on the behavior pattern, the output value of the renewable energy source and the charging state by adopting a collaborative optimization algorithm; and testing the scheduling strategy based on a simulation environment constructed by a digital twinning technology, and issuing a control signal based on a test result.

The invention discloses a heterogeneous graph neural network-based microgrid cooperative scheduling prediction and security check method. The method comprises the steps of constructing a heterogeneous graph model; different micro-grid operation scene graphs are generated through analogue simulation; solving an optimal collaborative scheduling strategy vector for each scene graph to serve as an optimal scheduling label, generating a training data set of a'scene graph-optimal scheduling label 'data pair, and completing offline training of the graph neural network model; dynamically generating an instantiated heterogeneous graph; taking the instantiated heterogeneous graph as the input of the trained graph neural network model, carrying out first forward reasoning, and outputting a predicted collaborative scheduling strategy vector; and injecting a virtual fault, generating a virtual fault heterogeneous graph, inputting the virtual fault heterogeneous graph and the predicted cooperative scheduling strategy vector into the trained graph neural network model, carrying out second forward reasoning, predicting the state of the faulty micro-grid system, obtaining the predicted fault node voltage, and carrying out safety check on the predicted cooperative scheduling strategy vector.

The invention relates to the technical field of automatic guided vehicle scheduling, in particular to an AGV cooperative scheduling method and system for multi-station joint tasks. The method comprises the following steps: constructing a task relaxation time evaluation model, and evaluating the urgency degree of a task by using the task relaxation time evaluation model; estimating energy consumption of task execution, and constructing a comprehensive optimization objective function according to the energy consumption; obtaining a task-AGV allocation result according to basic constraint conditions and the comprehensive optimization objective function; based on a task-AGV allocation result, screening an optimal path through a cost function; and in combination with the urgency degree and the optimal path, AGV cooperative scheduling is completed through multi-AGV path conflict detection and time window-game type coordination. According to the invention, the problem of cooperative scheduling in the automation process of the multi-station broken yarn splicing task of the textile workshop is solved.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention provides a cross-day two-stage random scheduling method for an industrial park integrated energy system, and the method comprises the steps: fitting the output fluctuation characteristics of new energy power generation equipment in a cross-day time scale based on historical data, and constructing an uncertainty scene set containing a new energy prediction error; establishing a cross-day two-stage stochastic programming model; according to the two-stage model, an optimal scheduling scheme is solved so as to minimize the overall operation cost, and the operation cost comprises the demand electric charge, the electricity purchase electric charge, the new energy power abandoning cost, the unit start-stop cost and the standby penalty cost; and on the basis of tie line power constraint and dynamic response characteristics of the multi-energy coupling equipment, feasibility verification and rolling optimization adjustment are performed on the scheduling scheme. According to the method, the new energy consumption capability of the industrial park integrated energy system under the cross-day time scale can be effectively improved, the total operation cost is reduced, and collaborative scheduling of demand cost optimization and spot market participation is realized.

The invention discloses a heterogeneous computing network resource collaborative scheduling optimization method based on self-adaptive multi-agent, and aims to solve the scheduling problem caused by resource heterogeneity, load dynamics and task high concurrency in a heterogeneous computing network system. According to the method, cross-domain resource collaboration is realized by constructing three sub-domain adaptive agents of a computing resource domain, a network resource domain and a storage resource domain and a global collaboration layer. According to the method, a deep reinforcement learning algorithm and an 'LSTM + GNN' fusion model are integrated, and multi-target adaptive optimization of resource utilization rate, task time delay, service quality and energy consumption is achieved through closed-loop optimization of state perception, strategy generation, value evaluation and strategy updating. The heterogeneous computing network resource fine-grained sensing, cross-domain cooperative scheduling and multi-target dynamic optimization are realized, the resource utilization rate and the task completion rate are high, the service quality and the energy consumption performance are good, and the dynamic response capability and the overall performance of the heterogeneous computing network system are improved.

The embodiment of the invention provides a multi-agent collaborative scheduling method based on federal reinforcement learning and digital twinning, which belongs to the technical field of computing, and specifically comprises the following steps: importing a test task list, environmental parameters and an agent capability matrix; constructing a mathematical model of a scheduling decision; based on a digital twinborn model of a physical entity, rehearsing a scheduling process and outputting a pre-optimization strategy set containing a conflict avoidance strategy; independently executing a deep reinforcement learning algorithm locally, generating a private experience pool, and updating local model parameters according to the private experience pool; the aggregation server calculates an aggregation weight based on the performance index of each agent, and adopts a weighted average strategy to aggregate local model parameters of each agent; dynamically allocating tasks to corresponding agents and monitoring task execution states; real-time resource competition among the intelligent agents is solved through an evolutionary game mechanism; and performing scheduling ending evaluation and iteration. Through the scheme of the invention, the scheduling efficiency, security and robustness are improved.

The invention discloses an intelligent collaborative scheduling system and method for a general computing and intelligent computing fusion scene, and relates to the technical field of computing power resource management and scheduling. In order to solve the problem that isomerous computing power resource islands are difficult to collaborate, the system comprises a computing power access layer used for executing specified operation on isomerous computing power resources through a standardized access template, abstracting the isomerous computing power resources into a unified logic computing power unit and registering the unified logic computing power unit into a computing power pool; the resource management layer is used for continuously monitoring and collecting static attributes and dynamic states of computing power resources to form a global resource real-time view; the business processing layer is used for receiving the business submitted by the user, analyzing and identifying the business demand, converting the business demand into a demand vector, and generating a dynamic arrangement scheme based on the real-time view and the multi-target strategy library; and the collaborative scheduling execution layer is used for converting the dynamic arrangement scheme into an instruction adaptive to various APIs and completing computing power resource allocation and service starting. According to the invention, unified management and intelligent cooperative scheduling of computing power resources can be realized.

The invention belongs to the technical field of energy management, and particularly relates to a light storage direct current flexible load self-evolution cooperative scheduling method, system and equipment, and the method comprises the steps: constructing a parameterized energy utility curve, quantifying the comprehensive utility of flexible load response in energy efficiency, comfort and equipment loss, and calculating the unit power marginal utility as the flexibility; establishing a multi-target collaborative scheduling model considering the time-varying carbon intensity, the electricity price and the utility curve, and solving by adopting a model predictive control and reinforcement learning mixed strategy; static and dynamic data are fused to construct a knowledge graph, and flexibility is predicted and cross-scene migration is realized through a sequence diagram neural network; and cooperatively optimizing a knowledge graph prediction result and a scheduling instruction through a Lagrangian relaxation method to form a self-evolution closed-loop control system. According to the method, flexible load refined modeling, carbon perception economic optimization scheduling and system adaptive learning are realized.

The invention discloses a hydraulic engineering scheduling simulation system based on digital twinning, and relates to the technical field of hydraulic engineering scheduling, the hydraulic engineering scheduling simulation system comprises an acquisition component, a processing component, an analysis component and a scheduling simulation component, the analysis component comprises an ecological analysis unit, a collaborative scheduling unit and a dynamic scheduling management unit, and the scheduling simulation component performs linkage simulation, simulation scene setting, multi-dimensional verification and an exception handling mechanism. Multi-dimensional ecological parameters are integrated through the ecological analysis unit, ecological health is comprehensively and quantitatively evaluated to fill up the limitation of a single index, and multi-target parameters such as flood control and power generation are integrated through the collaborative scheduling unit and converted into unified priority to solve the problem of fuzzy scheduling targets. And the dynamic scheduling management unit integrates multiple detail parameters to generate an accurate and adaptive instruction, so that the execution effect is ensured, and the hydraulic engineering scheduling simulation system has excellent practicability.

The invention relates to the technical field of power system dispatching, in particular to a distributed storage system for source-network-load-storage collaborative dispatching, which comprises a distributed data acquisition module, a regional collaborative sensing module, a distributed energy storage evaluation module, a dispatching instruction generation module and a dispatching instruction distribution module, wherein the distributed data acquisition module is used for acquiring real-time operation data of source, network, load and storage nodes in parallel; the regional collaborative sensing module is used for calculating a real-time power deviation value; the distributed energy storage evaluation module is used for generating an energy storage adjustment instruction set; and the scheduling instruction generation module is used for generating a final refined scheduling instruction. According to the invention, through constructing a cooperative scheduling mechanism of the source network load storage full chain, accurate identification of power deviation and optimal distribution of energy storage resources are realized, and scheduling response efficiency and operation stability of the system are significantly improved.

The invention provides a multi-agent reinforcement learning regional energy collaborative scheduling method and system, and belongs to the field of regional energy system scheduling. Coupling degrees and a coupling degree matrix between agents are constructed; inputting the observation vector into a strategy network to obtain a decision action; individual basic rewards and system economic rewards are calculated, and constraint reference rewards are constructed; individual differentiation basic rewards are calculated, and rewards are distributed; inputting the decision action into a physical quantity prediction network, calculating a physical consistency reward, obtaining a final reward and a global reward, and calculating a target return; splicing observation vectors and decision actions of all agents, splicing global joint observation vectors and joint action vectors, inputting the spliced vectors into a value network, and training; inputting the local state set into the trained strategy network, outputting a scheduling instruction, inputting the scheduling instruction into the trained value network, and outputting an evaluation result; the problems of depiction rigidness of an intelligent agent coupling relation, lack of a cooperative benefit distribution mechanism and insufficient decision physical consistency are solved.

The invention discloses a digital workshop multi-equipment collaborative operation control system based on industrial Internet of Things, which relates to the technical field of industrial Internet of Things collaborative control and comprises a data acquisition module, a health degree evaluation module, a collaborative scheduling optimization module, an instruction control module, a dynamic rescheduling module and a verification updating module. According to the method, a dynamic equipment association network is constructed, so that health degree evaluation considering the coupling influence between equipment is realized; and based on multi-source data fusion monitoring and real-time deviation analysis, a closed-loop dynamic rescheduling scheme subjected to physical and security verification is triggered and generated, and meanwhile, a system knowledge base is continuously updated by utilizing operation process data. According to the scheme, equipment isolation evaluation is converted into system coupling evaluation, so that static scheduling is upgraded to closed-loop dynamic control with self-adaption and continuous optimization capabilities, and the overall toughness and operation efficiency of multi-equipment collaborative operation are enhanced.

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 relates to a source-load-storage multi-twinborn collaborative interaction method. The method comprises the following steps: acquiring operation parameters of source, load and storage equipment to construct a source-load-storage multi-twin body; performing multi-scale time sequence analysis on the twinborn body, and extracting supply and demand fluctuation characteristics including fluctuation frequency, fluctuation amplitude and fluctuation energy ratio; based on the supply and demand fluctuation characteristics, optimizing output distribution of the source-side power generation equipment and the energy storage device by adopting a learning model, and generating a source-load-storage cooperative scheduling scheme; based on the scheduling scheme, an improved particle swarm optimization algorithm is used for solving an energy flow parameter between the load-side electric load and the energy storage device, and a source-load-storage collaborative optimization instruction is generated; fusing the real-time operation parameters and the collaborative optimization instruction, and performing equipment health state prediction through a neural network to generate a health diagnosis result; and monitoring the execution result of the collaborative optimization instruction, and generating a collaborative interaction report in combination with the health diagnosis result, the operation parameters and the supply and demand fluctuation characteristics, thereby realizing collaborative control and state monitoring of the source-load-storage system.

The invention discloses a heavy crane multi-task intelligent collaborative scheduling and path optimization control system, which is characterized in that by collecting operation state data, environment perception data and task queue data of a plurality of heterogeneous cranes, a time-space unified modeling module constructs and updates a collaborative operation digital map in real time; the collaborative operation digital map and the task queue are input into a multi-task dynamic collaborative scheduling module, a task-equipment allocation mapping relation and a plan time window are output, and then the collaborative operation digital map is combined to be input into a layered collaborative path planning module to generate a real-time motion control instruction sequence; and finally, issuing to a crane bottom layer control system through a protocol adapter to drive collaborative operation. According to the method, the problems of insufficient global optimization and high conflict risk when a plurality of heterogeneous cranes cooperatively work in a dynamic complex environment are solved, intelligent scheduling and safe planning are realized, and the working efficiency and safety are improved.

The invention discloses an electric vehicle man-machine cooperative scheduling strategy for multiple scenes of an electric power traffic coupling network, and aims to solve the problem of electric vehicle charging optimization scheduling caused by deep coupling of an electric power system and a traffic system in different scenes. The method comprises the steps that firstly, topological information and behavior characteristics are fused through a graph generative adversarial network, a graph structured model is constructed, and an electric power traffic coupling operation scene is generated; secondly, establishing a multi-objective optimization mechanism by utilizing hierarchical reinforcement learning, constructing an electric vehicle charging optimization scheduling finite Markov decision model in a conventional scene and a fault scene, and designing an algorithm based on knowledge distillation to solve a scheduling strategy; and finally, realizing strategy migration of charging redistribution and path emergency adjustment in a fault scene by combining a man-machine cooperative regulation and control technology and fusing a user instruction. Experimental results show that the strategy can effectively improve the toughness of the power grid, relieve traffic congestion, reduce charging queuing time and increase user satisfaction.