190 results about "Global scheduling" patented technology

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention discloses a multi-edge device collaborative reasoning method and system for a hybrid expert large model, and the method comprises the steps: collecting and analyzing system data, and adjusting the expert layout according to the system data; in the first stage, the number of experts required by each layer of a server is dynamically determined by balancing activation diversity and memory resource limitation; in the second stage, according to the expert number and the activation mode of each layer obtained in the first stage, low-time-delay reasoning is achieved by minimizing the calling times of remote experts, and the experts are distributed to all the servers. The system includes a global scheduler and an edge server participating in the system. By using the method, edge multi-machine joint reasoning is achieved, the deployment method is optimized, online adjustment deployment can be performed according to data changes, and the reasoning speed is increased compared with other deployment technologies. The method can be widely applied to the technical field of distributed machine learning.

The invention belongs to the field of power system operation and control, and particularly relates to an operation control method for a wind-light hydrogen storage multi-energy complementary system, which comprises the following steps: performing empirical mode decomposition on an original output sequence to obtain an IMF component, training parameters by combining a hidden Markov chain model and a forward-backward algorithm and solving an optimal state sequence, and fusing a predicted value by using Bayesian estimation to obtain an optimal state sequence; outputting a wind and light output and load demand global prediction value; by taking the minimum comprehensive operation cost, the minimum carbon emission intensity and the minimum power supply shortage rate as targets, obtaining equipment, power balance and hydrogen storage capacity constraints through a constraint verification algorithm, and constructing a multi-target optimization model; a scene trigger function identifies four types of typical scenes, a chaos particle swarm optimization algorithm is improved to solve a model, and density peak clustering is carried out to obtain a global scheduling scheme containing output of an electrolytic cell and a fuel cell and energy storage charge and discharge power. According to the invention, through scene adaptive optimization and intelligent clustering screening, the randomness and volatility of wind and light output are dealt with.

The invention discloses a workshop dynamic scheduling method considering equipment occupation under emergency order insertion, which comprises the following steps: receiving a trigger event in a production system, and obtaining workshop production real-time state data according to the trigger event; according to the real-time state data, a dynamic scheduling decision engine is activated, and a global scheduling problem is decomposed into deterministic sub-problems at the current decision moment; according to the deterministic sub-problem, identifying the state of a work-in-process and generating an unfinished process set, and re-bringing the unfinished process set into a rearrangement resource pool; selecting a rearrangement strategy based on a preset influence threshold according to the unfinished process set; according to the rearrangement strategy, a planning model is constructed, and the planning model aims at minimizing the maximum completion time and minimizing the order insertion change degree; according to the planning model, an improved genetic simulated annealing algorithm is adopted for solving, and a dynamic scheduling scheme is output and issued to a production execution layer. According to the invention, the disturbance of order insertion on the original production system can be minimized.

The invention relates to a virtual power plant cloud edge collaborative scheduling method based on multi-objective optimization and confidence coefficient screening. The method comprises the following steps: obtaining related data of a virtual power plant transaction terminal; constructing a multi-objective function based on the related data, selecting a comprehensive optimal scheme under the condition that the multi-objective function is satisfied, and generating an execution strategy set covering global scheduling of the virtual power plant; and carrying out cloud edge cooperative scheduling on the virtual power plant based on the execution strategy set. According to the method, multi-stage time sequence multi-target scheduling optimization and a Pareto frontier-based strategy generation method are fused, a multi-VPP joint optimal scheduling model is constructed in a cloud set, a high-reliability solution set is screened through confidence and is distributed to each VPP unit, and an edge controller intelligently selects an adaptive solution according to a local state, so that the optimal scheduling efficiency is improved. And collaborative optimization of the response efficiency, the execution reliability and the overall benefit of the system is realized. The method has engineering realizability, and is suitable for multi-scene complex environments such as a region-level virtual power plant platform and a source-network-load-storage cooperative control system.

The invention relates to the technical field of information communication, and discloses a 5G network slice resource scheduling method for a power distribution network, and the method comprises the steps: firstly collecting the geographic coordinates and priority labels of a service request, carrying out the regional division of a service based on the coordinates, and determining the cluster center of each region; then combining the priorities and the areas to which the requests belong, carrying out hierarchical sorting on all the requests to generate a global scheduling sequence; for each request in the sequence, calculating a path distance to each network resource node according to the coordinate of the request and the cluster center, and selecting a shortest path node as a target resource node; and finally, allocating resource quotas from the resource pool of the target node in sequence. According to the 5G network slice resource scheduling method provided by the invention, the resource matching accuracy and locality are effectively improved, the service transmission delay is reduced, the network flow space distribution is optimized, and the more efficient and reliable bearing of the power distribution network service is realized.

The invention provides a distributed large model reasoning method and system based on modular cache, and the method comprises the steps: disassembling a prompt into modules which can be independently reused through modular division driven by Schema, and calculating the attention Key-Value state of each module in advance before reasoning for caching. When a user request arrives, the system firstly performs module analysis and cache assembly on a prompt, loads a cache as required and supplements a missed part, and then injects a complete KV state into a reasoning process. Meanwhile, a layered and distributed scheduling mechanism is designed, a global scheduler selects a target GPU based on a cache hit rate and a node load, and a local scheduler manages cache copying and elimination in a node, so that load balancing and cache multiplexing are both considered. According to the scheme, repeated calculation can be remarkably reduced, the first Token delay is reduced, the system throughput rate is increased, and the method is suitable for high-repetition prompt scenes such as code generation and long text question and answer.

The invention discloses an SRv6 computing power network path optimization method and system based on multi-dimensional dynamic scoring, and belongs to the technical field of communication networks, and the method comprises the steps: building a multi-dimensional dynamic scoring system of load scoring, delay scoring and service priority scoring through collecting node loads, link congestion, geographic distances, topology hops and service types in real time; the method comprises the following steps: dynamically selecting an optimal path based on a DMPSC-T table (including a LastUpdate Time / ExpireTime timestamp mechanism); and when a better path is detected, an End.Redit instruction newly defined by SRv6 is utilized to trigger SID dynamic replacement: an Endpoint node directly replaces a target SID in a SegmentList, and a transfer node generates an ACL rule through a PBR to realize redirection. According to the invention, millisecond-level path switching can be realized, the service quality of high-priority business is effectively improved, and the global scheduling efficiency of network resources is optimized.

The invention provides a supply and demand matching degree-based multi-target collaborative optimization scheduling system for a light storage building group, and the system comprises a sensing layer which is used for collecting the physical operation state data of the light storage building group in real time; the network layer is used for transmitting data to the control layer; the control layer is used for generating a global scheduling plan by taking maximization of the comprehensive supply and demand matching degree as an optimization target according to the supply and demand matching degree prediction information of the first scale and the time-of-use electricity price information of the power grid; according to the supply and demand matching degree prediction information of the second scale and the physical operation state data, performing rolling correction on the global scheduling plan to generate a correction instruction; according to the supply and demand matching degree prediction information of the third scale and the physical operation state data, performing local autonomous judgment on the correction instruction, and triggering a local response when supply and demand fluctuation is detected; and the application layer is used for displaying the data acquired by the sensing layer and / or the output data of the control layer, so that the self-balancing capability of the system is fundamentally improved.

The invention discloses a carrier rocket avionics system based on a TSN (Time Sensitive Network) and a carrier rocket avionics data transmission method, and belongs to the technical field of carrier rocket control. A network is physically or logically divided into a periodic time-sensitive data stream, a high-speed time-sensitive data stream and a best effort data stream, the three data streams are subjected to isolation management, and transparent interconnection between a 1553B bus network and a TSN switching network is realized on a protocol layer through an intelligent protocol conversion gateway. Meanwhile, in a network management layer, through an instruction response type configuration protocol, common Ethernet equipment is seamlessly incorporated into the global scheduling and control category of the TSN network. According to the invention, by constructing the avionics network system with three-layer partition and dual-network fusion, multi-network seamless fusion on the rocket can be realized, the real-time performance of the network is improved, and the key requirements of rocket recovery are met.

The invention relates to the technical field of computers, and discloses a process scheduling method based on a tenant isolation strategy, and the method comprises the steps: receiving a process scheduling request of a tenant, and carrying out the standardization processing and legality verification; querying a predefined tenant mapping table, and determining an internal unified tenant identifier corresponding to the flow scheduling request and associated single-tenant concurrent quota, tenant resource weight, isolation level and strategy rule; obtaining a single-tenant concurrent quota and a tenant resource weight, generating a tenant isolation strategy in combination with the calculated rate limiting parameter and the tenant correction priority, and performing admission judgment; and based on the judgment result, distributing the flow scheduling request to the corresponding tenant scheduling unit according to the local priority, selecting a target tenant scheduling unit according to the scheduling qualification value and the global scheduling priority, generating an execution instance and triggering the execution instance. Through differentiated services, resource monopoly is prevented, task hunger is eliminated, system overload is avoided, multi-dimensional mutual restriction is carried out, and isolation between tenants is achieved.

The invention is suitable for the technical field of communication, and provides a 5G lightweight core network-based computing network integration intelligent scheduling method, which comprises the following steps of: analyzing a computing task and a resource demand vector thereof by acquiring a task demand, computing power and a network resource state, further matching the demand with the resource state, and applying an improved particle swarm optimization or convex optimization algorithm and the like to obtain a computing network integration intelligent scheduling result. Generating an optimal task scheduling strategy, determining a mapping relation between the task and a computing power node and a network path, distributing the task to an edge node for execution according to the strategy, guaranteeing the data transmission efficiency through a UPF local shunting technology, and finally updating a global scheduling model through federal learning iteration based on a task execution result. And a closed-loop scheduling system with continuous optimization capability is formed. Unified management and integrated operation of computing power and network resources are realized, and through a lightweight core network and an intelligent scheduling algorithm, the task time delay is remarkably reduced, and the resource utilization rate is improved.

A container simulation network system based on a link autonomous agent is composed of a visual interaction module, a control plane module, a network simulation module and a resource management module. The modules perform information interaction and task collaboration through a uniform interface so as to realize automatic construction and management of the simulation network, a two-layer architecture design of network bridge containers and user containers is adopted, and each user container corresponds to one network bridge container; a link autonomous agent module is deployed in the network bridge container and is used for collecting a link state, calculating a performance adaptation degree and executing a scheduling operation bit; the visual interaction module realizes visual monitoring and interaction control of a simulation state; the control plane module is responsible for simulation scene analysis, link parameter generation and global scheduling management; and the network simulation module is responsible for calling a container generation tool to generate a specific container instance and carrying out resource monitoring and adjustment on the container.

The invention provides a hierarchical man-machine cooperation multi-target dynamic job shop scheduling method, and relates to the field of man-machine cooperation dynamic scheduling. On the basis of a hierarchical collaborative reinforcement learning framework, a high-dimensional global scheduling problem is decomposed into a plurality of low-dimensional sub-problems by dynamically partitioning a job set and allocating robot resources through a high-layer agent in a decision-making period, and the sub-problems are processed by the low-layer agent in parallel; two-tuple actions of a process selection rule and a processing mode selection rule of the process selection rule are quickly generated for the responsible stations in each time step and are verified, and the state space dimension needing to be processed by the reinforcement learning agent is remarkably compressed. Besides, a proposed target sharing type collaborative mechanism ensures that global multi-target weight parameters calculated by a high-level agent guide local decision of a low-level agent in real time, the deviation between a local optimization target and a global target is effectively reduced, the problem of multi-agent target splitting is solved, and the multi-target collaborative optimization capability is improved.

The invention provides a task scheduling method, device and system, and relates to the field of data circulation and scheduling, and the method comprises the steps: obtaining a global task set; performing first-level global scheduling according to the global task set, and determining a task allocation matrix; the task allocation matrix is used for indicating the execution of a secondary domain of each global task; the global task is sent to a corresponding secondary domain, and the secondary domain splits the global task into sub-tasks which can be executed in the domain; scheduling in the second-level domain according to the subtasks and local tasks of the second-level domain, and determining a resource allocation matrix; the resource allocation matrix is used for indicating execution resources of each subtask; sending the subtask to a data transmission gateway corresponding to the execution resource, and executing the subtask by the data transmission gateway; according to the method and the system, a hierarchical scheduling mechanism is combined with first-level global scheduling and second-level intra-domain scheduling to construct a cooperative mode of global overall planning and local execution, so that task scheduling is realized, and the method and the system can adapt to distribution, dynamism and complexity of a data flow network.

The invention relates to the technical field of artificial intelligence, and provides a data processing method, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining to-be-processed data; segmenting the to-be-processed data along a sequence length dimension to obtain a plurality of data blocks; on the basis of a preset traversal sequence, the dimension of the to-be-processed data is traversed, a global task sequence is constructed according to dimension index information obtained through traversal, and the preset traversal sequence takes the sequence length dimension as an outermost layer cycle; and controlling the plurality of parallel processing units to parallelly acquire the calculation tasks corresponding to the data blocks from the global task sequence, and executing the calculation tasks. According to the method, the global scheduling sequence of the tasks is reconstructed, and the sequence length dimension is used as the outermost layer circulation to organize the calculation tasks, so that the calculation tasks distributed to different processing units have similar calculation amounts in the same time slice of parallel processing, and therefore, efficient load balancing is achieved, and the calculation efficiency is improved. And the hardware utilization rate and the overall processing performance are improved.

The invention discloses a supply chain fund flow intelligent scheduling method based on reinforcement learning, and the method comprises the steps: S1, collecting multi-dimensional heterogeneous data, constructing a dynamic digital twin environment, and carrying out the fusion to generate a standardized input tensor; s2, aggregating node features by using a heterogeneous graph attention network, and outputting a high-dimensional state feature graph; s3, analyzing market fluctuation and risk through a high-level agent, and outputting a global scheduling guidance strategy; s4, outputting a refined scheduling instruction set by using a DPPO algorithm in combination with tail risk minimization; s5, based on the improved MAPPO algorithm, noise is injected to cover privacy, and Nash equilibrium optimization is executed through inverse reinforcement learning; and S6, fund allocation is executed, and online iterative optimization is carried out. According to the invention, supply chain fund scheduling efficiency and security are improved, and individual and collective benefits are effectively balanced.

The invention discloses a gate distributed operation scheduling method and system. According to the method provided by the invention, a'global-regional-device 'three-level hierarchical collaborative architecture is constructed, and the overall operation condition of the drainage basin is identified and the global scheduling instruction with maximized revenue is generated through the global optimization server based on the reinforcement learning model under the driving of the dynamic reward function; and the achievement of high-level targets such as water resource utilization efficiency or flood control safety is ensured. And the cooperative control gateway responds to the global scheduling instruction so as to carry out data fusion and cooperative analysis on data in the region through a supply and demand balance model, and the global instruction is converted into a cooperative scheduling strategy adaptive to the region characteristics. And finally, the control device accurately executes the localized instruction to form a sensing-decision-execution closed loop. And self-learning and self-optimization of the scheduling strategy are realized, and gate number expansion and scheduling target dynamic adjustment are supported.

The invention provides an agricultural robot group intelligent scheduling method, which comprises the following steps: performing visual semantic conversion on an operation behavior video acquired by each agricultural robot to obtain a corresponding semantic feature vector; according to the similarity between the plurality of semantic feature vectors corresponding to each agricultural robot and the semantic feature center vector corresponding to each agricultural robot, determining an operation state response value of each agricultural robot at the moment corresponding to each frame of image; according to the plurality of operation state response values corresponding to any two agricultural robots, a cooperative pressure difference value corresponding to any two agricultural robots is determined; and according to a plurality of operation state response values corresponding to any agricultural robot and the cooperative pressure difference values between the agricultural robot and other agricultural robots except the agricultural robot, determining a system overall scheduling loss value, and performing global scheduling on the agricultural robots according to the system overall scheduling loss value. Therefore, the better system cooperation capability is realized.

The application provides a resource scheduling method and device, electronic equipment, storage medium and edge cloud system. The resource scheduling method comprises: in the case of identifying the resource requirement of a target workload, determining whether there is an edge node in the edge cloud cluster that meets the resource requirement; in the case that there is an edge node in the edge cloud cluster that meets the resource requirement, deploying the target workload on the edge node that meets the resource requirement; in the case that there is no edge node in the edge cloud cluster that meets the resource requirement at a specified time, or according to at least one of the resource requirement, the resource state of the edge cloud cluster, the running monitoring information of the edge cloud cluster and the current traffic corresponding to the target workload, performing global scheduling on the load deployment state of the edge cloud cluster. The technical scheme of the application can improve the resource utilization rate of the edge cloud cluster.

The invention provides an intelligent scheduling method, system and device based on a shunt electric power cold station and a medium, and belongs to the technical field of electric power cold stations. The cloud platform constructs a hierarchical reinforcement learning scheduling framework and generates a global scheduling target; according to the global scheduling target, a combined operation strategy of the water chilling unit frequency conversion rate, the cooling tower fan rotating speed and the valve opening degree is determined, and branch cooling capacity distribution control is achieved; establishing a mapping matrix of the power load and the cooling capacity demand, and keeping the temperature fluctuation of the low-load loop not to exceed a preset temperature threshold value; and embedding the requirements that start and stop do not exceed two times per day and the equipment load rate is maintained in a preset interval into the current scheduling strategy. According to the method, intelligent and efficient dispatching of the shunt electric power cold stations is achieved, the energy consumption cost and carbon emission are reduced while the cooling capacity distribution accuracy is improved, and the cooling capacity distribution accuracy, the operation stability and the economical efficiency are improved, and the service life of equipment is prolonged.

The invention discloses an intelligent mobile mechanical arm composite robot system and method controlled by the Internet of Things, and belongs to the field of application of intelligent robots and the Internet of Things. Aiming at the problems of disjunction between global scheduling and local security, confrontation congestion of robots, difficulty in optimizing data dispersion, difficulty in equipment migration and the like in the existing multi-robot system collaboration, a cloud-side-end collaboration architecture is provided, wherein a cloud side is responsible for task allocation and time sequence arrangement; the edge end is responsible for rolling execution and safety control; and the robots perform mutual avoidance, road occupation and relay based on collaborative semantics. The system comprises a cloud job arrangement module, an edge execution control module, a cooperative communication module and a data closed loop module. According to the method, conflict congestion is reduced through a complete collaborative semantic mechanism, a data-driven strategy optimization closed loop is established, decoupling of the method and equipment is realized, and the operation efficiency, safety and mobility of multiple robots in a complex scene are remarkably improved.

According to the low-power-consumption outdoor communication system and communication method provided by the invention, through the global scheduling capability of the network management platform, the problems of inaccurate switching and lack of collaboration in the existing multimode communication terminal scheme are solved. In a normal state of each intelligent terminal in the system, only the main communication module is started for public network communication, and the standby communication module is deeply dormant to reduce power consumption; and when the public network is abnormal, the network management platform accurately selects the target terminal based on the global positioning information, and awakens the standby communication module as required, thereby realizing local area network communication between the abnormal terminal and the target terminal. According to the mode of'global decision and on-demand wakeup ', the reliability and collaboration of communication in an outdoor scene are guaranteed, the overall power consumption of the terminal is greatly reduced, and the endurance demand of long-time outdoor operation is effectively met.

The invention discloses an agent application-oriented resource sharing method, and relates to the field of cloud computing. The scheme comprises offline and online two-stage processing: in the offline stage, constructing an agent dependency conflict graph, dividing a core and other agent sets and grouping, and determining candidate execution nodes through a convergence hash algorithm; in the online stage, a global scheduler selects a target node according to grouping information and node real-time loads, a local scheduler schedules a calculation sandbox to execute local logic, a communication sandbox calls a large language model, and a capacity sandbox is dynamically expanded and shrunk based on a preset strategy. The system correspondingly comprises an offline processing module, a global scheduler, a local scheduler and two types of sandboxes. Through calculation and communication decoupling, off-line grouping, double-layer scheduling and security isolation mechanisms, the problems of low resource utilization rate, high operation cost, insufficient concurrent bearing capacity and the like in the prior art are solved, and the resource reuse rate, the system stability and the throughput capacity are remarkably improved.

The application provides a plate tail intelligent grading equipment and method based on visual guidance, and the core is that through integration of multi-view visual perception, adaptive grabbing, digital twin and cloud-edge collaborative control technology, full-process intelligent management of plate tail from warehousing, grading storage to on-demand use is realized; multi-view images of the tail are collected by a camera array, three-dimensional point clouds are reconstructed through a stereo matching algorithm, and morphological characteristic parameters of the tail are extracted; with the help of digital twin technology, a virtual model of each tail is generated and synchronized, a stacking strategy is optimized based on a genetic algorithm, and space utilization is maximized; when a use request is received, the system can perform virtual cutting pre-performance in the digital twin environment, accurately match the demand, and use the stacked tail through the temporary storage-backfilling mechanism; finally, through the cloud-edge collaborative platform, visual monitoring and global scheduling optimization of the inventory and equipment state are realized, and the tail management efficiency and material utilization are significantly improved.

The invention belongs to the technical field of industrial digital scheduling, and particularly relates to a cloud computing industrial digital intelligent scheduling terminal and method for flexible production, and the terminal comprises the steps: firstly, breaking an equipment and system data island through a multi-source industrial data fusion collection module, and carrying out the butt joint of a multi-management system through the adaption of multi-protocol heterogeneous equipment; unified acquisition of production data is realized, and complete, accurate and continuous data support is provided for global scheduling decision through cleaning, noise reduction and cache transmission processing; on the basis, through cloud-edge computing power dynamic allocation and scheduling scheme optimization of a cloud computing collaborative scheduling center, and in combination with edge end real-time control instruction generation, the method has the advantages of realizing instant response of order change and process adjustment in flexible production and adapting to production dynamic change.

The application discloses a supply chain performance path optimization and collaborative decision-making method based on an AI algorithm, and particularly relates to the field of supply chain performance path optimization, and comprises the following steps: firstly, order goods information and warehouse resource data are acquired, and it is judged whether a single warehouse can independently perform the performance by matching; if yes, a comprehensive evaluation function containing total transportation time, time redundancy, path complexity and resource utilization rate is constructed, and an optimal path from the warehouse to a delivery place is generated; if multiple warehouses need to be cooperated, a centralized point warehouse is dynamically selected, the goods transfer cost and historical performance risk value of each warehouse are comprehensively calculated, a comprehensive early warning score is generated, and an optimal centralized point is selected, finally, the transfer path from the supply warehouse to the centralized point and the performance path from the centralized point to the terminal are planned, and a full-link collaborative scheme is formed; the application breaks through the traditional single-warehouse decision-making mode, realizes global scheduling of resources through a double-path optimization mechanism, significantly reduces the transportation delay risk, and improves the operation efficiency of the supply chain.

The invention discloses a multi-agent-based resource scheduling method, system and device and a medium, and belongs to the field of resource scheduling, and the method comprises the steps: receiving local scheduling actions reported by a plurality of agents in a network and corresponding network state information; constructing a global network state view based on each local scheduling action and each piece of network state information, determining a scheduling conflict result among a plurality of agents based on the global network state view, and grading the network influence degree of each agent based on the scheduling conflict result to obtain a conflict grading result, determining an optimization priority and a global constraint condition according to a conflict grading result; and issuing the optimization priority and the global constraint condition to each agent to enable each agent to generate a global scheduling action, and performing resource scheduling based on the global scheduling action. Therefore, by implementing the method and the device, uniform distribution of resources in a multi-agent distributed network environment can be realized.

The invention discloses an AI order dispatching intelligent management and remote resource scheduling project management SaaS system oriented to multi-workshop industrial collaboration, and relates to the technical field of industrial data analysis. In the system, a heterogeneous data fusion module breaks a data island and constructs a unified data set; the data quality evaluation module quantitatively calculates a data credibility index Kxds to provide a reliable basis for decision making; a self-adaptive scheduling decision module dynamically switches a high-precision and robustness scheduling model according to Kxds to avoid the risk of model failure; the scheduling scheme execution and feedback module constructs a decision execution closed loop; and the system efficiency comprehensive evaluation module finally fits a global scheduling optimization index Yxzs by calculating model efficiency factors Gjyz and Ljyz and a matching degree index Ppds, and provides an accurate efficiency evaluation and continuous optimization basis for the system.

The present application relates to the technical field of urban drainage and waterlogging prevention, and particularly relates to a plain river network large-scale drainage and waterlogging prevention engineering group optimization scheduling method and system. The method comprises determining a typical composite waterlogging event of the plain river network, establishing a hydrodynamic model to analyze the typical composite waterlogging event, identifying key nodes of waterlogging, determining a linkage relationship between a waterlogging facility scheduling behavior and a hydrological response of the key nodes, establishing a multi-objective optimization scheduling model with waterlogging risk minimization and operation cost optimization, gradually decomposing a global scheduling target into a local scheduling target, establishing a mapping relationship between the local scheduling target and a waterlogging facility scheduling decision, obtaining a real solution of the multi-objective optimization scheduling model for potential individuals predicted in different candidate scheduling scheme populations, constructing an expected improvement matrix to select an optimal solution to obtain an optimal scheduling scheme. The present application effectively meets the scheduling timeliness requirement by combining machine learning prediction capability and an optimization algorithm, and realizes the optimization scheduling of the plain river network large-scale drainage and waterlogging prevention engineering group.