312results about How to "Efficient scheduling" patented technology

The invention discloses an integrated energy system economic dispatching model method based on deep reinforcement learning, and the method comprises the steps: constructing a park equipment environment model, and carrying out the mathematical description of a park integrated energy system low-carbon economic dispatching problem through taking economy and carbon emission as targets; secondly, the scheduling problem is expressed as a Markov decision process framework, and a state space, an action space and a reward mechanism of the system are reasonably designed; then, a near-end strategy optimization algorithm is adopted to carry out low-carbon economic dispatching decision making, and an action noise dynamic updating strategy is provided to gradually improve the exploration capability and learning capability of a network model; according to the method, the action space, the state space, the reward mechanism, the neural network structure and the like of the intelligent agent are designed in detail, the minimization of the system operation cost is considered on the basis, and a deep reinforcement learning method is adopted for scheduling simulation. The method provided by the invention can perform optimal scheduling according to the load demand of the system, and has good scheduling performance and self-learning ability.

The embodiment of the invention provides a chip platform hardware abstraction layer construction method, which comprises the following steps: scanning chip platform hardware characteristic information, constructing a hardware characteristic analysis model, carrying out classified labeling on the hardware characteristic information to extract equipment interfaces, resource attributes and operation characteristics, establishing a characteristic vector library of hardware components, and constructing a hardware abstraction layer of the hardware components, grouping the hardware components by adopting a hierarchical clustering algorithm to form a function category tree; training an adaptive abstract model based on the function category tree, mapping hardware components with similar functions to a unified interface space, and calculating target Q value update strategy network parameters based on a time sequence difference algorithm to generate a hardware abstract rule base; loading a hardware abstraction rule base in a hardware abstraction layer engine, dynamically monitoring a resource use state, updating an allocation strategy, and returning a hardware operation result to an application layer through a uniform interface; according to the method, hardware features can be deeply understood, an intelligent interface packaging strategy is constructed, and efficient scheduling of resources is realized through technologies such as reinforcement learning.

The invention discloses an intelligent management platform suitable for a power plant, and relates to the technical field of power plant management, and the platform employs an intelligent scheduling system and a DDPG algorithm, and dynamically adjusts a power generation strategy according to the real-time equipment health score, residual life prediction, and the change of energy price and load demand. The power plant can quickly make adjustments when the load demand changes sharply, preferentially dispatch efficient and low-emission equipment, improve the overall energy efficiency and reduce energy waste, and through integration of a sensor network and edge computing, key operation parameters of the equipment are collected in real time, and a dynamic maintenance plan is generated based on health scores and residual life prediction of the equipment. The equipment about to break down is preferentially processed, the random forest model is used for health state evaluation and fault probability prediction, the health score of the equipment can accurately reflect the running state of the equipment, the maintenance time is dynamically adjusted in combination with the load condition of the equipment, and excessive maintenance or maintenance lag is avoided.

The invention discloses a dynamic scheduling method for a cloud computing resource pool in the technical field of cloud computing. Comprising the following steps: S1, analyzing resource demands; s2, performing performance prediction on the resource pool through load prediction; s3, predicting task demand resources; s4, resource allocation decision making; s5, task scheduling; and S6, resource monitoring and feedback. According to the invention, load prediction is carried out through the long-short-term memory network, an adaptive mechanism is introduced, and a load prediction model is updated along with continuous change of data, so that the accuracy is improved; the resource demand of the task is predicted through a machine learning algorithm, a reasonable resource allocation decision is made according to the state of the current resource pool and the resource demand predicted by the task, the utilization rate of the resource is improved, stable operation calculation of the task is guaranteed, efficient task allocation and scheduling are achieved, resource utilization maximization is guaranteed, and the resource utilization rate is increased. And the change of the system load is effectively responded.

The invention discloses a multi-underwater-robot cooperative control system and method based on predictive control, and relates to the technical field of cooperative control. During operation of the system, underwater environment data are collected through sensor equipment, an underwater three-dimensional dynamic environment model is generated by using a data fusion technology, and the underwater three-dimensional dynamic environment model is established based on task requirements; tasks are decomposed and distributed to all the robots, through an optimization algorithm, optimal paths of the robots and the mechanical arms are planned, the motion trails of the underwater robot mechanical arms are dynamically optimized based on model predictive control MPC, a real-time control strategy is provided for task division among the multiple mechanical arms, and the real-time control strategy of the underwater robot mechanical arms is provided based on environment modeling and the real-time state. Future motion states of the robot and the mechanical arm of the robot are predicted, an optimization objective function including task completion time, energy consumption and mechanical arm operation precision is constructed, a rolling optimization method is utilized, a control instruction is updated in real time, and real-time data sharing among multiple underwater robots is supported based on an acoustic communication technology.

The invention relates to the technical field of photovoltaic power prediction, and discloses a regional distributed photovoltaic power prediction method and system, and the photovoltaic power prediction comprises the steps: S1, obtaining meteorological data in real time, introducing a time sequence processing capability model, and capturing the nonlinear influence of the weather change on the photovoltaic power; s2, introducing an online learning and self-adaptive mechanism, enabling the prediction model to have an online learning capability, and dynamically adjusting model parameters along with the inflow of new real-time meteorological data; s3, implementing rolling prediction based on a short time window, and dynamically adjusting the power generation plan: when a sudden weather change is detected, triggering a quick response program, and adjusting the power generation plan according to the latest prediction result; and S4, integrating model prediction results, and adopting a probability prediction method to provide probability distribution of photovoltaic power output instead of a deterministic numerical value, so as to reflect an uncertainty range of future photovoltaic power change.

The invention provides a computing power network task scheduling method and device, a storage medium and a program product, and relates to the technical field of computers.The method comprises the steps that a task scheduling optimization problem is obtained by defining an objective function between network energy consumption of a computing power network and network carbon emission of the computing power network and constraint conditions corresponding to the objective function; the optimization objective of the objective function is to reduce the network energy consumption and the network carbon emission by optimizing the computing power node, the forwarding path and the forwarding time corresponding to the computing power task; solving the task scheduling optimization problem through a preset optimization algorithm to obtain a task scheduling method of the computing power network; the task scheduling method is used for indicating a forwarding path of a computing power task in a computing power network, a computing power node for executing the computing power task and forwarding time corresponding to the computing power node; according to the method, the problems of relatively low expandability and management efficiency of task allocation can be solved; and the task allocation flexibility and management efficiency are improved.

The invention discloses an optimized dispatching control method and system for transformer area energy storage, and relates to the technical field of energy storage dispatching, and the method comprises the following steps: integrating an intelligent load management system with an energy storage system, thereby monitoring the electrical load of a transformer area in real time, and feeding back the real-time data to the energy storage system; historical load data and electricity price data are collected, a prediction model is trained, future load and electricity price are predicted in real time, and prediction data are generated. And according to a prediction result, a preliminary energy storage system scheduling strategy is formulated, and a target function R is constructed, so that the target function R can dynamically adapt to changing load and electricity price conditions. According to the invention, through tight integration of the intelligent load management system and the energy storage system, real-time monitoring of the power grid load and efficient scheduling of the energy storage system are realized; in other words, the energy storage system can charge or discharge at the best time in combination with accurate load and electricity price prediction and scheduling strategy rules, power grid resources are utilized to the maximum extent, and unnecessary resource waste is avoided.

The invention discloses a water plant whole-process intelligent optimization scheduling method based on adaptive feedback control. The method comprises the following steps: S1, dividing into a low-demand stage, a medium-demand stage and a high-demand stage; s2, in a low-demand stage, a medium-demand stage and a high-demand stage, correspondingly obtaining real-time monitoring data sets of the water plant; s3, constructing a multi-stage evolutionary game model based on the water plant real-time monitoring data set; s4, generating a local optimal scheduling scheme of the current stage; s5, outputting a local optimal scheduling scheme according to the evolutionary game model by utilizing a self-adaptive feedback control mechanism; s6, performing global optimal scheduling of the system in each stage; s7, optimizing a scheduling scheme in advance; and S8, displaying the optimized scheduling effect through the integrated intelligent scheduling platform, monitoring the operation state of the whole water plant, and generating a scheduling report for reference of management personnel. According to the invention, the predictability and perspectiveness of the scheduling system are effectively improved through the prediction model, and it is ensured that the water plant is always in an efficient and safe operation state.

The invention provides an HIVE grouping operation performance optimization and scheduling method based on key column partitioning, which relates to the technical field of data processing, and comprises the following steps: preprocessing data by constructing a key column distribution histogram and determining a key column partitioning strategy; optimizing data partitions based on resource evaluation and a dynamic programming algorithm; and calculating an optimal data transmission path by adopting a minimum spanning tree algorithm, and dynamically adjusting an allocation scheme. According to the method, HIVE grouping operation performance can be improved, dynamic balance of calculation loads is achieved, the problem of data skew is effectively solved, and large-scale data processing efficiency is improved.

The invention discloses an intelligent operation sewage pipeline system and an implementation method thereof, and the intelligent operation sewage pipeline system comprises monitoring equipment, a cloud server, a GIS map model, a management platform and an operation management terminal which achieve data interaction through a 5G data network or the Internet. And the data is processed and analyzed by combining big data analysis, an intelligent algorithm and an image algorithm, so that prompting, decision making, renovation and evaluation of the defects of the sewage pipeline are realized. The specific implementation method comprises the steps of constructing the intelligent operation sewage pipeline system, arranging monitoring equipment, establishing a GIS map model, setting a management module, constructing functions of each module and the like. Effective scheduling of the sewage pipeline operation system and reasonable allocation of operation and maintenance resources are realized, and the efficiency and quality of sewage pipeline operation are improved. The problems of external water entry, sewage leakage, sewage overflow, road collapse and the like caused by sewage pipeline defects are solved.

The invention relates to the technical field of emergency management and big data processing, and discloses an emergency drilling system based on big data, and the system comprises a data collection and recognition module which is used for collecting on-site images and video data in real time through a camera, unmanned aerial vehicle equipment and a mobile terminal, and recognizing the key information of an accident site through an image recognition algorithm; the big data analysis and prediction module is used for processing and analyzing the real-time data acquired from the data acquisition and identification module and predicting the development of the emergency event; and the hierarchical scene analysis and simulation module is used for decomposing the emergency event into a plurality of hierarchies for independent analysis and simulation, and the hierarchies are associated through a preset rule. By means of weapon deduction, multi-level analysis, real-time data processing and intelligent scheduling modules, the problems of experience dependence, decision lag, unreasonable resource scheduling and the like in traditional emergency management are solved, and the ability of coping with complex emergencies is enhanced.

The embodiment of the invention discloses a task scheduling method and device based on process preemption, electronic equipment and a storage medium, and the method comprises the steps: under the condition that a first task needing to be executed through a target process is received, determining a first scheduling priority and an execution time slice of the target task, and executing the first scheduling priority and the execution time slice of the target task; obtaining a second scheduling priority of a second task being executed by the target process; and when the first scheduling priority is higher than the second scheduling priority, interrupting the second task, recording context information of the second task, and executing the first task through the target process, so that the first task with high priority can be preferentially processed, the real-time performance and the response speed of the system are improved, and the user experience is improved. And the context information of the interrupted second task is recorded, so that the continuity and the consistency when the tasks are recovered to be executed are ensured, and the stability and the reliability of the system are ensured.

The invention provides a distributed fault detection and dynamic task scheduling system for an unmanned ship cluster, and the system achieves the efficient cooperation of the unmanned ship cluster in a complex marine environment through the cooperation of a sensor data collection module, a data processing and feature extraction module, a fault state classification module, an information sharing module, a weighted voting decision module, a task scheduling module and the like. The system firstly collects state information of each unmanned ship and adjacent ships, performs feature extraction and classification on data, and judges whether a fault exists or not. Fault information is shared to each boat in a cluster through a multi-path redundancy communication mechanism, and weighted decision is carried out by a voting mechanism, so that the accuracy of fault detection is improved. After a fault is confirmed, the system dynamically adjusts task allocation according to task priorities, key tasks are transferred to the unmanned surface vehicle in a good state, and continuity and stability of cluster tasks are guaranteed. Different from traditional centralized control, the distributed control method disclosed by the invention enhances the damage resistance and the self-adaptability of the system.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention relates to a heterogeneous computing power fused cloud computing platform, a resource scheduling method and device, computer equipment and a computer readable storage medium. The cloud computing platform comprises a heterogeneous node management module used for managing hardware resources of different architectures to a computing power cluster; the heterogeneous computing power scheduling module is used for receiving a computing power calling request sent by a user, the computing power calling request carries resource type demand information and architecture demand information, and a computing power resource creation instruction is generated according to the resource type demand information and the architecture demand information; the cloud computing platform can automatically select appropriate hardware resources according to the computing power resource creation instruction, and the task is completed through a container or a virtual machine. By the adoption of the method and device, effective management and scheduling of heterogeneous hardware resources can be achieved through cooperation of the heterogeneous node management module, the heterogeneous computing power scheduling module, the container management module and the virtualization management module, and the utilization rate of computing resources is increased.

The invention relates to the technical field of resource allocation management, in particular to a first-aid medical resource allocation management method and system, a medium and a product, and the method comprises the steps: obtaining call-for-help appeal information, and determining first-aid preparation information based on the call-for-help appeal information, the first-aid preparation information comprising a first-aid route and first-aid vehicle-mounted resources; carrying out patient receiving and transporting based on the first-aid preparation information, and collecting real-time road condition information, patient sign information and patient medical history information in the receiving and transporting process; adjusting a first-aid route based on the real-time road condition information and the physical sign information of the patient, and recording receiving and transporting driving information; real-time hospital rescue resource information is acquired, and on-site medical resources and resource preparation time are determined based on the real-time hospital rescue resource information, the patient sign information and the patient medical history information; on-site preparation information is determined based on the on-site medical resources and the resource preparation time. According to the invention, rationality and accuracy in a first-aid medical resource allocation process can be improved, so that a patient can be ensured to be timely and effectively rescued.

The invention discloses a virtual digital twinborn traffic resource scheduling model based on cloud edge collaboration, and aims to further improve the resource operation efficiency of a traffic system. According to the model, on the basis of fully utilizing the characteristics of a digital twinning technology, efficient operation of resources is realized in a virtualized resource mode through a cloud edge collaborative architecture so as to adapt to on-demand scheduling of various traffic complex scenes. Besides, in order to further optimize scheduling, the invention provides a value distribution strategy gradient map deep reinforcement learning model based on a node importance strategy, and efficient collaboration and scheduling of digital cloud edge comprehensive traffic resources are realized through deep learning of key nodes and relationships and adaptive feedback of scheduling requirements. Experimental results show that the model provided by the invention is superior to a current mainstream resource scheduling model in multiple aspects.

The invention discloses a collaborative resource scheduling method and system based on a programmable network. The method comprises the following steps: performing performance modeling on heterogeneous computing resource data in all computing resource data to obtain a uniform resource performance index; defining target computing resource data required by multi-dimensional resource demand services, and determining matching relationship information between each multi-dimensional resource demand service and the current computer cluster; in the deployment process, according to all the matching relation information, the correspondingly selected multi-dimensional resource demand service is deployed to each computer cluster; determining a shortest deployment path between the routing nodes in each computer cluster based on a Floyd algorithm; and in the actual scheduling process, executing the computing power arrangement scheme generated in the deployment process. According to the collaborative resource scheduling method and system based on the programmable network provided by the embodiment of the invention, the utilization rate of computing power resources in a wide area network can be improved, and a computing power scheduling strategy is dynamically generated.

The invention discloses a scheduling method, device and equipment for a natural resource multimode computational analysis model, and the method comprises the steps: carrying out the virtualization of a computational analysis model, and achieving the model integration across multiple modes; performing calculation mode and calculation resource scheduling on the data in the calculation process, and subdividing the data into a plurality of calculation sub-tasks to support calculation of large-scale data; and executing scheduling strategies under different scales according to scale characteristics of data calculation and analysis. The method aims at solving the problems that in an existing method, calculation and analysis models of different modes are similar in function, parameter setting is not unified, meanwhile, scheduling of calculation resources is not refined enough, and timeliness and accuracy of calculation and analysis are insufficient under the condition that resources are deficient. According to the method, a unified virtualization model is adopted to integrate a multi-modal analysis model, and calculation resources are finely scheduled to improve timeliness and accuracy. Meanwhile, resource use is optimized, large-scale data calculation and analysis are supported, and efficiency can be improved by rapidly utilizing newly-added resources.

The invention relates to the technical field of optical transmission, in particular to a high-efficiency optical transmission line protection system and method.A standby transmission line is used for switching to a main transmission line for data transmission when the main transmission line breaks down and has a regular self-checking function; the real-time fault detection module is used for continuously monitoring the optical signal strength and quality of a main transmission line, and automatically triggering a protection mechanism when abnormality is detected; the fault positioning module is used for positioning a fault position of a main transmission line based on a GIS platform to obtain position information; and the alarm module is used for notifying maintenance personnel of the position information. Therefore, all-around protection of the optical fiber network is realized, the stability and the operation and maintenance efficiency of the network are greatly improved, and high quality and high reliability of data transmission service are ensured.

The invention discloses a process scheduling method and device, equipment, a medium and a computer program product, and relates to the technical field of server process scheduling, and the process scheduling method comprises the following steps: collecting parameter information of a target process running on a current node, and determining a first memory demand of the target process for the current node based on the parameter information; determining a second memory demand of the target process for the target node, and evaluating process performance and process migration loss based on the first memory demand, the second memory demand and a processor load of the current node; comparing the memory resources of the current node and the target node to obtain a comparison result; and analyzing the process performance, the process migration loss and the comparison result by using a preset Bayesian network model to predict a process scheduling probability, and determining whether to schedule the target process from the current node to the target node based on the process scheduling probability. Whether the process needs to be scheduled or not can be accurately evaluated, so that the system performance is optimized, and the system stability is improved.

The invention belongs to the technical field of data processing, and particularly relates to a container yard digital twinborn model construction and monitoring method based on the Internet of Things, and the method comprises the steps: collecting the related information of a container yard in real time, and constructing a digital twinborn model of the container yard; dynamic data of the container yard is obtained in real time based on a sensor, the dynamic data can represent the operation state of the container yard, and the digital twinborn model is updated in real time based on the dynamic data; obtaining a monitoring result of the container yard based on the digital twinborn model, and generating a stockpiling scheme and a scheduling scheme based on the monitoring result; and simulating the stockpiling scheme and the scheduling scheme in the digital twinborn model to obtain a simulation result, optimizing the stockpiling scheme and the scheduling scheme based on the simulation result, and simulating the optimized schemes in the digital twinborn model again until an optimal stockpiling scheme and an optimal scheduling scheme are obtained. According to the method, the accurate digital twinborn model can be constructed, and the operation efficiency of the container yard can be improved based on the digital twinborn model.

The invention relates to the technical field of data processing, in particular to a data compression and block storage processing method based on deep learning. The method comprises the following steps: acquiring integrated data to be compressed; analyzing the effective to-be-compressed integrated analysis data according to the to-be-compressed integrated data; constructing a heterogeneous data feature analysis tree model; extracting to-be-compressed feature data of the effective to-be-compressed integrated data through the heterogeneous data feature analysis tree model; designing a recursive sparse coefficient evaluation objective function based on the to-be-compressed feature data; performing adaptive sparse compression processing on the effective to-be-compressed integrated data based on the recursive compression sparse coefficient evaluation objective function to obtain compressed data; performing block processing on the compressed data to generate block compressed data; and performing compressed data block storage optimization processing on the partitioned compressed data to generate an optimized storage compressed data block. According to the method, data features are analyzed through a deep learning algorithm, so that efficient processing of data compression and block storage is realized.

The invention provides a hierarchical federated learning system and method in a wireless mobility scene. The system comprises a central server, a plurality of base stations and a plurality of edge servers, each base station is connected with at least one piece of user equipment, and the user equipment has mobility and can perform data acquisition and model updating at different positions; in each round of training of the hierarchical federated learning system, the user equipment performs local updating by using the local data set, and uploads a local model to the base station after updating; the edge server is configured in the base station and is responsible for receiving a local model uploaded by user equipment and performing partial aggregation, and the partial aggregation is based on a preset aggregation algorithm; after multiple times of aggregation, uploading an aggregation model to a central server; and the central server is used for receiving the aggregation models from the plurality of edge servers and performing global aggregation to complete a federal learning task. According to the method, the user energy overhead can be minimized under federal learning performance constraint and mobility constraint.

The invention discloses a cardiovascular disease intelligent diagnosis and treatment auxiliary system which comprises a hospital server terminal, a cardiovascular disease intelligent diagnosis and treatment cloud platform, a doctor mobile terminal, a medical care mobile terminal and a patient mobile terminal. The hospital server terminal is used for compiling a clinical patient medical record created and uploaded by a doctor and data uploaded by medical care for clinical examination of a patient, and creating a medical record model of the patient, and the hospital server terminal uploads the created medical record model to the cardiovascular disease intelligent diagnosis and treatment cloud platform for cardiovascular case big data collection; according to the intelligent diagnosis and treatment auxiliary system for the cardiovascular disease, in the intelligent diagnosis and treatment process, a medical record model of a patient can be analyzed, healing duration data of cardiovascular diseases of the same type in a patient healing and disease death record storage unit are retrieved and called, and the healing duration of the patient is calculated and predicted through big data; and the disease death duration data of the same type of cardiovascular diseases in the patient healing disease death record storage unit are retrieved and called, and the survival rate of the patient is calculated and predicted through big data.

The invention discloses a method for realizing multi-tenant task scheduling by using XXL-JOB based on Kubernetes, and relates to the technical field of task scheduling, and the method comprises the following steps: building and configuring a Kubernetes cluster; tagging task nodes in a Kubernetes cluster, deploying an application module, an XXL-JOB scheduling center and an actuator on which a data management platform depends, and integrating a Liquibase tool to automatically create a database table structure to support the registration of the actuator; resource monitoring and allocation are realized through a management center, an actuator instance is created and managed by utilizing XXL-JOB, and actuator information is written into a scheduling resource configuration table; a computing task is created through a front-end interface, resources are selected, the computing task and the selected resources are bound, a scheduling strategy is generated, and finally scheduling strategy information is registered to a scheduling center to manage and execute the computing task. According to the method, the problem of inclination of node resource use in the Kubernetes platform can be effectively prevented and reduced, multi-node load balancing is realized, and the utilization rate of node resources is improved.

The embodiment of the invention provides a resource scheduling system, method, device and equipment, a medium and a product, and the resource scheduling system comprises a host end and a substrate management controller chip. Video data processing sub-modules, random access memory management control modules and a random access memory resource overall management control scheduling module are carried on the substrate management controller chip, and each video data processing sub-module is correspondingly provided with one random access memory management control module. Obtaining random access memory resource information corresponding to each video data processing sub-module; and determining a public random access memory corresponding to each video data processing sub-module according to the random access memory resource information, and storing the video data to be shunted. According to the application, the random access memory resource in each sub-module in the video processing path is taken as an integral resource for management and control; the defect that random access memory resources in each sub-module can only be used by the current sub-module is avoided.

The invention discloses an energy regulation and control method and system of a virtual power plant based on a prediction model, and relates to the field of virtual power plant operation control, and the method comprises the following steps: collecting energy data in the virtual power plant; integrating the wind power data and the load data, and performing feature extraction on the integrated data; based on the predicted power generation data of the distributed energy and the predicted demand data of the load side resources, constructing a preliminary energy dynamic balance prediction model; performing day-ahead load demand and distributed energy prediction by using the preliminary energy dynamic balance prediction model; according to the preliminary scheduling strategy, using a niche grey wolf optimization algorithm to adjust the operation state of each distributed energy and load side resource in real time; dynamically adjusting and optimizing the energy dynamic balance prediction model; and carrying out load demand and distributed energy prediction again by using the optimized energy dynamic balance prediction model. According to the method, the change of the power system can be responded in advance, and more efficient energy scheduling is realized.

The invention discloses a scheduling method and system based on a ship traffic flow prediction model, belongs to the technical field of multi-step double-route ships, and is used for solving the problems that in the operation process of an existing multi-step double-line type ship lock, due to the fact that a ship arrangement plan is unreasonable, the utilization efficiency of lock chambers is low, and the cost is low. And the overall operation efficiency and the collaboration of the passing of the fleet are difficult to improve. The method comprises the following steps: carrying out passing analysis on a ship queue of a wait-to-pass water area under a time factor and a queue length factor, and determining a ship queue state; performing dynamic and static data statistics on all ships in the wait-to-pass water area to obtain dynamic attributes and static attributes of each ship; performing interval division of ship flow on the ship queue in the waiting passing area to obtain an initial ship interval; performing target optimization scheduling processing on the ship flow in the ship interval to obtain a final interval ship flow; and a ship flow scheduling planning scheme based on the multi-step double-linear ship lock is generated.