137results about How to "Efficient allocation" patented technology

The invention discloses a game theory improved model-based cloud computing node storage calculation training resource competition scheduling method. The method comprises the following steps of S1, constructing a global resource scheduling system; s2, carrying out initial division on the cloud computing nodes in each logic partition; s3, executing a local resource competition game model; s4, the global resource scheduling system collects a local resource competition result of each logic partition and residual available resources in each logic partition; s5, executing the global collaborative game model; s6, dynamically updating the resource state information of the cloud computing node; s7, the global resource scheduling system performs adaptive adjustment on resource demand strategies in the local game model and the global cooperative game model; and S8, the global resource scheduling system stores and analyzes past resource competition results and task execution conditions, and each cloud computing node updates a resource demand strategy according to the data. According to the invention, the problems of resource bottleneck and overload in the prior art are solved, and the resource utilization efficiency and the scheduling flexibility are greatly improved.

The invention relates to the technical field of power systems, and discloses a digital twinning-based power grid support performance evaluation system and method. Comprising a data acquisition and processing module, a digital twin model construction and integration module, a multi-scene simulation and prediction evaluation module, an intelligent scheduling and fault prediction module, a multi-source data fusion and big data analysis module, a performance evaluation and optimization decision module, and a visualization and man-machine interaction module. The data acquisition module is used for acquiring real-time states, load data and external environment data of equipment in a physical power grid. Through an intelligent scheduling algorithm, a digital twinborn model and a fault prediction technology, the system can monitor and optimize a new energy access process in real time, solves the challenge of new energy power generation fluctuation on power grid stability, and the algorithm can dynamically adjust power grid load distribution and predict potential faults in advance under the condition of centralized access or fluctuation of new energy. And the stability of power transmission and the safety of equipment are ensured.

The invention discloses an unmanned aerial vehicle group cooperative inspection control method for power station inspection, and the method comprises the steps: determining an unmanned aerial vehicle group according to a power station inspection task; performing task allocation of the heterogeneous unmanned aerial vehicle cluster by using a multi-objective optimization algorithm; performing unmanned aerial vehicle path planning by adopting an improved dynamic multi-population particle swarm algorithm; based on a graph theory and master-slave control, unmanned aerial vehicle group attitude consistency control is realized; integrating a target detection technology based on deep learning, and constructing an inspection anomaly detection model; performing remote fault diagnosis analysis on the real-time inspection data through an inspection anomaly detection model to obtain an inspection anomaly result; optimizing the inspection anomaly detection model according to the target condition of the real-time inspection data; and dynamically controlling task allocation and path planning of the unmanned aerial vehicle group according to an abnormal inspection result. Through the scheme, potential problems can be found and processed in time, the intelligent control level of power station inspection is improved, and the efficiency and accuracy of unmanned aerial vehicle group cooperative inspection are improved.

The invention relates to the technical field of chip task allocation, and discloses a task allocation optimization method and device for a multi-core heterogeneous ASIC (Application Specific Integrated Circuit) computing mainboard. Computing core setting information of the computing mainboard is collected, a mainboard computing performance model is generated through digital feedback processing, an executable task set is obtained, and the computing requirement of each task is analyzed; performing simulation analysis on the calculation demand set based on the performance model by using an optimization algorithm, generating a task allocation plan, identifying and generating task execution characteristics of a currently executed task, allocating the task execution characteristics according to the task allocation plan, and scheduling calculation resources to process the current task. The resource performance is accurately simulated through the performance model, the optimization algorithm ensures efficient resource allocation, responds to task changes in real time, maximizes the utilization of heterogeneous computing resources, improves the overall computing efficiency, and solves the problem of execution delay when real-time task allocation is carried out on the multi-core heterogeneous ASIC computing mainboard in the prior art.

According to the power control method and system based on energy storage unit adjustment priority evaluation, an energy storage unit calibrates the charge state, the battery capacity and the health state of the energy storage unit and feeds back the charge state, the battery capacity and the health state to a battery management system; the battery management system verifies a built-in battery state estimation model of the battery management system by using the calibrated charge state and health state; based on the calibrated charge state and the battery capacity, calibrating the energy state range; calibrating the voltage standard deviation coefficient, the voltage range and the temperature range based on the measurement precision of the sensor; taking the weighted sum of each evaluation index score after calibration of the energy storage unit as a comprehensive evaluation score; determining the adjustment priority of the energy storage unit allowed to be adjusted by using the comprehensive evaluation score and the charge-discharge cycle times of the energy storage unit allowed to be adjusted; and selecting the adjustment-allowed energy storage unit with the highest adjustment priority as an execution unit for responding to the scheduling target power instruction, and ensuring the safe and stable operation of the energy storage system at the same time.

The invention discloses a micro-kernel Hypervisor-based virtual machine cross-operating system communication method, which comprises the following steps of: creating virtual machines which independently run in respective operating system environments by a micro-kernel Hypervisor, then constructing a shared memory between two virtual machines with communication requirements, respectively initializing virtual machine shared equipment by the two virtual machines, and storing the virtual machine shared equipment in the shared memory; according to the cross-operating-system communication method, the shared memory is mapped to a virtual address of the shared memory, when one virtual machine needs to transmit data to the opposite side, communication is initiated to write the data into the shared memory, and the virtual machine receiving the data reads the data from the shared memory, so that cross-operating-system communication of the virtual machines is realized. Based on the shared memory technology, the communication process of the shared memory and the virtual machines is managed by using the microkernel Hypervisor, and cross-operating system communication between the virtual machines is realized.

The invention relates to the technical field of computing power optimization, and provides a computer and cloud computing power optimization method based on multi-task collaboration. The method comprises the following steps: receiving Q to-be-executed tasks of a local cloud collaborative network; performing priority constraint on the Q to-be-executed tasks according to the multivariate priority evaluation factors, and establishing a task chain; collecting callable computing resource information of a local cloud collaborative network, and establishing a resource space; based on the resource space, performing task chain resource allocation by utilizing a computing resource scheduling optimization algorithm to obtain a scheduling strategy chain; performing cloud computing power balance optimization according to the scheduling strategy chain to obtain an optimized chain; and executing the task chain according to the optimization chain based on the cloud collaborative network. The technical problems of unbalanced scheduling and low resource utilization rate between the cloud and the local computing resources are solved, and the technical effects of improving the cooperative work efficiency and the resource utilization rate of the cloud and the local computing resources through priority constraint and computing resource scheduling optimization are achieved.

The invention discloses a computing power aware routing distribution method and system based on SRV6, and the method comprises the steps: constructing a network topology undirected graph based on a target distributed cluster; in response to the service request, obtaining real-time resource state data of each node in the target distributed cluster, and analyzing the service request to obtain resource request data; matching each node in the network topology undirected graph with the resource request data to obtain a target node; calculating an optimal path from the source node to the target node in the network topology undirected graph by using an improved Di jkstra algorithm; and according to the optimal path, configuring a corresponding routing rule in the target distributed cluster through an SRV6 network programming technology. According to the method provided by the invention, the problem of insufficient computing power resource routing distribution flexibility is solved.

The invention discloses an E-SOP-based interconnected power distribution area voltage optimization regulation and control method, and the method comprises the steps: carrying out the analysis of an E-SOP-containing power distribution area interconnection system structure, and constructing an E-SOP mathematical model; establishing a voltage-power sensitivity calculation model, and determining the optimal access position of the E-SOP; an E-SOP interconnected power distribution area planning model with the lowest comprehensive cost as the target is constructed, and the optimal capacity configuration of the E-SOP is determined; establishing a multi-objective function E-SOP interconnection power distribution area operation optimization model and solving the model; and determining the optimal position, capacity and operation scheme of the E-SOP according to the solving result. According to the method, a three-terminal SOP (E-SOP) containing an energy storage system is applied to an interconnection system of a power distribution area, the flexibility and rapidity of a power distribution network can be improved from the multi-dimensional aspects of space, energy and power of a power distribution system, and voltage optimization of the power distribution area is achieved.

The invention provides an NPU instruction level context switching method and device, and relates to the technical field of data processing.The method comprises the steps that when a context switching instruction is received, a non-refresh instruction is forbidden, it is waited for completion of an instruction being executed, and context data is stored in a main storage module; after completion, recovering the context switching related instruction of the NPU module, and executing the target task; and after the target task is executed, recovering the context data to the NPU module, and recovering the execution of the non-refresh instruction. According to the method, optimization is carried out aiming at the problem that an existing NPU architecture generally can only support limited preemption at an algorithm boundary or a specific context switching point and even cannot preemption, so that a high-priority task must wait for a low-priority task to be completed, and remarkable priority inversion and / or key inversion are / is caused. A full-stack solution from a system on chip to an instruction set architecture and then to an operating system kernel is provided, and preemption of an NPU instruction level is achieved.

The embodiment of the invention discloses a large-scale competition medical resource scheduling guarantee method and system, a medium and a product. The method comprises the following steps: determining a plurality of initial medical points and first deployment positions of the plurality of initial medical points based on geographic data of a target track so as to obtain a rescue service radius of each initial medical point, and screening available medical points from the plurality of initial medical points based on the rescue service radiuses; dividing the target track into a plurality of track partitions based on a second deployment position of the available medical point, obtaining a demand level of the track partitions, and performing medical resource deployment according to the second deployment position and the demand level; when the participant has a safety risk, acquiring an optimal rescue route based on a third deployment position of the recently available medical point and the staying position of the participant; and according to the optimal rescue route, rescuing the participant by adopting the recent available medical point. According to the method, the utilization efficiency of medical resources can be improved, and meanwhile, the safety guarantee capability of a large competition is effectively enhanced.

The invention discloses a range-extending type automobile energy management method combining working condition recognition and a driving style, and relates to the technical field of range-extending type electric automobile energy management. Through an intelligent energy management strategy, the starting and output power of the range extender is dynamically adjusted according to the actual driving working condition and the driving style, invalid energy consumption is reduced, and the energy utilization efficiency of the whole vehicle is improved; the optimized energy management strategy can better respond to the intention of a driver, smooth and sensitive-response acceleration performance is provided, and meanwhile, the consistency of energy management and a driving mode is kept in an intelligent driving mode, so that the driving experience is enhanced; the optimized energy management strategy is beneficial to reducing the running cost of the whole vehicle, and maximization of economic benefits is achieved by reducing fuel consumption and battery replacement frequency; and moreover, the provided energy management system can adapt to different driving working conditions and driving modes, and efficient energy management can be realized no matter in urban congested road sections or expressways.

The invention discloses a new energy station management system and method based on an intelligent power grid, and the system comprises a real-time monitoring module which is used for predicting the charging load fluctuation of an electric vehicle based on real-time monitoring data, a scheduling module which is used for adjusting the output of new energy power generation equipment according to the predicted load fluctuation, and an optimization module. The power distribution network management module is used for optimizing charging scheduling in a charging station and avoiding overload caused by peak charging at the same time, the dynamic adjustment module is used for dynamically adjusting the voltage and frequency of a power distribution network, and the real-time monitoring module is used for monitoring the state of the power distribution network in real time and dynamically updating a management strategy; according to the new energy station management system and method based on the smart power grid, the problems that in the prior art, due to instability and rapid change characteristics of electric vehicle charging loads, severe fluctuation of the loads can bring extra pressure to a power distribution network, the overload problem of the power distribution network is caused, and then the reliability and safety of the power grid are affected are solved.

The present invention discloses a transport robot path planning system for a new energy charging parking lot. The transport robot path planning system for a new energy charging parking lot includes: a garage data acquisition module, an information interaction module, a central control module, and an AGV transport robot. The present invention obtains the vehicle energy drive mode through the garage data acquisition module, sends an energy replenishment prompt to the user through the information interaction module, and obtains the user confirmation signal. If confirmed, the central control module allocates a parking space according to the garage data and the energy drive mode, and uses the A* algorithm to plan the transport path, and transmits the transport path information to the AGV transport robot, which accurately transports the vehicle to the allocated parking space, thereby improving the user experience and solving the current problem of poor user experience.

The invention relates to the technical field of steady-state balance of an electric power system, and discloses a current transient fusion and steady-state balance method based on intelligent control, which comprises the following steps of: based on a transient current fluctuation signal in the electric power system, obtaining a current fluctuation amplitude, a voltage fluctuation amplitude and a frequency, and measuring the current fluctuation amplitude and the voltage fluctuation amplitude by measuring the variation amplitude of the current and the voltage; and calculating the change rate of the fluctuation amplitude and the frequency, and comparing the change rate with a preset threshold value to generate a transient fluctuation state signal. According to the method, transient fluctuation state signals are obtained through a particle swarm optimization algorithm, the distribution condition of current loads of all nodes is accurately calculated, fine adjustment is carried out, load balance is ensured, the stability and response speed of the system under transient disturbance are improved, a language type fuzzy system is adopted, and when transient current and steady current are fused, the stability of the system is improved. According to the actual energy distribution state, the optimal fusion mode is intelligently selected, efficient utilization and distribution of energy are achieved, and the long-term operation stability and safety of a power system are enhanced.

The invention relates to the technical field of information transmission, in particular to a meta-universe-based information transmission method, robot and group robot system.The meta-universe-based information transmission method comprises the following steps that a real-time data flow is based on a meta-universe platform, the network bandwidth utilization rate is monitored, the data packet type is recognized, classification is conducted according to the flow size and the data type, and the real-time data flow is obtained; and real-time data stream classification information is obtained. According to the method and the device, more efficient resource allocation and utilization can be realized through classification, priority evaluation and bandwidth dynamic configuration of the data streams, so that different types of data streams are optimized in transmission, the bandwidth requirement of key information is ensured to be preferentially met, communication parameters are adjusted in combination with the current interactive data of a user, and the user experience is improved. The method can adapt to the change of the network condition in time, improves the response speed and stability of the whole network, effectively reduces the resource waste through the prediction of the future bandwidth demand, and guarantees the interaction experience of the user in the virtual environment.

The invention discloses an inspection task real-time optimization scheduling method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: carrying out the total-factor space-time perception mapping for an inspection site, and determining an inspection view; supervising and training the scheduling optimization model based on the inspection view; receiving an inspection task, performing task collision analysis under high concurrency, generating an optimization instruction of inspection scheduling, performing balanced scheduling distribution of the inspection task in combination with the scheduling optimization model, determining a task scheduling strategy, transmitting and releasing the task scheduling strategy to an inspection target, and performing execution and tracking management of the inspection task. The technical problems of inflexible task scheduling and low resource utilization rate caused by the fact that a traditional inspection task scheduling method in the prior art is generally static and cannot adapt to field environment change in real time are solved, and the purposes of dynamically adjusting the task scheduling strategy according to real-time data of an inspection field and improving the task scheduling efficiency are achieved. And the real-time performance, the flexibility and the resource utilization efficiency of the inspection task are improved.

The invention discloses a leg structure of a biped robot, belongs to the technical field of humanoid robots, and solves the problem that the mass distribution of a series connection structure is too close to the tail end of a leg in the leg design on the basis of realizing two-degree-of-freedom rotation, namely left-right movement along an X axis and front-back movement along a Y axis. The movement of a human hip joint can be better simulated; the driver of the thigh part firstly drives the middleware, and then the middleware drives the driven part to move, so that relative sliding of the thighbone and the tibia and bending movement of the knee joint are simulated under the assistance of the middleware; the telescopic driving part of the shank part drives the universal joint to rotate at any angle in the telescopic process, meanwhile, the movable plate can conduct front-back pitching, left-right swinging and plane rotation, three-degree-of-freedom movement is achieved, the parallel structure is adopted, and better distribution of force and torque can be achieved.

The invention discloses a multi-energy micro-grid energy regulation and control method and device and a storage medium. The method comprises the following steps of: constructing a scene prediction constraint optimization problem by utilizing a trained Warisstein generative adversarial network, and predicting a group of multi-dimensional uncertainty multi-energy micro-grid operation scenes in a future period by following known information at each moment to serve as prediction scenes; forming a pre-scheduling model and a real-time power compensation model based on the prediction scene, and forming a two-stage multi-energy micro-grid real-time energy regulation and control model; a pre-scheduling model obtains a pre-scheduling instruction of the multi-energy micro-grid based on random model prediction control by using the prediction scene information; the real-time power compensation model coordinates schedulable units in the multi-energy micro-grid according to real-time observation of uncertain variables; therefore, the energy in the multi-energy micro-grid is regulated and controlled. Through quick response and adjustment of real-time data, the system can effectively cope with fluctuation caused by uncertainty, thereby realizing accurate regulation and control of the energy of the multi-energy micro-grid.

The invention discloses a special gas and chemical operation and maintenance service management system, and relates to the technical field of chemical management. In order to solve the problem of unreasonable resource allocation or inventory shortage caused by lack of intelligent optimization of demand prediction and inventory management of special gases and chemicals in the prior art. According to the method, material supply and demand balance is realized through adaptive demand prediction and a dynamic inventory optimization algorithm, resource supply of operation and maintenance tasks is ensured, supply chain interruption risk assessment is combined to enhance supply chain elasticity, material supply stability is ensured, early warning and emergency response support are provided, and the risk assessment efficiency is improved. According to task priority dynamic sorting and a path and resource scheduling optimization algorithm, the task completion rate and execution quality are ensured, meanwhile, cross-module information linkage is achieved, the operation and maintenance efficiency is improved, the compliance and efficiency of the operation and maintenance process are comprehensively evaluated, a continuous improvement strategy is provided, and continuous improvement of system performance is promoted.

The invention discloses a high heat dissipation type direct current permanent magnet synchronous servo motor and a using method, and relates to the technical field of motors, the high heat dissipation type direct current permanent magnet synchronous servo motor comprises a center shaft and a stator, a rotor is arranged in the middle of the outer surface of the center shaft, the stator sleeves the rotor, and a sealing cover is arranged on one side of the outer surface of the center shaft. As the temperature of the electric brush assembly rises, the thermal expansion coefficient of the material can cause slight deformation or position adjustment of the electric brush, and the change can help maintain good contact pressure between the electric brush and the commutation copper sheet, so that contact resistance increase caused by temperature rise is reduced; graphite powder generated by abrasion of an electric brush can form a layer of conductive film on the surface of the commutation copper sheet, although the graphite is a conductor, if the graphite is not uniformly distributed, poor local contact is caused, and proper temperature control and design can ensure that the graphite layer is more uniformly distributed on the commutation copper sheet, and on the contrary, the electric contact quality can be improved.

The invention discloses a centralized monitoring management method and system for special software licensing, and the method comprises the steps: collecting a source data sample used by the special software licensing, obtaining the basic data of the use state of the special software licensing, and storing the basic data of the use state of the special software licensing in the centralized monitoring management system for the special software licensing, according to the centralized monitoring and management system for the special software licenses, the monitoring and management system carries out statistical analysis and visual presentation on historical basic data, the use rule of the software licenses is mined, instructive suggestions are provided for increasing and decreasing work of the future licenses, and the centralized monitoring and management system for the special software licenses provides a flexible and elastic grouping authorization management strategy; and an idle permission elastic release mechanism based on authorization use activeness can effectively improve the use efficiency of the software and realize rapid, centralized and efficient management of the existing software authorization.

The invention belongs to the technical field of office automation, and discloses an intelligent office process automation service system and method based on reinforcement learning, and the system comprises a data obtaining module which is used for obtaining office process structure data; the data processing module is used for preprocessing the office process structure data to obtain an office interaction feature data set; the task time evaluation module is used for training according to the office interaction feature data set to obtain a task allocation matching degree prediction model, and predicting an office task allocation matching degree based on the task allocation matching degree prediction model; the work intelligent distribution module is used for performing task distribution on the employees by utilizing an intelligent distribution algorithm according to the predicted office task distribution matching degree; the feedback early warning adjustment module is used for establishing a feedback channel, acquiring the work feedback rate of the employees in real time, and judging whether adjustment early warning information needs to be generated or not according to the work feedback rate of the employees; the ability and task requirements of the employees are matched more reasonably, and the working efficiency is improved.

The invention discloses a vehicle controller diagnosis method, device and system, electronic equipment and a vehicle, and belongs to the technical field of vehicles. The method comprises the steps that a diagnosis request message sent by diagnosis equipment is acquired, the diagnosis request message comprises a request address, and the request address is an address distributed from a first addressing interval; the request address is used for identifying a target controller needing to be diagnosed; the diagnosis request message is sent to a target controller identified by the request address, a diagnosis response message returned by the target controller is obtained, the diagnosis response message comprises a response address, and the response address is an address allocated from the second addressing interval; the response address is used for identifying a target controller sending the diagnosis response message; and sending the diagnosis response message to the diagnosis equipment, so that the diagnosis equipment diagnoses the target controller which responds to the address identifier according to the diagnosis response message. The configurable number of the controller diagnosis addresses is increased, more controller diagnosis can be supported, and the diagnosis efficiency of the controller is improved.

The invention relates to a clinical specimen automatic call identification method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining the specimen information of each specimen, and calculating the specimen priority index of each specimen based on the specimen information; acquiring department information of each department, and calculating a department workload index of each department based on the department information; according to the specimen priority index of the specimen and the department workload index of the department corresponding to the specimen, calculating the transport priority of the specimen; calculating a call trigger index of the specimen according to the delivery priority of the specimen; according to the call triggering index of the specimen, calculating a path efficiency index used for optimizing a specimen conveying path; and when the call trigger index, the path efficiency index and the department workload index of the specimen meet preset conditions, triggering a visual specimen automatic calling device to call personnel to convey the target specimen. According to the invention, automatic and intelligent specimen automatic calling identification operation can be realized.

The invention relates to the technical field of electric energy quality management, and particularly provides an electric energy quality management priority determination method and device, and the method comprises the steps: obtaining an index value of each electric energy quality management priority evaluation index corresponding to each user; determining the power quality management priority of each user based on the index value of each power quality management priority evaluation index and the weight coefficient of each power quality management priority evaluation index; wherein the weight coefficient is obtained by adopting a fuzzy comprehensive evaluation algorithm. According to the technical scheme provided by the invention, the electric energy quality treatment work in the park can be carried out in a targeted manner, the electric energy quality is precisely and orderly treated, and the electric energy quality treatment efficiency and treatment level in the area are improved.

The invention discloses an Internet resource management method and system, and relates to the field of data management, and the method comprises the steps: receiving a resource data index, and obtaining a resource data index matrix; calculating the dynamic entropy weight of each resource index based on the matrix; constructing an adaptive entropy weight hybrid optimization algorithm based on the dynamic entropy weight to obtain a resource allocation scheme; performing real-time simulation on the resource allocation scheme through a digital twinborn technology, obtaining a simulation result, and optimizing parameters of an adaptive entropy weight hybrid optimization algorithm based on the simulation result; and obtaining state prediction of the Internet resources, and inputting the state prediction into the optimized adaptive entropy weight hybrid optimization algorithm to realize dynamic adjustment of a resource allocation strategy. Dynamic adjustment of resource allocation is realized by calculating the dynamic entropy weight of a resource data index, constructing a self-adaptive entropy weight hybrid optimization algorithm and generating a resource allocation scheme, performing real-time simulation and feedback optimization on the allocation scheme through a digital twin technology, and combining with resource state prediction.

The invention discloses an optimized deployment method for network mode coexistence in a multi-mode network environment, and belongs to the field of multi-mode intelligent networks. The method comprises the following steps: modeling a deployment problem of a network mode as a 0-1 integer programming problem, and comprehensively considering key constraints such as multi-mode network element resources, link resources and flow forwarding delay so as to ensure user service quality; meanwhile, through joint routing optimization, the connectivity of each network mode is ensured; for the non-convexity of an original problem, the problem is converted into an equivalent 0-1 integer linear programming problem, and a network modal deployment and routing algorithm based on random rounding is designed; according to the algorithm, efficient deployment of the network modals can be realized under the condition of limited resources, and the network performance is ensured. According to the method provided by the invention, the link load is effectively reduced, and the utilization rate of network resources is improved on the premise of ensuring the service quality.

The invention discloses an artificial intelligence auxiliary inquiry real-time data scheduling method and system. The method comprises the following steps: S1, forming a preliminary medical data set; s2, respectively constructing corresponding game tree models; s3, embedding the priority scheduling path into the game tree model; s4, establishing a cross-layer communication mechanism to realize state interaction and information transmission between the heterogeneous medical data hierarchical game tree models; s5, generating a final medical data scheduling plan; and S6, executing a final medical data scheduling plan, preferentially transmitting the medical data with high priority to a doctor end or a diagnosis model, enabling the medical data with low priority to enter a cache module, adjusting a subsequent transmission sequence of the medical data according to a global revenue function, and recording log information of medical data scheduling at the same time. According to the invention, the interference of low-priority data on key tasks is reduced, and the overall scheduling efficiency and the service quality of medical scenes are improved.

The invention discloses an automatic adjusting system for high-speed rail dispatching, which is characterized in that the occupation conditions of station tracks and trains are visually displayed through a visual interface, a dispatcher can quickly grasp the global situation, the decision-making efficiency is improved, meanwhile, the wrong dispatching risk caused by information disorder is reduced, an automatic arrangement function quickly solves conflicts according to a preset rule, and the dispatching efficiency is improved. Scheduling flexibility and response speed are improved, manual intervention and cost are reduced, operation efficiency is improved, statistics of station track change conditions provides data support for follow-up analysis and optimization, a dispatcher is helped to optimize resource allocation, decision accuracy is improved, in addition, a specific train number station track is fixed, specific operation requirements are met, and the service life of the dispatcher is prolonged. The locking function reduces the conflict risk related to a specific train number, and through comprehensive application of visual, automatic and intelligent means, the scheduling efficiency, safety and flexibility of the high-speed railway arrival and departure line application system are remarkably improved, the operation cost is reduced, and a powerful guarantee is provided for smoothness and high efficiency of railway transportation.