1896 results about "Optimal scheduling" patented technology

A system and method for performing modeling, prediction, optimization, and control, including an enterprise wide framework for constructing modeling, optimization, and control solutions. The framework includes a plurality of base classes that may be used to create primitive software objects. These objects may then be combined to create optimization and / or control solutions. The distributed event-driven component architecture allows much greater flexibility and power in creating, deploying, and modifying modeling, optimization and control solutions. The system also includes various techniques for performing improved modeling, optimization, and control, as well as improved scheduling and control. For example, the system may include a combination of batch and continuous processing frameworks, and a unified hybrid modeling framework which allows encapsulation and composition of different model types, such as first principles models and empirical models. The system further includes an integrated process scheduling solution referred to as process coordinator that seamlessly incorporates the capabilities of advanced control and execution into a real time event triggered optimal scheduling solution.

A system and process / method is provided, which economically optimizes the dispatch of various electrical energy resources. The disclosed process / method is linked to and communicates with various sources of input data, including but not limited to, EMS / SCADA legacy Energy Management Systems (EMS), legacy Supervisory Control and Data Acquisition (SCADA) Systems, Demand Response (DR) and Distributed Energy Resources (DER) monitor, control, schedule, and lifecycle management systems (DR / DER Management System), and Energy Markets, electrical energy commodity trading systems (Trading Systems), and Operations System (OPS) in order to compute optimal day-ahead, day-of, and real-time schedules of various durational length for generation, demand response and storage resources while taking into account bilateral contracts and market-based trade opportunities.

A novel protocol for scheduling of packets in high-speed cell based switches is provided. The switch is assumed to use a logical cross-bar fabric with input buffers. The scheduler may be used in optical as well as electronic switches with terabit capacity. The proposed round-robin greedy scheduling (RRGS) achieves optimal scheduling at terabit throughput, using a pipeline technique. The pipeline approach avoids the need for internal speedup of the switching fabric to achieve high utilization. A method for determining a time slot in a NxN crossbar switch for a round robin greedy scheduling protocol, comprising N logical queues corresponding to N output ports, the input for the protocol being a state of all the input-output queues, output of the protocol being a schedule, the method comprising: choosing input corresponding to i=(constant-k-1)mod N, stopping if there are no more inputs, otherwise choosing the next input in a round robin fashion determined by i=(i+1)mod N; choosing an output j such that a pair (i,j) to a set C={(i,j)| there is at least one packet from I to j}, if the pair (i,j) exists; removing i from a set of inputs and repeating the steps if the pair (i,j) does not exist; removing i from the set of inputs and j from a set of outputs; and adding the pair (i,j) to the schedule and repeating the steps.

To improve the delivery probability of a random access channel and increase the information amount notified on the random access channel. In a mobile communication system in which a mobile station apparatus and base station apparatus perform multicarrier communication with each other, the mobile station apparatus sets transmission control information about a random access channel based on a state of the mobile station apparatus in transmitting the random access channel, and the base station apparatus determines the state of the mobile station apparatus based on the transmission control information notified on the random access channel, and performs optimal scheduling corresponding to the state of the mobile station apparatus.

The invention relates to a microgrid multi-energy joint optimal scheduling method. The method includes: step 1, on the basis of force application conditions of energy supply equipment and operating states of energy storage equipment in different scenes, constructing a physical model of a multi-energy system; step 2, establishing unit mathematical models, including a wind power generator model, a photovoltaic power generation model, a micro-gas turbine model, a fuel cell model and an energy storage model, of the microgrid multi-energy system; step 3, in order to realize lowest comprehensive cost in system operation, constructing a microgrid multi-energy optimal scheduling model by taking DG operation constraints, system safety constraints and multi-energy coupling characteristics into comprehensive consideration; step 4, on the basis of reversed order dynamic planning, solving the microgrid multi-energy optimal scheduling model to obtain a microgrid multi-energy joint optimal operatingstrategy. By the microgrid multi-energy joint optimal scheduling method, operating demands in different scenes can be met, electricity-thermal coordinated optimal scheduling schemes are provided for operating of a comprehensive energy microgrid in different scenes, and high integral operating efficiency and high economic benefits are realized.

The invention provides a wind power optimizing scheduling method based on power forecast, which is applied to a power scheduling operation schedule solution containing wind power. In the method, the power forecast curve of a wind power station are utilized; the peak regulation capacity and the safe and stable limit of a system are calculated, so that scheduling of total output of the wind power within the scheduling range is divided into restricted time interval scheduling and non-restricted time interval scheduling; for the non-restricted time interval scheduling, the error scope of the forecast power generation is considered based on a forecast power generation curve; and for the restricted time interval scheduling, wind power forecast errors are considered based on the restriction of the aggregate limit of the wind power stations and the forecast power generation output of each wind power station, thus optimizing power generation power limit value allocation of each wind power station, so that the system can maximally digest the wind power under the condition of meeting the requirement of safe and stable operation. A wind power scheduling curve is executed by the power generation of the wind power station so as to strength safety and stability of the power grid operation, intensify the digestion capacity of the wind power and maximally utilize the wind power.

The invention discloses a cascade reservoir multi-objective optimization scheduling method based on an improved artificial bee colony algorithm. The method comprises the following steps that s11, the basic information data of a cascade reservoir system are acquired; s12, a multi-objective scheduling model including power generation, estuarine ecology and water supply is established according to the information of the reservoir system; and s13, the optimal scheduling scheme of the cascade reservoir system is solved by performing the improved artificial bee colony algorithm. Global optimization of the reservoir scheduling problem can be realized so that the calculation efficiency and accuracy can be enhanced, and a new approach can be provided for solving the multi-objective optimization scheduling problem of the cascade reservoir system.

The invention provides a distributed task scheduling method for an unmanned aerial vehicle fleet in a dynamic environment and aims to achieve that the unmanned aerial vehicle fleet get rid of the limits of a ground control station in the dynamic environment and autonomously conduct task scheduling, and the real-time performance of task scheduling is improved. The distributed task scheduling methodis implemented by the steps that a decision-making unmanned aerial vehicle allocates a task to each unmanned aerial vehicle and sends an execution instruction to each unmanned aerial vehicle; all the unmanned aerial vehicle fleet execute the tasks according to the execution instructions sent by the decision-making unmanned aerial vehicle, and communicate with the decision-making unmanned aerialvehicle through a heartbeat mechanism; the decision-making unmanned aerial vehicle conducts optimal scheduling on the tasks of all the unmanned aerial vehicle fleet; the unmanned aerial vehicle fleetexecute the tasks which are determined again according to the flight paths respectively, and formation is kept; the decision-making unmanned aerial vehicle monitors the task completion conditions of all the unmanned aerial vehicle fleet, and when all the tasks are completed, the unmanned aerial vehicle fleet makes a return voyage. By means of the distributed task scheduling method, the unmanned aerial vehicle fleet can select a correct scheduling method according to the change of the dynamic environment, the real-time performance of task scheduling is improved, and the unmanned aerial vehiclefleet can autonomously execute the task.

The present invention discloses a multi-year regulating storage reservoir optimal scheduling method considering runoff uncertainty. The method comprises the following steps of: (1) analyzing historical reservoir runoff data; (2) establishing a runoff random simulation model, and generating a reservoir simulation annual runoff sequence; (3) simulating annual inflow runoff sequence classification and performing intra-annual distribution to obtain a three-year inflow runoff process sequence on a month scale; (4) establishing a multi-year regulating storage reservoir optimal scheduling model that considers multiple water demands; and (5) reducing dimension by using an optimization algorithm and optimizing a resolution model so as to obtain an optimal discharging and water supply decision. According to the method provided by the present invention, the scheduling with maximized comprehensive benefits of long-term multi-target operation can be performed on a multi-year regulating storage reservoir by considering runoff uncertainty, so that the method is suitable for promotion in long-term optimal scheduling of the multi-year regulating storage reservoir in China.

Embodiments of the invention provide for managing attraction attendance levels through tracking current attendance levels and notifying patrons of incentives to alter their attraction selections in real-time. Examples identify an attraction queue that has a customer wait time failing to meet a threshold and determine a geographic relationship of a tracking device associated with a registered customer to the queue. Accordingly, an incentive is created from metadata associated with the queue and offered to the registered customer to urge the registered customer to take a queue balancing action. The queue balancing action may be leaving the queue if the customer wait time exceeds a threshold maximum wait time and the tracking device is within the queue, or choosing to enter the queue if the customer wait time is less than a threshold minimum wait time and the tracking device is outside of the queue.

The invention discloses a mobile edge computing task unloading method in a single-user scene, and relates to the field of treatment of a mobile computing system. The invention aims to reduce the reaction time delay and energy consumption of mobile equipment. construction of single-user scene task unloading model comprises construction of a system overall model and construction of each partial model, wherein the construction of each partial model comprises: a task queue model, a local computing model, a cloud computing model and a computing task loading model; a task unloading strategy: givinga task unloading scheme targeting at minimizing the system overall load K; executing all tasks on local CPU or an MEC server based on binary particle swarm optimization; correspondingly and locally executing a load optimal scheduling strategy, and executing a load optimal scheduling strategy by the MEC server based on pipeline scheduling. Through verification, the task unloading method in a sing-user scene provided by the invention reduces the reaction time delay and energy consumption of the mobile equipment.

The invention discloses an operation workshop scheduling modeling method based on a genetic algorithm. The method comprises the steps of JSP genetic algorithm design of reverse cross of a stored gene segment, eM-Plant simulation modeling, data collection, improvement of mutation operator and obtaining of an optimized scheme; the JSP genetic algorithm design of the reverse cross of the stored gene segment comprises the steps of randomly generating an initial group according to a sequence code, calculating the fitness of the initial group, judging whether the cycling times is satisfied, outputting an optimal result and program running time if the cycling times is satisfied, drawing an algorithm performance trace diagram, drawing an optimal scheduling trace diagram, selecting through a roulette wheel if the cycling times cannot be satisfied, reversely crossing the stored gene segment, randomly mutating the gene segment, calculating the fitness of a novel population, re-inserting a filial-generation population to the parental population, and recording the performance of the optimal result trace algorithm. By adopting the method, the running of the production workshop can be optimized and coordinated, the design effect is good, the process is simple, and the production danger and production cost can be reduced.

The invention provides an optimized scheduling method of a semiconductor production line based on a Petri net and immune algorithm. The method utilizes the Petri net for carrying out modeling to the semiconductor production line, comprising a path scheduling model, an equipment group scheduling model and an equipment scheduling model; and the immune algorithm is embedded in the Petri net model as the scheduling strategy. By the Petri net model, all possible acts of the semiconductor manufacturing system can be described, such as work-piece processing, equipment failure, batch processing, defected wafer rework and the like. The chromosome of the immune algorithm can be directly structured out of search nodes of the Petri net model, and each gene of each chromosome records the scheduling strategy of each equipment group. A better chromosome is obtained by the simulation of the Petri net model, and therefore a sub-optimal scheduling strategy is generated. The optimized scheduling method has the advantages of reducing the complexity of the model and improving the reusability of the model and the scheduling algorithm.

A HCPS (hierarchically clustered P2P streaming system) comprising peers grouped into clusters and hierarchies. The HCPS actively balances the uploading capabilities among clusters and executes an optimal scheduling algorithm within each cluster to ensure that system resources are optimally utilized. The HCPS comprises an architecture which can be used in practical applications, yet can achieve the streaming rate close to the theoretical upper bound.

The present invention relates to the supply-demand side combination random scheduling technology, especially to an optimal scheduling method considering wind power output and demand response nondeterminacy. The method comprises the step 1, based on an extension probabilistic sequence operation theory, establishing a probability model representing excitation load response nondeterminacy and price load response nondeterminacy, considering influence of the demand response nondeterminacy on cost; the step 2, in response to wind power fluctuation and randomness as background, constructing the current optimal scheduling model of an electric power system considering the wind power output and demand response nondeterminacy at the same time based on a risk constraint and risk cost theory; and step 3, converting a random optimization problem as a linear convex optimization problem for solution through conversion of a target function and a constraint condition. The method provided by the invention performs modeling and analysis aiming at demand response nondeterminacy and performs fine description of the nondeterminacy in the demand response process so as to provide practical reference for the scheduling of the electric power system and improve the stability of the electric power system.

The invention discloses a regional energy comprehensive coordination management and control system. The system comprises a regional operation monitoring subsystem, a distributed power forecasting subsystem, a load cluster prediction response analysis subsystem, a fault fast processing subsystem, an energy analysis and management subsystem, an electric vehicle optimal scheduling subsystem and a regional multi-level energy comprehensive coordination control subsystem. The regional energy comprehensive coordination management and control system achieves comprehensive operation monitoring of power sources, power grids and user loads in a region and forecasting, analysis and scheduling of a variety distributed energy resources on the basis of multi-source information integration, achieves rapid fault diagnosis and processing of the power grid, achieves load forecasting, energy consumption analysis, energy saving management and electric vehicle intelligent scheduling of users and achieves reasonable distribution and pluralistic complementary of regional energy through comprehensive coordination of energy interaction among the power sources, the power grids and the user loads, thereby greatly increasing the energy efficiency and enabling the power grids to be in economical and efficient operation.

The invention discloses a charging and discharging scheduling method for electric vehicles connected to a micro-grid. The method comprises the steps of: (1) determining a system structure of the micro-grid and the characteristics of various units; (2) building a micro-grid optimal scheduling target function of considering the battery depreciation cost of the electric vehicles under time-sharing electrovalence; (3) determining constraints of various distributed power supplies, batteries of the electric vehicles and the like and forming a micro-grid optimal scheduling model by the constraints and the micro-grid optimal scheduling target function; (4) determining the number, the start-stop time, the start-stop charged states and other basic calculated data of the electric vehicles connected to the power grid under the time-sharing electrovalence; and (5) solving the micro-grid optimal scheduling model through a particle swarm algorithm and determining the charge and discharge power when the electric vehicles are connected to the power grid. The batteries of the electric vehicles are connected to the micro-grid as mobile distributed energy storage devices to achieve the peak load shifting effect; the operation safety and stability of the micro-grid in the time-sharing electrovalence environment are improved; and the energy utilization efficiency and the power grid operation efficiency are simultaneously improved.

The invention is applicable to the technical field of take-out delivery of internet or mobile internet take-out service platforms or systems and provides a method and a system allowing a take-out deliverer to compete for orders. The method includes the steps of receiving a delivery order submitted by a smart terminal or a website server and including a delivery address, a merchant's address and details of items; pushing the delivery order to delivery terminals which deliverer accounts are logged in; according to received order competition information fed back by the delivery terminals, distributing the delivery order to deliverers related to the delivery terminals. The method and the system have the advantages that the deliverers are fully motivated by competing for orders, the delivery terminals allow the deliverers to make full consideration on problems such as delivery range, routes and duty state while competing for the orders, optimal scheduling is achieved, delivery is easier and more efficient, and delivery cost is lowered.

The invention discloses a medium-term and long-term combined power generation optimal scheduling method and a medium-term and long-term combined power generation optimal scheduling system in a cascade reservoir group. The method comprises the following steps that: S1, a scheduling server establishes and stores three medium-term and long-term optimal scheduling models, namely a model of largest cascade power generation capacity, a model of largest cascade power generation capacity which gives consideration to guaranteed output, and a model of largest cascade energy storage; S2, the scheduling server selects an optimal medium-term and long-term optimal scheduling model according to a power generation optimal objective; S3, a data acquisition device acquires materials required for solving the models, and the scheduling server solves the medium-term and long-term optimal scheduling models by selecting algorithms; and S4, the scheduling server generates and outputs a medium-term and long-term power generation scheduling optimal scheme. By the method and the system, the phenomenon that a large quantity of optimization variables and constraint conditions of cascade reservoir optimal scheduling influence computational efficiency and result reasonability can be avoided; and an optimal medium-term and long-term combined power generation optimal scheduling scheme is obtained.

The invention discloses a distributed optimal scheduling method for a multi-energy complementary microgrid containing multiple bodies based on an ADMM in the field of power system microgrid technology. According to the method, operators and users form optimal interaction based on an ADMM framework till supply and demand balance is reached. In the optimal iteration process, the operators and the users can complete optimal scheduling just by exchanging expected power supply capacity and heat supply capacity and actual power supply capacity and heat supply capacity, and therefore the privacy of the operators and the users is greatly protected. Due to efficient energy cascade utilization of cogeneration combined with a heat storage system, demand responses at the user side and renewable energy power generation, the method has the advantages of saving energy, reducing emissions, relieving the pressure of power grids and the like; a comfortable indoor temperature is set, the comfort of the users is considered, and the economical efficiency and the subjective intentions of the users are comprehensively considered in terms of cost. The method is an optimal method with lower cost and higher feasibility for economic operation of the multi-energy complementary microgrid.

The invention relates to the technical field of the optimization of computer performance and particularly relates to a dispatching method for working load of an elastic cloud computing platform. Thedispatching method comprises the steps of initializing virtual machines and tasks and combining the virtual machines according to the relative load among the tasks; dividing the total execution time of the tasks into setting number of scheduling phases, and working out an optimal scheduling policy according to the working load of a current task by a dispatching system; then dividing or combining the scheduling phases according to the degree of similarity of the working loads of two adjacent scheduling phases; finally adjusting the number of times of simulation to the sequence of the divided or combined scheduling phases in a self-adaption way so as to determine the computing time of the next scheduling phase until the end of the program. According to the dispatching method, the adaptive multi-tasking load change stage can be configured reasonably, so that the dispatching performance of a whole system is improved, and the test time is reduced.

A multi-mode combined cooling, heating and power micro-grid system considering ice storage air conditioning is composed of a distributed power generation system composed of photovoltaic power generation, wind power generation, a fuel cell, a micro gas turbine and ice storage air conditioning, primary system composed of an energy storage system and other co-supply equipment, and a secondary dispatching control system. A CCHP-MG multi-time-scale optimal scheduling method is proposed, in a day-ahead plan, the uncertainty of renewable energy is described through multiple scenes, economic operationof CCHP-MG in a running optimization period is focused on; Intra-day scheduling is based on the scheme of the day-ahead plan, the difference and correlation of cooling, heating and power on scheduling time scale are considered, a two-layer rolling optimal scheduling scheme is proposed, the upper layer dispatches the combined cold and heat supply equipment, the lower layer adjusts power equipment,taking minimization of the adjustment cost as the goal, the smooth output of each co-supply equipment is obtained. The invention can remarkably improve the comprehensive utilization efficiency of energy and the local absorption capacity of the distributed renewable energy, and realize the economic and safe operation of the multi-energy micro-network.

The invention discloses an energy management and coordination control system and method for a wind and solar energy distributed power generation independent micro grid. The energy management and coordination control system is composed of a distributed power generation system, a wireless communication system and a monitoring system, and an AC and DC hybrid topology structure is adopted. Specifically, main power generation units are composed of the wind power generation unit, the photovoltaic power generation unit and other micro power supplies, an energy storage device is of a hybrid energy storage type including a lead-acid storage battery and a super capacitor, the micro wind power generation unit and the solar photovoltaic power generation unit are converged to a DC bus through a power electronic conversion device, the hybrid energy storage device is connected with the DC bus through a bidirectional power converter, the DC bus exchanges energy with an AC bus through a DC / AC converter, and a medium wind power generator is connected with the AC bus. According to the energy management and coordination control system and method, a multi-agent coordination control system can be applied to a micro grid monitoring system and is developed based on a JADE platform, and energy management and optimal scheduling of the micro grid can be achieved.

The invention discloses a day-ahead optimal scheduling method for a wind-solar storage integrated power generation system. The method comprises the following steps: acquiring various optimization data and determining the optimization space of the wind-solar storage integrated power generation system; building an optimization model taking the maximum total active power as the target according to the grid model of a practical power grid; adding the punishment quantity of the total active power output curve variation relationship to the optimization target to obtain the optimal scheduling model putting smooth power generation curve into consideration; linearizing the nonlinear factors in the model, obtaining the active power generation curve of the wind-solar storage integrated power generation system through solving by the dual simplex method, submitting the active power generation curve to a superior scheduling center; and acquiring the charge-discharge plan of an energy storage device and issuing the plan to subsystems for execution. By adopting the method provided by the invention, the power resource optimal configuration capability is greatly improved.

The invention provides a method of wafer optimal scheduling. The steps of the method comprise obtaining the quantity of wafers needed to be operated in a system and the technique sequence of each wafer, analogously calculating operating paths of a plurality of wafers according to the quantity of the wafers and the technique sequence of each wafer, wherein the path is moving sequence aggregation of each wafer in time, evaluating the operating paths of the pluralities of wafers according to the technique time efficiency of the system, taking the path with the maximum processing time efficiency of the system as the optimal path to store into a scheduling sequence, and scheduling each wafer according to paths in the scheduling sequence. The invention performs n steps simulation to all potential scheduling sequences of the system through constructing the function of the technique time efficiency of the system, and can obtain the optimal path through further analysis. Therefore, the scheme of the invention can increase the productivity of the system and reduce the manufacturing cost, thereby bringing higher benefits for manufacturers.

The invention relates to a two-stage scheduling method of parallel test tasks facing a spacecraft automation test, which belongs to the field of parallel tests. The method comprises the following stages: in the first stage, the test tasks, task instructions and tested parameters are analyzed and determined, a constraint relation between the tasks is defined, a time sequence constraint matrix and a parameter competitive relation matrix are established, the tasks and the constraint relation between the tasks are changed into undirected graphs, a parallel task scheduling problem is changed into a minimum coloring problem in the sequence of the tops of the graphs, a method based on the combination of a particle swarm and simulated annealing is used for solving, and then a test task group with the maximal degree of parallelism is obtained; in the second stage, the obtained test task group with the maximal degree of parallelism is distributed on limited test equipment, and then an optimal scheduling scheme is obtained. According to the two-stage scheduling method, the constraint relation among a plurality of test tasks is quickly established, the independence between the test tasks is analyzed, the degree of parallelism of the test tasks is increased, the optimal scheduling of the tasks on the equipment is realized when constraint conditions are satisfied, and the test efficiency is improved.

The present invention discloses an optimization scheduling method based on mixing power supply multi-time scale complementation. The method comprises: 1, bundling a wind turbine generator, a photovoltaic power station and a hydroelectric generating set into a VP for combined operation, defining a load tracking index, and allowing a load curve deducting the VP output to be an optimal load curve; 2, establishing a day-ahead 24-hour planning model comprising a VP optimal scheduling layer and a thermal power optimal scheduling layer, and taking the minimum of the load tracking index and the thermal power generation cost as target functions; 3, establishing an intra-day 4-hour rolling planning model, taking the invariance of the optimal load curve in this period and the minimum of the thermal power adjustment cost as targets, and setting wind-abandoning and photovoltaic-abandoning constraint of progressive correction; and 4, establishing a real-time 15-mit planning model, and taking the invariance of the optimal load curve in the last moment and the minimum of the thermal power adjustment cost, and setting the wind-abandoning and photovoltaic-abandoning constraint of the progressive correction. The optimization scheduling method based on the mixing power supply multi-time scale complementation realizes the maximization of regulation benefit, environmental protection benefit and economic benefit, and provides valuable solution schemes for a plurality of new energy grid-connected problems.

The invention discloses a scheduling method and system. The method comprises the following steps: when a mobile terminal request is received, determining candidate objects to be scheduled based on preset attribute information included in the request, wherein the candidate objects have attribute information corresponding to the preset attribute information; and determining an optimal object from the candidate objects according to current states and working performance evaluation information of the candidate objects, and scheduling the optimal object on the basis in order that the optimal object interacts with a mobile terminal to process a service request of the mobile terminal. Through adoption of the scheduling method and system, the optimal object can be determined from the candidate objects to be scheduled in conjunction with the current states and the working performance evaluation information of the candidate objects, so that optimal scheduling of CDNs (Content Delivery Networks) of different producers can be realized when the scheduling method and system are applied to scenes in which videos are accelerated under the assistance of a plurality of third-party CDNs, and relevant services such as live video streaming have relatively good acceleration effects.

The invention relates to a scheduling method for steel product logistics based on real-time information. The scheduling method comprises the steps that (1) according to a cargo order, an initial logistics scheduling scheme is made by using a vehicle optimal scheduling mathematic model, and logistics is assigned according to the initial logistics scheduling scheme; (2) dynamic effect information in the delivery way is fed back to a logistics management server by using a vehicle-mounted terminal after delivery is carried out; (3) according to the dynamic effect information, an updated logistics scheduling scheme is generated by using a path updating algorithm, benefit evaluation is carried out on the updated logistics scheduling scheme, whether the logistics scheduling scheme is updated or not is judged according to a benefit evaluation result, the updated logistics scheduling scheme is transmitted to the vehicle-mounted terminal in the delivery way through the logistics management server if the logistics scheduling scheme is updated, and the delivery task is executed continually according to the initial logistics scheduling scheme by the delivery logistics if the updated logistics scheduling scheme is not adopted. The scheduling method has the advantages that data are updated in time, operation efficiency is high, the cost for logistics monitoring is low, and the information between enterprises is integrated and unified.