115results about How to "Reduce scheduling costs" patented technology

The invention discloses a method for constraining workflow scheduling in a cloud environment based on the ant colony optimization algorithm through deadline. The method comprises the steps: employing an ant colony system with a candidate list; respectively searching a workflow scheduling scheme in the cloud environment through a plurality of ants; carrying out the communication of a workflow scheduling result among the ants in a pheromone mode, thereby guiding the deciding of the subsequent search direction of ants and the workflow scheduling scheme. Compared with a conventional workflow scheduling method, the method can reduce the cost of the workflow scheduling in the cloud environment and improves the service quality for cloud users under the condition of meeting the deadline time QoS requirements of users.

The invention discloses a scheduling method, device and server for a shared vehicle. The scheduling method includes: detecting whether an event requesting to mark the current shared vehicle occurs, and if so, marking the current shared vehicle as an incentive vehicle in the vehicle map to Prompt user to use; issue reward to current account using incentive vehicle. Through the scheduling method of the present invention, users can be encouraged to use incentive vehicles, thereby realizing the scheduling of shared vehicles, and greatly reducing the scheduling cost.

The invention discloses a public bicycle renting forecasting method based on multi-source data fusion. According to the method, historical data about public bicycle renting / returning records, weather, temperature, holidays, festivals and the like are cleaned and preprocessed, and training datasets are acquired; the datasets are classified with a clustering algorithm, and different renting modes of public bicycles are divided; the classified datasets are used to establish a Bayesian classifier used for forecasting the renting modes according to conditions of holidays, festivals, weather and air temperature of one day in the future; a self-adaptive particle swarm neural network model corresponding to each mode is trained for different modes of datasets respectively; finally, the renting mode of one day is forecasted by the aid of the Bayesian classifier, a corresponding particle swarm neural network model is selected to forecast the renting law of public bicycles. The forecasting accuracy is high, the operation speed is high, reference basis is provided for bicycle renting and returning by a user, the duration time of the unbalanced state of a public bicycle station is shortened, and the users' satisfaction is improved.

The invention provides a two-level optimized dispatching method for price type flexible load. The two-level optimized dispatching method comprises the following steps: (1) analyzing interactive response and interactive cost for the price type flexible load; (2) establishing a two-level optimized dispatching model for price type flexible load, wherein the upper layer is of a price type flexible load dispatching module based on global optimization and the lower layer is of a multi-object multi-target model for flexible load; (3) converting an expected value of a flexible load response amount probability model; and (4) solving a two-level optimizing problem based on a genetic algorithm of a penalty function. According to the two-level optimized dispatching method disclosed by the invention, the maximized social benefits are realized with the purpose of reducing the power grid dispatching cost, the flexible load satisfaction degree is used as a constraint of the upper-layer power grid dispatching layer in a maximized manner, the flexible load is guided to participate in power grid dispatching operation in a facilitated manner, and the win-win aim of the power grid company and a flexible load individual or a load aggregator is realized.

The present invention provides a shared vehicle scheduling method and system. The method comprises the steps that: a server generates a scheduling task according to a shared vehicle partitioning situation of a shared vehicle distribution area and a vehicle scheduling time period to which the current time belongs; a client selects a target task; a target vehicle located at a departure point of thetarget task is unlocked; after the target vehicle is locked again, the server generates target task ending information and sends the target task ending information to the client. According to the method and the system, the scheduling task can be released according to a dynamic vehicle distribution condition, thus a terminal user is guided to dispatch over-centralized or over-distributed vehicles to other sites so as to realize the 'rebalancing' of the shared vehicles, the vehicle scheduling time period to which a current time belongs can be considered in releasing the scheduling task, thus theconflict between the scheduling task and traffic 'tide' is avoided, and thus the terminal user can be guided to schedule the vehicle towards the direction of 'inverse tide'.

The invention discloses a vehicle scheduling method and system, a server and a client. The vehicle scheduling method comprises the steps of obtaining scheduling information of the client, and selecting candidate scheduling units from a plurality of scheduling units included in a current scheduling area according to the scheduling information; selecting a predetermined number of candidate scheduling units to serves as recommended scheduling units according to scheduling parameters of each candidate scheduling unit, sending corresponding indication information to the client so as to trigger a user who uses the client, and selecting a target scheduling unit from the recommended scheduling units to implement vehicle scheduling. According to the invention, accurate vehicle scheduling is realized, the vehicle scheduling efficiency is improved, and the vehicle scheduling cost is reduced.

The invention relates to a multi-machine cooperative operation remote management scheduling method based on an improved ant colony algorithm. The method comprises the steps: task planning and path planning, and task planning is divided into task distribution and task sequence optimization. Firstly, according to initial task information and agricultural machinery information in a farmland operationenvironment, and according to a supply and demand matching principle of agricultural machinery and tasks, an optimal task allocation scheme is obtained by improving an ant colony algorithm; secondly,according to the optimal task allocation scheme, performing task sequence optimization on multiple tasks allocated to each agricultural machine through an improved ant colony algorithm to obtain an optimal task sequence scheme; and finally, establishing an environment map model according to the initial environment information, performing path planning through an improved ant colony algorithm according to the optimal task allocation scheme, the optimal task sequence scheme, the path cost between the dynamic and static obstacles and the task plot and the path smoothing factor, obtaining an optimal path planning scheme, and seeking a collision-free optimal path.

The invention relates to a requirement responding scheduling method for wind power uncertainty. The method comprises a scheduling trading center predicts a benchmark load curve and a wind power generation curve and releases scheduling information to load agents; the load agents perform learning and decision making according to scheduling requirement information, system operation information and historical trading information and formulate bidding strategies and reports to strategies to the scheduling trading center; the scheduling trading center performs optimal scheduling on generation and consumption power resources with the lowest scheduling cost as the target after obtaining load agent bidding information and generator set calling cost information and sends scheduling instructions and final compensation price to the load agent and the generator set; the load agents adjust consumption power by means of electricity price adjusting and stimulation with the lowest scheduling cost as the target after obtaining final scheduling instructions. According to the method, an effective way is provided for wide-area distributed medium and small volume load resources to participate in system scheduling, the wind power volume load admitting capacity is improved, and the scheduling cost is reduced.

The invention relates to a vehicle subletting system. The vehicle subletting system comprises a subletting client with a subletting module, a cloud server with a matching module and at least one renting client with a renting module, wherein the subletting module transmits a subletting request to the matching module, the matching module transmits the received subletting request to the renting module, the renting module gives a corresponding subletting answer request according to the subletting request and transmits the subletting answer request to the matching module, the matching module transmits the received subletting answer request to the subletting module, the subletting module generates a subletting selection request according to the subletting answer request and transmits the subletting selection request to the matching module, and the matching module receives the subletting selection request, gives a corresponding subletting operation request and transmits the subletting operation request to the renting module. By adopting the vehicle subletting system, the traditional geographic limitation is avoided, a user can rent a vehicle in any place at any time, and the vehicle returning cost of the user and the dispatching cost of an operation company are reduced.

The invention provides a track-type parking device for public bicycles. According to the track-type depositing device, locking devices are arranged on the sliding track in a sliding manner; inserted pins on the bases of the locking devices are matched with insertion holes in the sliding track; tracks are arranged on two sides of the baseplate of the sliding track; wheels walking on the tracks are hinged to the lower surfaces of the bases of the locking devices; the concave surfaces the wheels are matched with the convex surfaces of the tracks; sucking disk type electromagnets are connected with a power supply and a switch in series through a conducting wire; the sucking disk type electromagnets are fixed on the bases of the locking device; through holes are formed in the bases; sliding bars arranged at the bottom ends of the sucking disk type electromagnets penetrate the through holes; the sliding bars are sleeved with springs; inserted pin bodies attracted by the sucking disk type electromagnets are arranged at the bottom end of the sliding bar; slideways matched with the sliding bars are arranged on the inserted pin bodies; the inserted pin bodies are matched with the insertion holes in the sliding tracks. The track-type deposit device can be flexibly increased or decreased according to the practical rent amount change of a bicycle renting station and the renting and returning efficiency of public bicycles is efficiently improved.

A method for predicting natural demands on public bicycle rental spots includes the steps of 1, predicting a share ratio of public bicycles, to be specific, establishing a share ratio prediction model, quantifying trip characteristic indexes, and establishing a utility function; 2, predicting the natural demands on public bicycles, to be specific, subjecting the share ratio of public bicycles, predicted in the step 1, and an actual share ratio to correlation analysis, and based on large-sample historical data of rental spots, predicting the natural demands on pubic bicycle rental spots by means of correlation coefficient.

The invention relates to a taxi scheduling method and system based on deep reinforcement learning. The system comprises an area construction module, a demand prediction module, a vehicle scheduling module and a simulator. The method comprises the steps of S1 forming a regional network; S2 predicting the number of orders that will appear in any area at any time; S3 calculating the total vehicle supply of one area; obtaining a demand / supply state of each area; and S4 inputting the states of the area where any idle vehicle is located and the neighbor area into a trained taxi scheduling model to obtain a scheduling strategy of the vehicle, and determining whether the vehicle is continuously left in the local area or scheduled to the neighbor area specified by the system. According to the invention, idle taxis are dispatched, the success rate of order matching is increased, the waiting time of passengers is reduced, and the utilization rate of taxis is improved.

The invention discloses a methane-wind-light full renewable energy system based on an energy hub and a methodthereof. The method comprises the following steps: constructing a full renewable energy system based on a methane-wind energy-solar hub; carrying out detailed modeling on an energy hub containing an energy storage element, processing the energy hub into a linearized matrix which is more adaptive to system optimization scheduling, and then further considering the output uncertainty of wind-solar renewable energy sources; and constructing a day-ahead-real-time two-stage distributed robustoptimization scheduling model based on data driving. According to the invention, the absorption of renewable energy can be promoted, the absorption of biomass energy is promoted, the heat preservation and supply of the biogas digester can be realized in winter, and the stable supply of biogas is guaranteed; and the thermoelectric coupling degree is weakened, the consumption of wind-solar renewable energy sources is effectively promoted, and meanwhile, the comprehensive scheduling cost of the system is reduced.

The invention discloses a wind power forecast error-oriented demand response multi-time scale scheduling method. The method comprises the following steps of: firstly setting a real-time electricity price of the next day according to a wind power forecasted value and a load forecasted value of the current day, acquiring electricity utilization data set by a PDR user according to the electricity price, and making an operation plan of a unit in the next day by the PDR user; in the current day, forecasting wind power output H hours early, forming a new real-time electricity price according to a formula (as shown in the specification), freely selecting, by the PDR user, whether to respond scheduling according to the new electricity price to adjust the electricity utilization plan in the day, and recollecting a PDR electricity utilization plan by a power grid through a data acquisition system; and finally carrying out real-time wind power forecast, if the formula (as shown in the specification) is not 0, carrying out IDR and conventional unit scheduling, and when constraints are not satisfied, selecting to put into standby application (the load cannot be satisfied) or abandon the wind (the system electricity is surplus). The method is capable of effectively carrying out scheduling balance on different-time scale wind power forecast errors by utilizing different types of demand response characteristics.

The invention discloses a public bike scheduling monitoring method. The method comprises the following steps: step 1, data acquisition, i.e., obtaining lease site data through a wireless communication mode; step 2, vehicle scheduling, i.e., work personnel in a scheduling center scheduling release sites needing scheduling at any time through controlling scheduling vehicles in a command road network; and step 3, scheduling quantity statistics, i.e., finishing bike scheduling-in and scheduling-out quantity statistics of each lease site for the scheduling personnel to use. The public bike scheduling monitoring method can effectively improve the scientific public bike scheduling management, enhances the scheduling efficiency, decreases the scheduling cost, improves the utilization rate of public bikes and alleviates the conflict of difficult bike renting and difficult bike returning.

The invention relates to an unmanned vehicle formation scheduling method, device and system and computer equipment, and belongs to the technical field of networks. The method comprises the steps of obtaining a scheduling task, wherein the scheduling task carries region specified information and a formation task; determining a target driving area from a plurality of unmanned driving areas accordingto the area specifying information; determining a formation form and determining at least one target unmanned vehicle according to the formation task; sending a vehicle driving instruction to each target unmanned vehicle according to the formation form, wherein the vehicle driving instruction is used for controlling each target unmanned vehicle to drive in the target driving area according to theformation form. Centralized scheduling of multiple unmanned vehicles in multiple areas can be realized, the scheduling cost of the unmanned vehicles is effectively reduced, and the difficulty of vehicle management is reduced.

The invention relates to a routing scheduling method, a routing scheduling device and network equipment. Dynamic adjustment is added on the basis of static routing, and the dynamic adjustment added on the basis of static routing is not completely equivalent to dynamic routing. The routing scheduling method, the routing scheduling device and the network equipment are especially suitable for home routing systems, and are capable of improving the efficiency of traffic scheduling and convenient to manage. The routing scheduling method comprises the following steps of receiving a domain name resolution request, and acquiring a domain name from the domain name resolution request; acquiring an IP (Internet protocol) address which corresponds to the domain name when the domain name belongs to a domain name list; adding the IP address to a current routing table to form a target routing table; performing traffic scheduling processing on the target routing table to obtain routing which corresponds to the IP address.

The invention discloses a distributed intelligent power grid economic dispatching method based on finite time consistency under directed topology, and the method comprises the following steps: carrying out mathematical modeling which comprises a total cost objective function and a supply and demand balance and generator power limit constraint function of all generators under power grid economic dispatching; calculating the optimal cost increment of the generator set; calculating the final output power of each generator according to the parameters of each generator of the generator set; and re-determining the new optimal cost increment of the generator set. According to the invention, the intelligent power grid system can complete the economic dispatching process within limited time; the economic dispatching time of the intelligent power grid is shortened, the dispatching cost is greatly reduced, the modern intelligent power grid dispatching requirement is met, the proposed algorithm considers the economic dispatching process under directed communication topology according to the characteristic of difference of information obtained between generators in an actual intelligent power grid system, and the applicability is wider.

The invention discloses a shared vehicle scheduling method, device and server. The method comprises dividing a shared vehicle distribution area into a plurality of areas; according to the shared vehicle areas of the shared vehicle distribution area and the current vehicle scheduling period, generating a scheduling task; delivering the scheduling task, generating target task completing information,and delivering the target task completing information to clients. The shared vehicle scheduling method can deliver the scheduling task according to dynamic vehicle distribution to lead terminal usersto schedule excessively-centralized or dispersed vehicles to other areas and accordingly to achieve rebalance of the shared vehicles; meanwhile, during delivery of the scheduling task, the shared vehicle scheduling method takes the current vehicle scheduling period into consideration to avoid conflict between the scheduling task and traffic tides and to guide the terminal users to schedule the vehicles in a counter-tide direction.

The invention discloses a power system energy accumulation optimizing method. The method is characterized in that during energy accumulation of a power system, a first-category energy accumulation system and a second-category energy accumulation system are operated in a combined manner, and a power system mathematic model comprising the first-category energy accumulation system and the second-category energy accumulation system is configured and applied to the power system. By the method, the energy accumulation of the power system has the large-capacity energy accumulation performance of the first-category energy accumulation system and the timely dynamic response performance of the second-category energy accumulation system, the problems that the power system is poor in dynamic responds performance when only the first-category energy accumulation system is configured in the power system and the power system is small in energy accumulation capacity when only the second-category energy accumulation system is configured in the power system are solved, the operation efficiency of the power system is increased, and dispatching cost is saved.

The invention discloses a steelmaking and continuous casting scheduling method based on an artificial bee colony (ABC). The method remains a framework of the ABC (comprising an employed bee phase, an observation bee phase and a scouter bee phase), and is improved by combining a JADE algorithm. At the employed bee phase, a search strategy of the JADE is used for updating individuals in one population, and the diversity of the population can be increased by adopting an external archiving mechanism of the JADE; at the observation bee phase, updating operation of differential solution, and archiving composed of the current population is increased on the basis of a previous search strategy of the ABC; and at the scouter bee phase, the previous search strategy of the ABC at the phase is adopted. With the adoption of the method, the convergence of the population is accelerated under the condition of guaranteeing the diversity of the population, and an ideal scheduling result can be generated in relatively short time, so that the aims that the waiting time of workpieces in a steelmaking and continuous casting process and the machining cost is reduced are realized.

The invention discloses a shared bicycle charging method for encouraging user scheduling. By taking the measures of uphill reward and downhill charge increase, combined with travel time accounting andtravel slope accounting to determine the travel cost, to encourage riding users to participate in the shared bicycle scheduling spontaneously, the invention can effectively save the cost of the shared bicycle scheduling, at the same time takes into account the balance of revenue and expenditure of the shared bicycle service without loss.

The invention discloses a ship lockage optimization dispatching system and method, and the system comprises a mobile terminal located at a ship end, a background server which communicates with the mobile terminal through a network and is located at a ship lock end, and a display terminal and an operation terminal which are respectively connected with the background server and used by a dispatcher for watching and dispatching arrangement. The mobile terminal is used for transmitting ship information and lockage reporting information to the background server after the ship arrives in a lock region, and receives and reminds dispatching optimization information transmitted by the background server. The background server is used for calculating the time of a lockage reporting ship in passing each of lockage stages according to the ship information and the lockage reporting information, and outputting the time needed by the former three stages, wherein the stages are ordered according to the lockage sequence and the occupied time from the big to the small. The display terminal is used for displaying the time needed by each stage. The operation terminal is used by the dispatcher for inputting the dispatching optimization information to the background server and then transmitting the dispatching optimization information to the mobile terminal. The system achieves the quick and safe lockage of the ship, and is high in recognition rate of the ship.

The invention discloses a shared vehicle scheduling method, device and terminal. The method comprises acquiring a scheduling task from a server, and selecting a target task from the acquired scheduling task; unlocking target vehicles of the start point of the target tasks; after the target vehicles are locked again, receiving target task completing information from the server. The shared vehicle scheduling method can deliver the scheduling task according to dynamic vehicle distribution to lead terminal users to schedule excessively-centralized or dispersed vehicles to other areas and accordingly to achieve rebalance of the shared vehicles; meanwhile, during delivery of the scheduling task, the shared vehicle scheduling method takes current vehicle scheduling period into consideration to avoid conflict between the scheduling task and traffic tides and to guide the terminal users to schedule the vehicles in a counter-tide direction.

The invention discloses an intelligent agricultural machine management and scheduling system and method based on cloud-side cooperation. The system employs a cloud platform as a central node, employs a vehicle-mounted terminal of an agricultural machine as an edge node, carries out the time-delay sensitive tasks, including operation scoring, farmland information processing, agricultural machine management, and the like, at the edge node close to a data source, the tasks are not uploaded to a long-distance cloud platform for processing any more. The data which is not sensitive to delay, has small capacity and needs to store historical records is uploaded to a cloud platform for processing and storing. For scheduling of a plurality of operation orders, the agricultural machinery scheduling method aims at minimizing the agricultural machinery scheduling cost of each operation order, considers the priority order of the orders, and determines a scheduling scheme with the optimal cost. The agricultural machinery scheduling efficiency can be improved, the real-time performance of management is enhanced, and the agricultural machinery scheduling cost is reduced.

The invention provides a flexible load dispatching method for improving acceptable capacity of a power distribution network on renewable energy resource. The flexible load dispatching method comprises the steps that (1) a power distribution network side performs short-term power prediction on renewable energy resource electric energy production on the next day by adopting a neural network analysis method; and (2) different optimizing and dispatching methods are adopted according to the magnitude of the renewable energy resource electric energy production on the next day for optimization. By adoption of the flexible load dispatching method, effective consumption and acceptation on the large-scale renewable energy resource can be realized by the power distribution network, so that the acceptable capacity of the power distribution network on the renewable energy resource is enhanced, and benefits are brought to two parties of the power distribution network and a load user.

The invention discloses an industrial park comprehensive demand response scheduling method based on multi-energy cooperation. The method comprises steps that an operator executes day-ahead load prediction and reports the day-ahead load prediction to ISO or bids to the power market; the operator obtains a load reduction index from the ISO or the market clearing result; the operator determines a scheduling plan by solving a comprehensive demand response optimization model and informs a consumer and the CCHP of the result; if any consumer or CCHP cannot respond according to an instruction, the operator rearranges the scheduling plan, otherwise, the response is executed; on the second day, the consumer adjusts the load demand, reduces the power load or increases the heat and cold loads at theappointed time, and the CCHP correspondingly adjusts the yield to balance the load. The method is advantaged in that various interactive modes such as interruptible load, multi-energy demand coupling,multi-energy supply and demand interaction and the like are provided, the selectable space of a user is increased, scheduling means of the operator is increased, comprehensive energy consumption management is realized, and scheduling cost is reduced.

The invention provides an unmanned equipment scheduling device, system and method, and relates to the field of logistics. The device comprises edge equipment located in an edge equipment cluster areaand configured to obtain a scheduling result of controlled unmanned equipment and schedule the unmanned equipment based on the scheduling result, wherein the scheduling result is generated by using ascheduling model and a scheduling rule according to the current order data and the external environment data, and the scheduling model and the scheduling rule are generated based on the operation dataof each unmanned equipment. According to the invention, the overall execution capability of the cluster is utilized to schedule the individual unmanned equipment, the optimal planning of the unmannedequipment group can be realized, and the scheduling cost is reduced.

The invention provides a shared bicycle parking area recommendation method, device and system and a storage medium, and the method comprises the steps: screening a target user according to reward areainformation; sending prompt information to a terminal of the target user; and when it is determined that the target user completes vehicle parking under a preset condition, issuing a corresponding reward to the target user. According to the invention, the user can be guided to park the vehicle at a fixed point consciously by issuing awards to the target user, the vehicle scheduling cost is reduced, and the user stickiness is improved.