30results about How to "Reduce scheduling costs" patented technology

The invention provides a micro-grid robust optimization scheduling method considering a segmented multi-interval uncertainty set, and the method comprises the steps: carrying out the segmentation fitting of the source load prediction power and error data of wind, light and loads based on a Gaussian mixture model, and combining with a box-type interval uncertainty set to build a multi-interval uncertainty set of power segmentation segments; the uncertainties are used for describing wind, light and load uncertainties; constructing a microgrid robust optimization scheduling model considering the segmented multi-interval uncertainty set and the carbon emission by taking the minimization of the operating cost and the carbon emission cost of the microgrid as a target; and solving the micro-grid robust optimization scheduling model by adopting a column sum constraint generation algorithm. According to the method, the problems of uncertainty modeling and carbon emission of the micro-grid can be solved, the conservative property of a robust optimization scheduling strategy is reduced, and the economical efficiency of micro-grid scheduling is improved.

The invention provides an electric power communication network joint optimization method and electronic equipment, and belongs to the technical field of resource regulation and control. The method comprises the following steps: determining communication cost information of a power network according to the data volume of each data node in a communication network and a pre-constructed communication network equivalent model; determining generator scheduling cost information and load aggregator scheduling cost information of the power network according to the operation standby adjusting power of each generator in the power network, the operation standby adjusting power of each load aggregator and a pre-constructed power network model; and determining target total scheduling cost information of the power network according to the communication cost information, the generator scheduling cost information, the load aggregator scheduling cost information and a pre-constructed power communication network joint optimization model. According to the invention, the method can achieve the effects of reducing the power resource scheduling cost, improving the response speed and accuracy of power resource scheduling, and improving the implementability of power resource scheduling.

The invention relates to the field of virtual power plant regulation and control, and particularly discloses a virtual power plant side-end coordinated voltage regulation control method and system, a terminal and a medium, and the method comprises the steps: distributing a voltage regulation amount for each node cluster according to a voltage sensitivity coefficient and scheduling cost of the node cluster; constructing a cluster internal autonomous optimization model for each adjustable resource in each node cluster according to the optimization target; according to the distributed voltage regulation amount of each node cluster, solving the cluster internal autonomous optimization model of each node cluster through a distributed average consistency algorithm to obtain an output optimal value of each adjustable resource in each node cluster; and performing voltage regulation operation on the node cluster according to the output optimal value of each adjustable resource of the node cluster. According to the method, the output optimal value of each adjustable resource is obtained in the node cluster according to the distributed average consistency algorithm, side-end coordinated voltage regulation control is realized, the voltage regulation control efficiency and the regulation effect are improved, and the voltage regulation cost is reduced.

The invention relates to a warehouse logistics scheduling method and system based on cloud computing, and relates to the technical field of warehouse logistics scheduling, and the method comprises the steps: jointly building a logistics scheduling model through a plurality of edge cloud computing nodes, and the logistics scheduling model comprises an edge scheduling scheme generation model and a comprehensive scheduling scheme generation model; determining at least one first optimal edge cloud computing node based on the computing power resource demand of the warehouse logistics scheduling task; each first optimal edge cloud computing node generates a model through an edge scheduling scheme, generates a plurality of candidate warehouse logistics scheduling schemes based on the warehouse logistics scheduling task, the warehouse node state information and the logistics vehicle state information, screens the plurality of candidate warehouse logistics scheduling schemes, and determines a plurality of target warehouse logistics scheduling schemes; through the comprehensive scheduling scheme generation model, the optimal warehouse logistics scheduling scheme is generated based on the multiple target warehouse logistics scheduling schemes, warehouse logistics scheduling is carried out, and the method has the advantage of improving the intelligent level of warehouse logistics scheduling.

The invention provides an energy storage power station scheduling model establishment method and device and an energy storage power station scheduling method and device. The method comprises the steps that the electricity price of a power transaction center, the capacity compensation electricity charge, the power transmission and distribution electricity charge, the government fund and additional item electricity charge, the power grid on efficiency, the power grid off efficiency, the loss rate, the charging power, the discharging power and operation parameters are obtained; establishing a target function, an upper limit constraint formula, a lower limit constraint formula, a charging and discharging constraint formula, a capacity SOC constraint formula of the energy storage system and a power balance constraint based on the data; and establishing an energy storage power station scheduling model based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charging and discharging constraint formula, the capacity SOC constraint formula of the energy storage system and the power balance constraint. Therefore, by designing and controlling the objective function and the constraint condition, different energy storage power station scheduling models can be established according to actual design requirements and scheduling requirements, so that automatic and personalized scheduling of the energy storage power station is realized, the scheduling efficiency is improved, and the scheduling cost is reduced.

The invention relates to the technical field of power supply scheduling, and provides an energy storage vehicle emergency power supply coordination control method and system, and the method comprises the steps: querying a power supply energy storage vehicle in a power failure region, calculating the energy storage saturation corresponding to the power supply energy storage vehicle, and calculating the energy storage period corresponding to the power supply energy storage vehicle; based on the geographic positions corresponding to the power supply and energy storage vehicle and the power-off area, obtaining a parking position and an area position, calculating an optimal driving period corresponding to the power supply and energy storage vehicle, and determining a response period corresponding to the power supply and energy storage vehicle; determining power users in the power-off area, collecting power utilization behavior information of the power users, analyzing power utilization purposes corresponding to the power users, calculating power-off loss corresponding to the power-off area, and setting regulation and control priorities corresponding to the power-off area; and calculating the energy charging benefit corresponding to the power supply and energy storage vehicle, setting a scheduling sequence corresponding to the power supply and energy storage vehicle, and controlling the power supply and energy storage vehicle to supply energy to the power-off area. The invention aims to improve the emergency power supply coordination control efficiency of the energy storage vehicle.

The invention relates to the technical field of clothing supply chain management, and discloses a cross-regional clothing supply management system and method, and the method comprises the steps: obtaining the sales feature data, generated at each sales node, of a target clothing product from the generated sales volume; screening out a unique similar sales curve from N historical sales curves generated from each sales node based on the sales feature data, and obtaining the predicted sales volume of the target clothing product at the M sales nodes from the unique similar sales curve; calling the stock of all sales nodes about the target clothing product in the current time range, and obtaining the target sales nodes appearing in the predicted time range according to the quantitative relationship between the predicted sales and the stock; generating a scheduling strategy for each product shortage node by using a pre-configured genetic algorithm, and performing cross-regional clothing scheduling according to the scheduling strategy; the scheduling efficiency of the clothing supply chain can be effectively improved, and the management level of the clothing supply chain is improved.

The invention discloses a power distribution network electric vehicle combined low-carbon scheduling method and system, and is used for solving the technical problem of overhigh scheduling cost caused by the existing power distribution network electric vehicle combined scheduling method. The method comprises the following steps: setting a first constraint condition by taking the operation cost minimization of the electric vehicle as an optimization target, and constructing a lower-layer model by taking the aggregator data of the electric vehicle as a decision variable; taking the minimum carbon perception operation cost of the power distribution network as an optimization target, setting a second constraint condition, taking power distribution system operator data corresponding to the power distribution network as a decision variable, and constructing an upper layer model; based on the initial electric vehicle aggregator data, performing joint optimization solution on the upper layer model and the lower layer model, and outputting target power distribution system operator data and target electric vehicle aggregator data; and taking the target power distribution system operator data and the target electric vehicle aggregator data as a power distribution network electric vehicle joint low-carbon scheduling scheme.

The invention discloses a multi-type load power grid low-carbon optimization scheduling method and system, and the method comprises the steps: firstly constructing a load scheduling model which takes the maximum absorption of new energy in a region as a target, then solving the load scheduling model, obtaining the regional load scheduling power of each time period in a scheduling period, and finally obtaining the load scheduling power of each time period region in the scheduling period based on the load scheduling power required by each time period region in the scheduling period. The dispatching cost and carbon emission of regional resident loads, commercial loads, industrial loads and electric vehicle adjustable loads are comprehensively considered, a multi-type load power grid low-carbon dispatching model is established, finally, the multi-type load power grid low-carbon dispatching model is solved, and a multi-type load power grid low-carbon dispatching scheme is obtained. According to the method, scheduling characteristics of various types of loads such as a resident load, a commercial load, an industrial load and an electric vehicle adjustable load and spatial and temporal changes of power grid node carbon emission factors are comprehensively considered, new energy consumption improvement is taken as a target, and regional power grid carbon emission is reduced while power grid scheduling cost is reduced.

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.

The present invention discloses a cloud-edge collaborative service orchestration and request scheduling optimization method based on social learning, including: eAPs and cloud respectively construct a request scheduling decision model and a service orchestration decision model; the cloud combines the first resonance information sent by the eAP to obtain the state #imgabs0# using the attention mechanism to generate the second resonance information and transmit it to the eAP; the state #imgabs1# is input into the service orchestration decision model and optimized, combined with the lower guidance vector sent by the eAP to obtain the optimal service orchestration decision and transmit it to the edge server; the eAP combines the second resonance information sent by the cloud to obtain the state #imgabs2# using the attention mechanism to generate the first resonance information and transmit it to the cloud; the state #imgabs3# is input into the request scheduling decision model and optimized, combined with the upper guidance vector sent by the cloud to obtain the optimal request scheduling decision and transmit it to the edge server; the edge server processes the request according to the two decisions received. The present invention can effectively reduce scheduling costs and improve throughput.

The present invention provides a method for fusion judgment of device faults of voiceprint model evolution for power grid monitoring, including: real-time audio acquisition, interference noise removal preprocessing by dual classification denoising method based on wavelet transform; extracting MFCC feature vectors of device sound signal data under various normal states after preprocessing, respectively obtaining sample data sets under various normal states, establishing and updating Gaussian mixture models of target devices under various normal states; extracting audio sample data to be tested and establishing a multi-Gaussian mixture model evolution method based on voting to match the characteristic law of voiceprint of target devices that changes over time; based on the multi-Gaussian mixture model, performing fusion judgment of voiceprint faults of target devices. The method of the present invention can eliminate the influence of weak abnormal interference and strong abnormal interference of the environment, can match the changing law of sample data in real time, effectively monitor power grid monitoring scenes, realize abnormal monitoring and timely early warning.

The invention discloses a power grid optimal dispatching system considering various adjustable loads and carbon transactions, and belongs to the technical field of power systems. The invention discloses a power grid optimal scheduling system considering various adjustable loads and carbon transactions. The power grid optimal scheduling system comprises a data acquisition unit, a load model establishment unit, a carbon transaction model establishment unit and an optimal scheduling unit. According to the method, the problems that the scheduling advantage of the adjustable load cannot be fully played and the low-carbon property cannot be ensured in the prior art are solved, the load scheduling model and the load adjustment compensation model are respectively established by acquiring various adjustable load data of the to-be-scheduled power grid and based on the power utilization characteristics of various adjustable load resources, and meanwhile, the load scheduling model and the load adjustment compensation model are established. A carbon transaction cost model is constructed according to a carbon transaction mechanism, and load characteristic constraints are established by taking the minimum total scheduling cost as an optimization target, so that a scheduling method for multi-type adjustable load resources is established for optimal scheduling, the scheduling cost and the carbon emission of a unit can be effectively reduced, and the economical efficiency and low-carbon property of power grid operation are ensured.

The invention belongs to the technical field of power system automation, and particularly relates to an island micro-grid online hierarchical control method, device and equipment and a medium. The method comprises the following steps: after a power distribution network fault occurs, constructing each island micro-grid based on energy storage equipment and distributed photovoltaics; constructing a layered voltage / reactive power control model based on the three-phase unbalanced power distribution network model of the island micro-grid; the layered voltage / reactive power control model comprises an objective function with the minimum sum of the voltage deviation and the reactive power cost generated by the distributed photovoltaic inverter, and a constraint condition corresponding to the objective function; introducing an auxiliary variable vector to reconstruct the layered voltage / reactive power control model to obtain a problem equivalent model; and based on constraint conditions, solving the problem equivalent model by using a quasi-Newton ADMM method to obtain an island micro-grid online hierarchical control strategy. According to the scheme, the problem of voltage safety after photovoltaic grid connection of the island microgrid is solved by utilizing inherent photovoltaic resources in the island, and voltage autonomy is realized.

The invention discloses a container truck appointment scheduling method based on a train arrival and departure time window, and the method comprises the steps: constructing a single path set S based on a current station, setting a preset complete time window of a path as T, setting an actual complete time window of the path as T ', obtaining a complete time window difference value D on the path, and obtaining a complete time window difference value D of the path; obtaining a preset time window threshold value Y of the path and a corresponding original band domain, constructing gene chains based on respective time sequences, judging Yi by Y, and adjusting a scheduling plan so as to realize integral scheduling; by monitoring the whole running route of the train and calculating and adjusting the matching time of the train and the container truck, the time pressure and the space pressure of the current station are reduced, the overall matching, appointment and scheduling difficulty is reduced, when non-punctual running occurs, the scheduling plan is revised by adjusting the container truck appointment time in advance, and the scheduling efficiency is improved. The scheduling cost is reduced, and the operation efficiency of the station is also improved.

The invention discloses a reel type sprinkling irrigation machine dispatching method for soil mechanical compaction and reduction, and relates to the field of agricultural machinery dispatching. The problem that the influence of the machine on the soil compaction effect is not considered in an existing sprinkling irrigation scheduling method is solved. According to the method, the total scheduling target function is constructed with the lowest total scheduling cost of the reel type sprinkling irrigation machine and the highest total compaction satisfaction degree of the field, and the minimum scheduling target function is constrained according to the set constraint condition, so that optimization solution is carried out, and the optimization solution efficiency is improved. And distributing corresponding reel type sprinkling machines and service paths of the reel type sprinkling machines to the field parcels corresponding to the water source points. The method and the device are mainly used for scheduling the reel sprinkling machine.

The invention relates to the technical field of oil platform energy management and control, and discloses an oil platform energy management and control system, which comprises an energy acquisition and monitoring module, an energy use data calculation module, an energy scheduling module and a safety detection module, and is characterized in that the energy acquisition and monitoring module comprises an electric energy acquisition unit, a solar energy acquisition unit and a fuel oil acquisition unit; the energy use data calculation module comprises an energy use recording unit and an energy consumption calculation unit, the energy scheduling module comprises a scheduling request analysis unit and an actual scheduling unit, and the safety detection module comprises an energy reserve calculation unit and an equipment energy consumption anomaly detection unit. According to the method, the electric energy and the gas energy on the oil platform are counted, the optimal energy consumption model is established according to the energy consumption required by the ith equipment, the optimal energy consumption model is solved, the optimal energy distribution weight is obtained, scheduling distribution is carried out according to the energy distribution weight, the economical utilization of the energy of the oil platform is ensured, and the service life of the oil platform is prolonged. Therefore, the scheduling cost is reduced.

The application relates to an intelligent scheduling method and device of a medical waste transport vehicle, and relates to the technical field of vehicle scheduling.The method comprises the following steps: based on a received scheduling request of a medical waste transport vehicle, determining medical waste information of a medical institution and waste information of a centralized disposal center; determining a target area and a target medical waste transport vehicle of scheduling, and dividing the target area into multiple subareas; based on a second scheduling parameter, determining a target subarea and a first scheduling path of the target medical waste transport vehicle in the target area; scheduling the target medical waste transport vehicle based on a scheduling instruction generated based on the first scheduling path and the target subarea; in the scheduling process, adjusting the first scheduling path according to a third scheduling parameter to generate a second scheduling path, until the target medical waste transport vehicle is scheduled to be completed, the application improves scheduling efficiency and reduces scheduling cost, and realizes the intelligentization and safety of the scheduling process.

The invention provides a shared vehicle management method and device, and relates to the technical field of shared vehicle management, and the method comprises the steps: obtaining the position information of a shared vehicle in response to a vehicle returning request sent by a user terminal; determining a current parking point and the type of the parking point based on the position information; judging whether the user level corresponding to the user terminal is matched with the type of the parking spot; and if yes, feeding back prompt information of successful vehicle returning to the user terminal. According to the shared vehicle management method and device provided by the invention, the user needs to return the vehicle in the parking spot matched with the user grade, the user can be effectively guided to return the vehicle in the planned parking spot normatively, the scheduling cost caused by blind parking is further reduced, and meanwhile, based on the user grade and the type of the parking spot, the user experience is improved. In addition, the method can effectively improve the refinement level of parking management of shared vehicles, reduces the management cost, provides differentiated services for users of different user levels, and remarkably improves the operation efficiency and user satisfaction.

The invention discloses a single data center time domain migration task scheduling method, system and device and a storage medium. The method comprises the following steps: acquiring green power parameters in a power grid; a green electricity parameter is used as an optimal target, an optimization target function is constructed, and an optimization scheduling model and an optimal economic model mechanism based on a single data center are established; setting decision variables and constraint conditions of the optimization scheduling model based on the single data center, solving an optimal solution of the optimization scheduling model based on the single data center and the optimal economic model mechanism by adopting a deep learning algorithm, and obtaining a final optimization result and a scheduling optimization scheme by taking the optimal solution as a basis of power grid optimization scheduling; the system comprises a data acquisition module, a model construction module and a model solving module. According to the method, optimization and adjustment of green power and economic operation data are facilitated, the resource utilization rate and the new energy scheduling efficiency are improved, and the scheduling cost is reduced.

The invention discloses a rural light-biogas virtual power plant energy optimization operation method and system considering energy storage degradation. The method comprises the steps that an energy storage degradation cost model and virtual power plant basic operation constraints are established; the virtual power plant upper layer model prediction control layer considers the dynamic energy storage degradation cost, the electricity purchasing and selling cost and the total operation and maintenance cost to carry out virtual power plant energy operation optimization, and an upper layer optimization scheduling instruction is obtained; and inputting into a lower-layer model prediction controller, comprehensively considering deviation cost and energy storage scheduling cost of an upper-layer reference value to carry out source load power fluctuation stabilization to obtain a lower-layer operation result, and feeding back the lower-layer operation result to an updated upper-layer model prediction control layer to carry out virtual power plant energy loop iteration operation optimization. According to the method, the rural photovoltaic and biogas resources are flexibly adjusted, the method is suitable for a rural power distribution network to which a large number of renewable energy sources are accessed, and the average degradation cost of an energy storage battery and the average operation cost of the system are reduced on the premise of efficiently absorbing new energy.

The invention belongs to the technical field of power systems, and discloses a source-load-storage multi-element resource access regulation and control method, system and device and a medium, and the method comprises the steps: obtaining the adjustment characteristics of a source-load-storage adjustable resource through analyzing the adjustment mechanism of the adjustable resource; constructing a unified access scheduling architecture of the source-load-storage multi-element adjustable resources; determining an access demand of the source-load-storage multi-element adjustable resources according to the unified access scheduling architecture of the source-load-storage multi-element adjustable resources; and establishing a comprehensive cost and adjustment performance priority model based on the topological structure of the source-load-storage multi-element adjustable resources, and solving the comprehensive cost and adjustment performance priority model through a multi-target particle swarm optimization algorithm to obtain the optimal adjustment quantity of each adjustable resource of the source-load-storage. According to the invention, unified resource scheduling and management are realized, the resource access efficiency and supply stability are improved, large-scale access and optimal regulation and control of multi-element adjustable resources are also realized, the resource utilization efficiency is improved, and the scheduling cost is reduced.

The present invention relates to a vehicle-mounted edge server scheduling method, system and storage medium for minimizing the number of vehicle-mounted edge servers, and relates to the field of mobile edge computing. The method comprises: obtaining a set of regions and warehouses, establishing a network model; determining the total energy consumption of the vehicle-mounted edge servers in the warehouse in the driving path, establishing a problem of minimizing the number of vehicle-mounted edge servers; optimizing and solving the problem of minimizing the number, determining a scheduling scheme for the vehicle-mounted edge servers, and scheduling the vehicle-mounted edge servers in the warehouse according to the scheduling scheme. The method can realize dynamic scheduling of vehicle-mounted edge servers as well as scheduling flexibility and balance, reduce the number of vehicle-mounted edge servers, save the scheduling cost of vehicle-mounted edge servers, and solve the problem of resource waste and shortage caused by statically deploying edge servers in mobile edge computing scenarios.

The invention provides an unmanned port emergency logistics distribution method and a related device, and relates to the unmanned driving field, when a scheduling request of a port vehicle operation management platform is obtained, a freight truck is controlled to enter an unmanned driving mode, and the freight truck is navigated to a specified frontier cargo loading and unloading parking position based on a port area high-precision map, so that the unmanned port emergency logistics distribution method is realized. And then a loading request is reported to the vehicle operation management platform, when notification information used for representing that loading and unloading are completed is obtained, the box locking box is controlled to be locked, and the freight container truck is controlled to return to the initial position. In the process, the freight container trucks in the harbor area are controlled to run in an unmanned mode and are uniformly scheduled by the harbor vehicle operation management platform, so that smooth traffic in the harbor area can be ensured, the loading efficiency is improved, port scheduling terminal equipment does not need to be deployed in a freight container truck cabin, the scheduling cost is reduced, and the scheduling efficiency is improved. The process does not need participation of a driver, and the personal safety of the driver in the loading process is guaranteed.

The present invention discloses a method for diagnosing and rescheduling sudden spatial interference based on BIM, which generates a three-dimensional construction model using BIM technology, excludes unacceptable spatial interference, and generates a BIM model combining progress, cost, and resources; identifies and diagnoses spatial interference problems through manual reporting, alarm devices configured by construction equipment, and environmental equipment detection during the construction process, and extracts and processes problem information in a BIM information integration management platform; establishes three scheduling strategies to deal with the problem: a heuristic repair strategy, a mode reselection strategy, and a complete rescheduling strategy, and decision makers make choices based on actual conditions and the application of scheduling strategies, and update data in the BIM information integration management platform to form a new construction schedule and cost. Use BIM technology to control construction before construction, select specific solutions for actual problems to make decisions, ensure smooth construction, and improve construction site operation efficiency.

The invention provides a scheduling method for a mobile energy storage device after an extreme disaster, and belongs to the technical field of mobile energy storage device scheduling. Comprising the following steps: constructing a target function of a scheduling model by taking minimum scheduling cost of a mobile energy storage device and minimum equivalent load loss cost of a power distribution network as targets; constructing constraint conditions of the scheduling model; solving the scheduling model, and scheduling the mobile energy storage device according to a solving result; the constraint conditions comprise a traffic network constraint, a radial network constraint, a power flow constraint, a node balance constraint, a mobile energy storage device scheduling constraint and a distribution line repair constraint. According to the method, real-time scheduling of the mobile energy storage device is carried out under the condition that the influence of extreme disasters on traffic conditions is considered, and the obtained scheduling scheme has the optimal economical efficiency.

The application belongs to the technical field of power systems and discloses a power distribution network distributed resource optimization aggregation method based on an ant colony simulated annealing algorithm; the method first acquires parameters of distributed energy in a virtual power plant, constructs an objective function with the aim of minimizing the total cost of the virtual power plant, sets a constraint condition, and finally solves the objective function by combining an ant colony algorithm and a simulated annealing algorithm to output an optimal solution, namely, an optimal aggregation method of the distributed resources of the power distribution network; the application can jump out of the trap of a local optimal solution, enhance the global search capability, improve the convergence speed, and significantly improve the probability of finding a global optimal solution; the cooling strategy of the simulated annealing algorithm accelerates the late convergence, the pheromone mechanism of the ant colony algorithm strengthens high-quality solutions, and the cooperation of the two can improve the economy and stability of the scheme; by setting the constraint, the aggregation scheme can meet various operation requirements, reduce the scheduling cost, improve the resource utilization rate, realize the efficient utilization and optimal allocation of resources, and the like.

The invention discloses a container truck scheduling method, system and equipment and a computer readable storage medium, and relates to the field of container truck scheduling, in particular to application of the container truck scheduling method to a target tower crane, and the target tower crane communicates with container trucks based on a PC5 direct communication technology and issues container loading and unloading tasks to a plurality of container trucks. The container truck performs response processing based on the container loading and unloading task; obtaining the number of target container trucks and the distance between the target container trucks and the target tower crane, wherein the target container trucks are the target container trucks responding to the container loading and unloading task; determining a first container truck from the target container trucks based on the number and the distance; and issuing task confirmation information to the first container truck to realize scheduling of the container trucks. The communication quality can be ensured, the cost is reduced, and the flexibility and the anti-risk capability of port container truck dispatching are improved.

The application discloses a multi-type load power grid low-carbon optimal scheduling method and system, which comprises the following steps: firstly, a load scheduling model is constructed with the target of maximum new energy consumption in a region; then, the load scheduling model is solved to obtain the regional load scheduling power in each period in a scheduling period; then, based on the regional load scheduling power required in each period in the scheduling period, the scheduling cost and carbon emission of the regional residential load, commercial load, industrial load and electric vehicle adjustable load are comprehensively considered to establish a multi-type load power grid low-carbon scheduling model; finally, the multi-type load power grid low-carbon scheduling model is solved to obtain a multi-type load power grid low-carbon scheduling scheme. The application comprehensively considers the scheduling characteristics of the multi-type load such as the residential load, commercial load, industrial load and electric vehicle adjustable load and the time-space variation of the power grid node carbon emission factor, and takes the new energy consumption as the target, so that the power grid scheduling cost and the regional power grid carbon emission are reduced.

The invention discloses a new energy combination prediction and optimization method in a novel power system based on multiple neural networks, and the method comprises the steps: S1, building a CNN-LSTM-GWO combination prediction model, comparing the CNN-LSTM-GWO combination prediction model with a single LSTM neural network and a CNN-LSTM combination prediction model, carrying out the training through inputting a same training set, and comparing the prediction results of a test set; and S2, applying prediction results of different models to a subsequent optimal scheduling project of the novel power system, applying a weight-updated intra-day rolling optimization method to an optimal scheduling process of the novel power system, and comparing the cost of the novel power system with that of a traditional rolling optimization method. The present invention explores the recent progress by integrating two particularly effective neural networks for predicting wind and solar energy. The combination improves the accuracy of model prediction. In addition, in combination with the grey wolf optimization algorithm, the training process is accelerated, and the practicability of the model to ultra-short-term prediction and optimization scheduling tasks is enhanced.