16results about How to "Improve scheduling" patented technology

The invention relates to the technical field of logistics transportation, in particular to a logistics transportation route optimization method and system based on digital twinning. The method comprises the following steps: constructing a digital twin logistics base through multi-source heterogeneous data fusion and dynamic logistics network modeling; carrying out regional logistics distribution feature extraction and dynamic time sequence prediction based on a digital twinborn substrate to obtain a predicted regional transportation demand; performing real-time state evaluation on the line, generating a dynamic line performance matrix, and performing vehicle resource allocation in combination with the order task priority; and carrying out multi-objective solution and dynamic adjustment on the transportation scheduling scheme to finally obtain an optimal transportation scheduling scheme. And performing iterative optimization on the digital twin model through real-time deviation analysis and a feedback mechanism, and dynamically adjusting a transportation plan to form a closed-loop optimization process. Through the logistics transportation route optimization technology, the logistics transportation efficiency is remarkably improved, the cost is reduced, and the toughness of the system is enhanced.

The invention relates to the field of data visualization, in particular to a visualization method and system based on a vehicle service supply chain. The method comprises the following steps: acquiring whole-process information of a vehicle service supply chain; carrying out supply chain main body identification on the whole flow information of the vehicle service supply chain, carrying out spatial topology distribution analysis, and constructing a supply chain main body topology distribution map; obtaining supply chain main body interaction logic data based on the plurality of supply chain main bodies; performing topology interaction logic connection on the supply chain main body topology distribution graph by utilizing supply chain main body interaction logic data, and constructing a supply chain topology interaction network; performing intra-period path transportation frequency calculation on the whole-process information of the vehicle service supply chain to generate logistics demand trend characteristics; and performing operation behavior analysis of each link on the whole-process information of the vehicle service supply chain to obtain the capacity characteristics of each link. According to the invention, through visualizing the vehicle service supply chain, the resource scheduling efficiency and flexibility of the supply chain are improved.

The invention discloses a well industrial and mining unmanned driving simulation test method based on cluster hardware-in-the-loop, and relates to the field of unmanned driving simulation, and the method comprises the steps: constructing a simulation environment of an operation map; building a cluster hardware-in-the-loop simulation system on the constructed operation map simulation environment, and simulating an ore drawing machine, a draw shaft, a transport vehicle and a carry-scraper on the simulation system; when a plurality of loading points or unloading points exist in the constructed operation map simulation environment, calculating an optimal loading point, an optimal unloading point and an optimal driving route of each transport vehicle to realize transport vehicle scheduling simulation; the cluster hardware-in-the-loop simulation system adopts a distributed calculation architecture to perform parallel simulation calculation on a plurality of ore drawing machines, draw shafts, carry-scrapers and transport vehicles. Aiming at the low simulation test efficiency of the underground unmanned vehicle in the prior art, the underground simulation test efficiency is improved.

The invention relates to the technical field of solid waste sorting scheduling, in particular to a real-time monitoring optimization method and device for a solid waste collection, transportation and sorting ERP system. The method comprises the following steps: obtaining a real-time solid waste sorting log based on a solid waste collection, transportation and sorting ERP system; solid waste carrying vehicle tracking is carried out on the real-time solid waste sorting log, and a carrying track distribution visual view is constructed; acquiring an omnibearing monitoring image of a real-time sorting site; equipment sorting behavior visual identification is carried out on the real-time sorting site omnibearing monitoring image, dynamic sorting evolution is carried out, and a dynamic sorting twinborn model is constructed; carrying out multi-time-point solid waste sorting simulation on the dynamic sorting twinborn model, carrying out sorting load demand prediction, and generating a sorting load demand prediction value; and carrying out vehicle load calculation on the sorting load demand prediction value one by one, and then carrying out dynamic vehicle scheduling decision making to obtain a dynamic vehicle scheduling strategy. According to the invention, efficient and real-time solid waste sorting scheduling optimization is realized.

The invention discloses a power system source-load-storage scheduling method considering demand response and a related device, and belongs to the technical field of power system scheduling. The method comprises the following steps: establishing each scheduling unit model of the novel power system; each scheduling unit model comprises a distributed power supply model, a thermal power generating unit model, an adjustable load demand response model, a composite energy storage model and a carbon transaction model; based on each scheduling unit model, establishing a power system source-load-storage scheduling model considering demand response; and solving the power system source-load-storage scheduling model considering the demand response to obtain an optimal scheduling scheme so as to regulate and control the output of each scheduling unit of the power system. According to the invention, multi-element adjustment of different load side resources is fully utilized to realize source and load coordinated interaction, the elasticity of the power grid and the capability of accepting new energy power generation are enhanced, and the construction of a novel power system with flexible interaction between new energy and demand side resources is promoted.

This invention belongs to the field of airport public transportation allocation technology, and discloses an airport public transportation pre-allocation processing system and method based on a big data model. The method includes: acquiring historical data; using big data processing technology and machine learning and deep learning models to infer passenger demand and public transportation usage from historical data, generating a data projection model; inputting real-time weather, time, and passenger data into the data projection model, employing intelligent optimization algorithms, and dynamically adjusting public transportation resource allocation in conjunction with real-time traffic conditions; monitoring the public transportation resource allocation results in real time, continuously optimizing and dynamically adjusting the results using a feedback mechanism, and pushing the latest adjusted results to downstream third-party systems. This invention utilizes real-time data analysis and intelligent optimization algorithms to achieve a more efficient traffic scheduling scheme, and has significant practical implications and broad application prospects.

The present invention discloses a flexible job shop scheduling method based on an improved Seagull optimization algorithm, which belongs to the field of shop scheduling. The method includes: 1) solving the problem of small search space by improving the initialization population; 2) solving the problem that the Seagull optimization algorithm is prone to fall into the local optimum by redesigning the local search model; 3) enabling the offline trained model to quickly adapt to the new problem environment by integrating reinforcement learning. Through the present invention, the problem that the population diversity of the Seagull optimization algorithm decreases in the later iteration period and the algorithm is prone to fall into the local optimum can be solved; the convergence speed of the algorithm is accelerated and the accuracy is improved, so that the processing efficiency of the flexible job shop can be effectively improved, and the economic benefits of the factory can be improved. Finally, the method provided by the present invention is verified by examples and compared with other algorithms, which reflects the superiority of the method proposed by the present invention.

The invention discloses a carbon emission reduction supervision method based on artificial intelligence, and relates to the field of data analysis, and the main scheme is that the total carbon emission X is calculated according to the actual fuel carbon emission XA, the steam turbine carbon emission XB, the auxiliary equipment carbon emission XC and the transport vehicle carbon emission XD, the carbon emission condition can be comprehensively and systematically evaluated from the overall operation perspective of a power generation enterprise, and the carbon emission reduction supervision method can be applied to the power generation enterprise. The problem that the accuracy of carbon emission calculation is reduced due to the fact that collected data are single, the influence of boiler combustion equipment on carbon emission is ignored and carbon emission generated by other power generation equipment is ignored at the same time is solved, whether the carbon emission meets the standard or not is judged according to the total carbon emission X and the carbon emission upper limit value YG, different supervision methods are selected according to the judgment result, and the accuracy of carbon emission calculation is improved. The condition that carbon emission of part of links reaches the standard and other links exceed the standard is avoided, supervision judgment is more accurate and effective, and meanwhile reasonable allocation of supervision resources and accurate implementation of supervision measures are facilitated.

The invention provides a park energy scheduling method and system based on supply and demand balance, and relates to the technical field of data processing. The method comprises the following steps: constructing a park energy network, wherein the park energy network comprises a state grid, photovoltaic power generation equipment, candidate equipment and pure electric equipment; acquiring an electrical load predicted quantity; obtaining photovoltaic power generation predictive quantity; determining the pre-purchased electric quantity purchased from the State Grid Corporation of China according to the electrical load predicted quantity and the photovoltaic power generation predicted quantity by taking supply-demand balance as a target; acquiring device data of each candidate device in the current park energy network environment; determining a priority parameter of each candidate device according to the device data of each candidate device; and the pre-purchased electric quantity and the photovoltaic power generation amount are preferentially used for power supply, and the candidate equipment is scheduled to charge and discharge in combination with the priority parameters of the candidate equipment by taking the minimum power consumption cost and the most balanced supply and demand as targets so as to supply power to the park. According to the invention, the balance and stability of power supply can be ensured.

The utility model relates to the technical field of vehicle-mounted electronic equipment, in particular to a vehicle-mounted intelligent LED display system for concrete transportation. The system comprises an LED display screen, an intelligent sensor and a data processing terminal, the LED display screen and the intelligent sensor are connected through Bluetooth, the LED display screen and the data processing terminal are wirelessly connected through 4G, the LED display screen comprises an LED display module and a main control circuit board, the intelligent sensor comprises a rotating speed sensor and an oil level sensor, and the data processing terminal is arranged in a dispatching center; the LED display screen comprises an LED display module and a master control circuit board, and the LED display module is inserted into a pin seat of the master control circuit board through a pin and is fixed with the aluminum alloy frame through a fixing screw. According to the utility model, through cooperative work of the LED display screen, the intelligent sensor and the data processing terminal, real-time monitoring and intelligent management of the concrete transport vehicle are realized, and an efficient and safe solution is provided for the concrete transport industry.

The present invention relates to a fine-grained task offloading and resource allocation method for MEC network, including a model training phase and a decision reasoning phase, including: (1) creating an Internet of Things system; (2) deploying a fine-grained task offloading and resource allocation algorithm model based on deep reinforcement learning on an SDN controller; (3) collecting training data; (4) training the fine-grained task offloading and resource allocation algorithm model; (5) redeploying the trained fine-grained task offloading and resource allocation algorithm model to the SDN controller for implementing decision reasoning; (7) the trained SDN controller makes a computing offloading decision, a MEC computing resource allocation decision and a bandwidth allocation decision, and sends them to an intelligent Internet of Things device and an MEC server in the form of a control instruction. The present invention takes into account the dynamically changing network environment and random system environment in the Internet of Things scenario, and the present invention effectively reduces the long-term task processing delay of the system.

The application belongs to the field of workshop scheduling, and particularly discloses a mixed flow shop dynamic scheduling method and system considering machine predictive maintenance, which comprises the following steps: taking minimizing total completion time, maintenance cost and processing cost as the target, establishing a mixed flow shop scheduling problem as a multi-objective joint optimization model, setting the same number of intelligent agents as the number of processing stages, and constructing a Markov decision process; each intelligent agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on the Markov decision process, the intelligent agents are trained, the workshop is maintained at the operation and maintenance point, and the workpiece and machine selection are respectively performed by calling the workpiece scheduling network and the machine scheduling network at the scheduling point; after the training is completed, the trained intelligent agents are used to realize the dynamic scheduling of the workshop. The application effectively overcomes the mixed flow shop dynamic scheduling problem considering machine predictive maintenance by integrating the workshop scheduling of machine operation and maintenance, and has good dynamic and adaptability.

According to the virtual power plant low-carbon economic optimization scheduling method based on the improved sparrow search algorithm, the virtual power plant model related to a carbon transaction mechanism is established, the improved sparrow search algorithm is used, and a self-adaptive weight updating mechanism and a multi-strategy fusion exploration method are introduced, so that the search capability and the convergence speed of the algorithm are improved; the improved sparrow search algorithm is used for solving the established model, a scheduling scheme which is lowest in cost and meets all constraint conditions is found, the defects in the prior art are overcome, higher convergence speed and higher optimization efficiency are achieved, a better scheduling scheme is obtained, and the method is high in efficiency, high in universality and ideal in effect.

The present invention discloses a two-stage flow shop rolling production scheduling method, including: obtaining the processing information of the workshop; calculating the judgment conditions of the continuous production of the two-stage flow shop according to the processing information, establishing a linear programming model by means of 0-1 decision variables, the linear programming model including an objective function with the goal of minimizing the completion time and the constraint conditions of the objective function, the constraint conditions of the objective function are set according to the judgment conditions of the continuous production of the two-stage flow shop; solving the linear programming model, obtaining the initial scheduling scheme of the processing sequence of different batches of workpieces in the same machine to be determined, as the initial parameters of the Q learning algorithm; updating the reward and punishment table of the Q learning algorithm according to the semi-finished product inventory generated by different processing sequences of the workpiece, and using the Q learning algorithm to determine the scheduling scheme with the highest reward and punishment value, so as to determine the processing sequence of the workpiece in each machine. The present invention can ensure that the work can be uninterrupted when the debugging equipment is frequently switched due to a wide variety of products, thereby reducing the manufacturing cost.

The present invention relates to the field of data visualization, and in particular to a visualization method and system based on a vehicle service supply chain. The method includes the following steps: obtaining the full process information of the vehicle service supply chain; identifying the supply chain subject of the full process information of the vehicle service supply chain, and performing spatial topological distribution analysis to construct a supply chain subject topological distribution map; obtaining supply chain subject interaction logic data based on multiple supply chain subjects; using the supply chain subject interaction logic data to perform topological interaction logic connection on the supply chain subject topological distribution map to construct a supply chain topological interaction network; calculating the path transportation frequency within the cycle of the full process information of the vehicle service supply chain to generate logistics demand trend characteristics; analyzing the operation behavior of each link of the full process information of the vehicle service supply chain to obtain the production capacity characteristics of each link. The present invention improves the resource scheduling efficiency and flexibility of the supply chain by visualizing the vehicle service supply chain.

This invention discloses a dual-cycle collaborative scheduling method, system, equipment, and medium for sea-rail intermodal AGVs, comprising: constructing a multi-resource collaborative scheduling model with minimizing completion time as the objective, setting train arrival time as a dynamic trigger parameter; judging the execution timing of direct-fetch tasks based on this, splitting them into sub-tasks from shore to yard and then to train if the arrival time is earlier, otherwise maintaining direct-fetch; constructing dual-cycle constraints in time periods and matching import and export tasks to generate operation sequences; finally, through multi-layer coding mapping, using an improved whale algorithm to iteratively solve and output the optimal scheduling scheme. This invention significantly improves equipment utilization, operation continuity, and overall system scheduling efficiency under the sea-rail intermodal mixed operation mode.