888 results about "Operation scheduling" patented technology

The invention discloses a supply chain warehouse management system and method based on AI digital intelligence, and relates to the technical field of intelligent warehousing, and the method comprises the steps that a data processing unit collects supply chain system data through a data processing module, and constructs an associated data network containing goods information, shipping space information and order information based on the supply chain system data; the goods allocation unit executes a goods allocation algorithm according to the associated data network, extracts an association score based on goods information, analyzes a collaborative ex-warehouse frequency, and calculates an affinity matrix; the storage scheme unit generates a storage position scheme based on the affinity matrix; the job scheduling unit constructs a scheduling model, performs task optimization and outputs a task allocation sequence; the order picking navigation unit is provided with an order picking path planning module, presents a path and guidance, collects execution data, and optimizes the system through analysis. The problems that in traditional warehouse management, the goods allocation efficiency is low, task scheduling is not optimized, and the goods picking path is unreasonable are solved.

The invention provides a tower crane group collaborative operation scheduling and optimizing system. The system comprises a task dynamic allocation module, a conflict sensing path planning module and a collaborative scheduling module. Based on construction site task requirements, tasks are dynamically allocated to tower cranes, and path conflict pre-judgment cost is calculated; then, according to a task allocation result, generating a tower crane motion path in real time, and dynamically avoiding position conflicts and time overlapping among tower cranes by taking a space-time grid as a constraint; and finally, monitoring the state of the tower crane and the environment change in real time through a sensor, and dynamically adjusting the priority of task allocation and the parameters of path planning to form a global collaborative optimization model. The global collaborative optimization model integrates task allocation, path planning and real-time scheduling, and is realized through joint optimization. The system can effectively solve the problems of task delay, resource waste and potential safety hazards caused by insufficient management in collaborative operation of the tower crane group, and improves the collaboration and flexibility of the operation of the tower crane group.

The invention belongs to the technical field of agricultural automation, and provides a closed park-oriented tractor multi-machine collaborative operation scheduling system and a control method, and the method comprises the steps: employing a multi-source heterogeneous sensor network to construct an environment perception layer, generating an environment state tensor and a dynamic environment model through space-time registration and data fusion, and obtaining a dynamic environment model; a complex task is decoupled into an atomic operation unit, a single tractor optimal task sequence is generated by utilizing dynamic planning, a genetic algorithm is improved to realize global operation distribution, and an optimal path is planned for tractors in a closed park through an ant colony algorithm in combination with a dynamic environment model to control the tractors to perform collaborative operation. Deploying an edge computing node to receive the tractor pose data in real time, judging an actual conflict risk by calculating space-time conflict density, fitting a prediction trajectory function to detect a prediction conflict risk, and if the actual conflict risk or the prediction conflict risk exists, triggering conflict coordination; and selecting a hierarchical coordination strategy for the tractor according to the conflict risk type to realize obstacle avoidance control.

The invention relates to the technical field of steel production plan optimization based on multi-dimensional constraint dynamic coupling, in particular to a molten iron operation plan arrangement method based on dynamic constraint modeling hybrid optimization, which comprises the following steps: acquiring order data, equipment state data and constraint rule data to generate structured input parameters comprising order priority labels; constructing a multi-objective optimization model based on the molten iron distribution constraint and the maintainable time length of the equipment, extracting the maintenance conflict relationship between the equipment groups, constructing a maintenance rule knowledge graph, and generating a dynamic constraint condition set; generating an initial molten iron distribution and maintenance plan through global search by adopting a genetic algorithm, and outputting a molten iron distribution path and an equipment maintenance time sequence through local optimization by combining a linear programming algorithm; according to the method, dynamic collaborative optimization of molten iron distribution and equipment maintenance is achieved, and the resource utilization rate, the order delivery rate and the production system stability are improved.

The invention discloses an intelligent port scheduling and energy efficiency optimization method based on digital twinning. The method comprises the steps that S1, three-dimensional point cloud data and sensor data of a target port are acquired; s2, constructing a port digital twin model comprising a quay crane model, an AGV model and a storage yard model; s3, agents are constructed for a quay crane model, an AGV model and a storage yard model in the port digital twinborn model, and a multi-agent reinforcement learning environment is built; s4, training each agent in a multi-agent reinforcement learning environment through a multi-agent reinforcement learning algorithm; s5, mapping the real-time task data and state data of the port to each trained agent to simulate the scheduling of the target port, and generating a port scheduling strategy; and S6, realizing operation scheduling of the quay crane equipment, the AGV equipment and the storage yard equipment of the target port based on the port scheduling strategy. According to the invention, the operation efficiency and the energy efficiency level of the port can be improved, and powerful technical support is provided for sustainable development of the intelligent port.

The invention belongs to the field of civil aviation, provides a civil aircraft field service operation scheduling control system, and aims to solve the problem of low cooperative efficiency of multiple links of ground service scheduling. File management is based on an Ali cloud OSS development interface, supports multi-format files, and realizes functions of permission access, encryption and the like; communication adopts an MQ / MQTT architecture to construct an IM system, and supports multi-type message transmission and http network communication; data processing packages a multi-language database interface, and internal and external network secure intercommunication is realized through a gatekeeper agent. Flight functions cover and guarantee node management and control, dynamic updating, cross-department communication, real-time flight data butt joint, task distribution of unified operation according to work types, and support of multi-terminal such as a web terminal and a handheld terminal and nailing access. Ground service multi-scene requirements can be met, and the operation efficiency and the management level are improved.

The invention discloses a power transmission line fault diagnosis and operation and maintenance scheduling method and system based on networking learning, and relates to the technical field of power transmission line fault diagnosis operation and maintenance scheduling. Related data is extracted to construct a high-risk equipment area and a visual high-risk area thermodynamic diagram, a visual risk grading diagram is constructed in combination with electrical quantity data, and meanwhile, an intelligent recognition storage network and a fault type classification recognition model are constructed in combination with a convolutional neural network-long and short-term memory network hybrid model; the model is optimized through networking learning and an attention mechanism, maintenance teams and resources are autonomously allocated in combination with an operation and maintenance management system, then autonomous optimization and closed-loop operation are achieved, full-process coverage of fault sensing, intelligent decision making and efficient response is achieved, the response time after a line fault occurs is remarkably shortened, and the maintenance efficiency is improved. And the fault handling and operation maintenance capabilities of the power grid system are comprehensively enhanced.

The invention provides a water supply pipe network optimization scheduling method and system based on multi-model coupling, and the method comprises the steps: collecting the basic data of a regional pipe network, and building a water supply pipe network hydraulic model; obtaining a water supply network operation scheduling target, and establishing a water supply network scheduling scene model; historical monitoring data of a water supply network are acquired, a node water demand prediction model and a node water demand change curve prediction model are constructed by using a neural network algorithm, and node predicted water demand and a node water demand change curve during prediction are respectively output; constructing a water supply pipe network optimization scheduling model based on multi-model coupling, performing optimization by using an improved particle swarm algorithm, outputting a water supply pipe network optimization scheduling scheme, and introducing an optimization scheduling evaluation index to perform scheduling scheme evaluation; and constructing a water supply pipe network optimization scheduling scheme screening model, and obtaining an optimal water supply pipe network optimization scheduling scheme. According to the method, a multi-model coupling mode is adopted, the prediction error is effectively reduced, the optimization efficiency is improved, and intelligent and precise scheduling of the water supply pipe network is realized.

The invention relates to the technical field of automatic control of tool magazines, in particular to a tool magazine operation control method and system, and the method comprises the steps: obtaining a machining operation task, constructing a tool scheduling time sequence according to the machining operation task, and carrying out the operation scheduling of tools of the same type in a tool magazine according to a time sequence through the tool scheduling time sequence; obtaining use information of each type of tools of the same type, and analyzing according to the use information of the tools of the same type to obtain a priority scheduling strategy of the tools of the same type; setting a tool allocation time sequence node for the tool scheduling time sequence based on the machining operation task, wherein the tool allocation time sequence node is a time node when the tool enters and exits from the tool magazine; and traversing the tool magazine according to the tool allocation time sequence node, determining tool apron distribution information of the same-type tools, and selecting the same-type tools for executing the machining operation task according to the priority scheduling strategy and the tool apron distribution information. The invention aims to solve the problems of unbalanced use of tools, overlong shutdown time and poor anti-interference performance of the system in the operation process of the tool magazine.

The invention discloses a multi-park cross-space-time comprehensive energy consumption multi-objective optimization method and system based on deep learning, and the method comprises the steps: carrying out the feature extraction of data in the operation of a multi-park energy system through an extended long-short term memory network model, and constructing a multi-park multivariate energy efficiency evaluation index model according to the extracted multi-dimensional energy consumption features; evaluating the correlation degree of the operation process of the multi-park integrated energy system, and constructing an energy complementary relation matrix; according to the multivariate energy efficiency evaluation index model and the energy complementary relation matrix, constructing a multi-park system multi-target optimization model; solving the optimal solution of the optimization model under the operation constraint through a Transform-based multi-target particle swarm optimization algorithm, and obtaining an energy operation scheduling strategy of the integrated energy system of the plurality of parks; user interaction feedback is adopted in optimization, so that the result better meets the actual demand and target of the user; according to the method, cross-space-time comprehensive energy multi-target optimization of multiple parks can be realized, and the comprehensive energy utilization efficiency of the parks is improved.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention discloses an intelligent assembly loading control system of a geotextile laying ship, and belongs to the technical field of ship intelligent control. According to the invention, through the intelligent stowage planning module, based on standardized scheme matching and an adaptive optimization algorithm, objectives of balanced material distribution, shortest conveying path and ship stress rationalization are realized, stowage precision is improved, and sailing stability is optimized; the operation scheduling and arranging module dynamically generates an efficient operation time sequence graph through real-time cabin state recognition, process decomposition, operation time sequence optimization and remote cooperative control, precise scheduling is achieved, manual intervention is reduced, the automatic operation execution module is combined with real-time monitoring, abnormal early warning and intelligent feedback, the safety and adaptability of the operation process are ensured, and the working efficiency is improved. According to the method, loading deviation overrun and ballast abnormity are avoided, the decision-making capacity of the system is continuously optimized, manual operation errors are effectively reduced, the safety and economical efficiency of ship operation are improved, and green, energy-saving and efficient operation is helped to be achieved.

The invention discloses a flexible job shop scheduling method based on deep reinforcement learning, and belongs to the field of intelligent manufacturing and intelligent scheduling, and the method comprises the steps: firstly, taking the minimization of the maximum job completion time and the maximization of the average machine utilization rate as optimization targets, and obtaining a target model; then representing a flexible job shop scheduling problem by using a disjunction graph model to obtain a scheduling state; according to the scheduling state and the target model, an end-to-end learning framework is constructed, and the framework is combined with a multi-attention network for deep feature extraction and a decision network for extensible decision, and a Markov decision process is utilized to obtain a job scheduling model; and finally, the multiple attention network constructs a production adaptability operation-AGV-machine scheme by using the complex relationship among the operation, the AGV and the machine to support the decision of the decision network, and obtains a scheduling schedule by using the job scheduling model to complete flexible job shop scheduling. According to the method, complex modes and relations in data can be better captured, and the performance and generalization ability of the model are improved.

The invention relates to the technical field of collaborative construction, in particular to a collaborative construction method and system based on BIM, and the method comprises the following steps: obtaining component information and a construction path, calculating the center distance, the number of intersection lines and the blocking ratio, evaluating the density and the staggering degree, generating an urgent distribution set and a task list, extracting spatial parameters, and calculating the overlapping level. And judging the compression feasibility of the time window, and generating a non-parallel task list. According to the method, through quantitative evaluation of blocking risks between construction nodes, a dynamic sorting list generated based on urgency degree values and construction plan binding tasks, conflict levels between component pairs are divided through an operation space overlapping degree index, compression judgment is performed on an adjustable time window, and a minimum interference construction time period is allocated; strong interference task pairs are calibrated to avoid parallel construction, the sorting rationality of construction tasks and the space decoupling efficiency of job scheduling are effectively improved, and fine matching and efficient cooperation of component scheduling sorting and resource allocation in a dynamic scene are achieved.

The invention relates to the technical field of big data management and data analysis, and discloses a visual data platform full-link data flow traceability method, system and device and a medium, and the method comprises the steps: monitoring a job scheduling process submission event, analyzing the input and output configuration information of each job node, converting the information into a data flow model, and storing the data flow model in a traceability database; static analysis is conducted on node scripts and codes, and analysis results are stored in a traceability database; generating a unique identifier for each data record, and recording related information in a data circulation process; and writing the data flow relationship into a graph database Neo4j through a Kafka asynchronous message, and performing data traceability query by a user through a visual interface. The method has the beneficial effects that the data quality and safety are improved, and the limitation and complexity of a mainstream open source tool in the aspect of a data blood relationship analysis function are solved.

The invention discloses an island micro-grid operation scheduling method and device. According to the scheme, the method comprises the steps that power generation characteristic modeling is conducted on a distributed power source and an energy storage system of a diesel storage island micro-grid; constructing a function taking the minimum daily total operation cost as a target, wherein the function covers the operation cost of a diesel generator and an energy storage system and the pollutant treatment cost; establishing a power impact type load mathematical model including power characteristics, system dynamic frequency response characteristics, micro-grid dynamic frequency safety constraints and diesel generator frequency control constraints; establishing a micro-grid operation constraint model which has constraints such as power balance, Distflow power flow, energy storage, charging, discharging and capacity, diesel generator output and climbing rate; and solving the model by using a particle swarm optimization algorithm, generating a diesel generator output, energy storage system charging and discharging and frequency modulation reserve capacity distribution strategy, and obtaining an island microgrid optimal operation scheduling decision.

A database system is operable to generate an energy utilization-based operation scheduling function based on power usage policy data for the database system. A storage-formatted data generation operation is determined for execution, and energy utilization-based operation scheduling input data is determined for the storage-formatted data generation operation. The storage-formatted data generation operation energy utilization-based operation scheduling function is performed upon the energy utilization-based operation scheduling input data to generate energy utilization-based operation scheduling data for the storage-formatted data generation operation. A plurality of storage-formatted data structures are generated from a plurality of records for storage based on executing the storage-formatted data generation operation in accordance with the energy utilization-based operation scheduling data.

The invention provides a virtual power plant scheduling method and system based on distributed resource operation domain description, and relates to the technical field of virtual power plant optimization scheduling, and the method comprises the steps: determining a virtual power plant distributed resource operation domain; considering the influence of demand response load active power and reactive power adjustment on the virtual power plant output model, and constructing a comprehensive load model; dividing a feasible region of a variable in a bilinear term in the comprehensive load model into several non-intersecting regions by adopting a segmented McCormick relaxation method, and converting the regions into a mixed integer linear programming form; the operation constraint condition of the virtual power plant is considered, a mixed integer linear programming form is combined, boundary points of the operation domain of the virtual power plant are solved, and the range of the distributed resource operation domain of the virtual power plant is depicted; and in the distributed resource operation domain range of the virtual power plant, constructing a virtual power plant optimization scheduling model by taking the sum of the total operation cost and the demand response compensation as a target, and applying the obtained optimal operation scheduling strategy to the virtual power plant for execution.

The invention discloses an intelligent scheduling and transportation system for a port unmanned flat car, which belongs to the technical field of port unmanned transportation, and is technically characterized by comprising a cloud intelligent scheduling platform, an unmanned flat car transportation system and a car cloud collaboration module, the cloud intelligent scheduling platform comprises a vehicle cloud control system, an operation task management system, an automatic driving operation monitoring system, an intelligent network connection operation scheduling system and a port scheduling collaborative management system; the unmanned flat car transportation system comprises intelligent sensor hardware, global fusion situation awareness, multi-redundancy fusion positioning, high-precision motion control and anthropomorphic driving decision planning. And the vehicle cloud cooperation module comprises task instruction interaction and vehicle real-time state reporting, and has the advantages that through seamless butt joint with automatic container hoisting equipment, the whole-process unmanned operation of transportation and loading and unloading operation is realized, the port operation efficiency and the intelligent level are improved, and the intelligence and the high efficiency of the port unmanned transportation system are greatly improved.

The invention discloses a collaborative planning operation scheduling method and system for a power distribution network and a micro-grid, and the method comprises the steps: obtaining the historical operation data of the power distribution network and the micro-grid, and obtaining typical scene features according to the historical operation data; the typical scene features comprise typical power features; according to the typical scene features and a preset power grid cooperative operation requirement, a constraint condition and a target function are established, and a cooperative planning operation model of the power distribution network and the micro-grid is obtained; the collaborative planning operation model comprises a power grid planning layer and a power grid operation layer; performing linearization conversion on the collaborative planning operation model, and solving the linearized collaborative planning operation model to obtain a configuration and scheduling strategy; and performing configuration and scheduling operation on the power distribution network and the micro-grid by applying the configuration and scheduling strategy. By adopting the embodiment of the invention, the demand response capability of flexible resources can be mined, the economical efficiency of investment is improved, the optimal configuration of the power distribution network and the micro-grid is realized, and meanwhile, the method also has relatively high safety.

The invention provides a multi-AMR robot collaborative operation scheduling optimization method and system and a medium. The method comprises the steps that a collaborative operation scheduling map is constructed based on environment information; allocating a corresponding first target task based on the first state information of the AMR robot and the first task information of each to-be-executed task; planning an initial operation route of the AMR robot based on the collaborative operation scheduling map in combination with the second state information of the AMR robot and the second task information of the first target task; performing path conflict detection optimization based on the initial operation path of the AMR robot to obtain a first target operation path; and based on the real-time state information of the AMR robot in the process of executing the first target task, optimizing the first target operation route and the first target task of the AMR robot, and determining a second target operation route and a second target task of the AMR robot. According to the method, the efficiency and the stability of collaborative operation of the multiple AMR robots are improved.

The invention relates to the technical field of energy storage battery equalization adjustment, in particular to an energy storage power station intelligent operation scheduling management system which comprises a battery parameter estimation unit, a dynamic equalization threshold generation unit, an equalization circuit control unit, a data fusion prediction unit and a scheduling strategy generation unit. The battery parameter estimation unit calculates key parameters of a single battery, the dynamic equalization threshold generation unit generates a dynamic equalization threshold through a fuzzy logic algorithm in combination with a battery aging stage, multiple parameters and temperature factors, and the equalization circuit control unit determines an energy transfer strategy according to a model prediction control algorithm. A switch tube is controlled through an adaptive phase shift angle adjustment technology, a fault diagnosis and fault-tolerant control mechanism is provided, the data fusion prediction unit predicts key indexes of the energy storage power station, the scheduling strategy generation unit formulates an optimal operation scheduling strategy, and the ability of the energy storage power station to participate in power grid auxiliary service is improved.

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

The invention belongs to the technical field of power systems, and particularly relates to a virtual power plant carbon-green transaction optimal scheduling method considering a green power and carbon emission coupling relationship, which comprises the following steps: S1, by taking the lowest total cost as an objective function, constructing an optimal scheduling model for a virtual power plant to participate in carbon transaction and green certificate transaction; the optimal scheduling model comprises a calculation model or a calculation formula of carbon transaction cost, green certificate transaction cost and operation cost of each device; s2, solving the optimal scheduling model according to actual parameters of a virtual power plant to be subjected to optimal scheduling to obtain an optimal scheduling scheme of the virtual power plant participating in carbon transaction and green certificate transaction; and S3, optimizing the operation scheduling of the virtual power plant by using the optimized scheduling scheme obtained in the S2. The method can ensure that the operation scheduling of the virtual power plant comprehensively complies with and optimizes the complex operation mechanism of the power market while considering the carbon transaction cost and the green certificate transaction cost, and realizes accurate regulation and control.

The invention provides a medical environment air quality management method, system, equipment and medium, and relates to the technical field of environment air quality management.The medical environment air quality management method includes the steps that according to real-time data of a surrounding environment monitoring station outside a hospital and an air quality monitoring device in the hospital, a distribution map reflecting indoor and outdoor pollutant concentration is obtained through data fusion and calibration processing; performing pattern recognition on the distribution map by using a machine learning algorithm to obtain key features of medical environment air quality, and intelligently generating a customized air quality improvement scheme; processing through a near-end strategy optimization algorithm to obtain an equipment operation scheduling strategy; carrying out dynamic processing by utilizing a PID control algorithm to obtain dynamically adjusted equipment operation parameters; and the air quality of the medical environment is correspondingly managed through processing of an anomaly detection algorithm. According to the invention, a more intelligent and efficient air quality management scheme can be provided for hospitals.

The invention discloses a passenger ship operation scheduling method and system based on big data analysis, and the method comprises the steps: building a port passenger ship operation digital twin model, synchronizing the ship positioning data, facility operation states and passenger dynamic distribution data of a port in real time through Internet of Things equipment, and generating a virtual simulation scene; calling a parallel simulation engine of the digital twin model, performing space-time conflict detection and resource load rehearsal on the candidate shift scheduling schemes, and screening initial scheduling schemes meeting conditions; boarding bridge distribution, waiting area capacity matching and ship berthing time sequence optimization are simulated in the digital twin model, and an operation scheduling plan is generated; and establishing a scheduling evaluation model, performing multi-target iterative optimization on the passenger retention risk, the facility vacancy rate and the carbon emission intensity of the operation scheduling plan according to a dynamic weight adjustment algorithm, and outputting an optimal operation scheduling plan. According to the method, the smoothness of the port operation process is improved, the boarding time of passengers and wait area resource waste are reduced, and the ship berthing efficiency is improved.

The invention provides a multi-bucket wheel machine collaborative operation scheduling system and method based on a swarm intelligence algorithm, and relates to the technical field of electronic information. Inputting the equipment information, the stacking information and the environment information into a first model to generate constraint conditions; performing global search based on an artificial bee colony algorithm introducing harmony search, and screening solutions according to constraint conditions in each iteration to obtain a group of feasible solutions; performing local search in each feasible solution neighborhood by adopting mixed integer programming to obtain a globally optimal solution; and generating working parameters of the equipment according to the globally optimal solution, and performing job scheduling. The artificial bee colony algorithm is improved by introducing a harmony search mechanism, the global search capability is enhanced, and premature convergence is avoided; and meanwhile, local optimization is carried out in combination with mixed integer programming, so that the solution accuracy is improved, the algorithm can adapt to yard scheduling requirements of different scales and different constraint conditions, and the overall operation efficiency is improved.

A DAG job scheduling method includes following steps when a progress state of a job is updated: acquiring native feature data of each of jobs; performing embedding feature vector extraction on the native feature data based on a graph convolution neural network, and generating state feature vectors based on an extraction result; inputting the state feature vectors into a first prediction network, and predicting scheduling probability of respective candidate tasks by the first prediction network to obtain scheduling priorities of the respective candidate tasks; and selecting allocation nodes for the candidate tasks sequentially in an order of the scheduling priorities from large to small based on the state feature vectors and generating a scheduling strategy based on a selection result. The scheduling strategy provided in the disclosure can improve efficiency globally and can also reduce caching overhead of intermediate data in a job execution process.

The invention discloses a large power grid security check calculation method suitable for multiple time scales, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining the calculation content of a multi-user security check task, evaluating the complexity of the calculation content, and selecting a parallel calculation mode based on an evaluation result; s2, based on a parallel computing device pool set in a parallel computing mode and a job scheduling strategy, extracting computing sub-tasks of the multi-user security check task, and dynamically distributing the computing sub-tasks to corresponding computing nodes by adopting a load balancing mechanism; and S3, processing the calculation subtasks of the plurality of calculation nodes by adopting a parallel algorithm of a CPU + GPU hybrid architecture, merging calculation subtask results of the plurality of users, and generating a final security check report. According to the scheme, the computing resource utilization rate of the resource pool is improved, and the large-scale power grid safety checking efficiency is remarkably improved.

The invention discloses a multi-objective optimized intelligent job scheduling system and a scheduling method thereof, and belongs to the technical field of supercomputers. According to the scheduling method, a Pareto solution set in multiple targets is searched through a simulated annealing algorithm, simulated annealing algorithm search and job scheduling are combined, and an optimal job execution sequence is sought. According to the method, the simulated annealing algorithm and the job scheduling scene demand are combined, and the optimal scheduling mode is searched, so that the job scheduling is more intelligent, and the scheduling efficiency is improved to a certain extent. In the super computer sharing the burst buffer architecture, the burst buffer requirement of the job can be effectively utilized, the backfilling limitation existing in traditional scheduling can be broken through, and the job scheduling efficiency and the system resource utilization rate are improved.