724 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 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 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 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 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.

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 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 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.

The invention discloses a cluster job planning method and system based on a dual time window mechanism. The method comprises the steps that jobs are divided into a multi-level structure with an execution sequence, and a directed acyclic graph is constructed to form a task topological structure; constructing a dual time window mechanism; designing a multi-objective optimization function; modeling cluster job scheduling into a multi-agent Markov decision process, setting a hierarchical action space, and designing a reward function reflecting scheduling balance; the method comprises the following steps: constructing a hybrid neural network architecture, extracting a time sequence correlation feature of a task, encoding a heterogeneous feature between the task and a server by using a heterogeneous graph neural network, and generating an intelligent matching score between the task and the server; a multi-agent near-end strategy optimization algorithm is applied, and agent collaborative optimization is achieved through a collaborative mechanism updated by a centralized value function and a distributed strategy. According to the method, fine modeling and state dynamic perception of complex operation topology are realized, and scientificity and controllability of scheduling decision are improved.

The invention discloses an AI auxiliary operation scheduling and compliance identification method for operation under a high-voltage line. The method comprises the following steps: firstly, according to an actual layout path, a voltage grade and a protection boundary of a high-voltage line, constructing a three-dimensional coordinate system matched with the terrain for delimiting a dangerous grade area; in the operation process, the spatial position and attitude parameters of the operation machine and the position point location information of an operator are continuously collected and converted into a track input data set. Through construction of a continuous track sequence and spatial difference analysis, operation behaviors exceeding a safety boundary or approaching a critical height are identified, and key space-time and equipment information is extracted. And performing risk classification on the abnormal behaviors in combination with the construction plan and the field progress, and generating a dynamic scheduling suggestion according to the current operator density and the equipment arrangement condition. The suggestion can be synchronously pushed to a field terminal, and is linked with a voice broadcast and warning device to carry out early warning, so that active intervention and scheduling optimization of high-risk operation behaviors are realized.

The invention relates to the technical field of data processing, and particularly discloses a cross-platform and cross-cluster hybrid operation method and system. By constructing the initial mixed environment adaptation model, pre-adjustment of the job scheduling strategy can be realized according to the resource feature parameters and job attributes of the physical machine cluster and the cloud environment cluster, so that resources in different environments can be fully coordinated, the flexibility and efficiency of job scheduling are improved, and the job scheduling efficiency is improved. Seamless sharing and circulation of job data and states between a physical machine and a cloud environment are achieved by dynamically adjusting the execution sequence and the resource allocation quantity of jobs and utilizing a cross-environment data sharing space, optimization factors are generated by collecting the operation states and resource change data of sub-job units in real time, and the optimization efficiency is improved. The real-time performance and the accuracy of job scheduling are ensured, an initial mixed environment adaptation model can be continuously optimized to form closed-loop optimization by means of a mixed environment job execution evaluation report feedback mechanism, and the stability and the high efficiency of job execution are ensured.

The invention discloses a distributed job scheduling method and device, a storage medium and computer equipment, relates to the technical field of distributed task scheduling, is suitable for the field of financial and medical services, and mainly aims at solving the problem that an existing distributed job scheduling system lacks task dependence management and dynamic scheduling capability. Comprising the following steps: performing task modeling processing on each to-be-executed task by adopting a binary tree structure to obtain a task binary tree corresponding to each to-be-executed task; performing serialization processing on the task binary tree by adopting a job arrangement engine to obtain a corresponding task sequence; issuing the task sequence to an event flow bus; after the event flow bus is started, tasks to be executed are allocated to different distributed actuators for execution based on the central control scheduler; and the distributed executor returns an execution result to the event flow bus in real time, so that the event flow bus triggers the downstream task after the upstream task is completed until the task sequence is completely executed.

The invention relates to the technical field of wharf accessories, in particular to a wharf accessory intelligent management method and system based on big data analysis. Comprising the following steps: setting a plurality of sub-devices according to accessory parameters, and constructing a state monitoring model and an operation scheduling model based on all the sub-devices; setting a plurality of scheduling sub-strategies according to the to-be-processed cargo parameters and the operation scheduling model, and generating an equipment control strategy according to the state monitoring model and all the scheduling sub-strategies; generating an operation risk value of each sub-device according to a preset diagnosis time node, and judging whether an early warning instruction is generated or not according to all the operation risk values; a cargo identification sub-model is constructed based on big data analysis, characteristic parameters of to-be-processed cargoes are identified in time, a plurality of scheduling strategies are selected according to the characteristic parameters, real-time operation states of all accessories are obtained according to a state monitoring model, and all the scheduling strategies are optimized, so that the overall scheduling operation efficiency of the accessories is improved, and the scheduling efficiency of the to-be-processed cargoes is improved. The loading and unloading efficiency and the equipment operation efficiency are improved.

The invention relates to the technical field of intelligent scheduling, in particular to a mining area operation scheduling system based on big data, which comprises an operation feature construction module, a strength label generation module, a density mutation recognition module, a rhythm type judgment module and an operation time sequence sorting module. According to the method, by constructing the operation behavior data of the transportation equipment and extracting the mean value combination of the load change and the residence time, a load feature distribution structure is formed among multiple mining areas, and the operation intensity label division among the mining areas is realized by a similarity measurement mechanism among normalized feature pairs, so that the quantitative expression of the operation load distribution state can be realized; a mining area density time sequence is further constructed in combination with equipment track information and a distribution sequence, operation rhythm change nodes are determined through a density abrupt change point identification means, and time period rhythm types are classified on the basis of double variables of operation intensity and operation density, so that mining area operation rhythm types in different time periods are accurately divided.

The invention discloses an automatic shipping operation scheduling method for a container wharf, and the method is realized through the following steps: determining a current workable candidate container list based on a preset on-board position dependence relation and a storage yard real-time state through feasibility screening; performing execution cost evaluation and opportunity utility evaluation on the candidate jobs respectively, quantifying the costs of equipment movement, overturning and ramming operation and the like by the execution cost evaluation, and actively and quantitatively unlocking the key jobs by the opportunity utility evaluation, thereby forming earnings brought by double-box collaboration or matching of wharf resources; and calculating a final decision score through a weighting algorithm fusing utility and cost, and selecting an optimal job generation instruction according to the final decision score. According to the method, a complex scheduling problem is decomposed into a plurality of evaluation levels, refined and intelligent management of ship loading operation decisions is realized, and the overall efficiency, balance and economical efficiency of automatic wharf ship loading operation can be effectively improved.

The invention discloses a multi-robot collaborative job scheduling optimization method based on a graph neural network. The method comprises the following steps: S1, constructing a collaborative scheduling graph comprising robot nodes, task nodes and meeting point nodes; s2, initializing a feature vector of each node; s3, node feature aggregation and scheduling representation vector generation are executed through the graph neural network; s4, an improved Co-DPSIPP algorithm is adopted to calculate a handover score of a meeting point node, and optimization is carried out based on task completion time, energy consumption change and communication load; s5, selecting an optimal handover mode according to the handover score, and updating the scheduling graph; s6, executing task redistribution and path planning based on the updated scheduling graph; and S7, repeatedly executing reasoning and scheduling control until all tasks are completed, and outputting a scheduling scheme and a path sequence. The scheduling efficiency and path planning of the multi-robot system are remarkably improved, and the method is widely applied to automatic production and robot cooperation tasks.

The invention discloses a micro-grid capacity optimization configuration method based on an improved grey wolf algorithm. The method comprises the following steps: S1, constructing a capacity optimization configuration model of a micro-grid based on each distributed power supply of the micro-grid; s2, setting a micro-grid operation scheduling strategy; s3, under the microgrid operation scheduling strategy, solving the capacity optimization configuration model through an improved grey wolf algorithm to obtain an optimal configuration scheme; wherein the solving process comprises the following steps of: initializing a population by adopting a good point set, and confirming an initial configuration scheme; self-adaptive weight coefficients in different advancing directions are determined according to gray wolf positions, and population updating is carried out in combination with convergence factors; using a lens reverse learning strategy to update an optimal individual, and introducing a greedy strategy to carry out individual screening; the optimal configuration scheme is obtained through iteration; the method has higher optimization precision and better optimization stability, and can effectively reduce the annual value comprehensive cost and the power deviation rate of the micro-grid.

The invention discloses a surgical instrument automatic scheduling method, system and terminal based on pre-operation scheduling, and the method comprises the steps: obtaining pre-operation scheduling information which comprises an operation style, an operator, a time period and inter-operation scheduling; a standard instrument set template is obtained according to the operation mode, historical instrument use behavior data of the operator is obtained, and the historical use behavior data is used for dynamically correcting the standard instrument set template to obtain a personalized template; acquiring instrument inventory information in the time period, and intelligently scoring the standard instrument package template and the personalized template according to the instrument inventory information and the recommendation model to obtain a target template; obtaining a scheduling task list according to the target template, the operation style, the time period and the inter-operation arrangement; and inputting the scheduling task list into an automatic transfer system, generating a route task package, and packaging, delivering and delivering the instruments according to the route task package and the scheduling instruction. According to the method, the instrument set is automatically generated and optimized, and the surgical instrument configuration efficiency is remarkably improved.