387 results about "Integer programming" patented technology

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention discloses an unmanned aerial vehicle cargo distribution management method and system based on strategy optimization, and particularly relates to the technical field of cargo distribution management. Through an improved incremental K-means clustering algorithm, a KD-Tree index is combined to optimize nearest neighbor search, order area grouping efficiency is improved, based on an order cluster set O, mixed integer programming and a Hungary algorithm are adopted to carry out unmanned aerial vehicle task matching, a task distribution moment M is optimized, a distribution path is dynamically adjusted to deal with real-time weather and airspace limitations, and in the distribution process, the distribution efficiency is improved. Data such as order liquidity index, battery consumption rate and task balance degree are acquired through an unmanned aerial vehicle sensor, and the scheduling delay condition is judged by using fuzzy logic reasoning, so that the system can still realize efficient, balanced and intelligent unmanned aerial vehicle scheduling in a high-load scene, the distribution delay is greatly reduced, the no-load rate is reduced, and the overall logistics efficiency is improved.

The invention relates to the technical field of production scheduling, and discloses a production scheduling visualization method based on big data. According to the method, multi-dimensional production data such as equipment real-time states, material inventory, personnel skills and order delivery periods are collected, and a four-dimensional dynamic constraint model is generated through a dynamic constraint modeling algorithm; and obtaining an initial scheduling scheme by using a mixed integer programming and improved genetic algorithm dual-drive optimization engine, inputting the initial scheduling scheme into a visual interaction module, displaying a result by using a drag-and-drop Gantt chart, and detecting resource conflicts in real time. The feasibility of the adjusted scheduling scheme is verified through a dynamic re-calculation algorithm, and then the model is updated. According to the method, multi-dimensional data are integrated, production scheduling optimization and visualization are realized, production resource conflicts can be effectively solved, the production efficiency is improved, the cost is reduced, and an efficient solution is provided for enterprise production management.

The invention discloses a large model scheduling multi-agent power grid fault coping strategy knowledge graph extension method and system, and the method comprises the steps: carrying out the modeling of task distribution as a mixed integer programming problem, and achieving the solving through a relaxation-correction algorithm; extracting and normalizing a core task of knowledge, and outputting structured data; summarizing the obtained text segments, and prompting a large language model to retain key entities, relationships and domain-specific terms; using an LLM-based named entity recognition technology, combining with prompt and dictionary / ontology filtering in the power dispatching field, recognizing related entities in a text, and normalizing the related entities into a standard form in a knowledge graph; detecting logic contradictions between the newly extracted triples and existing relationships in the knowledge graph, and classifying and solving the contradictions by utilizing debate prompts based on LLM (Logistics Library Model); summarizing the plurality of verification signals, and calculating the global confidence, definition and correlation score of each triple;

The invention provides a micro-grid group-containing AI active distribution network scheduling optimization method, a medium and a system, and belongs to the technical field of power grid scheduling. The method comprises the following steps: firstly, constructing a micro-grid group and main and distribution network interaction model, and determining boundary constraints; predicting key parameters of the micro-grid group by using deep reinforcement learning; constructing an active distribution network power balance equation and topology constraint conditions; solving by adopting mixed integer programming to obtain power flow distribution of the distribution network; constructing a power grid dispatching optimization objective function based on a quantum evolutionary algorithm; processing multi-source data by using an MGPN deep neural network to output an optimal scheduling strategy; monitoring a running state verification effect in real time through a state estimation technology; updating the strategy in real time by applying a rolling optimization mechanism; and establishing an evaluation system to dynamically optimize neural network model parameters, realizing efficient collaborative scheduling of the micro-grid group and the active power distribution network, and solving the technical problem of low distributed energy consumption rate in the collaborative scheduling optimization process of the micro-grid group and the active power distribution network.

The invention discloses a collaborative unmanned aerial vehicle cluster path planning and scheduling system, and particularly relates to the technical field of unmanned aerial vehicle intelligent control, and the system comprises a multi-mode sensing unit which is composed of a heterogeneous sensor array composed of LiDAR, binocular vision and millimeter wave radar, and an output dynamically updated three-dimensional Gaussian mixture map; the decision control unit is used for implementing double-layer optimization of mixed integer programming task allocation and artificial potential field path planning; the dynamic communication network adopts a hybrid networking protocol of TDMA backbone nodes and 802.11 ax terminal nodes; an energy management module; aiming at the insufficient environment perception and dynamic modeling capability in the prior art, the method achieves the effects that the centimeter-level positioning precision and the dynamic obstacle recognition rate are greater than 92%, the environment model is delayed and compressed to be within 200ms, the response speed is increased by 5 times by setting multi-modal sensor fusion, constructing a dynamically updated 3D Gaussian mixture map and combining an LSTM network to predict the obstacle trajectory in real time, and the dynamic obstacle recognition rate is greater than 92%. And the obstacle avoidance reliability in a complex scene is obviously enhanced.

The invention discloses a multi-source equipment health collaborative operation and maintenance method in a complex environment based on edge federation, and relates to the technical field of equipment collaborative operation and maintenance. Vibration acoustic emission current waveforms are mapped to a unified time-frequency grid at an edge gateway, and an encrypted sparse index is generated and uploaded; training a global model by combining a graph regular base network with a gradient direction and a distribution distance, and injecting fault information by using a new working condition protection door; after being issued by a sparse adaptation layer of double-temperature-zone distillation and random projection compression, fine adjustment is carried out on site under few samples through temperature gradual fusion and reversible orthogonal mapping, and only unit gradient direction and health labels are uploaded; the center adopts entropy constraint Bayesian filtering to fuse information to generate a health index, a maintenance schedule and a spare part plan are formed by integer programming according to confidence intensity mapping risk popularity, a result is differentially pushed and audited, and a closed loop of collection, learning, evaluation and decision is realized.

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention relates to the technical field of energy load prediction distribution optimization, in particular to an energy terminal-oriented dynamic load prediction distribution optimization method and system. The method comprises the following steps: generating a load prediction value by using a long short-term memory network based on acquired terminal load data; constructing a load priority matrix and a load distribution conflict rule set, and constructing a multi-factor integration constraint model in combination with the optimization parameters of the load predicted value; dynamically correcting the model based on the load prediction value; an integer programming problem is solved, and a distribution instruction of each terminal is obtained; and performing execution control based on the distribution instruction. The distributed acquisition unit supports multiple communication modes, acquires and normalizes various types of terminal data, loads a virtual value compensation model for checking abnormal nodes, guarantees data integrity and reliability, improves the processing capability of the system for multi-source heterogeneous data, and enhances compatibility and robustness.

The invention discloses a supply chain production scheduling optimization method and system based on multi-source data. According to the method, supplier purchase data is extracted through a data crawler, raw material sharing is analyzed in combination with a knowledge graph, and a raw material sharing list is generated; on the basis of delivery records and product attributes, a quality stable value is predicted through linear regression, process similarity is calculated through a text similarity algorithm, and a process similarity matrix is constructed; in combination with historical sales data, a market demand prediction model is constructed by applying multiple linear regression and time sequence analysis. A complex relation among raw material sharing, process similarity and market demands is described through a graph neural network, a product association network graph is generated, an optimal balance point is found between delivery time and a resource utilization rate through Pareto optimization, and a production scheduling scheme is formed; and finally, integrating the performance score, the quality stable value, the raw material sharing list and the production scheduling scheme through an integer programming algorithm, and optimizing a supplier combination. According to the invention, the production scheduling efficiency and precision are improved, and complex business requirements are met.

The invention provides an elastic computing power scheduling method and device based on space-time prediction and a medium, and belongs to the technical field of cloud computing and edge computing. The method comprises the following steps: acquiring a historical load, a satellite cloud picture, meteorological data, a burst flow mark, an energy price and a green electricity proportion in real time; constructing a fusion prediction model which comprises a multi-modal feature coding module, a space-time attention fusion module and a prediction output layer; a multi-objective optimization function is constructed, cost, delay and resource fluctuation are minimized, and green rewards are maximized; solving a task allocation variable and a resource adjustment amount by adopting mixed integer programming; the actual green electricity utilization rate is monitored, and the green reward coefficient is dynamically adjusted to correct prediction and scheduling deviations. According to the method, the computing power demand fluctuation of different areas in the future time period can be predicted in advance, so that resource allocation is dynamically adjusted, resource idleness and waste are reduced, and the utilization rate of computing power resources is remarkably improved.

The invention relates to the technical field of computer aided design, in particular to a distributed building structure intelligent design system and method under a low-carbon background, and the method comprises the steps: a BIM parameter optimization module constructs a building group adjacent matrix and a sunlight shielding coefficient based on building structure data, calculates a ventilation score, combines a window heat transfer coefficient, and employs an SQP algorithm to optimize a window-wall ratio; and the route generation module constructs energy nodes and connection paths by using the adjacent matrix of the building group. And the carbon emission analysis module comprises construction carbon emission calculation, multi-factor coupling prediction and net carbon emission polymerization, can predict future carbon emission based on an LSTM algorithm, and evaluates the net carbon emission of the building group in combination with ecological restoration. And the routing optimization module generates an initial feasible solution by adopting a heuristic algorithm according to a carbon emission analysis result, and optimizes energy node configuration and an optimal connection path of the building group through mixed integer programming, so that the intelligent design efficiency of the building structure is improved.

The invention relates to the technical field of control systems, and particularly discloses an electric vehicle and renewable energy source integrated energy management measurement and control system and method, and the system comprises an acquisition module, an optimization control module, a control execution module, a constraint module, and a fault tolerance module. The acquisition module acquires photovoltaic, wind power and electric vehicle load data through a distributed sensor network, and the control execution module issues an instruction through a multi-priority message queue and performs closed-loop feedback; the constraint module decouples space-time constraints through causal derivation, and updates strategy network parameters in combination with federated learning; and the fault-tolerant module performs dynamic fault tolerance based on the Bayesian network and the generative adversarial network. The method covers multi-source data processing, hierarchical optimization instruction generation, closed-loop verification and anti-interference training processes, solves the problem of optimization path deviation caused by difficult solution of high-dimensional mixed integer programming and time-space correlation interference, and realizes dynamic balance of energy supply and demand and low-carbon operation.

The invention discloses a photovoltaic centralized charging control method and device for a light commercial electric vehicle and a medium, and belongs to the technical field of new energy charging. The method comprises the following steps: acquiring weather data, a vehicle battery charge state and task scheduling data; photovoltaic module parameters and environment data are processed through an LSTM neural network model, and a photovoltaic power generation power prediction curve is generated; constructing a multi-dimensional evaluation index system including task emergency degree, charging efficiency and power grid interaction factors, and generating a charging priority sequence in combination with an entropy weight TOPSIS algorithm; solving a real-time power distribution scheme based on a mixed integer programming algorithm, and dynamically adjusting the output power of the charging pile; monitoring the operation state of the system, and triggering a multi-stage power compensation mechanism when the photovoltaic power is insufficient or the electric quantity is low; and the battery state and the photovoltaic power are updated in real time, and a charging strategy is corrected through a rolling optimization algorithm. By means of the method, centralized charging control over the light commercial electric vehicle is achieved, and the technical effect of meeting the requirements of a motorcade is achieved.

The invention relates to the technical field of textile production scheduling and resource allocation planning, in particular to a textile equipment scheduling management and optimization system of a textile factory, which comprises a data perception and integration module, a scheduling optimization decision module, a plan execution and equipment control module and a closed-loop feedback and self-learning module. The data sensing module collects and fuses order data, equipment operation state data and production environment data in real time, and a unified real-time data view is generated through cleaning and alignment processing; the scheduling module runs a mixed integer programming dynamic model, and minimizes the comprehensive cost and synchronously optimizes the equipment utilization rate and energy consumption in combination with rolling horizon optimization under the condition of meeting the process constraints of order delivery time limit and process dependency matrix representation; the plan execution module analyzes the scheduling instruction into an equipment executable instruction, drives equipment operation and collects execution deviation; and the closed loop module triggers rescheduling when the deviation exceeds the limit or the order is plugged. The scheduling accuracy and adaptability are improved, the cost is reduced, and efficient and stable production is guaranteed.

The invention discloses a power distribution network power scheduling optimization method and system, which are used for solving the technical problem of poor calculation efficiency of a power distribution network power scheduling optimization scheme caused by great calculation complexity of joint solution in a traditional power distribution network power scheduling optimization method. The method comprises the following steps: constructing an initial classification tree model according to an obtained optimal topology scheme in a plurality of power distribution network operation scenes and network operation states in the plurality of power distribution network operation scenes; performing global optimization on a tree structure of the initial classification tree model by using a mixed integer programming method based on a preset feature selection and division mechanism to generate an optimal classification tree model; and when a current power distribution network operation state of the power distribution network is received, based on a mathematical programming method, solving an optimization problem corresponding to the power distribution network by adopting the optimal classification tree model according to the current power distribution network operation state, and generating a target power distribution network power scheduling scheme.

The invention provides a construction carbon emission dynamic simulation and optimization decision-making method based on BIM and digital twinning, relates to the technical field of building construction carbon emission management and control, and solves the problem of limitation of an existing management and control process in the aspects of accounting precision, response speed, emission reduction effect and economical efficiency. The method comprises the following steps: firstly, establishing a multi-dimensional BIM model, constructing a digital twinborn body linked with a physical construction site in real time based on the model, and mapping real-time energy consumption and material data; and then monitoring and calculating the real-time carbon emission intensity of the whole construction process in combination with a dynamic carbon emission factor library, further performing multi-target optimization solution by adopting a mixed integer programming algorithm based on the intensity value and an optimization decision, outputting a low-carbon construction optimization scheme, and guiding field execution. Through BIM, digital twinning and dynamic optimization full-chain technology integration, real-time monitoring, accurate accounting, intelligent optimization and closed-loop management and control of building construction carbon emission are smoothly realized.

The invention discloses an energy storage configuration and scheduling method and device considering section power flow power fluctuation optimization and source network load storage coordination, and belongs to the technical field of energy storage configuration and scheduling. The method comprises the steps that after a new energy output curve, a load curve and a power grid topological structure are obtained, a constructed double-layer mixed integer programming model is solved, and a new energy output curve is obtained; obtaining an energy storage rated capacity and an energy storage intra-day optimization scheduling curve; and then calculating a standard deviation of daily cross-section tide power fluctuation, if the standard deviation of the daily cross-section tide power fluctuation is greater than a preset threshold value, solving the double-layer mixed integer programming again until a preset convergence condition is met, obtaining an optimal energy storage rated capacity and an optimal intra-day optimal scheduling curve of energy storage, and performing energy storage configuration and scheduling according to the optimal energy storage rated capacity and the optimal intra-day optimal scheduling curve. According to the method, the reasonable configuration scheme is ensured by designing the optimization target and the constraint, the reasonable energy storage configuration capacity is ensured by reducing the economic weight of the energy storage capacity, and the redundancy or insufficiency of the capacity is avoided.

The invention provides a container shipping stowage method and system based on a multi-stage optimization algorithm, is applied to the technical field of operational research and port logistics, and decomposes a complex stowage process into a plurality of optimization stages based on a four-stage mixed integer programming method. The method comprises the following steps: firstly splicing 20-foot boxes into 40-foot equivalent boxes, quickly locking a shipping space by taking the minimum box area-ship-shellfish connection number and weight deviation as targets, then eliminating vertical overweight and transverse unbalance layer by layer, and finally finely adjusting the shipping space through local neighborhood search, thereby reducing the box turnover rate and improving the dispatching efficiency. Therefore, higher efficiency, lower conflict rate and better process matching degree of container space distribution are realized, the adaptability and efficiency of actual wharf operation are remarkably improved while the safety of a ship structure is guaranteed, and intelligent, systematized and efficient development of a port operation system is promoted.

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.

In an example embodiment, a solution for matching entities in a query table with one or more entities in a target table, in the presence of a constraint on the value sum of the matching targets (hereinafter called the “value constraint”), using machine learning techniques, is provided. Specifically, a non-linear objective function is converted to a linear objective function and a machine learning model is trained using the linear objective function, allowing for the use of solver functions from libraries in order to speed matching over existing methods.

The invention relates to the technical field of electric vehicle intelligent energy management, and discloses a charging management method and system for an electric vehicle, and the method comprises the steps: obtaining vehicle data, user charging demand data and charging station data; according to the charging station data and historical data of the vehicle data, calculation is carried out to obtain load data; comparing the load data with the available upper limit data, and generating priority charging sequence data in combination with the charging demand data; the idle charging piles are grouped, and an integer programming algorithm is adopted to obtain an optimal power distribution scheme; generating a charging plan according to the optimal power distribution scheme; in the plan execution process, collecting real-time charging data and updating the charging plan; and according to the real-time charging data, in combination with historical operation data of the charging station, a deep learning algorithm is adopted, an intelligent scheduling strategy is optimized, and intelligent distribution of resources of the charging station is completed. According to the method, historical data and real-time charging data can be fused to realize dynamic optimization of the charging plan of the electric vehicle.

The invention discloses an island energy system opportunity constraint weekly optimization scheduling method and system, and the method comprises the steps: converting the opportunity constraint of the actual output of wind and light into a deterministic inequality constraint based on the prediction error probability distribution characteristics of wind power and photovoltaic; according to the obtained wind and light output constraint, a dynamic operation model is established, and waste heat output of the hydrogen fuel cell is coupled with an absorption refrigerator; with the purpose of minimizing the total operation cost of the system, combining the obtained deterministic inequality constraint and the dynamic operation model, defining the power balance constraint of electricity / heat / cold / fresh water, the hydrogen storage tank capacity constraint and the equipment climbing rate constraint, and constructing an optimization model; and solving the optimization model by adopting mixed integer programming to obtain an optimization scheduling scheme. The requirements of electricity, cold, heat and fresh water of an ocean island energy system are met, and meanwhile energy storage and cross-period utilization during wind and light resource enrichment are achieved.

The invention discloses an intelligent agricultural plant protection operation control system based on a big data Internet of Things, and the system comprises a sensing layer which is an agricultural environment parameter collection module which collects the environment parameters of a farmland in combination with a fine-grained expert splitting technology; the crop growth state monitoring module is used for monitoring the growth state of crops in real time; a network layer: an Internet of Things communication module which is responsible for data transmission between the sensing layer and the platform layer; the platform layer comprises a big data processing and analysis module which is used for cleaning, integrating, analyzing and mining data acquired by the sensing layer in combination with a hybrid expert model in a MoE architecture; the intelligent decision support module is used for constructing a dynamic decision optimization model and dynamically adjusting a decision scheme; the plant protection operation management module is used for designing a hybrid solver of a space-time heuristic rule and integer programming, generating a preliminary operation plan, and then adjusting and optimizing the preliminary operation plan in real time according to real-time data in combination with a dynamic decision optimization model and an expert model in the MoE architecture.

The invention discloses a digital shared profile automatic storage system and a management method thereof, and particularly relates to the technical field of profile storage. The sectional material identification dynamic tracking module is used for realizing full-life-cycle real-time positioning and environment state monitoring of sectional materials through a multi-mode sensing technology, and the intelligent storage strategy engine module is used for dynamically generating a storage layout scheme and an equipment scheduling strategy based on a spatio-temporal data analysis and optimization algorithm. Through fusion of 128-bit EPC coding and a multi-source sensor, Kalman filtering and a space-time alignment algorithm are combined, sub-meter-level real-time positioning and deformation monitoring of the profile are achieved, the abnormal response speed is shortened to be within 5 seconds, the optimal path of the truss robot is dynamically generated through an improved optimization algorithm, the shelf layout is reconfigured through mixed integer programming, and the system performance is improved. And multi-user fair auction is realized based on a block chain smart contract, a cross-enterprise data isolation channel is constructed, and zero leakage of data is ensured while 200 + concurrent access per second is supported.

The invention provides a demand response bus intelligent scheduling method and system driven by real-time data. A demand response bus intelligent scheduling method driven by real-time data comprises the following steps: S1, collecting and fusing multi-source data in real time, and constructing a dynamic data set; s2, carrying out short-time demand prediction and clustering analysis based on LSTM and DBSCAN; s3, dynamic service area division and greedy algorithm line generation; s4, elastic vehicle scheduling based on combination of mixed integer programming and a simulated annealing algorithm; s5, dynamically adjusting the real-time ticket price driven by the supply and demand game model; s6, passenger personalized service matching is achieved through a collaborative filtering algorithm; s7, performing multi-target simulation verification on the digital twin platform; and S8, reinforcing a learning-driven dynamic feedback optimization mechanism. According to the real-time data-driven demand response bus intelligent scheduling method and system provided by the invention, the operation efficiency, the passenger satisfaction degree and the enterprise benefit of the demand response type customized bus can be remarkably improved, and the method and the system are suitable for construction of an urban intelligent bus system and have remarkable advantages.

The invention discloses an intelligent charging scheduling method and system, and the method comprises the steps: collecting a real-time multi-source data set, building an LSTM charging demand prediction model based on an LSTM neural network, inputting the real-time multi-source data set into the LSTM charging demand prediction model for prediction, and defining a dynamic priority weight function according to the charging demand and the power grid schedulable margin. Generating a priority list; establishing a multi-target hierarchical optimization algorithm by utilizing MIP mixed integer programming and an NSGA-II genetic algorithm, and solving a charging scheduling scheme in a hierarchical manner by taking a priority list as a constraint based on the multi-target hierarchical optimization algorithm; active peak load shifting is performed by using a grading response strategy, the regional peak load is reduced, the capacity expansion investment of the transformer is delayed, and the service life of equipment is prolonged. And in combination with the V2G reverse power supply capability, electric vehicle energy storage resources can be called in an emergency state of a power grid, so that the local power supply reliability is improved, and the carbon emission of a traditional peak regulation power plant is reduced.

The invention provides a relay core component double-layer production line AGV scheduling strategy considering task completion time and energy consumption optimization, and aims to optimize task completion time and AGV energy consumption distribution of a double-layer relay welding production line and improve the operation efficiency of an AGV in a complex production environment. The method comprises the following steps: firstly, constructing an environment model of a double-layer production line, extracting operation constraints and task requirements of an AGV, and planning an initial path by using an A * algorithm; secondly, whether dynamic or static position conflicts exist is judged according to the real-time position of the AGV, obstacle avoidance is performed in combination with an improved BUG2 algorithm and a priority strategy, and an AGV route after obstacle avoidance is updated; and finally, combined optimization is carried out on the path planning and task allocation of the AGV by using mixed integer programming in combination with an adaptive weight adjustment strategy so as to ensure that the AGV can complete the established task under the lowest energy consumption. The method can be widely applied to the fields of intelligent manufacturing, automatic logistics, unmanned warehousing and the like, the production efficiency is improved, and the operation cost is reduced.

The invention provides a semi-active suspension vibration control method and system based on preview fusion and game decision, and the method comprises the following steps: obtaining pavement preview information through a forward sensor, carrying out the signal processing, and generating a future pavement preview sequence in real time; establishing a two-degree-of-freedom 1 / 4 whole vehicle vertical dynamical model; under a model prediction control framework, participants of a non-cooperative Nash game model are defined, a corresponding cost function is designed, a damping coefficient is discretized by adopting mixed integer programming, Nash equilibrium is solved by adopting a branch and bound method, and an optimal damping coefficient is obtained; mapping the optimal damping coefficient into optimal damping force meeting constraint conditions; hysteresis force compensation is conducted through a hysteresis model, and target control current is obtained through damping force obtained after hysteresis compensation according to the mapping relation between the damping force of the magnetorheological damper and the control current; and outputting the target damping force according to the instruction of the target control current. According to the invention, autonomous balance adjustment of multiple control targets is realized.