276 results about "Integer programming" patented technology

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 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 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 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 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.

The invention discloses a quantum Isin model construction method for a security constraint unit commitment optimization problem, and relates to the field of quantum computation.The quantum Isin model construction method comprises the steps that a security constraint unit commitment optimization model is constructed, and parameters of a mixed integer programming problem are obtained; using a Benders decomposition method to decompose a mixed integer programming problem into a main problem and a sub-problem; substituting the optimal binary solution to solve the sub-problem to obtain a new cut plane and expand a cut plane set; constructing a compact high-dimensional quadratic function fitting cutting plane set; solving a positive semidefinite programming problem to obtain a high-dimensional quadratic function parameter; converting a quadratic unconstrained binary optimization model constructed based on a high-dimensional quadratic function into an Isin model; solving the Isin model to obtain a quantum bit state, and solving an optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solution. According to the invention, the problem of huge consumption of quantum bit resources in the prior art is solved, especially the problem of difficulty in processing NP with complex constraints and more variables is solved.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention relates to the technical field of aircraft simulation training, and discloses a multi-mode man-machine interaction aircraft simulation training system. A multi-modal feature extraction module of the system generates a multi-modal feature map including a flight environment situation map and an equipment operation health map through a hierarchical feature extraction network; the incremental analysis module divides an incremental data set, updates flight state prediction model parameters through an incremental clustering algorithm and outputs an incremental state prediction result; the parameter optimization layer initializes a tabu search population according to a prediction result, and iteratively optimizes parameters of an operation rule base through a dynamic efficiency evaluation matrix; the decision generation module fuses the optimized rule base parameters and the real-time multi-modal feature map, and generates a flight operation instruction sequence based on dynamic confidence score; and the execution optimization layer analyzes the instruction sequence, satisfies constraint conditions through a constraint projection algorithm, and completes instruction sequence integer programming in combination with the operation portrait deviation. The system improves the interaction accuracy and scene adaptability of simulation training.

The invention discloses a method for evaluating the coordinated mining degree of coal and allogeneic symbiotic bauxite based on production progress planning, which comprises the following steps of: constructing a space coordinate system, and dividing a coal seam and allogeneic symbiotic bauxite into three-dimensional ore blocks; based on multi-objective 0-1 integer programming, establishing a coal and allogeneic symbiotic bauxite coordinated mining mathematical model, and determining an optimization objective and constraint conditions; a genetic algorithm is adopted to intelligently solve the model, and an approximate Pareto optimal solution set is obtained, so that an optimal production progress scheme in a specific period is formed; and based on the optimal production progress scheme, constructing a coordinated mining degree evaluation index system, and performing quantitative evaluation on the coordination degree of the actual mining scheme. According to the method, through production progress planning and intelligent optimization, space-time conflicts in the mining process of the coal mine and the allosymbiotic bauxite can be effectively avoided, comprehensive balance of economic benefits, safe production and environmental protection is achieved, and a scientific basis is provided for optimization and sustainable development of a mine production scheme.

The invention discloses an extreme high-temperature scene multi-energy complementary optimization scheduling method considering uncertainty, which mainly comprises the following steps of: 1, fusing bidirectional time convolution, a bidirectional long-short-term memory network, an attention mechanism and a quantile regression forest, realizing high-precision prediction and uncertainty modeling of wind speed, solar irradiation and load, and constructing a typical day scene set; 2, constructing a two-stage scheduling model fusing epsilon-constraint multi-objective optimization and opportunity constraint mixed integer programming, optimizing and adjusting margin day ahead, and rolling and correcting a scheduling path within the day; 3, three physical correction mechanisms of wind power air density correction, photovoltaic temperature response and hydroelectric evaporation-water level coupling are provided for extreme high-temperature disturbance; and 4, a prediction-optimization-feedback-correction closed-loop process is integrated and constructed, and the stability and response toughness of the system under extreme climate are improved. The method has the beneficial effect that the prediction precision, the scheduling flexibility and the operation toughness of the system under the extreme climate are remarkably improved.

The invention provides a power grid traveling wave synchronous positioning method based on mixed integer convex quadratic programming, and the method comprises the steps: building a mathematical model for describing the topological structure and line length of a power grid, obtaining the fault traveling wave arrival time recorded by each monitoring point in the power grid, and building an actual measurement time difference matrix based on the fault traveling wave arrival time and the mathematical model; constructing a mixed integer convex quadratic programming model based on the actual measurement time difference matrix, calling a mixed integer programming solver to solve the constructed mixed integer convex quadratic programming model to obtain a global minimum mismatch degree, and meanwhile, obtaining a global optimal fault line, a fault position and a traveling wave velocity corresponding to the global minimum mismatch degree. And traveling wave synchronous positioning is realized. The method can improve the positioning accuracy and reliability under the condition of unknown wave velocity and complex network topology.

The invention discloses an intelligent coal blending method and system for dual-engine staged solution in a power coal scene. The method comprises the following steps: performing structured processing on coal source data and establishing a hundred-ton integer batch variable; the method comprises the following steps of: constructing a hard constraint integer programming (ILP) model, and sequentially executing three stages of solving of cost minimization, heat value deviation minimization under the fixed cost and coal source quantity minimization under the fixed cost and deviation; when any stage is not feasible, automatically switching to a soft constraint target planning (GP) model, performing soft constraint on the coal quality index through a deviation variable, and sequentially performing deviation minimization, cost minimization and coal source quantity minimization; and calculating a combustion stability index (CSI) on the basis of volatile component fluctuation, fixed carbon, ash content change and sulfur content change, and carrying out stability verification on a final coal blending scheme. The system comprises a data management module, a first engine module, a second engine module, an automatic rollback module, a combustion stability index module and a result output module.

The invention discloses an active fluctuation collaborative stabilizing method and system for a high-proportion distributed new energy power grid, and relates to the technical field of power grid dispatching. According to the method, a physically consistent weather prediction model is established through multi-source meteorological data fusion, and a regional fluctuation propagation rule is accurately captured; identifying a high-risk fluctuation cluster based on dynamic time warping and spectral clustering, simulating a fluctuation propagation path by using a digital twin platform, and quantifying resource requirements; energy storage resource configuration is optimized by adopting mixed integer programming and a column generation algorithm, and multi-dimensional stability verification is carried out through a digital twin environment; a self-adaptive optimization mechanism based on reinforcement learning is established, continuous evolution of the system is realized, the technical bottlenecks of a traditional method in the aspects of fluctuation perception, resource allocation, system self-adaption and the like are solved, a collaborative stabilization mechanism with accurate prediction, intelligent recognition and decision optimization is formed, and a complete solution is provided for safe and stable operation of a high-proportion new energy power grid.

The invention relates to the field of communication, in particular to a method for delivering cache content of a mobile edge computing network for energy consumption optimization. For a cache-based MEC cellular network, the method reasonably utilizes user movement information, and calculates corresponding path fading, channel gain and shadow fading by utilizing future position information of a user. Determining the minimum transmission power by using the calculated channel gain and path fading, then setting a time slot, carrying out content delivery decision on data packets requested by different users, and allocating wireless communication resources; and with minimization of the total emission energy consumption of the base station as a target, modeling a cache content delivery problem into a mixed integer programming problem to carry out optimization solution so as to obtain an energy consumption minimization decision for the base station to deliver all data packets.

The invention provides an electric vehicle charging station planning method and system, and the method comprises the steps: obtaining road network data and power distribution network topology information of a planning region, and constructing a time and space distribution model of an electric vehicle distributed load; based on historical travel behaviors and path probability distribution of the electric vehicle, constructing an electric vehicle behavior prediction model in combination with a graph neural network, and determining a candidate charging station service radius and a clustering region; according to the distributed photovoltaic access, the dynamic carbon emission factor and the vehicle charging and discharging behavior, constructing a dynamic optimization model of the coupling energy flow and the carbon flow; constructing an upper-layer charging station site selection model in the candidate charging station nodes, and determining an optimal site selection result by adopting a multi-objective optimization algorithm of an attention-based self-evolution Transform structure; establishing a lower-layer dynamic mixed integer programming configuration model based on the site selection result; and outputting an optimal deployment scheme considering energy efficiency, green low carbon and supply and demand balance by jointly solving the double-layer optimization model.

The invention relates to the technical field of strategy optimization, in particular to an ASIC (Application Specific Integrated Circuit) layout optimization method based on hybrid shaping programming, which comprises the following steps of: analyzing a circuit netlist, and establishing a signal path physical model containing static topological connectivity; a candidate layout scheme is generated based on the model, and layout state data describing the state of the candidate layout scheme is generated through physical effect simulation; based on the layout state data, calculating a signal processing performance index and a wiring cost value, and generating a group of dynamic shaping constraints for guiding subsequent layout according to the index and the cost value, the dynamic shaping constraints being quantitative adjustment of a path congestion perception graph in a signal path physical model; and updating the model through the constraint, evaluating the stability of a Pareto optimal boundary formed by the performance index and the cost value so as to judge convergence, and finally outputting a layout scheme associated with a stable state. According to the method, dynamic shaping constraints are generated through a strategy optimization algorithm, and global collaborative optimization of multiple targets such as time sequence and wiring performance is achieved.

The invention provides a power distribution network comprehensive planning method and system in electric power engineering design. The method comprises the following steps: firstly, acquiring geographic space data and historical operation data in a planning area and cable vibration spectrum data monitored by an optical fiber sensing network; carrying out distributed storage and correlation analysis on the geographic space data and the cable vibration spectrum data through a mixed integer programming parallel acceleration algorithm, and generating a cable aging risk distribution diagram based on spatial topology constraints; performing multi-target collaborative optimization based on a second-order cone programming model, and generating a high-voltage distribution network line capacity increasing scheme and a cable replacement priority sequence; and finally, in combination with a partial discharge frequency domain characteristic change trend in the cable vibration spectrum data, correcting a line corridor extension parameter, and outputting a dynamic planning map including a cable life prediction node and a capacity increasing path. According to the technical scheme provided by the invention, precise planning and optimization of the high-voltage power distribution network are realized, and the accuracy and efficiency of cable life prediction and capacity increasing path planning are improved.

The invention provides a micro-grid black start recovery optimization method and device based on adaptive Benders decomposition, and the method comprises the steps: providing a Benders two-stage solving algorithm fused with an adaptive penalty factor for the hierarchy and constraint complexity of a micro-grid black start two-stage optimization model. In the first optimization stage, a micro-source recovery sequence and a recovery path are optimized based on a Benders decomposition method of an adaptive penalty factor, and in the second optimization stage, load recovery is optimized in combination with mixed integer programming. According to the method, the precision of the optical storage model is improved through dynamic characteristic correction, the recovery efficiency and stability are balanced through a two-stage framework, the solving performance is improved through an adaptive algorithm, and efficient and safe recovery of black start of the micro-grid is effectively achieved.

The invention discloses a new energy automobile charging scheduling method and system based on deep learning, and the method comprises the steps: carrying out the preprocessing of data, and obtaining multi-source data; a self-attention mechanism of Transform is combined with a GNN graph neural network to establish a deep learning model, a time sequence is processed, a topological relation between geographic distribution of charging piles and a power grid load is modeled through GNN, and spatio-temporal joint features are output; meta-learning is introduced to dynamically adjust the weight of the spatio-temporal joint feature according to a real-time environment, and a charging demand prediction index is obtained; and solving an optimal charging pile distribution scheme through MIP mixed integer programming according to the charging demand prediction index, and generating a charging scheduling scheme by adopting a greedy algorithm. The total charging cost is reduced, the average waiting time of users is shortened, and the power grid load variance is reduced.

The invention relates to a group control optimization method and system for a cold station system, and belongs to the technical field of automatic control, and the method comprises the steps: carrying out the minimum optimization of a first-stage group control optimization objective function through a mixed integer programming algorithm with the start-stop state of each device as an optimization variable during the operation of the cold station system, and obtaining a second-stage group control optimization objective function; the start-stop state of each device of the cold station system is controlled during first-stage group control; and on the basis of the first-stage group control, performing minimum optimization on the second-stage group control optimization objective function by taking chilled water outlet water temperature, chilled pump frequency, cooling pump frequency and cooling tower frequency as optimization variables through a model prediction control algorithm, and controlling the operation state of operation equipment when the cold station system is in the second-stage group control. Therefore, energy consumption is reduced as much as possible and energy efficiency is improved.

The invention relates to the technical field of subkeys and communication, discloses a quantum key distribution and wavelength division multiplexing cooperative scheduling method for a metropolitan optical network, and aims to solve the problems of insufficient key rate and classic service quality reduction caused by mutual interference of quantum signals and classic services in a shared optical fiber. The method comprises the following steps: modeling Raman scattering and crosstalk noise between quantum and classical signals; constructing a joint optimization model which aims at minimizing classic service degradation and ensures that the quantum key rate is not lower than a threshold; solving wavelength, power and routing allocation by combining mixed integer programming and an intelligent algorithm; the key generation window and the reconfiguration wavelength are dynamically scheduled based on the service load prediction; according to the technical scheme, dual optimization of safety performance and network resource utilization efficiency can be realized.

A method and system for expanding a knowledge graph for multi-agent power grid fault response strategies for large-scale scheduling, comprising: modeling task dispatch as a mixed integer programming problem and solving it using a relaxation-correction algorithm; extracting and normalizing core knowledge tasks and outputting structured data; summarizing the resulting text fragments to prompt a large language model to retain key entities, relations, and domain-specific terms; using LLM-based named entity recognition technology, combined with prompts and dictionary / ontology filtering in the power scheduling field, to identify relevant entities in the text and normalize them into a standard form in the knowledge graph; detecting logical contradictions between newly extracted triples and existing relations in the knowledge graph, and using LLM-based debate prompts to classify and resolve conflicts; and aggregating multiple verification signals to calculate the global confidence, clarity, and relevance scores for each triple.

The invention provides a hybrid energy scheduling method and system. The method comprises the following steps: acquiring predicted meteorological information and predicted power generation demand in a preset future time period; adopting a multi-target mixed integer programming algorithm to obtain a planning scheme; starting preliminary power generation by each energy unit according to a preset power generation priority, acquiring actual output data in real time, and generating an updated power generation demand according to a preset condition; acquiring real-time meteorological information; dynamically adjusting the operation state of each energy unit by adopting a model prediction control algorithm, and adjusting the actual output according to a surplus scene / gap scene / normal scene subdivision adjustment strategy; calculating a deviation ratio between the adjusted actual output and the updated generating capacity demand, and judging whether the deviation ratio exceeds a preset threshold by adopting a dynamic trigger mechanism; and if the deviation rate exceeds a preset threshold value, starting graded energy complementation. The scheme has the characteristics of high-precision prediction capability, dynamic planning mechanism, subdivision scene adjustment strategy and flexible expansion.

The invention discloses an intelligent security early warning management method and system based on machine learning. The method comprises the steps of multi-source data fusion and acquisition, preprocessing and feature extraction, dynamic model construction and training, security situation analysis and early warning, multistage linkage response and system self-learning optimization. According to the system, heterogeneous data is collected by integrating a camera, an infrared sensor, a temperature sensor and a humidity sensor, edge calculation is used for synchronous fusion, features are extracted through a convolutional neural network, a recurrent neural network and the like, model parameters are dynamically optimized in combination with reinforcement learning, and safety situation level evaluation and advanced early warning are achieved. Emergency resource allocation is optimized based on a genetic algorithm and integer programming, and the model is continuously iterated through a feedback mechanism. According to the scheme, through multi-modal data fusion and dynamic machine learning, the adaptability and early warning accuracy of the security and protection system to a complex environment are improved, full-process intelligent management from data collection to intelligent decision making is achieved, and the method is suitable for multiple scenes such as commercial complexes and industrial factories.

The invention provides an industrial energy consumption monitoring and early warning system based on artificial intelligence. According to the system, the real-time performance and the intelligent level of energy consumption anomaly detection in an industrial scene are improved. The method comprises the following steps: firstly, acquiring equipment state data through a high-precision sensor group, and realizing nanosecond time synchronization based on an FPGA (Field Programmable Gate Array) to ensure time-space consistency of data; thirdly, a feature extraction and fusion mechanism is introduced into the edge end, anomaly recognition is achieved by combining an isolated forest and an auto-encoder model, and meanwhile an energy consumption prediction model is constructed based on historical data to generate a prediction curve; and finally, dynamically optimizing a detection threshold value by utilizing reinforcement learning, generating a scheduling strategy based on integer programming, and continuously optimizing model parameters and a control strategy by a closed-loop module. Through the integrated design of perception, analysis, prediction and optimization, the accuracy, predictability and response speed of energy consumption monitoring are remarkably improved, and the industrial deployment value is good.

The invention discloses a digital twin-driven intelligent construction site resource dynamic scheduling system and method, and belongs to the technical field of building construction management, and the system comprises a four-dimensional resource topology construction module which constructs a four-dimensional topological graph of construction resources based on UWB and RFID technologies; the mixed engine scheduling module adopts a mixed integer programming engine to generate a reference scheduling scheme in an initial stage, and adopts a reinforcement learning optimization engine to perform dynamic adjustment in an execution stage; the conflict prediction network module is used for establishing a conflict prediction model based on historical data and triggering a resource redistribution mechanism; the virtual interaction visualization module displays a scheduling scheme in a virtual reality environment and supports manual intervention, depth relation modeling, double-engine collaborative optimization and active conflict prevention of construction resources are achieved, the resource utilization rate is increased by 35% or above, the construction period is shortened by 15% or above, and conflict events are reduced by 50% or above.

The invention relates to the field of internet big data and port energy scheduling, in particular to an inland port vehicle transfer and charging scheduling method based on unified information interaction, which comprises the following steps: acquiring tasks, vehicles and battery information of an inland port; task and vehicle matching is carried out through an integer programming-criticer solution framework based on reinforcement learning; constructing a Markov decision process model which aims at maximizing the income of completing all tasks, minimizing the charging cost and minimizing the battery capacity aging rate; solving the Markov decision process model through a deep reinforcement learning algorithm; information interaction requirements are included in the target task allocation scheme and the optimal transfer and charging scheduling scheme, and tasks of the inland river port are completed; and executing an information unified interaction scheme to realize information interaction among tasks, vehicles and batteries. According to the invention, the accuracy and efficiency of inland port vehicle transfer and charging scheduling can be improved.

The invention discloses a flexible job shop energy-saving scheduling optimization method considering a light storage condition and a load characteristic, and relates to the technical field of industrial scheduling, and the method comprises the steps: building a flexible job shop energy-saving scheduling problem model based on mixed integer programming, and setting an optimization target and a constraint condition; an ant colony algorithm is improved, an ant colony is divided into dynamic multi-level search, and a pheromone matrix is optimized; the updating effect of pheromones in the ant colony algorithm in the iteration process is optimized; optimizing the optimal solution of each generation of the ant colony algorithm by adopting a graph neural network off-line learning neighborhood search method; and stopping iteration when a preset termination condition is met, and outputting an optimal scheduling scheme. According to the method, a more efficient and flexible energy-saving scheduling strategy is developed to effectively coordinate the productivity and the energy efficiency, energy optimization and cost reduction in the production process are achieved, actual technical support is provided for energy-saving scheduling of the flexible job shop, and a method system for the workshop scheduling problem is enriched.

The invention relates to a regional pump station group collaborative optimization scheduling method based on reinforcement learning. According to the method, a structured state data set is constructed by collecting energy consumption, operation time and pipeline flow data of a pump station group; on the basis, a pre-trained reinforcement learning model is driven to generate an initial scheduling scheme containing pump station flow distribution information and pump set starting and stopping time information; verifying the initial scheme through flow simulation, and dynamically adjusting to generate a global optimization scheme; performing energy consumption balance optimization calculation on the global scheme by adopting a mixed integer programming solution method to generate a configuration table; mapping the configuration table data into an operation situation map through three-dimensional visualization; receiving a correction instruction through human-computer interaction according to the map to generate a manual optimization scheme; and finally, automatically comparing the artificial scheme with the configuration table parameters through a threshold judgment mechanism, and dynamically triggering the reinforcement learning model to adaptively adjust the key parameters of the artificial scheme. The technical effects of accurate dynamic load matching of the pump station group and continuous improvement of the overall energy efficiency of the system are achieved.

The invention provides a power distribution network cloud energy storage configuration and operation optimization method and system, and relates to the field of power distribution network renewable energy source access. The method comprises the following steps: collecting historical data of a power distribution network and a renewable energy access plan to obtain a power distribution network feature data set; determining an optimized capacity configuration scheme of the cloud energy storage system by applying a state-dependent conformal perception boundary model and mixed integer programming; establishing a day-ahead-real-time two-stage coordinated optimization model, and determining a cloud energy storage multi-time scale operation strategy by using a kernel-based parameter change feedforward control learning method; establishing a multi-agent game model and an incentive compatibility mechanism, and designing a marketization clearing mechanism; and constructing a comprehensive evaluation system to generate a technical consultation service scheme implementation evaluation report. According to the method, the cloud energy storage capacity configuration accuracy and the operation efficiency are improved, and efficient access and consumption of renewable energy sources are realized.