291 results about "Integer programming model" patented technology

The invention discloses a blasting design-oriented multi-source data fusion preprocessing method, and particularly relates to the technical field of blasting data scientific processing. Comprising the following steps: acquiring multi-source heterogeneous data of a blasting area through a multi-source sensor array and a geological modeling system, establishing a multi-modal data unified representation framework based on ontology, constructing a depth feature fusion network with physical constraints, developing a blasting parameter optimization-oriented mixed integer programming model, and establishing a blasting parameter optimization-oriented mixed integer programming model; integrating a multi-target genetic algorithm and an expert experience knowledge base to perform parameter optimization; according to the method, a multi-mode unified representation framework based on the ontology is constructed, millimeter-level space alignment and millisecond-level time synchronization of multi-source data are achieved through a space-time reference coordinate system and an improved DTW-B spline composite algorithm, the spatial resolution of blasting design is improved to the centimeter level, the time synchronization precision reaches the millisecond level, and the time synchronization efficiency is improved. Compared with a traditional method, the data utilization rate is increased by more than 40%, and a solid foundation is laid for fine blasting design.

The invention relates to a supply chain risk assessment decision method and system. The method comprises the following steps: carrying out single-factor evaluation modality and multi-factor weighted evaluation modality on a risk original data set of each link of a supply chain, and creating a multi-dimensional risk quantification model; according to the multi-dimensional risk quantification model, performing multi-stage time scale evaluation on the risk original data set to obtain a multi-stage risk evaluation result; based on the multi-stage risk assessment result, modeling a supply chain network into a directed graph, and constructing an integer programming model for solving to obtain a risk-oriented path allocation scheme; and according to the risk-oriented path distribution scheme, risk index calculation and early warning grade division are carried out on the multi-stage warehousing system to obtain a collaborative early warning strategy of the multi-stage warehousing system. According to the invention, reasonable distribution of supply chain resources is realized, the congestion risk of logistics nodes is effectively relieved, and the operation efficiency of the whole supply chain is improved.

The invention relates to a power distribution network two-stage recovery method based on an electric vehicle and mobile energy storage, and belongs to the technical field of disaster prevention and reduction and emergency recovery of a power system. According to the technical scheme, the method is divided into two stages of recovery: stage 1 pre-disaster pre-configuration and stage 2 post-disaster collaborative recovery; establishing a power distribution network line vulnerability evaluation model, and generating a fault scene in extreme weather based on a wind speed-fault probability curve; constructing a charging station and maintenance station pre-configuration optimization model, and determining the optimal site selection in the traffic network by adopting integer nonlinear programming; establishing a post-disaster recovery mixed integer programming model, coordinating and dispatching the electric vehicle, the mobile energy storage system and the maintenance team, and realizing collaborative optimization of load recovery and line repair; and designing a traffic network and power distribution network coupling mechanism, quantifying a system toughness index and optimizing a resource scheduling strategy. Through multi-resource cooperative scheduling, the post-disaster recovery capability of the power distribution network is remarkably improved, and an innovative solution is provided for improving the toughness of the power distribution network in extreme weather.

The invention discloses a multi-source power distribution network energy-saving optimization scheduling method, system, equipment and storage medium based on big data processing, and relates to the technical field of power systems and big data processing. After power distribution network multi-source data is collected, through preprocessing and feature extraction, loads are predicted and classified through a long and short-term memory network; the load dynamic optimization module combines cost, network loss and photovoltaic consumption, generates a scheduling instruction through a mixed integer programming model, compares power loss before and after scheduling after CPLEX solution, and generates a difference thermodynamic diagram. According to the scheme, accurate load prediction and dynamic optimization scheduling of the multi-source power distribution network are realized through big data processing, the cost and energy efficiency are effectively improved, the consumption capability of photovoltaic energy is enhanced, the energy-saving effect is visually displayed through the power loss difference thermodynamic diagram before and after scheduling, and powerful support is provided for intelligent management of the power distribution network.

The invention discloses a full-equipment energy consumption optimization management system in a building, and relates to the technical field of intelligent control, and the system is provided with a multi-modal data collection module which collects building environment parameters and equipment operation data through a distributed sensor network, and generates a multi-modal sensing matrix; an environment state prediction module is set to construct a dynamic knowledge graph based on a multi-modal sensing matrix, a graph attention network is used, an energy flow matrix of equipment topology is output, micro-environment state prediction data is generated through prediction of a finite element solver, a scheduling scheme solving module is set, a mixed integer programming model is constructed based on the micro-environment state prediction data, and a scheduling scheme is set. And solving the mixed integer programming model to obtain a scheduling scheme of the energy consumption equipment, setting a control instruction optimization module, and driving the edge execution equipment to operate based on the scheduling scheme. The global scheduling optimization of the whole building energy consumption equipment is realized, the energy consumption is effectively reduced, and the environmental comfort is improved.

The invention relates to the technical field of smart power grids, and provides a power distribution network power failure probability analysis and resource scheduling optimization method based on a knowledge graph, which realizes minute-level node association weight updating through a streaming updating mechanism, and remarkably improves the power failure risk identification accuracy in extreme weather by combining with dynamic adjustment of a meteorological risk function. A topology / environment double-attention architecture is adopted to fuse multi-source features, so that the fusion efficiency of equipment node features and environment node features is improved; and a fault deduction model with dynamic weight constraint is introduced, so that the secondary fault early warning capability is enhanced. In a resource scheduling link, a mixed integer programming model of a carbon emission factor and island power balance constraint is integrated, and Pareto optimization of first-aid repair efficiency and an environmental protection target is realized. Through transfer learning, knowledge graph cross-region adaptation is realized, and the novel transformer area access error is controlled to be reduced in a distribution network reconstruction scene. The method supports minute-level topology reconstruction and second-level weight updating, has low delay and high expansion capability, and is suitable for provincial power grid real-time deployment.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a high-temperature environment multimodal transport method and system based on composite heat insulation and active temperature control. The method comprises the steps that a multimodal transport capacity grid model simulating high-temperature transport cooperation is constructed; determining model condition hypothesis, and defining decision variables, parameters and state variables of the model; high-temperature special items and safety penalty are included in the total cost, a high-temperature associated total cost model is constructed, and a multi-scene cost calculation formula is deduced; with the purpose of minimizing the total cost, a mixed integer programming model including path connection and flow conservation, temperature constraint, refrigeration / heat dissipation energy and power constraint, node refrigeration / sunshade capacity constraint, battery and key component temperature safety constraint, butt joint / node capacity constraint and time window constraint is constructed; solving the model by adopting an improved hybrid immunity-particle swarm dual-optimization algorithm, and outputting an optimal transportation path, transportation mode, transfer node and high-temperature scene exclusive scheduling parameter; and collaborative optimization of high-temperature transportation safety, energy efficiency and economy can be realized.

The invention discloses a power grid topology toughness enhancement method based on pre-disaster prediction of a graph neural network. The method comprises the following steps: constructing a power grid graph structure taking power grid equipment as nodes; predicting a node damage probability through a graph attention network, and defining a high-risk node set; according to the high-risk nodes and the propagation paths thereof, a mixed integer programming model is constructed and solved, and a pre-disaster optimal power grid structure adjustment strategy is generated; in combination with the strategy, a resource scheduling scheme is generated by using a deep reinforcement learning algorithm; calculating a power grid toughness core index after simulation operation, and if the index is lower than a preset threshold value, optimizing a structure adjustment strategy; and uploading the optimized strategy and scheduling scheme to a scheduling platform to complete pre-disaster active defense deployment. According to the method, collaborative linkage of pre-disaster risk prediction, topology reconstruction and resource deployment is realized, a differentiated pre-disaster defense scheme is automatically generated, post-disaster first-aid repair is converted into pre-disaster deployment, and the pre-disaster prevention and control response efficiency is remarkably improved.

The invention provides a method, a device and a product for realizing intelligent scheduling and cooperative work of multiple unmanned aerial vehicles. The method comprises the following steps: step S1, receiving a task to be processed in real time and acquiring a real-time operation state of each unmanned aerial vehicle in an unmanned aerial vehicle group; s2, performing intelligent dynamic allocation on the tasks by using a pre-established task value scoring model, an unmanned aerial vehicle task adaptation degree model and a global multi-task dynamic allocation optimization model; the global multi-task dynamic allocation optimization model is a mixed integer programming model established by taking maximization of the total task income as a target under a predetermined constraint condition; and S3, after task allocation is completed, a pre-constructed three-dimensional grid airway model G (V, E) is used to plan an airway for each unmanned aerial vehicle executing the task. According to the technical scheme, the mixed integer programming algorithm and the particle swarm optimization are combined, and the global task allocation optimization model is constructed, so that task allocation can be efficiently solved, and various actual constraints can be met.

The invention relates to the technical field of cross-border logistics, in particular to an artificial intelligence algorithm for cross-border overseas warehouse location storage and picking path optimization, which comprises the following steps: S1, obtaining order details, commodity characteristics and warehouse environment data, and monitoring warehouse states and cargo storage conditions in real time; s2, data preprocessing is carried out, features are extracted, clustering analysis is carried out, and association rules are mined; s3, performing rule matching, constructing a mixed integer programming model, performing constraint processing, and outputting a commodity storage strategy; and S4, converting the storage strategy into a warehousing operation instruction, and executing the warehousing operation instruction. The method has the beneficial effects that the accuracy of goods classified storage and the space utilization rate of the warehouse location can be improved, and the storage cost of the warehouse location is reduced.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

The invention discloses a distributed server load balancing system based on multi-modal data fusion. The distributed server load balancing system comprises a multi-modal sensing layer, a fusion analysis layer, an intelligent decision-making layer and an elastic execution layer, the multi-modal sensing layer collects multi-source heterogeneous data; the fusion analysis layer is used for mining an association relationship between data of different dimensions; meanwhile, converting multi-source data into a unified feature vector, and outputting a dynamic load balancing strategy; the intelligent decision-making layer generates an executable load balancing strategy according to the fusion analysis result; and the elastic execution layer converts the strategy generated by the intelligent decision-making layer into an actual action, and deals with load change through an elastic mechanism. According to the method, server performance, network topology and request semantic data are fused, a unified feature tensor is generated by using a cross-modal attention mechanism, and load association is accurately quantified; on the basis of cooperation of a global mixed integer programming model and a local lightweight DQN agent, load migration is completed in a very short time, and the response speed is increased.

The invention relates to the field of business management, in particular to a production and manufacturing resource scheduling-oriented mixed integer programming optimization system, which comprises six modules: a data acquisition module acquires industrial big data streams; the data mapping module processes the data flow and constructs a real-time data model; the dynamic modeling module is used for automatically generating a mixed integer programming model according to a built-in steel production process model library based on a real-time data model; the model solving module generates a resource scheduling scheme through an optimization engine; the instruction analysis module analyzes a specific management and operation instruction; and the scheduling recalculation module starts a new round of mixed integer programming modeling and solving according to the increment information. According to the system, information such as resources is summarized through a mixed integer programming method, a specific production instruction is generated through a solver, a new round of modeling and solving are started when an accident occurs, rapid and accurate response to resource scheduling is achieved, and the response efficiency of resource scheduling and the reliability of the system are improved.

The invention provides a scene port entering and leaving aircraft joint optimization scheduling method. The method comprises the following steps: aiming at aircrafts which take off and land on a plurality of parallel runways in an airport, establishing a mixed integer programming model for a scene port entering and leaving aircraft IASP; setting a target function and a constraint condition of an IASTP problem by taking minimization of the total delay duration of the port entering and leaving aircrafts as a target; and based on the constraint conditions, solving a target function of the IASTP problem by adopting a heuristic algorithm based on VNS, and obtaining a comprehensive scheduling scheme of runway and sliding path distribution, take-off and landing sequence and time of the port entering and leaving aircraft. According to the method, actual operation constraints of various scenes are considered, runway distribution and sorting, taxiing path selection and taxiway scheduling of the port entering and leaving aircrafts, and a deducing sorting scheduling scheme of the port leaving aircrafts are planned and optimized as a whole, the take-off and landing delay time of the port entering and leaving aircrafts is shortened as much as possible, the hub airport operation efficiency is improved, and the airport landing time is shortened. And decision support is provided for efficient scheduling of aircrafts.

The invention provides an intelligent traffic scheduling method based on multiple modes, and the method comprises the following steps: 1, obtaining a traffic data flow, and constructing a standardized space-time tensor X; step 2, preprocessing the standardized space-time tensor X to obtain an alignment matrix Dalign; 3, constructing a mixed integer programming model for the intelligent traffic scheduling problem, and solving to obtain a static optimal solution scheduling matrix according to the alignment matrix Dalign; and 4, constructing a deep reinforcement learning model, carrying out real-time fine adjustment on the static optimal solution scheduling matrix, obtaining a final intelligent traffic scheduling scheme, and completing intelligent traffic scheduling based on multiple modes.

The invention relates to a low-voltage distribution network topology identification method based on voltage-current correlation feature mining, and the method comprises the steps: carrying out the time synchronization processing of user-side multi-source electric parameter data after obtaining the user-side multi-source electric parameter data, and forming a unified electric parameter data set; after further preprocessing the integrated electric parameter data, extracting a voltage feature sequence based on the preprocessed point parameter data; the method comprises the following steps: establishing a user group, calculating voltage curve similarity among users, establishing a feature matrix and performing clustering analysis to preliminarily identify the user group, then establishing a mixed integer programming model by combining a Kirchhoff's current law, optimizing a phase-line-user relationship, and finally outputting a topological structure result. The problems that the low-voltage power distribution network topological structure is insufficient in recognition precision, poor in data consistency and low in dynamic adaptive capacity are solved, so that efficient and reliable network topology recognition is achieved, and monitoring and fault management of the power distribution network are effectively supported.

The invention discloses a topological communication coverage path planning method and system of an unmanned ship cluster for wide-area target search and detection, and belongs to the technical field of multi-agent collaborative path planning. According to the method, turning frequency optimization is converted into a minimum rectangular coverage problem, an integer programming model is established, a layered relaxation integer programming acceleration strategy based on maximum extension candidates is proposed, and the bottleneck of model expansion and calculation efficiency caused by candidate solution space explosion in a large-scale scene is broken through; then, constructing a rectangular topological connected graph, designing an improved spectral clustering algorithm fusing load balancing constraint and subgraph connectivity guarantee, realizing efficient and balanced distribution of regions in a complex environment, and ensuring internal connectivity of subregions; and finally, providing a dynamic pruning optimization method for the minimum corner spanning tree based on strong and weak connection characteristics, iteratively evaluating the corner cost and removing redundant edges, and finally generating a coverage path spanning tree with the minimum corner cost, thereby providing an accurate and efficient full-coverage operation path for scenes such as regional exploration and environment monitoring.

The invention discloses a multi-unmanned aerial vehicle distribution and bus charging combined path optimization method, which aims at minimizing task total time, constructs a mixed integer programming model based on a space-time network, and comprehensively considers unmanned aerial vehicle electric quantity constraint, demand point full coverage, bus time window and charging pile number limitation. An original model is decoupled to limit a main problem and a sub-problem by adopting branch pricing and a Dantzigzag-Wolfe decomposition theory, the main problem deals with demand coverage and charging resource allocation, and modeling is a set coverage problem; for single-machine path generation, the latter is modeled as a resource-constrained and replenishable shortest path problem with a time window dependent feature. A multi-unmanned aerial vehicle initial solution is constructed through a random generation method, a dual variable is iteratively solved after a main problem is initialized, a sub-problem is solved based on a multi-label algorithm, and global optimal solution search is realized in combination with a column generation mechanism and a branch strategy. According to the invention, through joint optimization of the departure time and path planning of the unmanned aerial vehicle, the collaborative optimization problem of distribution and charging is accurately solved.

The invention provides a power system unit combination method and system. The method comprises the following steps: training a pre-constructed mixed integer programming model through a pre-constructed graph data sample set and a residual graph convolutional neural network to obtain a trained graph neural network, and forming a mapping relationship between input data features of the graph neural network and data labels of graph data samples in the graph data sample set; explaining the trained graph neural network through the mapping relation and a graph neural network interpreter based on mask learning to obtain an interpretable graph neural network; converting the mixed integer programming model into a linear programming model through a unit start-stop strategy output by the interpretable graph neural network; and according to a solving result obtained by solving the linear programming model, selecting and outputting an optimal solution of the linear programming model or outputting an optimal solution of the mixed integer programming model as a unit commitment scheduling result. According to the invention, the credibility of the unit commitment scheduling decision is improved.

The invention relates to the field of automatic guided vehicle scheduling, in particular to an AGV collaborative scheduling system and method based on mixed integer programming, and the system comprises a data collection module, a central processing module, a scheme output module, an AGV scheduling module and a conflict detection module, and the data collection module collects storage and transportation operation demand data and task data; the central processing module receives the data and performs solution analysis based on a mixed integer programming model to generate a scheduling scheme, and the model comprises a dual-objective weighting function and a multi-dimensional constraint dynamic balance matrix; the scheme output module displays the scheme and receives an instruction; the AGV scheduling module schedules the AGV according to the scheme and feeds back execution information; and the conflict detection module detects a conflict state and generates conflict scheduling information, and realizes balanced optimization of the resource utilization rate and the energy consumption by establishing a dual-target weighting function which considers the minimum idle time of the AGV and the minimum total driving distance of the AGV at the same time.

This disclosure proposes a shift-based employee scheduling method and related apparatus. The shift-based employee scheduling method includes: acquiring task information and inter-task conflict information; acquiring employee information; acquiring shift information; constructing an integer programming model by setting constraints and an objective function, the constraints including a first constraint requiring that conflicting tasks assigned to each shift can be executed, the objective function being based on multiple objectives; solving the integer programming model using the acquired information to determine the correspondence between employees and shifts and between tasks and shifts, and decoding the correspondence between employees and tasks from the employee-shift and task-shift correspondence information, thereby scheduling employees.

The invention belongs to the technical field of power grid optimization, and particularly relates to a GAN driven learning-based power grid planning equalization optimization method and device, and the method comprises the steps: constructing a physical constraint-based space-time generative adversarial network PI-ST-GAN, building a composite risk index, and screening out a high-risk scene from a generated source-load probabilistic time sequence scene; constructing a risk-cost balanced double-layer optimization model; reconstructing the double-layer optimization model into a single-layer mixed integer programming (MIP) model based on a strong dual theory, and setting constraint conditions of the single-layer mixed integer programming (MIP) model; based on a single-layer mixed integer programming MIP model, a solver is utilized to carry out joint solution on a line investment scheme and operation scheduling variables, PI-ST-GAN is dynamically guided to enhance high-risk scene generation according to a solution result, an optimization-generation-feedback closed-loop collaborative iteration solution process is realized, and finally an optimal power grid planning scheme is output. The method can improve the toughness and economical efficiency of a power grid under the high permeation of renewable energy sources.

The invention relates to an information physics multi-agent cooperative control method in a mixed traffic flow environment, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, at the upstream section of an intersection, according to the driving direction and dynamic state of vehicles, taking a first vehicle reaching the boundary of a lane changing area as a trigger vehicle, detecting the vehicles in a preset time window, dividing the triggered vehicle and all vehicles in front of the triggered vehicle within the detection range into the same target vehicle set; s2, for the target vehicle set, constructing a 0-1 integer programming model, and optimizing an organization scheme of a hybrid queue; s3, on the basis of the optimal formation scheme, constructing a vehicle and hierarchical control framework which comprises two parts of cloud deep reinforcement learning DRL and vehicle-end distributed model prediction control DMPC; the cloud DRL outputs a reference action; and the vehicle end DMPC takes the reference action and state output by the cloud end DRL as a target, and realizes cooperative passing of the whole motorcade while satisfying local constraints.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

Disclosed herein are a method and apparatus for optimizing designs of product packaging units. A method based on one aspect of the present disclosure comprises: for each product having a corresponding order quantity among a plurality of products, using an integer programming model to determine the capacity of each level of packaging unit among one or more levels of packaging units above a basic packaging unit of the product, wherein the integer programming model uses minimizing the logistics-related costs of the product having the corresponding order quantity as its objective function; for each clustering scheme among a plurality of clustering schemes applied to the plurality of products, calculating the total logistics-related cost savings compared with a situation where the clustering scheme is not applied, wherein each clustering scheme corresponds to a different number of clusters and is used for dividing the plurality of products into the corresponding number of clusters on the basis of the sizes of the basic packaging units of the plurality of products; and on the basis of the clustering scheme having the maximum total logistics-related cost savings among the plurality of clustering schemes, determining an optimal packaging unit design.

The invention discloses an alarm processing method, a related device and a system, and relates to the technical field of data processing, a mixed integer programming model is constructed by adopting a mixed integer programming modeling technology, priority information of alarm information is calculated in real time based on multi-dimensional alarm feature data, sorting is performed according to the priority information, and the alarm information is processed according to the priority information. The alarm processing sequence of each piece of alarm information in the to-be-processed alarm information set is calculated in real time, and the alarm processing sequence is determined based on the multi-dimensional alarm feature data acquired in real time, so that the accuracy of the alarm processing sequence can be effectively improved; moreover, the mixed integer programming model is adopted to realize discrimination and screening work of the alarm information, so that limited operation and maintenance resources can be quickly and preferentially allocated to the alarm information with high priority; the alarm processing requirement that alarm information which is related to the core fault and needs to be processed preferentially is quickly and accurately screened out from massive alarms and processed is met.

The invention discloses an AC / DC hybrid grid planning method based on climate response capability risk analysis, and the method comprises the steps: constructing a time sequence generation model based on a denoising diffusion Transform, capturing a multi-time scale nonlinear coupling relation between new energy output data and meteorological data in a planning region through a diffusion process and a cross-scale attention mechanism, and carrying out the prediction of the multi-time scale nonlinear coupling relation. Generating a combined sample set; extracting a risk feature vector of the new energy output scene from the joint sample set, and establishing a mixed Gaussian distribution representation risk feature space; based on an outlier detection algorithm, identifying a low probability-high influence scene from the risk feature space to form a typical day set for planning; based on the typical day set for planning, constructing a mixed integer planning model with minimization of line investment cost, operation cost and various penalty costs as targets; and solving the mixed integer programming model to obtain an alternating-current and direct-current hybrid grid planning scheme.

The invention discloses a message issuing speed control method and system based on an AI large model. The method comprises the steps of collecting multi-source data in real time and constructing a multi-dimensional fusion feature vector; based on the BERT-CNN distillation architecture, detecting abnormity and executing hierarchical fusing; based on the fusion feature vector, using a reinforcement learning model to predict message issuing rates of a plurality of time windows in the future and dynamically adjusting priorities; in the offline stage, a mixed integer programming model is adopted to generate minimum cloud resource cost, and in the online stage, a deep reinforcement learning model is adopted to carry out real-time resource fine tuning; based on the node health degree, calculating a traffic allocation weight through a smooth weighted polling algorithm so as to carry out load balancing; different business scenes are adapted through domain adversarial training and multi-task learning, and different languages are adapted through multi-language BERT coding. The message rate is accurately controlled to improve the system stability and the resource utilization rate; low-cost scheduling fine tuning of cloud resources is realized; various business scenes and languages can be quickly adapted, and the development cost and time are reduced.

The invention discloses an electric power and carbon management collaborative optimization method and system for a multi-industry symbiotic zero-carbon park, and the method comprises the steps: constructing a mixed integer programming model which comprises an electric power dispatching sub-model, a carbon collection sub-model and a source-sink matching sub-model, and defining a coupling relation between an electric power dispatching variable and a carbon management variable; designing a multi-objective optimization function, and bringing coal consumption cost, start-stop cost, carbon transaction cost, wind curtailment penalty cost, deep peak regulation cost, peak regulation compensation benefit and transportation cost into a unified optimization framework; and setting time-space coordination constraint conditions, including power balance constraint, output upper and lower limit constraint, climbing rate constraint, capture-transport volume balance constraint and carbon sink capacity limitation constraint, to realize coordination optimization of power production and carbon logistics in time and space dimensions. According to the method, the total cost of the system is effectively reduced, the utilization efficiency of carbon sink resources is improved, real-time controllability of carbon emission intensity is ensured, and the problem of global optimum deficiency caused by a traditional two-stage optimization method is solved.