226results about How to "Improve optimization effect" patented technology

The invention provides an intelligent traffic data processing method and system based on edge computing, and the method comprises the steps: collecting multi-source traffic data through an edge computing node, carrying out the preprocessing of the data, and obtaining the preprocessing data; and performing space-time alignment and data association processing on the preprocessed data to generate an associated data set, and completing feature extraction and preliminary analysis at an edge computing node to obtain a local analysis result. The local analysis result is transmitted to the cloud server, deep data mining and mode recognition are carried out, and a global analysis result is generated. And based on the global analysis result, generating a local traffic control strategy at the edge computing node, and generating a global traffic optimization scheme at the cloud server. The local traffic control strategy is issued to traffic facilities for execution, and the global optimization scheme is distributed to each edge computing node to realize cooperative control. The advantages of edge computing and cloud processing are combined, and the real-time performance of traffic data processing and the overall optimization capability of an intelligent traffic system are effectively improved.

The invention provides a multi-AGV path planning and obstacle avoidance method based on an improved artificial bee colony algorithm, and aims to solve the key technical challenges of multi-AGV system path planning and obstacle avoidance in modern industrial automation. According to the method, a dynamic updatable gridding environment model is constructed to accurately represent a starting point, an end point, static obstacles and dynamic obstacles in a working environment, an improved artificial bee colony algorithm is adopted to globally optimize path planning of multiple AGVs, constraint conditions of obstacle avoidance and multi-AGV path conflicts are met, and the optimal path planning of the multiple AGVs is realized. And the efficient operation of the system and the path collision-free property are ensured. In addition, the method further comprises path tracking control and conflict detection and obstacle avoidance control so as to ensure that the AGVs can accurately run along the planned path, conflict points in the paths of the multiple AGVs are detected in real time, and different coping strategies are adopted to achieve path coordination and scheduling. Finally, the cooperation efficiency and safety of the multi-AGV system can be effectively improved.

The invention discloses a method for converting a two-dimensional image of a building component into a three-dimensional model, which relates to the technical field of image processing, and comprises the following steps of: comparing the size of the three-dimensional model of the component with the size of an actual building component to obtain error data between the two, and generating corresponding accuracy through a model error data set; if the accuracy of the component three-dimensional model does not exceed the expectation, carrying out coincidence analysis on the component three-dimensional model and the building component and obtaining the coincidence degree, generating a complete coefficient according to the coincidence degree, screening out defect areas in the component three-dimensional model according to the complete coefficient, and associating to obtain the optimization priority of each defect area; and after a model optimization scheme is given for the defect areas by the building component model optimization knowledge graph, optimizing the defect areas in sequence according to optimization priorities. A corresponding model optimization scheme is provided for the defect area through the building component model optimization knowledge graph, and the optimization pertinence is higher when all the defect areas are optimized.

The invention discloses a multi-objective optimization method for double-material injection molding process parameters. The multi-objective optimization method comprises the following steps: firstly, obtaining a simulation result data set based on Moldflow simulation analysis and a Taguchi test; then, on the basis of the simulation test data set, a GAM model is constructed and then trained, super-parameters of GAM are optimized through a GTO algorithm, and a GTO-GAM model between injection molding process parameters and a quality optimization target is established; then, performing multi-target optimization on the GTO-GAM model through an MODA algorithm to obtain a Pareto leading edge of a quality optimization target; and finally, determining an optimal combination of process parameters in the obtained Pareto leading edge of the quality optimization target through a CRITIC-TOPSIS evaluation method. According to the method, the buckling deformation and the volume shrinkage rate of the double-material injection molding plastic part can be effectively reduced, and a theoretical basis and data support can be provided for obtaining the optimal technological parameter combination of double-material injection molding.

According to the workshop scheduling optimization method based on reinforcement learning provided by the invention, the optimization effect and generalization ability of a scheduling strategy are remarkably improved by constructing a diversified environment model, dynamically updating model parameters and applying a deep reinforcement learning algorithm. According to the method, the production cost, the production efficiency and the product quality are comprehensively optimized through a multi-target reward function, and real-time adjustment and optimization of a scheduling decision are realized. Specifically, key features are extracted from historical production data of a workshop, and a plurality of environment models reflecting different production situations and scheduling requirements are constructed, so that the generalization ability of a scheduling strategy is enhanced. And then, through deep reinforcement learning methods such as a deep Q-network (DQN), the environment models are pre-trained, so that the intelligent agent can effectively learn and master an optimal scheduling strategy. On the basis, parameters of the environment model are dynamically adjusted through real-time data monitoring and acquisition, so that a scheduling strategy can flexibly adapt to continuously changing production conditions. The method has the advantages that the adaptability of a scheduling strategy to different production situations is remarkably enhanced, the optimization efficiency and the real-time response capability of a scheduling algorithm are improved, the limitation of a traditional method in coping with a complex workshop scheduling problem is successfully overcome, and the method has higher practicability and application value.

The invention discloses a distributed roof photovoltaic processing method and device based on Bayesian optimization, electronic equipment and a storage medium. Advanced data acquisition and preprocessing technologies, accurate grid division and feature extraction, comprehensive multi-dimensional evaluation index calculation and efficient Bayesian optimization modeling are combined. Through a Gaussian process model and a Bayesian optimization algorithm, accurate capacity prediction and resource allocation can be realized in a complex multi-target optimization space. The method has the advantages that multiple factors such as power generation potential, economic benefit and power grid load are comprehensively considered, and the method has dynamic adjustment capability to adapt to changes in actual operation. The invention discloses a distributed roof photovoltaic processing method and device based on Bayesian optimization, electronic equipment and a storage medium, and aims at remarkably improving the overall benefit and resource utilization efficiency of a photovoltaic system and promoting wider application of renewable energy sources.

The invention discloses an anti-DDoS attack data security protection system for a big data platform, and belongs to the technical field of data security. Historical network traffic features are learned by using a DDoS attack detection model constructed by a deep learning algorithm, so that a rule for identifying DDoS attacks can be constructed, DDoS attack detection is preliminarily realized, and the security of the DDoS attacks is improved. Meanwhile, an improved intelligent optimization algorithm is adopted to train a DDoS attack detection model, a better parameter optimization effect can be achieved, the DDoS attack detection accuracy is finally improved, DDoS attack relieving operation is executed according to a DDoS attack detection result, and the data security of a big data platform can be effectively improved.

The invention provides a college campus planning and design multi-objective optimization method based on cGAN and NSGA-II, and the method specifically comprises the steps: obtaining a large number of existing different campus planning and design related data, and carrying out the correlation of the data with corresponding carbon emission and incremental cost data, and forming a model data set; inputting the model data set into a pre-constructed cGAN model for training, and optimizing an initial campus planning and design scheme output by the cGAN model through an NSGA-II algorithm; and performing optimization adjustment and scheme visual display on the cGAN model based on the target campus planning design. The college campus carbon emission level and incremental cost collaborative optimization model constructed by the invention provides scientific decision support for an architect to fully consider the balance between the carbon emission reduction performance and the economic benefit in a scheme stage, and is helpful to further realize a campus carbon neutralization construction plan.

The invention discloses a logistics remote control method and system based on virtual reality, relates to the technical field of logistics transportation management, and reduces the possibility of human errors through automatic encasement scheme generation and path planning. The system automatically calculates and generates a boxing scheme according to goods characteristics and loading constraints, and the risk of manual operation errors is avoided. And if the efficiency evaluation index is higher than 0.8, the system confirms that the current scheme is the optimal scheme. The trajectory planning module combines the anchoring position of the target ship and the container yard position, generates a plurality of paths by using a dynamic path planning method, and calculates a trajectory coefficient and transportation time. The path planning method based on real-time data can flexibly cope with sudden changes in the transportation process, the transportation time is shortened, and it is ensured that tasks are completed in time. The dynamic path planning module can adjust the path and the position of a hoisting point in real time, and collision or other potential safety hazards possibly occurring in the operation process are avoided.

The invention discloses a listed company risk assessment method based on cross validation and Bayesian optimization, and the method comprises the following steps: 1, obtaining the comprehensive factor data of a listed company, carrying out the dimension reduction of the factor data through employing a principal component analysis method, obtaining a principal component factor, carrying out the effectiveness verification of the principal component factor through an IC and IR analysis method, and carrying out the calculation of the risk of the listed company; selecting a plurality of principal component factors as input features; 2, on the basis of the multi-factor model, constructing an ensemble learning index enhancement model by taking a decision tree and an AdaBoost algorithm as cores; 3, performing system optimization on parameters of the ensemble learning index enhancement model by adopting cross validation and a Bayesian method, and finally determining an optimal parameter combination to obtain a trained ensemble learning index enhancement model; and 4, evaluating the risk of the listed company by using the trained integrated learning index enhancement model. According to the method, the calculation resource consumption of the model can be reduced, the calculation efficiency is improved, and the over-fitting risk of the model can be reduced.

The invention discloses a multi-objective optimization method for stress-strain and high-frequency crosstalk of a laminated welding spot, which comprises the following steps of: establishing a finite element analysis model and a crosstalk analysis geometric model of the laminated welding spot through simulation software, and respectively carrying out stress-strain analysis and high-frequency crosstalk analysis to obtain a maximum stress-strain value, a near-end crosstalk value and a far-end crosstalk value of the welding spot; a response surface design method is adopted, different structure parameter factor horizontal combinations of the laminated welding spots are designed, simulation analysis is carried out, regression analysis is carried out according to the obtained result, a regression equation of stress strain, near-end crosstalk, far-end crosstalk and welding spot structure parameters is established, a multi-objective optimization model is established, and the welding spot structure parameters are optimized. And then multi-objective optimization is carried out in combination with response surface design and an MOEA / D algorithm, a Pareto optimal solution set is obtained, an optimal welding spot structure parameter combination of multi-objective optimization is obtained through entropy weight TOPSIS decision analysis, and simulation verification is carried out. The multi-objective optimization method is good in optimization effect and multiple in optimization objectives, and has a certain guiding effect on the aspect of welding spot structure optimization.

The invention relates to the technical field of audio processing, in particular to an audio optimization method and device based on artificial intelligence, computer equipment and a medium. According to the method, a linear layer is used for mapping a spectrum envelope of a to-be-optimized audio to obtain an envelope feature, an embedding layer is used for embedding a standard audio parameter into a parameter feature, a prediction model is used for predicting a fusion feature of the envelope feature and the parameter feature to obtain a prediction pitch curve, and a noise adding model is used for adding noise to a Mel spectrum of the to-be-optimized audio to obtain a predicted pitch curve. Calculating the noise of the noise addition result by using a noise estimation model to obtain predicted noise, updating the noise estimation model according to the predicted noise, the real noise and the predicted pitch curve, calculating the reference noise of the noise addition result by using the updated noise estimation model, and de-noising the noise addition result according to the reference noise. The noise estimation model is optimized in combination with pitch information, so that the denoising process meets the pitch requirement, and the audio optimization effect is improved.

The invention discloses a ceramic sintering optimization method and system based on dynamic monitoring, and relates to the technical field of sintering. The method comprises the following steps: synchronously obtaining a sintering data set of a ceramic sample in a sintering process under a plurality of sintering process conditions in situ; based on the sintering data set, densification kinetic models corresponding to the multiple sintering process conditions respectively are established; and determining a plurality of isodensity lines on the basis of the densification dynamic models corresponding to the plurality of sintering process conditions, and constructing a dynamic prediction model on the basis of the plurality of densification dynamic models and the plurality of isodensity lines, so as to optimize the sintering process parameters on the basis of the dynamic prediction model. The method depends on a dynamic monitoring means, the mass change rate and the size change rate in the sintering process can be synchronously obtained in situ, comprehensive dynamic monitoring data are provided, a densification mechanism in the sintering process can be accurately understood or captured, the optimization effect of sintering process parameters is improved, and therefore the performance of a ceramic material is improved.

The invention provides an active distribution network scheduling optimization method comprising a micro-grid group, a medium and a system, and belongs to the technical field of active distribution network scheduling, and the method comprises the steps: obtaining an active distribution network parameter and a micro-grid parameter of each micro-grid in an active distribution network; calculating the similarity between any two micro-grids by using a preset micro-grid similarity function; establishing an active power distribution network stable scheduling equation set considering the power distribution network parameters and a micro-grid stable scheduling equation set considering the micro-grid parameters; a double-layer optimization model based on a grey wolf hunting optimization algorithm and a genetic algorithm is constructed, the grey wolf hunting optimization algorithm is used for active power distribution network optimization, and the genetic algorithm is used for microgrid optimization; when the grey wolf population is initialized, setting an initial population of a genetic algorithm for each micro-grid to represent various possible scheduling schemes in the micro-grid; by not executing outer-layer optimization and inner-layer optimization, the problem that the characteristics of the whole active power distribution network and the interior of the micro-grid are difficult to consider in the prior art is solved.

The invention relates to the technical field of fault diagnosis, and discloses a fault diagnosis method and system applied to an automobile air conditioner compressor. The method comprises the following steps: firstly, introducing auxiliary noise to decompose a vibration signal, calculating a decomposition result by using a sample entropy method to obtain a sample entropy, and identifying and removing noise; secondly, variational mode decomposition is carried out on the vibration signals after noise elimination, an improved crayfish optimization algorithm is used for optimizing a decomposition scale and a penalty factor in the variational mode decomposition process, and a final IMF component signal set is obtained; mapping the final IMF component signal set to polar coordinates through a symmetric polar coordinate transformation method to form a fault diagnosis image set; and finally, training to obtain a convolutional neural network fault diagnosis model, identifying a fault diagnosis image set, and outputting a fault diagnosis result of the automobile air conditioner compressor. The vibration signal of the automobile air conditioner compressor is processed and analyzed, the purpose of fault diagnosis is achieved, and the method is objective and accurate.

The invention discloses a discrete orthogonal code sequence optimization method based on an iterative greedy code search memetic algorithm, and belongs to the technical field of radar waveform design. The performance of the method provided by the invention is superior to that of the existing method, and the self-correlation and cross-correlation performance of the discrete orthogonal code sequence set optimized by the method provided by the invention is further close to the existing lower bound, so that the difference between the self-correlation and cross-correlation performance of the discrete orthogonal code sequence set and the existing lower bound is reduced. Compared with a greedy code search method, the iterative greedy code search strategy provided by the invention is stronger in local search capability, better in optimization effect and higher in search efficiency. And the method can improve the final search optimization result of the algorithm under the condition of not increasing the operation time of the algorithm. By simultaneously adopting crossover and mutation operators in each iteration, the search diversity of the algorithm is increased, and the search space of the algorithm is expanded, so that a discrete orthogonal code sequence set with better orthogonality can be obtained more possibly.

The invention belongs to the technical field of industrial scheduling, and discloses a flexible job shop lean production self-organization optimization method for a peak load shifting strategy, and the method comprises the steps: building a problem model, and determining an optimization target and a constraint condition; encoding and decoding chromosomes, and initializing a population; sorting the individuals, executing selection, crossover and variation, and forming a primary filial generation population by using an equipment efficiency cooperative operator and an energy efficiency cooperative operator; combining the parent population and the filial population, performing sorting, selection, crossover and variation again, forming a new filial population by using an equipment efficiency cooperative operator and an energy efficiency cooperative operator, and taking the previous filial population as a new parent population; and repeatedly executing the iteration step, and outputting an optimal scheduling scheme after a termination condition is reached. According to the method, the productivity and the energy efficiency can be effectively coordinated, the actual technical support is provided for the flexible job shop to realize peak clipping and valley filling and reduce the peak-valley electricity proportion, and the method system of the shop scheduling problem is enriched.

The invention relates to an off-peak electricity peak use control method and a temperature control strategy and a power management system applying the off-peak electricity peak use control method, and belongs to the technical field of energy-saving control. Respectively establishing objective functions of the total cost of the storage battery module, the volume of the storage battery, the recovery period and the loss in the electricity storage process of the storage battery; s2, solving the plurality of objective functions through an NSGA-II algorithm to obtain a Pareto frontier solution; and S3, calculating the weighted sum of each objective function by using a minimize function, obtaining an optimal solution with the minimum weighted sum in the solutions of the Pareto frontier, configuring the storage battery according to the optimal solution, and switching the commercial power or the storage battery to supply power to the compressor according to the electricity price of the peak valley electricity period. According to the valley electricity peak use control method, the problems that the capacity configuration of the storage battery is not clear and the recovery period is not clear are solved, the power consumption cost is minimized, and the pressure of the peak period of the power grid is relieved.

The invention relates to the technical field of data processing, in particular to a transformer fault monitoring method and system based on artificial intelligence. The method comprises the steps of collecting an input current data set and an output current data set, obtaining a characteristic deviation value of each datum in the input current data set, and obtaining a first oscillation amplitude of the input current data set based on the characteristic deviation value; acquiring a second oscillation amplitude of the input current data set, and acquiring a differential weight according to the first oscillation amplitude and the second oscillation amplitude; obtaining the load change of the power transformer according to the change consistency of the input current and the output current of the power transformer, and correcting the differential weight according to the load change to obtain a corrected differential weight; and inputting the corrected differential weight into a DE algorithm to optimize convolution kernel parameters of the CNN network, and then monitoring the fault of the power transformer, thereby improving the accuracy of transformer fault monitoring.

The invention relates to the technical field of loss reduction optimization of a power distribution network, in particular to a power grid loss reduction method and a power grid loss reduction system considering Lorenna series power flow approximation under data driving, and the method comprises the following steps: converting a line loss optimization task of the power distribution network into an intelligent agent interaction environment; the intelligent agent interaction environment is used for converting a line loss optimization task of the power distribution network into a Markov decision process under a data driving framework; and by adopting the load flow approximation method based on the Loran series, a nonlinear problem in the traditional load flow calculation is converted into a linear problem, so that the calculation complexity is greatly reduced. According to the method, the load flow calculation speed can be remarkably increased on the premise that the calculation precision is guaranteed, and power grid loss reduction optimization can meet the real-time requirement. Especially in a high-permeability distributed new energy access complex power grid, the method can quickly respond to the dynamic change of the power grid state, and effective support is provided for loss reduction optimization in actual operation.

The invention is applicable to the technical field of neuroscience, and provides a scale-free attribute brain dynamic effective connection network estimation method, which comprises the following steps of: acquiring time sequence signals of different regions of a brain according to acquired fMRI data, and calculating the dynamic effective connection network of the brain on the basis of the time sequence signals; constructing a time variation vector autoregression model containing group space regularization and time smoothness regularization, and performing multiple regularization optimization on the time variation vector autoregression model by adopting an alternating multiplier method based on an augmented Lagrangian multiplier so as to estimate a model coefficient of the time variation vector autoregression model; and generating the brain dynamic effective connection network according to the optimized model parameters, so that the generated brain dynamic effective connection network keeps the scale invariant characteristic of the brain network, and the optimization capability of the brain dynamic effective connection network is improved.

The embodiment of the invention provides a workflow scheduling method and device, equipment, a storage medium and a program product, and is applied to the technical field of cloud computing. The method comprises the steps of obtaining target attribute data corresponding to a target workflow; according to the target attribute data, constructing a multi-target optimization model corresponding to the target workflow; the multi-target optimization model comprises a target hybrid optimization algorithm; determining a target solution set corresponding to the target workflow according to the target attribute data and a multi-target optimization model; and decoding the target solution set to obtain a target workflow scheduling mode corresponding to the target workflow. Therefore, the electronic equipment calculates and optimizes the workflow scheduling based on the multi-objective optimization model, so that the diversity of the workflow scheduling process and the scheduling result can be improved, and meanwhile, the optimization effect of the workflow scheduling can also be improved.

The invention provides a charging station optical storage and charging configuration optimization method and device, terminal equipment and a medium, and the method comprises the steps: obtaining charging station load data of a target region in a plurality of historical periods, and extracting a charging demand feature and a charging time feature of the target region from the charging station load data; calculating photovoltaic power generation output power of the target area according to pre-collected illumination data of the target area and the area of the photovoltaic power generation panel; according to the charging demand characteristics, the charging time characteristics and the photovoltaic power generation output power, a photovoltaic energy storage optimization model is constructed by taking the maximum annual net income of the life cycle as an optimization target; and according to the photovoltaic energy storage optimization model and the constraint condition, determining an energy storage configuration and scheduling strategy of the charging station in the target area. According to the invention, the optimization effect of the optical storage charging configuration can be improved.

The present invention relates to the field of data processing technology, and in particular to a transformer fault monitoring method and system based on artificial intelligence. The method includes the steps of: collecting an input current data set and an output current data set, obtaining a characteristic deviation value of each data in the input current data set, and obtaining a first oscillation amplitude of the input current data set based on the characteristic deviation value; obtaining a second oscillation amplitude of the input current data set, and obtaining a differential weight according to the first oscillation amplitude and the second oscillation amplitude; obtaining a load change of the power transformer according to the consistency of the change of the input current and the output current of the power transformer, and correcting the differential weight according to the load change to obtain a corrected differential weight; inputting the corrected differential weight into the DE algorithm to optimize the convolution kernel parameters of the CNN network, and then monitoring the fault of the power transformer. The present invention improves the accuracy of transformer fault monitoring.

The invention discloses a BIM-based reinforcing steel bar information extraction and cutting material breaking system and an operation method, based on Revit software, the Revit software comprises a BIM model containing reinforcing steel bar information, the reinforcing steel bar information extraction and cutting material breaking system comprises a BIM reinforcing steel bar information extraction plug-in loaded in the Revit software, the BIM reinforcing steel bar information extraction plug-in is used for analyzing the BIM model, and the BIM model is loaded in the Revit software; identifying and extracting steel bar information data of the pre-processing part so as to automatically generate a steel bar blanking list; the BIM reinforcing steel bar information extraction plug-in is loaded in the Revit software, the reinforcing steel bar cutting and cutting software is loaded in the Revit software and associated with the BIM reinforcing steel bar information extraction plug-in, and the reinforcing steel bar cutting and cutting software is used for setting an objective function and constraint conditions by adopting a simplex method based on a reinforcing steel bar blanking list so as to solve and obtain an optimal reinforcing steel bar blanking scheme. And an optimal blanking strategy is obtained through optimization analysis and is used for guiding on-site construction, so that cost reduction and benefit increase are realized.

The invention relates to an alkaline electrolytic cell multi-cell series-parallel hydrogen production optimization method, and the method comprises the steps: predicting photovoltaic power generation data in a certain time through an LSTM-CNN recurrent neural network, and enabling the interval of prediction time points to be 15 minutes; the predicted photovoltaic power generation power data are combined with a particle swarm optimization (PSO) algorithm to optimize the state of the electrolytic cell in the multi-cell series-parallel connection of the alkaline electrolytic cell, and the interval of optimization time points is 1 hour; each alkaline electrolytic cell is set to be in the optimal state at each time point for hydrogen production, the number of times of startup and shutdown of the alkaline electrolytic cell is reduced as much as possible, the hydrogen production efficiency of the alkaline electrolytic cell is improved to the maximum extent, meanwhile, the service life of the alkaline electrolytic cell is prolonged, the operation cost is reduced, and the safety problem of gas cross permeation is effectively avoided; and the stability and the safety of the system are improved.

The invention discloses a medical self-service machine flow optimization system and method based on interface-free data interaction, and relates to the field of medical information processing. According to the invention, a plurality of parameters of an interface-free data interaction technology used for synchronous updating of sign-in data in a medical self-service machine of a hospital and patient information in a hospital information system are updated and optimized from two perspectives of offline sign-in and online sign-in; the sign-in data in the medical self-service machine of the hospital and the updating of the patient information in the hospital information system are more synchronous; by collecting the current offline sign-in data and the current online sign-in data, a judgment basis is provided for subsequently judging whether a plurality of parameters of an interface-free data interaction technology need to be updated or not; by optimizing the interface-free data interaction parameter data of the hospital, a setting basis is provided for performing primary update setting or secondary update setting on a plurality of parameters of the interface-free data interaction technology of the current hospital subsequently.

The invention discloses a man-machine cooperation-based intelligent modeling and optimization integration method for a steel-concrete composite beam bridge, and the method comprises the following steps: 1) determining a structural system, span and a hole dividing scheme of the steel-concrete composite beam bridge, and drawing an initial condition graph; the initial condition graph comprises a bridge initial condition plane graph and a bridge initial condition elevation graph; 2) based on the initial condition graph, constructing an initial intelligent model of the steel-concrete composite beam bridge; and 3) optimizing the initial intelligent model of the steel-concrete composite beam bridge to obtain the intelligent model of the steel-concrete composite beam bridge. According to the intelligent modeling and optimization integrated frame of the steel-concrete composite beam bridge based on man-machine cooperation, intelligent modeling and optimization of the steel-concrete composite beam bridge can be integrally achieved, high flexibility and adaptability are achieved, and the intelligent modeling and optimization integrated frame can be popularized to design of different types of bridges.

The invention relates to a new energy power distribution network collaborative optimization system based on a distributed regulation and control framework. The system comprises a cluster module, a virtual power plant module, a power distribution network module and an optimization module. The cluster module gathers a plurality of small-capacity dispersedly accessed photovoltaic power supplies to form a plurality of photovoltaic power supply clusters; the virtual power plant modules form a plurality of virtual power plants, each virtual power plant comprises a high-capacity photovoltaic power station, an energy storage device and a load, and the virtual power plants serve as an optimization main body to supply power to the internal load and reversely transmit electric quantity to the power distribution network module so as to generate benefits; and the power distribution network module is used for connecting a plurality of photovoltaic power supply clusters and a plurality of virtual power plants. The invention relates to the technical field of new energy power distribution networks. Dispersed small-capacity photovoltaic power supplies are divided into photovoltaic clusters, large-capacity photovoltaic power stations are combined with energy storage and loads to form a virtual power plant, it is guaranteed that the photovoltaic power supplies connected in different forms fully participate in collaborative optimization, and the adverse effect of photovoltaic power generation intermittency and randomness on a power distribution network is reduced.

The invention discloses a multi-objective optimization-based surface mine unmanned mine card scheduling method and system, and relates to the technical field of smart mines, and the method comprises the steps: building a road network topological graph of a loading and unloading interval of a surface mine, constructing an unmanned mine card multi-objective optimization scheduling model, and carrying out the multi-objective optimization scheduling of the unmanned mine card. And solving the unmanned mine card multi-target optimization scheduling model to obtain a loading and unloading expected traffic flow matrix under various scheduling principles, thereby performing mine card real-time scheduling. According to the method, factors such as road conditions, road traffic flow saturability and real-time performance are considered, the queuing waiting time in an operation area is shortened, the mine operation efficiency and the equipment utilization rate are remarkably improved, and economic benefits are increased.