148 results about "Grid optimization" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention relates to a water-light storage area power grid optimization method based on federated and layered multi-agent reinforcement learning. The method comprises the steps of constructing a federated and layered multi-agent model, defining states, actions and reward spaces of layered agents, implementing federated reinforcement learning offline training with privacy enhancement, deploying the model and executing online distributed optimization. Through the federal learning framework, it is ensured that private data of all the energy main bodies are locally calculated, the privacy leakage risk caused by data collection in a traditional centralized method is avoided, and a technical basis is provided for building collaborative trust among multiple main bodies. Meanwhile, the designed hierarchical intelligent agent architecture decouples a complex power grid system into subproblems with clear hierarchies, and when the scale of the system is expanded, new energy units can be accessed in a modular mode, so that the overall maintenance and upgrading difficulty of the system is reduced, and the expandability of the system is improved.

The invention relates to the technical field of image processing, and discloses an intelligent spraying construction control method and system based on image processing and a medium. The method comprises the following steps: performing image acquisition and defect analysis on an outer wall base layer to generate reference data; performing lattice optimization and path planning based on the reference data; environment parameters are collected, a compensation database is established, and a process regulation matrix is generated; coating quality is monitored in real time, and a data set and an early warning library are generated; the spraying track is optimized, and a control instruction is generated; and evaluating the construction quality to form an evaluation system. Intelligent spraying quality real-time monitoring and self-adaptive adjustment based on image processing are realized, so that the automation level and the quality control precision of outer wall spraying construction are improved.

The invention discloses a laser point cloud-based industrial equipment digital twin model rapid reconstruction method and system, and the method combines a model matching mode and a point cloud reconstruction mode, and effectively solves the problem of twin model reconstruction in different scenes. According to the first mode, on the basis of an existing three-dimensional model, through shape matching, feature extraction and model retrieval methods, registration of point cloud data and the three-dimensional model is combined, and rapid reconstruction of the equipment twinborn model is successfully achieved. The mode is suitable for equipment with an existing standard three-dimensional model, the repeated utilization rate of the model can be improved, and the reconstruction time and cost are reduced. In the second mode, the reverse grid model construction method based on the point cloud data is provided for equipment which cannot be retrieved through an existing model. Through an improved greedy projection triangulation algorithm, point cloud data is converted into a grid model, and the precision and authenticity of the model are improved in combination with a grid optimization algorithm and a surface texture mapping technology. According to the method, the digital twinborn model of the industrial equipment can be efficiently and accurately reconstructed, the modeling difficulty in an industrial scene is relieved, the actual demand of field modeling is met, and the method has high application value and practicability.

The invention discloses an industrial park micro-grid optimization scheduling method based on electric furnace load active adjustment. The method comprises the following steps: constructing an economical operation objective function for generating power by using a thermal power generating unit to share blast furnace gas of a steel mill; dynamically adjusting the maximum exchange power between the micro-grid and the public grid by iterating the shrinkage constraint, and calculating a minimum feasible value to reduce basic electricity price expenditure; taking the gear of the electric furnace transformer as an adjusting variable, and constructing a load adjusting constraint model containing a fixed load and an adjustable load; the micro-grid adopts a double-bus topology, power mutual assistance is realized through a back-to-back converter, and a distributed power supply, an energy storage system and a new energy power generation set are configured. The method can efficiently utilize energy and reduce the operation cost; the public network expenditure is reduced by dynamically optimizing power exchange; the system flexibility is improved by flexibly regulating and controlling the load; new energy fluctuation is stabilized, and stability is enhanced; the double-bus structure realizes grid-connected and off-grid switching, improves reliability, and is suitable for iron and steel enterprises to improve the utilization rate of renewable energy sources and reduce operation cost.

The invention discloses a micro-grid optimization scheduling method based on an improved Loueer fox algorithm, relates to the technical field of micro-grid operation, and realizes collaborative optimization scheduling of a distributed power supply, an energy storage device, a load and the like in a micro-grid system by simulating various perception and behavior strategies of the Loueer fox. Aiming at the demand of distributed power supply system micro-grid optimization scheduling, the micro-grid system operation economy is taken as a main target, a function corresponding to a micro-power supply scheduling scheme is constructed, improvement is carried out based on an RFO algorithm, and a quantum fractal annealing disturbance strategy, a hyperbolic tangent self-adaptive olfactory disturbance strategy and a Riemannian metric accumulation disturbance strategy are introduced, so that the optimal scheduling of the micro-power supply system is realized. Through the fusion of the triple innovation strategies, the defects of poor population diversity, easy premature convergence, insufficient balance capability, low precision and the like when the original RFO algorithm and the traditional intelligent algorithm are used for solving the complex optimization problem are solved, and the efficient solution of the micro-grid optimization scheduling scheme is realized.

The invention relates to the technical field of power system real-time control, and discloses a multi-source distributed resource collaborative real-time control method, system and device and a medium. The method comprises the following steps: constructing a multi-target scheduling model fusing user comfort and power grid operation indexes; constructing an adaptive hybrid intelligent algorithm based on a non-dominated sorting genetic algorithm, a deep reinforcement learning algorithm and a particle swarm optimization algorithm; based on the real-time data, performing adaptive solution on the multi-target scheduling model according to an adaptive hybrid intelligent algorithm to obtain a minute-level optimization decision scheme; and issuing a control command according to the minute-level optimization decision scheme so as to realize deep cooperative control of the multi-source distributed resources. According to the method, the user comfort is used as a constraint to be fused into a power grid optimization target, and deep cooperative control of multi-source distributed resources, namely photovoltaic-energy storage-load, is realized through autonomous learning and optimization of an adaptive hybrid intelligent algorithm, so that the power grid peak clipping and voltage regulation capabilities are effectively enhanced while the user satisfaction is improved.

The invention discloses a multi-target collaborative optimization scheduling control method and system for an optical storage and charging micro-grid, and aims to solve the multi-target conflict problem of economy, environmental protection and stability in the operation of the micro-grid by constructing a micro-grid model containing photovoltaic, energy storage, load and a power grid. A multi-objective function set for economic cost minimization, carbon emission minimization and supply and demand balance optimization is constructed, and an adaptive multi-population genetic algorithm and a Pareto optimization algorithm are innovatively proposed to collaboratively iteratively solve a Pareto optimal solution set. The search efficiency is improved through an adaptive variation rate mechanism and a multi-population strategy, and the problem of multi-target conflict is effectively solved. The method is suitable for optimal scheduling of the independent micro-grid containing photovoltaic power generation, an energy storage system and a charging load, and the energy utilization efficiency and the system stability are improved.

The invention discloses a micro-grid scheduling strategy generation method and device, equipment, a medium and a product, and belongs to the technical field of micro-grid scheduling. The method comprises the following steps: constructing a micro-grid optimization scheduling physical model, wherein the micro-grid optimization scheduling physical model at least comprises a flexible load response model; based on the flexible load response model, establishing a multi-objective optimization function including a power supply reliability objective and an operation cost objective; system operation constraint conditions of the multi-objective optimization function are set, and the system operation constraint conditions at least comprise distributed power supply output constraints; and carrying out optimization solution on the multi-objective optimization function by adopting an optimization algorithm to obtain a micro-grid scheduling strategy, and carrying out cooperative control on various resources in the micro-grid based on the micro-grid scheduling strategy. According to the micro-grid scheduling strategy generated by the embodiment of the invention, the efficiency and robustness of micro-grid scheduling can be effectively improved.

The invention belongs to the technical field of energy management and power system optimization, and particularly discloses a port microgrid optimization scheduling method, device and equipment, a storage medium and a product. According to the application, the microgrid operation optimization model is constructed according to the predicted load demand, the predicted wind power generation power and the predicted solar power generation power; solving a target scheduling strategy according to the power grid operation optimization model and the multi-dimensional microgrid operation constraint conditions; and performing optimal scheduling on the port microgrid according to the target scheduling strategy. Through the above mode, comprehensive optimization of microgrid operation is realized by using the microgrid operation optimization model on the basis of considering the operation cost and the environment cost, the operation stability is ensured according to the multi-dimensional microgrid operation constraint condition, and then optimization scheduling is performed on the port microgrid according to the target scheduling strategy. Therefore, the accuracy of dispatching the port micro-grid can be effectively improved, the operation economy and the energy utilization efficiency of the micro-grid are improved, and carbon emission is reduced.

The invention provides a wind and light storage micro-grid optimization scheduling method and system containing small hydropower storage, and the method comprises the steps: obtaining historical operation data, and carrying out the prediction according to the historical operation data, and obtaining a full-dimension prediction data set; according to the full-dimension prediction data set, constructing a multi-objective optimization model with the goal of minimizing residual load, minimizing power abandoning cost, minimizing cascade water abandoning amount, maximizing green energy collaborative utilization efficiency and minimizing time weighted voltage-power comprehensive error; and solving the multi-objective optimization model by adopting a hybrid optimization algorithm fused by a Hanniac optimization algorithm and an improved sparrow search algorithm to obtain a final output. According to the invention, efficient consumption of green energy can be realized, the electricity abandoning phenomenon is reduced, carbon emission is reduced, and the stability, economical efficiency and environmental protection property of a power system are improved.

The invention provides a scene-based random optimization method for micro-grid energy and flexibility scheduling, and belongs to the technical field of micro-grid optimization scheduling. The method comprises the following specific steps: minimizing the expected cost of the micro-grid as a target; setting a power balance constraint in the micro-grid according to a relation among photovoltaic power generation capacity, power load power consumption, battery energy storage charging and discharging power and power loss in the micro-grid internal transmission and conversion process in the micro-grid; determining a flexible service constraint of the micro-grid according to the flexible service model and the battery energy storage model; predicting and updating by using a rolling time domain method, modeling uncertainty of the micro-grid by combining scene generation and scene reduction technologies, determining a day-ahead prediction error of the micro-grid, and constructing a random optimization model of energy and flexibility of the micro-grid; based on a flexible intra-day framework, solving a random optimization model of micro-grid energy and flexibility according to a scene at different time stages, and obtaining a flexible scheduling strategy; meanwhile, the stability and efficiency of the micro-grid are considered.

The invention relates to the technical field of electric power operation and maintenance, in particular to a building micro-grid multi-element collaborative optimization method and system, electronic equipment and a medium, and the method comprises the steps: setting a micro-grid optimization reference index based on building energy consumption characteristics, equipment safety specifications and power grid access requirements; based on a preset acquisition frequency, acquiring multivariate state characteristics of the building micro-grid in real time; inputting the microgrid optimization reference index and the multivariate state characteristics into a pre-constructed multivariate collaborative optimization model to obtain an optimization control instruction of each regulation and control unit; for each regulation and control unit, obtaining a corresponding current control instruction, and calculating an optimization amplitude according to the current control instruction and the optimization control instruction; based on the optimization amplitude, an optimization adjustment strategy corresponding to the regulation and control unit is generated, and the optimization adjustment strategy is executed; the method can deeply integrate building energy consumption characteristics, equipment operation constraints and power grid interaction requirements.

The invention provides a rural micro energy grid optimization scheduling system and method based on self-adaptive dynamic planning, and belongs to the field of distributed energy management and intelligent power grid scheduling. A multi-period optimization model is established for the state of the rural micro energy network by using a Markov decision process, a multi-period optimization scheduling scheme is obtained by using an adaptive dynamic programming algorithm and combining a piecewise linear function approximation value function, and a distributed energy unit, an energy storage unit and an electric vehicle unit are scheduled by using the multi-period optimization scheduling scheme. According to the method provided by the invention, real-time online optimization of the state of the micro energy network is realized, dependence on short-term prediction information is reduced, the problem of dimension disaster in traditional dynamic planning is avoided, and global coordinated optimization scheduling of each distributed energy source, each electric vehicle unit and each energy storage unit in the micro energy network is realized.

The invention provides a micro-grid optical storage capacity optimal configuration method and system, and relates to the technical field of micro-grid optimization, and the method comprises the steps: setting a sectional capacity configuration parameter according to a load characteristic parameter and an energy access condition of a micro-grid; setting a first compensation correction value and a second compensation correction value based on impedance matching characteristics of the micro-grid and charge and discharge efficiency parameters of the energy storage unit; reversely analyzing the capacity optimization configuration parameter combination obtained through optimization into the sectional capacity configuration parameters; and collecting real-time monitoring data in real time through an industrial Ethernet, and performing dynamic refreshing and rolling optimization on a capacity optimization configuration parameter combination according to a preset power balance threshold value and an actual operation deviation of an optical storage cooperative configuration execution scheme. According to the invention, the technical problem of capacity redundancy or insufficiency caused by static and fixed configuration of the optical storage capacity of the micro-grid in the prior art is solved, and the configuration precision of the optical storage capacity of the micro-grid is improved.

The invention relates to the technical field of new energy micro-grid optimization, and discloses an optical storage micro-grid optimization method and system based on the Internet of Things, and the method comprises the steps: carrying out the local processing, and generating an edge data set; calculating node data credibility; generating corrected light storage data; energy scheduling optimization is carried out in combination with the operation cost and the power balance constraint; and generating a control instruction according to the scheme and updating the data set in a closed-loop manner. In the prior art, it is generally assumed that node data is completely reliable or scheduling uncertainty under the condition that node communication is abnormal or aged is not fully considered, especially under the condition that communication conditions of a remote island type micro-grid are limited and node data quality fluctuates. The technical problem that the reliability difference of multi-source data cannot be dynamically identified and safely dispatched in the prior art is solved, and due to the fact that a dynamic credibility evaluation mechanism in the Internet of Things environment is introduced and the credibility is embedded into an optical storage micro-grid energy dispatching model, real-time correction and self-adaptive dispatching optimization of node data are achieved.

A method and system for data-driven parameter coordination control of a tea leaf initial production line is provided. The method uses experimental equipment and production line software to collect process parameters, image information and moisture content information during the tea production process, preprocesses the collected data and removes abnormal data to improve model performance, selects important features and chemical components through eigenvalue importance and correlation analysis, applies the tea process dataset to train a machine learning model, obtains the optimal hyperparameters of multiple models using grid optimization methods, and compares the modeling results for different units of different models. The optimal model for the roll drying unit is PLSR, and targets the moisture content of each unit and the chemical components of the tea leaves. A multi-objective algorithm is used to optimize the process parameters, and the optimization results are applied to the tea production line control software to achieve high-quality tea production.

The invention relates to the technical field of micro-grid operation control and energy management, and discloses an AI-based micro-grid optimal scheduling method, which comprises the following steps of: acquiring original data in real time and performing data preprocessing through a deployed intelligent electric meter, a sensor and a data acquisition unit; inputting the original data into a preset load prediction model to obtain a net load prediction curve of the next day of the real-time data acquisition time point; taking the net load prediction curve and the time-of-use electricity price as input, constructing and solving a multi-target mixed integer linear programming optimization model, and obtaining a global optimal energy storage charging and discharging plan in the next 24 hours; and calculating a deviation value between the actual net load and the net load predicted value so as to determine whether to execute a global optimal energy storage charging and discharging plan in the next 24 hours. According to the method, a three-layer progressive technical architecture of data-driven prediction, model accurate optimization and rule real-time correction is adopted, the problems of poor economical efficiency and weak adaptability of a traditional method are solved, calculation is efficient, practicability is high, and engineering implementation is easy.

The invention discloses a solver error control development method and system based on isogeometric analysis, and relates to the technical field of computational mechanics and numerical analysis, and the method comprises the steps: building an NURBS primary function according to isogeometric analysis, carrying out the grid division, generating initial grid data, calculating the stress error index of each grid unit through employing a Zienkiewicz-Zhu method, and carrying out the calculation of the stress error index of each grid unit. Grid optimization parameters are generated, local encryption processing is carried out on grid units, the order of an NURBS primary function is improved by adopting a p-refinement method, and an optimized grid structure is obtained; and inputting the optimized grid structure into a solver for iterative calculation, and when a calculation error is smaller than a preset error threshold value, outputting a solving result. According to the method, the optimal balance between the calculation precision and the resource consumption is realized through the precise geometric expression and the self-adaptive grid division of the NURBS primary function in combination with the Zienkiewicz-Zhu posterior error estimation technology.

The present invention discloses a grid optimization method for a disordered super-uniform solid device, comprising: firstly, designing a base point region; secondly, inserting regularly distributed base points within the base point region; thirdly, generating a random point distribution outside the base point region and performing point optimization to obtain a disordered super-uniform point distribution; thereafter, performing triangulation on the regular point distribution to form regularly distributed lattice points within the base point region; performing triangulation on the disordered super-uniform point distribution to obtain lattice points of the mesh structure of the disordered super-uniform solid device; then, connecting adjacent lattice points among all lattice points to form grid walls, constructing the disordered super-uniform solid device, and completing grid optimization; finally, introducing waveguide defect lines into the disordered super-uniform solid device to construct a waveguide integrated device. The present invention regulates the boundary grid of the disordered super-uniform solid to generate regular grid walls, avoiding the generation of additional defect holes after introducing waveguide defect lines, thereby reducing the loss of the disordered super-uniform solid waveguide device and optimizing the photonic bandgap characteristics of the device.

The application discloses a two-dimensional coherent signal DOA estimation method based on double parallel relatively prime array movement, which comprises the following steps: a double parallel array comprises a first subarray and a second subarray, phase compensation is performed on the output of the first subarray and the output of the second subarray to obtain first subarray compensation output and second subarray compensation output; a first vector is obtained based on the first subarray compensation output and the second subarray compensation output, and a second vector is obtained based on the first subarray compensation output; a sparse off-grid optimization function is constructed; accurate angle estimation of an incident signal and an X axis is obtained based on the sparse off-grid optimization function; angle estimation of the incident signal and a Y axis is obtained based on the first vector and the second vector; and angle estimation value of the two-dimensional coherent signal is obtained based on the accurate angle estimation of the incident signal and the X axis and the angle estimation of the incident signal and the Y axis. The application uses the double parallel relatively prime array to perform angle estimation of the two-dimensional coherent signal, and makes up for the deficiency of the relatively prime array in angle estimation of a coherent source.

The application discloses a kind of wind light storage micro-grid optimization operation method and system.The method adopts three-layer architecture of top optimization, middle layer cooperation and bottom execution, and forms the intelligent closed loop of " prediction-planning-coordination-execution-feedback ".Top is based on LSTM prediction and traffic jam algorithm to carry out global power flow congestion identification and dredging, and utilizes improved particle swarm algorithm in feasible interval to carry out multi-objective fine optimization, and generates optimal operation instruction;Middle layer dynamically adjusts optimization target weight according to operation scene, and decomposes instruction into multiple device cooperation strategy;Bottom execution customized control algorithm, realize device accurate regulation and millisecond level fast protection.The application effectively solves the problems of micro-grid multi-objective cooperative optimization, prediction control disconnection and operation risk foresight protection, and significantly improves the voltage stability, economy, dynamic response speed and safety and reliability of system operation.

The invention discloses a station micro-grid optimization scheduling method and system considering internal and external double circulation, and belongs to the field of micro-grid operation optimization. According to the method, an internal circulation model is established based on classification of power load data of a thermal power plant and in combination with the start-stop sequence and process of equipment and the stoppable interval of power generation, the internal circulation model comprises economical efficiency, operation time sequence and environmental protection modules, and coordinated optimization is realized through a dynamic adaptive weight adjustment mechanism. An embedded optimization method is adopted, dynamic programming and mixed integer linear programming are combined, an equipment start-stop path and load distribution are optimized, loads are further reduced and transferred, and economic benefits are improved. The outer circulation comprises wind power, photovoltaic and energy storage devices, is responsible for scheduling optimization, and is coupled with the inner circulation through energy storage discharge. On the basis of independent optimization, an internal and external loop optimization problem is decomposed into parallel sub-problems, each iteration is independently optimized and a Lagrange multiplier is updated, power distribution and target coordination are dynamically adjusted, and global optimization is realized.

The present invention discloses a topological grid optimization method for a permanent magnet flat wire motor based on micro-reluctance units, establishes an equivalent model of a rotor, a stator and an air gap micro-reluctance network, obtains a complete rotor structure by symmetry and replication of a 1 / 16 magnetic pole region of the rotor; establishes a two-dimensional Gaussian basis function covering an optimized design region, obtains the material characteristic parameters of the micro-reluctance units on the optimized design region by weighted summation of the two-dimensional Gaussian basis function, aggregates the micro-reluctance units with the same material properties on the optimized design region to generate a new motor topology structure; calculates the average magnetic flux density of each micro-reluctance unit, the magnetic permeability of each iron core micro-reluctance unit and the magnetic permeability convergence coefficient; utilizes a genetic algorithm to optimize the weight coefficient of the Gaussian basis function, and adjusts the material distribution of the micro-reluctance units in the design region; the micro-reluctance unit network model adopted by the present invention is a multi-purpose model that combines motor structure generation and performance analysis, which can greatly improve the design freedom and ensure optimization efficiency.

This invention provides an adaptive grid optimization method for improving the capacity of large-scale hydropower transmission, comprising: constructing a two-layer transmission network optimization objective function considering the transmission capacity of hydropower stations; wherein the upper-layer optimization objective function is used to evaluate the economic efficiency of the transmission network expansion planning scheme, and the lower-layer optimization objective function is used to evaluate the renewable energy absorption level and system reliability of the transmission network; employing a multi-agent reinforcement learning method to configure an independent agent for each layer of optimization objective to search for schemes and obtain schemes that satisfy the planning model through inter-layer constraints; wherein corresponding reward functions are set according to the structure of the upper and lower layer optimization objective functions. This invention constructs two independent agents, each using the upper and lower layer model optimization objective functions as rewards to search for schemes and independently training its own neural network based on its optimization process data, ultimately obtaining a transmission network expansion planning scheme that satisfies the two-layer model, thereby improving the capacity of large-scale hydropower transmission.

The invention discloses a multi-modal multi-target microgrid optimization method based on constraint multi-modal dominance. The method comprises the following steps: obtaining a plurality of targets of microgrid optimization; a convergence file and a diversity file which have complementary effects are initialized, the convergence file guides search to develop towards the CPF direction, and the diversity file uses a two-stage environment selection process to reserve a plurality of equivalent solutions of different fields; evaluating target values and constraint violations of the convergence archives and the diversity archives; iteration is carried out until the maximum number of function evaluation times is reached; and outputting a final micro-grid optimization scheme. According to the method, feasibility is preferentially considered by adopting a limited multi-modal dominating relation, meanwhile, solutions of different modals are reserved, and the modal recognition radius is dynamically adjusted according to distribution; a two-stage environment selection strategy is adopted, the decision space is widely explored at first, local optimum is reserved, and then high-quality solutions are gradually concerned.

The invention discloses a micro-grid optimization scheduling method containing interface solar steam power generation and wave energy power generation. The method comprises the following steps: establishing an interface solar steam power generation device discretization model and a wave energy power generation fitting model; with electrical load, hydrogen demand and water demand as the core, electric energy storage and hydrogen storage are considered in a combined manner, and a micro-grid optimization model containing interface solar steam power generation and wave power generation is established; establishing constraint conditions of the micro-grid optimization model; performing simulation calculation in different scenes; and testing the system stability under extreme working conditions. According to the scheduling method, the dual dilemma of energy shortage and fresh water shortage in the island region can be solved, the dependence on traditional energy can be reduced, the energy transportation cost and the environmental risk are reduced, and the energy utilization efficiency and the environmental friendliness are improved.

The application discloses a method for setting a transfer bus station and a line based on grid optimization, and comprises the following steps: 1, based on travel big data, respectively calculating travel demand distribution indexes of each unit grid in a region; 2, respectively calculating total travel demand and regional competitive advantage of each evaluation grid by using an evaluation grid optimization method; 3, searching and optimizing the evaluation grid of the site coverage range to respectively calculate and obtain quantitative evaluation values of the evaluation grid and the centroid longitude and latitude of the evaluation grid, thereby obtaining an evaluation grid quantitative evaluation value set in a certain range according to a certain step; 4, determining the optimal unit grid of the site selection of each evaluation grid as the optimal site selection position of the transfer station based on travel cost, and forming a bus driving route scheme; and 5, establishing a multi-objective optimization model to optimize the bus driving route to obtain a final bus driving route scheme, wherein the decision variables of the model optimization include the bus driving route and the number of vehicles equipped on the line. The application realizes scientific and accurate site selection for a city public transport stop.

The present invention discloses a three-dimensional fabric numerical simulation method based on microstructure simulation, which relates to the technical field of fabric modeling and includes the following steps: S1, initial model establishment: using a gridless method, according to the microstructure information of the fabric, an initial model of the three-dimensional fabric is established, and its weaving process is simulated; S2, macro-mechanical analysis: converting the gridless model into a finite element model, and analyzing the overall deformation and stress distribution of the fabric when it is impacted; S3, micro-behavior simulation; in the present invention, by combining a variety of advanced simulation technologies, high-precision, multi-scale comprehensive analysis is achieved, which provides strong support for the design and performance evaluation of the fabric. At the same time, it also has the effects of flexibly handling complex structures and boundaries, in-depth research on microscopic phenomena, adaptive grid optimization and computational efficiency improvement, collaborative optimization and iterative improvement, and guiding new product development and design optimization.

The present application relates to the technical field of power system grid optimization control, in particular to a data-driven grid voltage and reactive power control optimization method and system, which comprises constructing a voltage and reactive power control mathematical model of a three-phase unbalanced grid, taking the minimum node voltage deviation rate, the lowest grid line loss and the optimal three-phase unbalance degree of bus voltage as the optimization goal; using Latin hypercube sampling to cover the photovoltaic-load typical scene of the whole operation condition, combining with the historical operation data of the grid to generate sample data, training the highway neural network combined with the improved NSGA-II algorithm, and obtaining the globally optimal grid voltage and reactive power control strategy through population iteration solution. The present application adopts a data-driven method, replaces the traditional physical model with a proxy model, effectively adapts the uncertainty of new energy access, shortens the single time period decision time to seconds, stabilizes the node voltage and three-phase unbalance degree within the safety limit, and at the same time realizes the minimization of the whole grid loss.