923 results about "Site selection" patented technology

The invention relates to the technical field of unmanned aerial vehicle application and urban airspace management, in particular to an intelligent site selection method and system for unmanned aerial vehicle take-off and landing points in an urban building group, and the intelligent site selection system for the unmanned aerial vehicle take-off and landing points in the urban building group specifically comprises the following steps: S1, carrying out the multi-source heterogeneous data fusion and high-precision three-dimensional environment modeling; s2, fluid dynamics simulation and airflow field pre-calculation are calculated; s3, real-time meteorological monitoring and dynamic risk correction; s4, establishing a quantitative safety evaluation model and multi-objective optimization site selection; and S5, visual decision support and feedback learning. According to the scheme, by deeply fusing multi-source heterogeneous geographic information data and constructing a high-precision three-dimensional digital twinborn city environment, accurate simulation and risk assessment of an airflow field around a building group are realized, geometric, material and semantic information is automatically extracted by using oblique photography, laser point cloud and a building information model, and the construction efficiency is improved. A unified three-dimensional grid with physical attributes is formed, and a reliable basis is provided for subsequent fluid calculation.

The invention relates to an improved NSGA-III-based multi-target geographic site selection optimization method and system, belongs to the technical field of optimization in business management and resource planning, and particularly relates to resource allocation and site selection decision making by using a calculation model. The technology aims to solve the problems of slow scheme convergence, non-uniform solution set distribution and low calculation efficiency when multi-target site selection is carried out under limited resources. According to the scheme, geographic space data is preprocessed, and an initial population representing different site selection schemes is generated; carrying out optimization iteration by adopting an improved NSGA-III algorithm, and calculating a plurality of objective functions such as coverage rate, normalized population density and medical service accessibility; and performing individual selection and population updating by utilizing improved reference point generation and vertical distance measurement. The method is mainly used for improving the efficiency and effect of commercial and municipal decisions such as urban planning, medical resource configuration, energy station layout and logistics network optimization.

The present application belongs to the technical field of co-delivery center site selection. Disclosed are an urban-end co-distribution center site selection method and system based on air-ground coordination, which are used for solving the technical problems of an existing air-ground coordination co-delivery center site selection model being insufficiently integrated with an unmanned aerial vehicle delivery technique, failing to take into consideration constraint conditions on unmanned aerial vehicles, and the solving efficiency being low. The method comprises: establishing a planned layout set of an air-ground coordination co-delivery center; taking the minimization of the overall delivery time and the minimization of the total network operation cost as objectives to construct an air-ground coordination layout planning model; setting constraint conditions of the air-ground coordination layout planning model; by means of a grey wolf population algorithm, generating an initial site selection solution set for a pre-selected point set of the co-delivery center; filtering out invalid solutions in the initial site selection solution set on the basis of the constraint conditions; and performing an individual iterative update on the filtered initial site selection solution set, and performing a fitness assessment and no-fly zone crossing determination on a new solution set obtained after each iteration, so as to obtain a final site selection scheme.

The invention relates to an AI-based planned land site selection multi-objective optimization method and system. The method comprises the following steps: constructing a city data cube; constructing a cellular automaton model and generating a land utilization potential map; constructing a game decision model, and generating a preliminary site selection scheme set; inputting the preliminary site selection scheme set into a cellular automaton model to generate an evolution simulation data set; constructing a multi-target evaluation model, and calculating the relative closeness between each scheme in the preliminary site selection scheme set and an ideal solution; generating a site selection decision report according to the relative close degree; in conclusion, the urban data cube is constructed by integrating multi-source real-time and historical data, the dynamic land potential map is generated by applying the cellular automaton model, and the multi-target scientific balance is realized by introducing the multi-agent game decision. Automatic processing and consistency guarantee of planning rules are realized, a scientific quantitative tradeoff mechanism in a multi-target conflict scene is provided, and the method has the effect of improving data timeliness and dynamic response capability of site selection decision.

The invention relates to a multi-scene facility site selection method based on a gridding population mapping and hybrid optimization algorithm, and the method comprises the following steps: S1, constructing regular rectangular grids covering a research area, each network having a unique grid identifier, a centroid abscissa and a centroid ordinate; s2, obtaining different forms of population data, performing conversion processing, integrating the population data into regular rectangular grids, and calculating the population quantity for each network to obtain a grid data set with standardized population weight; and S3, based on the data set and the coverage determination function, respectively constructing target functions of three scenes of full-facility re-optimization, partial-facility optimization and newly-added facility site selection, and solving the target functions to obtain optimal multi-scene facility site selection results of the three scenes. Compared with the prior art, the method has the advantages of improving the calculation efficiency of the optimal facility number range and the site selection position and the like.

The invention relates to the technical field of urban planning, and particularly provides an intelligent site selection method and system based on a spatio-temporal data platform, and the method comprises the steps: collecting multi-source spatio-temporal data of key industry categories of a target region, constructing an enterprise demand feature set based on the multi-source spatio-temporal data, and building a quantitative demand standard based on the feature set; constructing an industrial ecology adaptation model based on the current industrial basic data of the target area, calculating the matching degree between the feature set and the industrial ecology of the target area through the industrial ecology adaptation model, and outputting candidate parks according to the matching degree by taking industrial ecology clustering as a target; establishing a multi-element adaptation relationship between the target enterprise and the candidate parks based on the quantitative demand standard, performing matching verification through a multi-dimensional compliance verification unit, and removing parks which do not meet the constraint condition of the quantitative demand standard; and based on the verification result, outputting a site selection scheme, finally realizing high matching of enterprise demands and public resources, improving site selection efficiency and quality, and assisting sustainable development of regional industries.

The invention discloses a wave energy array development site selection method, which belongs to the field of wave energy development and utilization, and comprises the following steps of: 1) processing geographical distribution data by utilizing a GIS (Geographic Information System) spatial analysis method to obtain a limited development area graph layer; and 2) constructing an evaluation index system of wave energy array development site selection, and carrying out standardization processing to obtain a comprehensive evaluation result. And 3) calculating an offshore distance, and generating a refined developable range layer. And 4) carrying out wave direction statistical analysis in the developable range to obtain an array development range. And 5) on the basis of determining the array development range of the target sea area, determining a potential development site. 6, quantitative analysis and trend analysis of the array development capability are carried out with the potential development site as the center point, and the key development site is determined.According to the method, multi-dimensional evaluation indexes such as environment compatibility, resource endowment, technical feasibility and economic rationality are integrated, and a scientific basis is provided for array development site selection of the wave power station.

The invention discloses a shopping mall site selection method and system based on big data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: employing a big data algorithm to analyze city data, calculating the hotspot score of a commercial candidate address, recognizing a potential commercial hotspot region, and forming a preliminary alternative address pool; by calculating the cosine similarity between the to-be-built shopping mall in the alternative address pool and the existing shopping mall, screening the preliminary alternative address pool to generate an advantageous alternative address pool; taking the existing shopping mall as a discrete point, constructing a Thiessen polygon, and removing an overlapping region in the advantage alternative address pool to obtain a high-value alternative address pool; and performing comprehensive scoring on the high-value alternative address pool to screen out a final shopping mall construction address. According to the method, a high-competition area is strategically avoided by constructing the Thiessen polygon, green ecological assessment and business district characteristics are fused, and homogeneous competition is avoided by calculating the repulsive force between the to-be-constructed shopping malls.

The invention discloses a risk zoning method for active fault engineering influence, which relates to the technical field of engineering construction evaluation, and comprises the following steps: I, fusing multi-source data of a detection fault, constructing a three-dimensional geometric model of the fault, and analyzing and correcting a fault distribution rule by utilizing a GIS (Geographic Information System) space; iI, combining fault sliding rate, paleoearthquake recurrence period and deep engineering crustal stress inversion, and evaluating activity characteristics of the hidden fracture in the engineering service period. Through multi-source data fusion, three-dimensional model construction and GIS correction, multi-factor overlay analysis and a dynamic correction mechanism, multivariate characteristics of the active fault and complex geological conditions of an engineering site are comprehensively considered, the problem of limitation of a zoning result is solved, the accuracy of risk assessment is improved, limitation of a single index is broken through, and the risk assessment efficiency is improved. Closed-loop management of data acquisition-risk assessment-engineering response is realized, and scientific and reliable technical support is provided for safe site selection, optimization design and operation and maintenance of major infrastructures.

The invention discloses a fire station site selection method and system based on an optimal transmission theory, and the method comprises the steps: building a multi-dimensional urban fire-fighting demand field, and building a three-dimensional quantitative index system which comprises a fire risk core evaluation factor, space population density and traffic accessibility; layered planning logic of high-risk area priority coverage, dense population area response guarantee and traffic reachability correction is realized, fire station site selection is abstracted into a probability measurement matching problem of a rescue capability field and a fire protection demand field, and a Wasserstein distance is introduced to measure a distribution gap between the rescue capability field and the fire protection demand field. The systematic balance of the coverage range and the response speed of the fire station is realized by minimizing the'transportation cost '(namely the response time of the fire fighting truck), and uncertainty factors in the site selection process are reasonably processed by introducing description of probability distribution, so that a site selection model which meets the actual fire-fighting planning requirements and also meets the site selection requirements is formulated. And a fire station site selection and distribution scheme of resource optimization configuration can be realized.

The invention relates to a locating and sizing method and system for an electric taxi battery swap station, and belongs to the technical field of intelligent transportation, and the method comprises the steps: carrying out the grid division of a target region, and determining a battery swap station position set and battery swap demand information corresponding to a battery swap demand position; by constructing a target function, the construction cost of newly building and rebuilding a battery swap station and the battery swap distance cost of a user are considered; setting constraint conditions such as total number of battery swap stations and demand distribution; a particle swarm algorithm based on simulated annealing is used for solving, a particle swarm is initialized by setting algorithm parameters, fitness is calculated in the iteration process, constraint conditions are judged, a new position is accepted or rejected according to the characteristics of simulated annealing, and particle position and temperature information is updated. And outputting a target battery swap station locating and sizing scheme after the maximum number of iterations is reached. According to the invention, the purpose of effectively reducing the construction and operation cost is achieved, the battery replacement experience of a user is optimized, and the efficiency and economical efficiency of the whole battery replacement network are improved.

The invention relates to a land site selection recommendation method and system fusing constraint conditions and an optimization algorithm, and the method comprises the steps: obtaining a constraint feature set, dividing the constraint feature set into strong constraint features and weak constraint features, and constructing a site selection knowledge graph; obtaining project demand information, and screening a strong constraint set and a weak constraint set based on the site selection knowledge graph; obtaining a compliance land use information set based on the strong constraint set, extracting fuzzy terms in the weak constraint set, and generating auxiliary decision suggestions based on the site selection knowledge graph; solving the compliance land use information set and the weak constraint set, generating a site selection recommendation solution set, and outputting the site selection recommendation solution set to the user side; according to the method, the planning provisions and the spatial data are automatically analyzed through the multi-channel feature extraction model, the site selection knowledge graph is constructed to achieve intelligent classification of constraint conditions, the recommendation solution set is generated in combination with the multi-target optimization algorithm, and the method has the advantages that the automatic processing degree of the complex planning provisions is improved, and multi-target optimization decision making is achieved.

The invention discloses an optimal reservoir building horizon site selection method for a multi-layer water-bearing gas reservoir underground gas storage. The method comprises the following steps: performing reservoir feature evaluation on each layer in a target gas reservoir; analyzing a reservoir cover system by utilizing the relationship between the lithologic configuration relationship of two sides of the fault and the fault mud ratio, and evaluating the change of a three-dimensional stress field under the injection-production pressure disturbance; from the perspective of production dynamics, historical data of a target gas reservoir are researched for productivity analysis, the water invasion amount and the gas-water ratio are calculated layer by layer, fine description and quantitative characterization are performed on the water invasion dynamics, and the gas-water distribution and migration conditions are determined; and finally, carrying out comprehensive evaluation, and selecting an optimal library building horizon. From two aspects of reservoir geologic characteristics and production dynamics, quantitative analysis and evaluation are carried out on all layers and small layers of the target gas reservoir, then the optimal target layer position can be relatively accurately selected, the reservoir building operation effect is good, the oil ring damage degree is small, and bottom water invasion can be effectively blocked.

The invention provides a dynamic traffic scene-oriented unmanned aerial vehicle charging base station site selection and scheduling collaborative optimization method. The method comprises the following steps of: 1, establishing a dynamic traffic flow space-time distribution model and an emergency event probability model based on a GIS (Geographic Information System) platform and traffic monitoring data; step 2, constructing a multi-target dynamic optimization model; 3, solving the multi-target dynamic optimization model by adopting an improved multi-target genetic algorithm; 4, a site selection and scheduling double-layer optimization structure is constructed, and unmanned aerial vehicle task allocation and base station utilization rate balance is realized through an unmanned aerial vehicle charging task scheduling model; and 5, realizing dynamic deployment and scheduling optimization closed loop of the base station through real-time data feedback. The method has remarkable advantages in the aspects of coverage rate, energy consumption, response time delay, system stability and the like. The emergency response speed and the energy utilization efficiency are remarkably improved, the system operation and maintenance cost is reduced, and the method is suitable for scenes such as intelligent traffic and emergency communication.

The invention discloses an eVTOL vertical take-off and landing airport site selection method considering travel mode selection behaviors, and relates to the field of urban low-altitude traffic facility site selection. The method comprises the following steps: firstly, considering a travel mode selection behavior, and constructing an urban air traffic (UAM) travel demand prediction model; secondly, solving an eVTOL vertical take-off and landing airport candidate point set by adopting an iterative weighted clustering algorithm according to an original traffic mode time value of a demand point; and finally, constructing a site selection model by taking reduction of the total cost of the network, reduction of the total travel time of the network and improvement of the UAM service rate as targets, and solving by adopting an NSGA-III algorithm. According to the method, the optimal eVTOL vertical take-off and landing airport site selection scheme can be given, meanwhile, the optimal travel mode of each journey of the whole network is solved, the model is more practical, and the urban travel efficiency is effectively improved.

A reservoir basin dam line site selection method based on an improved adaptive variable neighborhood search algorithm is realized based on an established dam line site selection optimization target mathematical model, and comprises the following steps: obtaining area DEM data needing site selection, and dividing the area DEM data into four identical sub-areas R1-R4; obtaining an initial parameter of each sub-region boundary, initializing the initial parameter of each sub-region boundary, and generating an initial solution Sol0; the initial solution Sol0 is used as a starting point, an IAVNS algorithm is adopted to solve the optimization target mathematical model, and the optimal solution Solbest of each sub-region is obtained; and outputting a global optimal solution Solbest fullness of the site selection area by using an SHP file, and carrying out visual display on the global optimal solution Solbest fullness. By proposing an IAVNS algorithm and adopting a double-layer collaborative optimization architecture, a dynamic hierarchical variable neighborhood search strategy and a rapid evaluation method based on terrain elevation variance analysis, global exploration, fine local search and multi-target dynamic balance optimization are realized, and fine optimization requirements under complex terrains are met.

The invention discloses an intelligent site selection method and system for farmland occupation and compensation balance, and belongs to the technical field of intelligent site selection, and the method comprises the steps: S1, collecting and preprocessing multi-source spatio-temporal data, so as to construct a basic database; s2, constructing a spatial adjacency relation graph, inputting data in the basic database into a graph neural network model, and generating a dynamic evaluation weight vector for a to-be-selected plot; s3, calculating a final utility value of each evaluation index, and calculating a multi-scale ecological balance score of the land parcel by adopting a multiplicative aggregation model in combination with the dynamic evaluation weight vector; s4, predicting the future quality evolution trend of the land parcels by adopting a long-short-term memory network model, and the method can accurately capture and respond to the spatial-temporal dynamic characteristics of the cultivated land ecosystem, so that the evaluation result is more fit with the physical reality.

The invention discloses a low-altitude take-off and landing infrastructure grading site selection method, device and equipment, and relates to the technical fields of low-altitude economy, urban planning and the like, and the method comprises the steps: obtaining a low-altitude scene demand, and carrying out the quantification processing, and obtaining demand data; a facility grading system is established based on the demand data, configuration data corresponding to the facility grading system is obtained, and the facility grading system comprises at least three grades; performing overlay analysis on the limiting factor data, and determining a candidate region; and performing hierarchical analysis on the candidate region to obtain suitability distribution data under the low-altitude scene requirement, and performing solution based on the suitability distribution data and the configuration data to obtain site selection data corresponding to the facility grading system.

The invention provides a whole vehicle logistics site selection and transportation path planning method and device and electronic equipment, and the method comprises the steps: converting the address information of each distribution network point in a preset region range into corresponding geographic position information; cleaning and preprocessing the address information, the geographic position information and the sales volume data of each distribution network to obtain target data, and clustering the target data to obtain a preset number of initial clusters corresponding to a preset area range; and performing iterative optimization on the preset number of initial clusters based on the target data and a preset freight rate data table to obtain a preset number of optimized clusters corresponding to a preset area range, and performing vehicle logistics transportation path planning based on the target data, the optimized clusters and the preset freight rate data table, and obtaining a target whole vehicle logistics transportation path corresponding to the preset area range. By adopting the method, the scientificity and the accuracy of site selection of the whole vehicle logistics transfer warehouse can be improved, and integrated coordination of site selection of the whole vehicle logistics storage center and whole vehicle logistics transportation path planning is realized.

The invention discloses a shipping fuel filling port site selection method, device and system, a storage medium and a computer program product, and belongs to the technical field of shipping fuel filling port site selection, and the method comprises the steps: obtaining the multi-dimensional technical attribute data of an alternative port; calculating the global weight of each piece of technical attribute data by using a preset algorithm; determining the relative proximity of each alternative port according to the technical attribute data and the global weight; and determining an adaptive target site selection port from the alternative ports based on the relative proximity. According to the scheme, the comprehensiveness and systematicness of shipping fuel filling port site selection evaluation are improved, and the matching degree of the site selection result and the actual operation requirement is effectively enhanced.

The invention relates to the technical field of geological data analysis, discloses a bank slope deep unloading developmental zone identification and evaluation method and system based on a sliding window, and aims at solving the problems that an existing method is low in efficiency and high in subjectivity, and evaluation results are discrete and cannot be quantized. Based on set window parameters, space convolution calculation is carried out through a sliding window, and a continuous feature signal sequence is generated; segmenting the sequence by using a preset background noise threshold value, and identifying candidate sections; matching and calibrating the boundary of the candidate section and the position of a known geological structural plane, and delineating a depth-keeping unloading development zone; the comprehensive development strength index of each deep unloading development zone is calculated, and quantitative evaluation is achieved. According to the method, automatic and continuous space recognition and quantitative representation of the deep unloading development zone are achieved, efficiency and accuracy are improved, and the method is particularly suitable for safe site selection and construction of major projects.

The invention discloses an LNG (liquefied natural gas) station site selection method and device based on multi-scene dynamic coupling, a computer and a storage medium, belongs to the technical field of LNG infrastructure planning and intelligent transportation, and solves the technical problem that LNG station site selection is difficult to accurately predict under multi-scene dynamic change. The method comprises the following steps: collecting multi-scene site selection data of the LNG filling station; data processing is carried out, and a space-time fusion big data medium table is constructed; constructing a long short-term memory network LSTM and reinforcement learning RL coupling model, and obtaining a real-time demand prediction result; a multi-constraint site selection optimization model is constructed, and an LNG gas station candidate site set is obtained; performing optimization through scene classification and feature modeling and defining a multi-scene layout criterion to obtain an LNG gas station candidate set adaptive to multiple scenes; according to the method, the site selection planning of the LNG filling station is realized by fusing the requirements and constraints of multiple scenes such as high speed, suburban, city and industrial districts, and the method is suitable for the field of administrative planning of the government and site selection planning of gas supply enterprises on the LNG filling station.

The invention relates to the technical field of wind power plant design, in particular to a wind power plant booster station site selection optimization method and system based on a multi-objective genetic algorithm and three-dimensional terrain constraint. The method comprises the following steps: firstly, constructing unified program grid data fused with geographic constraints; establishing a static land use cost model containing an orientation weight by using a rotation convolution algorithm; then, dynamic cost calculation logic of fan-power grid collaborative topology based on candidate coordinates, multi-constraint road dynamic path finding and site earth-rock balance iteration is defined; and finally, constructing a multi-target optimization model of comprehensive multi-dimensional cost, and performing iterative optimization through a genetic algorithm to obtain a Pareto optimal site selection coordinate. Through coupling of geographical constraints, the cost model and the intelligent algorithm, the problem of complex mountain site selection is solved, global collaborative optimization of land, earthwork, road and electrical cost is realized, and scientificity, economy and construction feasibility of wind power plant design are remarkably improved.

The invention provides a site selection method and device of a service site, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The site selection method of the service site comprises the following steps: acquiring address information of all orders in a target area and business sites to which the orders belong; performing clustering processing on all the orders based on the address information and the business points to obtain a plurality of clustering arrays; obtaining associated data of each clustering array, and determining a site selection demand point of the service site from the clustering centers corresponding to the plurality of clustering arrays according to the associated data; the associated data comprises at least one of the number of waybills, the transportation distance, the waybill collection result and the site selection cost. The site selection of the service site can be accurately recommended, and convenience is provided for effective construction of the service site.

The invention discloses a battery swap station planning method considering an electric vehicle user queuing factor, and the method comprises the steps: taking the site selection and capacity configuration of a battery swap station as decision variables, and taking the minimization of investment cost and user queuing length as optimization targets, and constructing a battery swap station planning model; firstly, the battery replacement demand of the electric vehicle is analyzed based on a trip chain; secondly, a Dijkstra algorithm is adopted to realize service area division of the battery swap station, and the dynamic operation process of the battery swap station is deeply studied; and finally, an immune genetic algorithm (IGA) is used for solving the constructed model, and the constructed model is used for solving the model. The method can effectively optimize the layout of the battery swap station, reduces the waiting time of a user, and has a high engineering application value.

The embodiment of the invention provides an urban public service facility site selection method, which belongs to the technical field of data processing, and specifically comprises the following steps: collecting facility data of a target area; modeling an urban public service facility site selection problem as a continuous maximum coverage problem with a distance attenuation effect and capacity constraint, wherein an objective function is a demand quantity which is satisfied to the maximum; dividing a target area by using a multi-scale grid, constructing a multi-scale space decision tree, and taking the multi-scale space decision tree as a site selection environment of the intelligent agent; enabling a plurality of agents to respectively control site selection plans of a plurality of facilities, and setting attributes of the agents; based on a target function, enabling multiple agents to interact with the multi-scale space decision tree, and optimizing agent network parameters by using a near-end strategy optimization mode to obtain an optimal site selection strategy; and performing site selection planning in the multi-scale space decision tree by using the optimal site selection strategy to obtain an urban public service facility site selection scheme. Through the scheme of the invention, the site selection efficiency and quality are improved.

The invention discloses an in-service town gas pipe network remote emergency cut-off valve transformation point selection method, and relates to the technical field of gas pipe network safety control, and the method comprises the steps: constructing a three-dimensional evaluation system comprising a pipe network connectivity index, a valve overflow rate index and an influence user number index, and correcting the importance index weight of the three-dimensional evaluation system; constructing a comprehensive evaluation model based on the three-dimensional evaluation system and the corrected importance index weight, and identifying a plurality of key valves based on the comprehensive evaluation model; based on the leakage consequence severity and rescue timeliness of the key valve, a remote emergency cut-off valve transformation priority evaluation model is constructed, and based on the remote emergency cut-off valve transformation priority evaluation model, the remote emergency cut-off valve transformation point location of the in-service town gas pipe network is determined. The problems of high transformation cost and low gas safety caused by the fact that a transformation point position cannot be reasonably selected by a valve transformation method of an urban gas pipe network which is put into operation are solved.

The invention discloses a business flow site selection optimization method based on multi-source data fusion, and relates to the technical field of big data processing and multi-source data fusion, and the method comprises the following steps: obtaining multi-source data of a to-be-detected region, constructing multi-source features, carrying out the cross-modal fusion of the multi-source features through an adaptive weight distribution mechanism, and generating a comprehensive feature representation; based on comprehensive feature representation, passenger flow trends and causal contributions of the candidate points in different scenes are output through a causal inference method; taking the passenger flow trend and the causal contribution as input, establishing a multi-objective optimization model, performing solving, and outputting a comprehensive score and a sorting result of the candidate points; constructing an explanatory index matrix according to the comprehensive score and the sorting result in combination with a causal contribution result, and outputting a site selection scheme through a visual platform; according to the method, the problems of single data source and incomplete candidate point description in the prior art are solved by fusing the spatial geographic data, the space-time traffic data and the consumption behavior data.

The invention belongs to the technical field of optimal planning and site selection of electric vehicle charging stations, and particularly relates to an electric vehicle charging station planning and site selection method, which comprises the following steps: S1, constructing an uncertainty set containing road and node traffic capacity loss; constructing a vehicle starting point-terminal point travel scene set in combination with the historical travel data of the vehicle; s2, constructing a network equalization model considering the arrival time fairness of the electric vehicle users according to the passing time cost difference of the electric vehicle users on the traffic road when the electric vehicle users go to the charging station from the starting point so as to solve regional traffic flow distribution; and S3, based on the traffic flow distribution in the S2, constructing a charging station planning model considering the user arrival time fairness, and determining an optimal site selection and capacity configuration scheme of the charging station. According to the method, the time fairness of the electric vehicle user arriving at the charging station can be ensured while the traffic uncertainty of the traffic network road section is effectively dealt with, and the method has a relatively good market application prospect.

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