79 results about "Trip chain" patented technology

The invention belongs to the field of urban rail transit network passenger flow simulation, and particularly relates to an urban rail transit passenger travel behavior characteristic estimation method. Obtaining, analyzing and processing travel data; searching a feasible path set meeting the maximum transfer frequency constraint between the OD pairs by adopting a breadth-first algorithm with constraint; reconstructing a passenger trip chain; through a path time similarity index, passengers on a non-transfer single path and passengers on a single-transfer single path are distributed to a train, and then a travel chain of the two types of travel passengers is obtained; and taking the standard deviation of the train arrival time as the standard deviation of the waiting time of the passengers on the platform, and calculating to obtain passenger in-out walking time distribution and transfer walking time distribution. According to the passenger travel behavior characteristic estimation method for the urban rail transit, station entering and exiting walking time distribution of passengers in all stations in an urban rail transit network, transfer walking time distribution of the passengers in each transfer direction of a transfer station and waiting time distribution of each station can be accurately estimated.

The invention discloses a subway passenger flow simulation deduction method considering an individual trip chain and an activity mode, and relates to the technical field of subway operation management, and the method comprises the following specific steps: constructing a topological framework, calculating an external factor attention weight, constructing an individual trip behavior library, carrying out dynamic topological adjustment, and executing passenger flow simulation and deduction. By constructing the space-time heterogeneous topology model and dynamically adjusting the weight values of the nodes and the edges, the precision and the real-time performance of subway passenger flow simulation are improved, physical parameters and historical operation data of urban subway lines are collected, an external factor input layer and a calibration layer are additionally arranged, and the accuracy of subway passenger flow simulation is improved. The spatial-temporal heterogeneous topology model can reflect the influence of external factors on the subway passenger flow in real time, the station retention weight and the line passing weight are dynamically corrected by calculating the attention weight of the external factors and combining data in the individual travel behavior library, and the prediction of the subway passenger flow change is realized. And a scientific basis is provided for subway operation managers.

The invention belongs to the field of big data processing, and discloses a crowd travel mode identification method and system for multi-view subspace learning based on dynamic and static feature fusion, and the method comprises the steps: carrying out the preprocessing of original signaling data, and cleaning invalid data; extracting a travel track of each user, and processing possible abnormal conditions of ping-pong data and drift data; after track data of a single user in a sample time period is obtained, segmenting the track data, and extracting a single trip chain track; extracting static characteristics such as travel behaviors and travel traffic of a travel chain in combination with multi-source data such as mobile phone signaling data, an OSM road network and public traffic data; extracting time sequence features by using an LSTM model, and modeling dynamic features of a travel track; static and dynamic features are fused, and travel modes are captured from multiple views. According to the method, the complex travel mode can be more accurately captured, data redundancy and noise are reduced, and the accuracy of travel mode identification is remarkably improved.

The invention discloses a truck trip chain identification method based on trajectory data, which comprises the following steps: S1, acquiring GPS trajectory data of a truck, and preprocessing the GPS trajectory data, including data cleaning, noise removal and trajectory segmentation; s2, by setting a speed threshold value and a time threshold value, the stop point of the truck is recognized; s3, spatial matching is carried out on the stay point and road network data, and the accurate position of the stay point is determined; s4, segmenting the track of the truck based on the time sequence and the space distribution of the stop points to form a plurality of travel segments; s5, analyzing the driving characteristics and the stop characteristics of each travel section, and removing stop points related to non-freight; and S6, identifying key staying points related to loading and unloading activities, and connecting the key staying points according to a time sequence to form a truck travel chain. According to the method, the actual running path and the logistics movable chain of the truck can be clearly restored, and high-precision technical support is provided for freight path analysis and industrial chain space structure recognition.

The invention discloses an urban circle traffic demand prediction method and system, and belongs to the technical field of traffic data processing, and the method comprises the steps: taking a four-stage theory as a basis, efficiently obtaining a travel demand OD matrix of urban circle global traffic middle area granularity in the future, and mastering the overall traffic demand pattern of an urban circle from a macroscopic level; on the basis of a trip chain theory, a city traffic cell granularity trip demand OD matrix with higher time-space precision in the future year is accurately obtained, and the method can adapt to more application scenes and reflect traffic prediction and evaluation analysis of traffic demand details; through a traffic distribution algorithm of motor vehicles and public traffic, traffic flow indexes borne by urban circle traffic supply facilities are obtained, and continuity and consistency of spatial and temporal distribution of the current situation and indexes such as traffic supply facilities, travel demands, road flow and bus passenger volume in the future are achieved. And therefore, the traffic demand prediction and evaluation index result of the urban circle in the future year can scientifically meet the needs of users.

The invention provides a transformer area distribution transformer available capacity evaluation method and system considering an electric vehicle access state, and a storage medium. The method comprises the following steps: obtaining transformer area transformer distribution transformer capacity and transformer area residential electricity consumption data in a preset time period; based on the inventory of the electric vehicles in the court, constructing a trip chain model for the charging behaviors of the electric vehicles by adopting a trip chain method, and calculating a daily charging load value of the court according to a travel schedule generated by the trip chain model; calculating a total load value of residential electricity consumption of the transformer area to obtain a transformer area basic load value, and calculating a total load value of the power distribution transformer area based on the daily charging load value and the transformer area basic load value; and on the basis of the transformer distribution transformer capacity of the transformer area and the total load value of the distribution transformer area, the available capacity evaluation of the distribution transformer area considering the access state of the electric vehicle is completed.

The invention relates to a railway passenger trip chain identification method based on mobile phone signaling data, and the method comprises the steps: extracting the mobile phone signaling data of a passenger, and identifying a track sequence and an activity point of a passenger individual; identifying railway junctions of the starting city and the terminal city based on the activity points and the track sequences of the passengers; judging whether the inter-city stage of the passenger is a railway trip or not by utilizing railway network GIS (Geographic Information System) data; on the basis of the activity points and the track sequence of the passengers, identifying an in-city connection trip of the starting city and the terminal city; primary activity points of the endpoint city are identified and a reservation time is estimated. Compared with the prior art, the method has the advantages that the whole-process travel requirements of the urban agglomeration can be comprehensively reflected, and reference can be provided for urban agglomeration railway operation organization optimization, urban connection traffic service improvement and the like.

The invention provides an electric vehicle charging load prediction method based on travel characteristics, which adopts a Monte Carlo method to simulate travel and charging behaviors of electric vehicles, and comprises the following steps: based on a travel chain model, extracting the initial travel time, the initial travel place, the initial charge state and the travel purpose of each time period of each electric vehicle; according to the travel purpose, extracting the functional area which each electric vehicle needs to reach in each time period; according to the functional area needing to be reached, path planning is carried out on the driving path of each electric vehicle; calculating the driving energy consumption on the driving path, and updating the charge state of the electric vehicle; the charging probability of the electric vehicle is judged according to the charge state; and finally, according to the charging probability of each electric vehicle in each area in different time periods, obtaining the charging load space-time distribution of the electric vehicles. By comprehensively considering various travel characteristics, the reliability of the prediction method is enhanced, and the problem of insufficient prediction precision is solved.

The invention relates to the technical field of traffic planning and control, and particularly discloses a regional public traffic reachability measurement method and device based on multi-source data fusion and a storage medium, and the method comprises the steps: firstly obtaining the station data of a subway and a bus, then dividing the travel modes of residents according to whether the subway and the bus are transferred, and finally, carrying out the division of the travel modes of the residents; and establishing a transfer connection chain between different traffic modes based on the transfer travel mode. And finally, index extraction is performed through the transfer link chain and the single trip chain, and the public transport station accessibility of the single trip and the transfer trip can be measured according to the model, so that the measurement of the regional public transport accessibility is completed by aggregating the station accessibility. According to the method, the travel modes are classified based on the multi-source data, a single travel and transfer link chain is established by considering continuous transfer services among different vehicles, and the station reachability of the single travel and transfer travel modes can be measured at the same time.

The invention provides a traffic mode prediction method considering parking lot charging and related equipment thereof. The method comprises the following steps: acquiring a grid map of a target area, parking lot data and resident trip chain data; calculating the grid parking fee of each grid unit in the grid map based on the parking lot data and the resident trip chain data; based on the resident trip chain data and the grid parking fee, constructing a private car generalized trip cost expression; constructing a multivariate utility selection model according to the private car generalized travel cost expression and a plurality of preset other travel cost expressions; generating a travel distribution matrix among the grid units according to the grid map and the resident travel chain data; and carrying out traffic mode prediction on the travel distribution matrix according to a multivariate utility selection model to obtain a refined travel distribution matrix considering traffic mode selection. According to the invention, by considering the influence of the parking fees on resident travel mode selection, the accuracy of urban traffic travel prediction is improved.

The invention relates to a city fast rail and urban rail joint travel demand identification method based on simulation deduction, which comprises the following steps of: based on historical passenger travel data, screening an effective path set by combining a density peak clustering algorithm and a depth-first algorithm, and calculating time distribution of each stage of passenger travel; a travel path and time distribution of each stage are fitted into a passenger travel chain, a travel demand identification method with travel simulation deduction of urban fast rail and urban rail connection passengers as a core is established, simulation units including passengers, trains, stations, a line network and the like are designed, travel tracks of the passengers in historical AFC data are deduced through simulation, and travel demands of the urban fast rail and urban rail connection passengers are identified. And obtaining refined passenger flow distribution and linked travel demand data in the line or the station under any time granularity. According to the method, a linked travel demand index system is established, refined passenger flow data recording in a passenger simulation process is realized, an actual passenger travel scheme is obtained, and detailed urban fast rail and urban rail linked travel demand passenger flow index output in a rail transit network is further realized.

The invention discloses a regional charging load prediction method, device and system, and a storage medium. The method comprises the following steps: S1, constructing a travel chain model, a working condition prediction model, an energy consumption prediction model and a charging decision model; and S2, according to coupling of the stroke chain model, the working condition prediction model, the energy consumption prediction model and the charging decision model, prediction of the regional charging load is carried out. By adopting the technical scheme of the invention, the charging load of the selected area can be predicted more accurately.

The present invention discloses a method for identifying and monitoring high-speed rail inter-city travel and commuting behavior based on mobile phone signaling. First, the mobile phone signaling data is cleaned, and then the mobile phone signaling data of the selected base stations along the high-speed rail line are divided into high-speed rail travel railway section travel chains; the actual high-speed rail passenger mobile phone signaling data is screened according to the average speed of each section of the high-speed rail travel chain, and the mobile phone signaling high-speed rail passenger identification results are adjusted and expanded by using the mobile phone signaling data through the passenger volume direction ratio analysis, and the high-speed rail inter-city commuting type is determined according to the cross-city flow through the travel direction of the high-speed rail passengers, and the cross-city section travel law and cross-city travel frequency are obtained according to the cross-city flow, and the passenger flow and distribution characteristics of various commuting passengers are obtained after analysis, so as to realize the real-time dynamic identification of high-speed rail inter-city travel and commuting behavior from massive mobile phone signaling data. The method of the present invention can promote the integration of the comprehensive transportation system and the national land space planning, and can serve the integrated development of urban transportation planning and regional transportation.

The invention relates to the technical field of data analysis, and discloses a trip chain supplement method and device based on user riding data, and the method comprises the steps: obtaining the riding data corresponding to a user; judging whether a to-be-supplemented riding record in the riding data meets a combined travel mode condition or not; when it is judged that the to-be-supplemented riding record meets the combined travel mode condition, determining a target combined travel mode matched with the to-be-supplemented riding record according to a mode classification condition; based on a trip chain supplement formula corresponding to the target combined trip mode, analyzing the riding data to obtain first supplement information; when it is judged that the to-be-supplemented riding record does not meet the combined trip mode condition or the trip chain supplement formula has no solution, the riding data are analyzed based on a trip chain prediction algorithm, and second supplement information is obtained; and supplementing the trip chain according to the trip chain supplement information. Therefore, by implementing the method, the supplement accuracy of the trip chain can be improved, so that the accuracy and the reliability of the trip chain are improved.

The present invention discloses an electric vehicle charging scheduling method considering demand matching, relates to the technical field of power system control, and alleviates the problems of under-response, over-response and slow solution convergence speed in the process of electric vehicle charging scheduling. The method comprises the following steps: establishing an electric vehicle charging load prediction model of a travel chain; inputting the sorted information into the electric vehicle charging load prediction model to obtain a prediction result; establishing an electric vehicle scheduling model of demand matching; inputting the prediction result in step 1 into the scheduling model in step 2, solving the problem through a fuzzy cuckoo algorithm, and obtaining a scheduling plan; the present invention utilizes an improved travel chain to perform electric vehicle load prediction, and predicts the charging load of electric vehicles at different times and spaces. The method can take into account multiple factors such as the number of electric vehicles and battery capacity, making the prediction result more realistic and reasonable.

The application provides a low-carbon travel carbon emission reduction amount verification method and system based on travel chain big data, which comprises the following steps: step 1) obtaining sample data of travel data; step 2) when the sample data is walking data, going to step 3); when the sample data is bicycle data, going to step 4); when the sample data is electric bicycle data, going to step 5); step 3) verifying the sample data by using a walking travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of walking according to the final effective travel distance; step 4) verifying the sample data by using a bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of the bicycle according to the final effective travel distance; step 5) verifying the sample data by using an electric bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; and calculating the carbon emission of the electric bicycle.

This invention relates to the field of electric vehicle trip chain simulation technology, specifically to a method and system for simulating the trip chain of electric heavy-duty trucks considering charging strategy optimization. The steps are as follows: Real-time updates of target station distance and battery charge; upon arrival at a loading / unloading station and when the loading status meets loading / unloading conditions, updating the loading / unloading status and executing an event; after the event, updating to a driving status; upon arrival at a charging station and when the battery charge meets charging conditions, if there is an available charging pile or the overall satisfaction level is greater than a threshold, updating the charging status and executing an event; after the event, updating to a driving status; if the overall satisfaction level is less than the threshold, directly updating to a driving status; updating the target station for the vehicle in the driving status and repeating the above steps until the entire trip chain simulation is completed. This invention has significant reference value for electric heavy-duty truck capacity demanders in terms of operational scheduling plans and station configuration, and can promote improvements in fleet operating efficiency and economy.

The application provides a cross-border passenger travel chain identification method and system, which comprises the following steps: screening the base stations of the exit port / airport based on the base station position information, and preliminarily screening the potential cross-border passengers; obtaining the full-chain travel of the potential cross-border passengers in the country through the residence area pre-identification algorithm based on the mobile phone signaling of the passengers in the country; constructing a residence mathematical model, screening the residence point with the highest credibility in the exit port / airport as the cross-border entry and exit point through the residence mathematical model, and determining the position of the cross-border entry and exit point; distinguishing the entry and exit behaviors of the cross-border passengers according to the residence time of the cross-border passengers in the port / airport and the mobile phone signaling disappearance characteristics; and combining the full-chain travel of the cross-border passengers in the country, the position of the cross-border entry and exit point and the entry and exit behaviors to construct the cross-border passenger travel chain. Through the scheme, the accuracy of the cross-border passenger travel investigation data can be improved, and the coverage time and coverage range of the investigation data can be improved.

The invention discloses a metro trip chain satisfaction evaluation method based on three-stage configuration factor analysis, and the method comprises the following steps: S1, dividing a metro trip chain, and formulating a metro trip satisfaction rule; s2, making questionnaires, issuing and collecting the questionnaires to obtain a data set; s3, acquiring basic information and travel sensing information of travelers and assigning values; s4, determining perceptual variables and observation variables participating in analysis; s5, converting the measurement variable into a sensing variable; s7, processing the data of the data set; s6, converting the data into a fuzzy set; s7, constructing a fuzzy set truth table; s8, obtaining configuration factors influencing subway travel satisfaction; and S9, counting the occurrence frequency of a single factor in the configuration factors, and carrying out statistical sorting. According to the method, the existing evaluation process and mechanism are perfected by obtaining the state influence factors, subway operation and planning are guided through the obtained result to make rectification opinions, and the subway sharing rate is improved.

The invention discloses a charging network guiding method and system based on trip chain simulation, and the method comprises the steps: firstly, building an electric vehicle trip probability model based on a trip chain theory, generating a virtual scene containing a vehicle driving track and charge state evolution through employing a Monte Carlo simulation method, and when a charging triggering condition is satisfied, carrying out the simulation of an electric vehicle trip probability model; recording the demand information to form a charging load basic data set for charging network planning; then, acquiring short-time load prediction data of a charging station; and finally, based on the prediction data, generating a guidance index related to the charging congestion degree, issuing the index to a user terminal for charging navigation, and reporting the prediction data to a power grid system at the same time. The problems that charging network planning lacks data support and information islands exist in operation are solved, planning basis is provided through scientific simulation, information linkage among the charging stations, the users and the power grid is established, and the cooperative operation efficiency of the whole energy traffic system is effectively improved.

The present application provides a method, device, electronic device and computer program product for obtaining user travel labels, and relates to the field of data analysis technology. The method includes: performing data cleaning on signaling data based on specific information; generating and classifying travel chains according to user trajectory data, and calculating the information entropy of each type of travel chain list; extracting a travel chain from each travel chain list and combining it with the corresponding information entropy to generate a travel chain information entropy list; when it is determined that the travel regularity index of the target user meets the standard, the travel chain information entropy list is matched with the map point of interest classification table to obtain the travel label of the target user. The embodiment of the present application cleans the acquired signaling data based on specific information, and filters the user's travel information based on the extracted user travel regularity characteristics, thereby matching travel labels that can accurately reflect the user's travel habits.

The invention discloses an electric vehicle space-time charging adjustable flexibility quantification method and device, and the method comprises the steps: building a charging space-time flexibility model based on a trip chain, and covering slow charging time and fast charging space flexibility modeling; a double-criterion random dynamic user balance model is constructed, and fast and slow charging loads in a balance state are obtained; and finally, quantitatively evaluating the slow charging time flexibility and the fast charging space flexibility potential by using the flexibility model. According to the method, traffic, price and user perception differences can be comprehensively considered, the charging load space-time schedulable characteristics are comprehensively reflected, and powerful support is provided for traffic-power system cooperative scheduling.

The present invention discloses a method and system for improving the public transportation share rate of a transportation community. The method comprises: obtaining public transportation travel parameters for all transportation communities based on multi-source data analysis; selecting target transportation communities that meet preset requirements based on filtering parameters; selecting travel directions from the target transportation communities to each other area that do not meet a first preset public transportation share rate as key public transportation passenger flow channels to be analyzed; performing a travel chain analysis on the key public transportation passenger flow channels to be analyzed to obtain corresponding travel parameters, and optimizing the key public transportation passenger flow channels that do not meet the travel parameter requirements using an expert knowledge base. The present invention analyzes the public transportation share rates of the selected target transportation communities, selects key public transportation passenger flow channels from them, and performs a travel chain analysis on them, thereby regularly analyzing the public transportation share rates of transportation communities in a geographically fine-grained manner and improving and optimizing key public transportation passenger flow channels with low public transportation share rates.

The present invention discloses a method and system for setting up expressway passenger transfer points and routes based on multi-source big data, which divides traffic areas according to traffic information and establishes a map in GIS software; calculates the OD matrix 1 of inter-city road passenger routes between highway passenger stations in each city according to ticket data; calculates the OD matrix 2 of inter-city travel based on mobile phone signaling data; extracts the travel chain and passenger flow of inter-city road passenger routes that can be used for travel according to traffic information; sets up passenger transfer points according to OD matrix 1, OD matrix 2, travel chain and passenger flow; and completes the arrangement of inter-city road passenger routes according to OD matrix 1, OD matrix 2, travel chain, passenger flow and passenger transfer points. The present invention can set up passenger transfer points according to travel passenger flow, forming an intensive operation mode of "expressway main line + city transfer branch line", which is simple and practical, and has strong operability and effectiveness.

The application provides a travel service recommendation method and device, an electronic device and a storage medium. The method comprises the following steps: performing regional clustering based on station position information of a traffic station to obtain a plurality of candidate travel region pairs, and constructing a candidate travel chain; obtaining first historical travel data in a preset historical period, and extracting corresponding second historical travel data for a preset operation time interval; for each candidate travel region pair, determining a maximum number of transfers, and obtaining a target historical passenger flow in a continuous historical time interval; combining a preset screening condition to screen a target travel region pair; extracting third historical travel data, generating a travel inappropriateness evaluation index of each operation time interval based on the first and third historical travel data for each target travel region pair, and recommending a travel service to a travel object according to the travel inappropriateness evaluation index. The application can provide personalized traffic services for travel objects with the same travel demand and provide a good travel experience.

The invention relates to the technical field of intelligent traffic, and particularly discloses a traffic flow prediction system based on trip chain dynamic evolution. The system comprises a trip chain real-time sensing and reconstruction module, a trip decision behavior dynamic deduction module, a group trip demand evolution calculation module and a macroscopic traffic flow dynamic prediction module which are connected in sequence. Through real-time sensing and reconstruction of an individual trip chain, individual behaviors are dynamically deduced, group demands are simulated and aggregated based on an intelligent agent, and a macroscopic model is driven to carry out prediction. The system constructs a complete technical closed loop from microscopic individual behavior real-time perception to macroscopic road network flow dynamic prediction. According to the system, through cooperation of the travel decision behavior dynamic deduction module and the group travel demand evolution calculation module, fine simulation of a group travel behavior emergence effect under an emergency situation is realized.

The application discloses a kind of motor vehicle trip chain intelligent generation method based on attention mechanism, including obtaining the trip observation data of individual vehicle using AVI detector, constructs the association system of trip observation and behavior attribute, and generates embedding vector representation by semantic and syntax embedding method;Combined with the characteristics of fragmented data, the multi-head attention mechanism of spatio-temporal association is fused, the coding and decoding structure is improved, and the trip chain is generated;Adopt the multitask pre-training strategy of autoregressive generation and contrast learning;Through trip group clustering and hierarchical fine-tuning strategy, implement individual heterogeneity adaptation using lightweight adapter, generate candidate trip chain in instruction-reply form, filter high-confidence results according to generation probability and spatio-temporal consistency, provide accurate and diversified support for motor vehicle trip chain reconstruction;The application can efficiently and accurately infer and generate complete motor vehicle trip chain under fragmented observation conditions, and consider the generalization ability and individual difference adaptability of the model.

The invention provides a cross-border passenger trip chain identification method and system, and the method comprises the steps: screening out a base station of a port / airport based on the position information of a base station, and carrying out the preliminary screening of potential cross-border passengers; the method comprises the following steps of: acquiring full-chain travel of potential cross-border travelers at home through a residence area pre-identification algorithm based on mobile phone signaling when the travelers reside at home; constructing a residence mathematical model, screening a residence point with the maximum credibility in the port / airport through the residence mathematical model as a cross-border access point, and determining the position of the cross-border access point; distinguishing entry and exit behaviors of the cross-border passengers according to the residence time of the cross-border passengers at the ports / airports and the mobile phone signaling disappearance characteristics; and merging full-chain travel, cross-border entry and exit point positions and entry and exit behaviors of the cross-border passengers in the home, and constructing a cross-border crowd travel chain. Through the scheme, the accuracy of the cross-border passenger travel survey data can be improved, and the coverage time and coverage range of the survey data are improved.

The invention discloses a bus OD derivation and road network flow matching method based on multi-source data and electronic equipment, and the method comprises the steps: obtaining and preprocessing bus multi-source data in a target area, and building a station-line-vehicle three-dimensional mapping relation with direction information; the bus multi-source data comprises bus GPS (Global Positioning System) data, station GIS (Geographic Information System) data and IC (Integrated Circuit) card swiping data; determining passenger types and historical travel laws according to the IC card swiping data, analyzing travel chain features according to the historical travel laws, and then deducing getting-off stations and time of passengers in combination with POI types of stations; according to the deduced get-off station of the passenger and the corresponding route, determining a station sequence interval of the travel path of the passenger on the bus route; then, according to the corresponding relation between the line station and the road section number in the road network, the passenger travel path is mapped to the actual bus road network, the total passenger flow of each road section is counted, and flow statistical data with the road section as the unit is generated.

The invention discloses an electric vehicle charging scheduling method and electronic equipment, relates to the technical field of new energy, and can accurately understand a travel intention and a charging demand of a user by acquiring information such as a starting point, a terminal point, a departure moment and an initial charge state of a target electric vehicle. And the charging scheduling scheme and the actual travel plan of the user are ensured to be closely fit. The trip chain is constructed based on the starting point and the ending point, and the optimal path is generated, thereby ensuring the reasonability and high efficiency of path selection. And the optimal path and the initial charge state are combined to accurately predict the time, the place and the required charge state of the charging service of the electric vehicle, so that the charging station can be planned in advance, and the time cost for searching the charging pile is reduced. Charging can be reasonably arranged in an idle window of a user travel by calculating a charging schedulable time period. And a moss growth optimization algorithm is adopted to solve the charging power scheduling model, so that the feasibility of a charging scheduling scheme and the stability of the whole system are ensured.