675 results about "Travel time" patented technology

The invention provides a method for quickly establishing a three-dimensional near-seafloor speed model in a shallow sea area; the method comprises the following steps of extracting initial informationand extracting a common-center-point gather; calculating the slope of the initial travel time in the common-center-point gather; judging whether a shot point is located in the seawater layer or not,and if yes, correcting the shot point to the seafloor; calculating the speed and the depth of inflection points corresponding to respective offset distances; performing linear interpolation on the speed at each depth to obtain a one-dimensional speed model of the common center point; after all the common-center point domain gathers are processed, adopting the linear interpolation method, calculating the speed values of the rest grid points by utilizing the one-dimensional speed model of all common center points, and a speed model is output. By adoption of the method for quickly establishing the three-dimensional near-seafloor speed model in the shallow sea area, the influence of the seawater layer on ray propagation is overcome, and the influence of multiple excitation receiving modes on an inversion result is eliminated, and the problem that the conventional method needs to carry out multi-ray tracing forward modeling is avoided.

The embodiment of the invention provides a multi-system rail transit junction streamline optimization method and device, and the method comprises the steps: carrying out the three-dimensional modeling of a multi-system rail transit junction, and obtaining a transportation junction model; dividing the transportation junction model into a plurality of digital blocks according to geographic information, facility layout information, passenger flow data and operation data of the multi-system rail transportation junction; according to passenger flow and logistics demands among different digital blocks, determining a connection relationship among the different digital blocks; according to the method, a streamline path is determined based on a connection relationship between different digital blocks, a streamline operation condition is simulated, a preset optimization target is taken as a target function, the streamline path is optimized, an optimized streamline is obtained, and the operation efficiency of the hub is effectively improved through division of the digital blocks in the hub and optimization of streamline organizations and stream nodes between the blocks; the travel time of passengers is shortened, and the passenger satisfaction is improved.

The invention provides a rescue scheduling method and device, electronic equipment, a storage medium and a product, and the method comprises the steps: building a multi-target optimization model based on a target function for minimizing travel time and resource cost, and constraint conditions of the target function; solving the multi-objective optimization model by adopting an evolutionary algorithm, determining an optimal rescue point corresponding to each accident point, and determining an optimal planning path with the shortest travel time from the optimal rescue point to the accident point; and based on the optimal rescue point and the optimal planning path corresponding to each accident point, determining a rescue scheduling strategy and carrying out rescue. According to the method, the optimal rescue point can be quickly and scientifically selected under the conditions of limited rescue resources and simultaneous occurrence of a plurality of accidents, and the optimal path can be planned while the optimal rescue point is selected, so that the overall rescue efficiency and the rescue effect are improved, and efficient and scientific rescue scheduling is realized.

The invention discloses a campus multi-scene service reservation management system and method, relates to the technical field of campus service management, dynamically calculates the optimal transit time between scenes based on three-dimensional building topology and real-time environment data, and breaks through the limitation of static path planning. Personalized dynamic buffer time is generated by fusing user historical performance data, a fixed threshold value is replaced, and risk control and the resource utilization rate are both considered; and when detecting that the time exceeds the limit, starting a progressive response closed loop: tracking the position and the speed of the user in real time, re-estimating the path time through a dynamic compensation algorithm, and circularly deciding. If the risk continuously exists, an acceleration prompt is intelligently pushed or a reservation time period is adjusted; the cross-scene default risk is reduced through space-time conflict pre-judgment and personalized buffering, and the utilization rate of the venue is improved through a reservation time period dynamic adjustment mechanism.

This disclosure describes, in part, systems and methods for dynamic allocation, monitoring, and reallocation of work machines (108) at a worksite (100) by using a controller (112) to monitor ranked production circuits (102) and dynamically reallocate work machines (108) based on their energy consumption, energy availability, machine operating parameters, flow rates, delays, wait times, and other such factors to provide compliance with the ranked production plan (204). The controller (112) is used to determine slow-downs, delays, queues, or other potential delays, including insufficient energy availability for a particular work machine allocation (110), and to dynamically adjust to provide for work machines (108) to reduce empty travel time and to maintain material flow rates or other metrics of the production plan (204).

The invention discloses an entrance ramp fleet confluence method based on reinforcement learning, and the method comprises the steps: carrying out the scene modeling according to an actual scene, and designing a plurality of fleet scales and vehicle speeds; vehicle acceleration and deceleration are designed as action selection, and the running speeds of main road vehicles and ramp vehicles are changed, so that the ramp vehicles select proper confluence opportunities; the change of the total travel time reflects the motorcade afflux efficiency, a reward function is established according to the change of the vehicle speed, the change of the time headway and the queuing waiting time of a ramp vehicle on an acceleration lane, an action mask is designed to improve the training efficiency, an intelligent agent is trained by using a PPO algorithm, and a trained deep reinforcement model is taken as a vehicle afflux model. The method gives consideration to the better passing efficiency of the main line and the shorter queuing waiting time of the vehicles on the ramp, enables the passing of the main line to be smoother, reduces the waiting time of the vehicles on the ramp as much as possible, and reduces the interference on the main line caused by the convergence of the vehicles on the ramp.

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.

The invention relates to a multi-mode network accessibility calculation method for comprehensive traffic, a calculation device and a storage medium, and the method comprises the steps: constructing a network structure of a comprehensive traffic network which comprises a road network, a railway network and an aviation network; recording longitude and latitude coordinates of a starting point and an ending point; a heuristic function of an A * algorithm is improved; the hierarchical search algorithm comprehensively considers travel paths in the traffic network, including intra-city travel paths of cities where a starting point and a terminal point are located, and inter-city travel paths connecting the cities where the starting point and the terminal point are located; calculating total travel time; and calculating reachability and diagnosing reachability depression. According to the method, the path search algorithm is improved with the aim of improving the efficiency, and the path search algorithm suitable for the multi-mode traffic network is constructed from the two aspects of optimizing the search path and improving the search strategy. And by introducing a hierarchical search strategy, unnecessary repeated search is effectively reduced, and the search efficiency of the algorithm is improved. Meanwhile, the algorithm fusion considers the influence of the transfer times and the train connection relationship on the travel path selection, and the actual travel behavior of the traveler is simulated more accurately.

An evacuation inducing method and system are disclosed. The evacuation inducing method includes: detecting the location of an emergency situation in a target region; generating prediction data for each unit path included in the target region, which predicts until when each unit path will remain safe, by predicting a spread of the emergency situation from the location of the emergency situation by using a spread prediction algorithm; predicting the degree of congestion on each unit path included in the target region and the expected travel time by taking into consideration the number of objects included in the target region that is preliminarily identified; creating an evacuation route based on the prediction data, the degree of congestion on each unit path, and the expected travel time; and initiating guidance for distributing and evacuating the objects based on the created evacuation route.

Lane segment-level traversal information is obtained. The lane segment-level traversal information is converted into probabilities for a state transition function. A policy is derived from a decision model using the state transition function. The policy directs vehicle movement of a vehicle between neighboring lane segments based on a cost function integrating a user preference with respect to at least two objectives and a slack time for alternative routes. The slack time indicates an allowable deviation in travel time relative to the user preference. A destination is received. The vehicle is then autonomously controlled on a route to the destination using the policy for lane transitions based on current lane positions.

The invention relates to the technical field of traffic, and discloses a railway junction passenger station division scheme optimization method, which comprises the following steps of: establishing an objective function of minimizing total travel time of passengers in a city and total operation cost in a railway junction; establishing a target and passenger flow distribution constraint condition; the method comprises the following steps: constructing a train operation scheme optimization model based on the target function of the minimum total travel time of passengers in a city and the minimum total operation cost in a railway junction and the target and passenger flow distribution constraint condition, solving based on the passenger flow distribution condition under the train operation scheme to obtain a train operation scheme optimization result, and inputting the train operation scheme optimization result into a Logit model to carry out passenger flow distribution. Obtaining a passenger flow distribution condition under the current train operation scheme, inputting the passenger flow distribution condition into the train operation scheme optimization model for solving again to obtain a current train operation scheme optimization result, and continuously iterating to obtain an optimal train operation scheme; according to the method, by optimizing the labor division of the passenger stations in the hub, passengers can take the bus nearby, and the urban travel time of the passengers is saved.

The invention discloses a commercial vehicle cruise control method and system giving consideration to physiological strain and energy conservation. The method comprises the following steps that a human body physiological strain index is constructed, a vehicle oil consumption and energy conservation index is constructed, an optimal cruise speed planning model is established, and the vehicle oil consumption and energy conservation index and passing time are comprehensively considered to form an overall reward function; a reinforcement learning algorithm is introduced for solving, and the optimal cruise speed within the predicted duration is obtained; constructing a vehicle-seat-human body system longitudinal and vertical coupling dynamic model, and calculating a human body physiological strain index under the optimal cruise speed; vehicle state constraints and optimal cruise speed sequence tracking constraints are coded as control constraints, and a longitudinal model prediction control strategy is formed based on a human body physiological strain index, a vehicle fuel consumption energy-saving index and tracking deviation; and a pitching regulation and control mechanism is added to realize vertical ceiling hybrid control, and the vehicle suspension is regulated and controlled. The vehicle cruising performance can be improved, and collaborative optimization of human body physiological strain and vehicle energy saving is achieved.

The invention relates to an origin-destination route deduction and travel time estimation method, which can deduce and generate a personalized travel route between origin and destination in combination with user preference on the premise of reducing data annotation quantity as much as possible by combining an antagonistic inverse enhancement network of an active learning mechanism. Furthermore, a plurality of weak learners fusing the graph convolutional network and the Transform network are integrated through an AdaBoost algorithm, and the graph convolutional network can obtain the high-dimensional spatial-temporal characteristics of the traffic network, thereby assisting the Transform network to better capture the long-term and short-term dependency relationship corresponding to the personalized driving route. In combination with learning results of a plurality of weak learners, a more abundant and comprehensive time sequence feature representation of a personalized driving route is obtained, finally, the travel time corresponding to the route is obtained by using the time sequence feature representation, and accurate origin-destination travel time estimation is realized.

The invention relates to a modular bus jump and relocation scheduling method considering stop time, and belongs to the field of urban public transport operation scheduling. The method comprises the following steps: acquiring geographical location information of a certain conventional bus route and a station and travel data information of bus passengers, and determining the number of passengers getting on and off the bus at the station; the method comprises the following steps: defining a jump station and relocation coupling operation as a decision variable, and establishing a relational expression between the stop time of a modular bus at a stop and the number of passengers getting on and off according to a modular bus single-door design characteristic and a bus combination form; taking the minimum sum of the passenger travel time cost and the vehicle operation cost as an objective function, setting a modular bus flow constraint and an operation time constraint, and constructing a modular bus scheduling optimization model; and solving to obtain an optimal modularized bus jump station and relocation scheduling scheme under the passenger demand of the corresponding line. According to the invention, for a bus route scene where part of bus stations are relatively high in load, modular bus operation scheduling which efficiently utilizes bus transport capacity resources is realized.

The invention discloses a bus travel time prediction method based on a space-time diagram attention network, and the method comprises the steps: carrying out the preprocessing of bus arrival time data, including bus number definition, duplicate record deletion, abnormal record deletion and missing record restoration; calculating the interval travel time of each train number based on the bus arrival time data to obtain the average travel time of each interval in different time periods; constructing a directed network by taking midpoints of the station intervals as nodes of the network and connecting lines between the midpoints of the station intervals as edges of the network, and fusing a distance matrix and a communication matrix of the nodes to represent spatial features between the nodes so as to weight the network of the station intervals; the historical average travel time of the station interval and the travel time of the latest train number serve as input, a transfomrer encoder, a graph attention network and a full connection layer are adopted to build a space-time graph attention network, time features and space features are extracted and fused, and multi-step prediction of the travel time of the station interval is achieved. According to the invention, more accurate bus travel time prediction can be realized.

The invention discloses a traffic flow management method combining dynamic and static induction information in a complex traffic environment, and the method comprises the steps: constructing a temporal-spatial data cube containing a temporal label, fusing static road network constraints (forbidding rules and lane attributes) with dynamic real-time data (flow and events), purifying abnormal data through a double-layer verification mechanism, and guaranteeing the compliance. Based on a hierarchical multi-objective optimization model, an NSGA-AM algorithm with an attention mechanism is adopted, a Pareto optimal induction strategy considering travel time, carbon emission and road network fairness is generated, the calculation complexity is reduced to O (NlogN), and the strategy generation time is less than 10 ms. Through a three-level feedback mechanism (immediate response, short-term adjustment and long-term evolution) of edge computing and cloud collaboration, system self-evolution is realized, and traffic environment changes are dynamically adapted. Experiments show that according to the method, the violation induction rate is reduced from 15% to 2% or below, the average travel time is reduced by 18%-25%, carbon emission is reduced by 12%-18%, the passing efficiency and safety of a complex road network are remarkably improved, and the method is suitable for engineering application of an intelligent traffic system.

The invention provides a rail transit platform passenger flow prediction method and system, computer equipment and a storage medium. The method comprises the following steps: extracting directed OD pairs from entry and exit records of an automatic fare collection system based on a completely directed network; determining travel time of non-transfer and one-time transfer; determining the real-time position of the passenger according to the directed OD pair and the travel time; according to the real-time position of the passenger, determining the passenger flow volume of each station in the target time period; acquiring and processing weather factors; a CNN-BiLSTM-Attention passenger flow prediction model is constructed; and taking the passenger flow volume of each station in the target time period and the weather factors as input variables, and predicting the passenger flow volume of each station by using the CNN-BiLSTM-Attention passenger flow prediction model. According to the method, the influence of weather factors, time dependence and spatial features is considered, the attention on key features is enhanced, and the prediction precision of the platform passenger flow is improved.

The invention relates to a method for reducing driver fatigue (2) of a vehicle (1). According to the invention, it is provided that when the comfort mode for fatigue reduction is activated and when driver fatigue is detected by an attention assistant of the vehicle (1), - an interior temperature in an interior area of ​​the driver (2) is reduced to a predetermined temperature and / or - increased ventilation in the interior area and / or - a fragrance device for the emission of a lemon and / or mint fragrance is activated at least in the interior area and / or - the seat heating of a driver's seat is deactivated and / or - the driver's seat ventilation is activated and / or - a seat kinetics of the driver's seat to promote blood circulation in the driver (2) is activated intermittently and / or - a distance and / or travel time to the next stopping point for the vehicle (1) is displayed or is and / or - by means of ambient lighting (6) emitting blue, green or white light and / or - dynamic light sequences are emitted by means of the ambient lighting (6) and / or - the brightness of a display unit (5) assigned to the driver (2) is increased and / or - a blue light filter of the display unit (5) is deactivated.

The present invention discloses a path planning method, system and transportation control system, wherein the path planning method includes the following steps: determining a feasible path according to the starting point and end point of a transport vehicle; determining the estimated travel time of the transport vehicle through each feasible path; when determining the estimated travel time, determining and selecting the feasible path with the shortest estimated travel time as the optimal path according to the static time, dynamic time of each line segment of the feasible path and the static time between each line. When performing path planning, the present invention estimates the travel time of the transport vehicle through each feasible path according to the dynamic situation of each feasible path, and selects the feasible path with the shortest estimated travel time as the optimal path. When determining the estimated travel time, not only the static time of the transport vehicle passing normally is considered, but also the delay that may be caused by the existing transport vehicles in different line segments of each feasible path is considered, which effectively improves the accuracy of path planning and is conducive to improving travel efficiency.

The invention provides a multi-destination travel itinerary planning method, system and device and a storage medium. The method comprises the following steps: receiving a travel planning request of a user; based on keywords in the tourism planning request, matching a plurality of related scenic spots and tourism destination cities to which the scenic spots belong; at least according to the traffic information between the tourism destination cities, generating a tourism sequence based on the tourism destination city playing sequence, inter-city traffic planning between the tourism destination cities and tourism sub-planning of each tourism destination city, and obtaining daily tourism tracks, daily accommodation hotels and scenic spot routes; and generating a travel itinerary plan according to the daily travel track, the scenic spot itinerary, the daily accommodation hotel and the inter-city traffic plan, and feeding back the travel itinerary plan to the user. According to the invention, travel, accommodation and travel time arrangement and route planning schemes in the travel itinerary of the user can be finely designed, the travel time cost of the user is reduced, and the experience in the travel itinerary is improved.

The invention discloses a cross-layer cross-roadway shuttle vehicle warehousing system configuration optimization method. The method comprises the following steps: determining a warehouse-in and warehouse-out operation process of a cross-layer cross-roadway shuttle vehicle warehousing system; establishing a mixed integer optimization mathematical model according to the operation process, and defining a model hypothesis; determining a target and a constraint condition of a mixed integer optimization model; designing an improved non-dominated sorting genetic algorithm II fused with a clone selection algorithm to solve the mathematical model; and the optimal layer-column ratio configuration of the cross-layer and cross-roadway shuttle vehicle warehousing system is determined. According to the method, a mixed integer optimization model is established by taking a cross-layer cross-roadway shuttle vehicle storage system as a research object and taking travel time and energy consumption required for completing a task as targets, and an improved non-dominated sorting genetic algorithm II is adopted to solve a problem to determine the optimal configuration of the system. According to the method, a design scheme can be provided for designers in the warehouse design stage, and the optimal configuration of the warehousing system and the model selection of the equipment speed acceleration are determined.

A method of calibrating a monitoring device (20) for monitoring movement of a movable component (2, 12, 19) of an elevator system (2) comprises detecting (120) a travel time (Δtk) between a starting time (tk) and a stopping time (t′k) as well as acceleration (a(t)) of at least one movement of the movable component (2, 12, 19); determining (130, 140) a travel distance of the movable component (2, 12, 19) by integrating the detected acceleration (a(t)) twice with respect to the detected travel time (Δtk); correlating (150) the determined travel distance (sk) with the detected travel time (Δtk) to form a pair of travel time and travel distance; and storing (160) the pair of travel time and travel distance (Δtk,sk) as part of a travel profile (34).

The invention provides an unmanned aerial vehicle path planning method and system based on an intelligent logistics cabinet, and relates to the field of logistics planning, and the method comprises the steps: obtaining a distribution task set, and calculating the total travel time corresponding to all distribution tasks in the distribution task set, so as to determine a scheduling time range; grouping the distribution task set to obtain a plurality of task groups; configuring constraint conditions for modeling; introducing a binary variable and using a time extension network method to introduce a time dimension to construct a target planning model; and solving the target planning model through a preset solver. Through time discretization, a dynamic problem is converted into a static network problem, so that the problem is more visual and easy to understand and analyze. The target planning model is easy to expand, the problem that a continuous time model is difficult to model can be converted into the problem that modeling can be carried out, and modeling and solving of complex problems are achieved.

The invention provides an airport passenger travel data processing method and device based on artificial intelligence, and relates to the field of data processing. The method comprises the following steps: responding to a dining request input by a passenger through user equipment; boarding duration, boarding gate position information and carry-on luggage information of passengers are obtained; according to the boarding duration and the carry-on luggage information, determining the dining duration of the passenger; acquiring current position information of the passenger; acquiring real-time meal delivery duration, real-time queuing duration and dining duration corresponding to the dining place in the preset range; determining the dining duration based on the current position information and the real-time serving duration, the real-time queuing duration and the dining duration corresponding to the dining place; according to the dining duration, screening from the plurality of dining durations to obtain a first dining duration meeting a preset condition; determining a first dining place corresponding to the first dining duration; and sending the first dining place to the user equipment. By implementing the technical scheme, the problem of reasonable arrangement of travel time of airport passengers can be solved.

The invention provides a road navigation method and system based on artificial intelligence, and relates to the technical field of road navigation, the method comprises the following steps: determining information of each passenger and the longest travel time and the shortest travel time of the travel by using an internal video processing model based on a shot video in an automobile; matching a plurality of navigation routes based on the longest driving distance, the shortest driving distance, the longest travel time, the shortest travel time and the target scenic spot information of the travel; screening out a plurality of target midway scenic spots of each navigation route from the plurality of initial midway scenic spots in each navigation route by using a target midway scenic spot determination model; displaying a plurality of navigation routes and a plurality of target midway scenic spots of each navigation route in the plurality of navigation routes; and navigating based on the selected target navigation route. The method can recommend appropriate scenic spots and the optimal navigation route, and the travel experience of the user is improved.

The invention relates to the technical field of public transport planning, and discloses a calculation method for quantitative evaluation of a bus track connection relationship, and the method comprises the following steps: 1, collecting data: collecting travel time, travel cost data and track travel time data between any stations of a bus line; according to the calculation method for the quantitative evaluation of the bus track connection relationship, a comprehensive quantitative evaluation model is provided by comprehensively evaluating the travel time, the cost and the competition coefficient between the bus line and the track line; the comprehensive evaluation method can more accurately identify and quantify the advantages and disadvantages in the bus track connection relationship, thereby helping a planner to optimize the layout of bus lines and track lines, reducing resource waste, improving the overall transportation efficiency, and improving the transportation efficiency by optimizing connection points and adjusting operation strategies. The transfer time and waiting time of passengers can be reduced, the travel experience of the passengers is improved, and the operation cost is reduced.

The invention belongs to the technical field of intelligent network connection scheduling, and particularly discloses a network connection bus real-time scheduling optimization method and system based on reservation passenger demands. The method includes: acquiring passenger travel information; according to passenger travel information, starting from three targets of bus operation cost, passenger travel time cost and waiting time cost, and considering constraint conditions of a departure time window, inter-station operation speed and bus capacity, establishing a time-varying capacity bus and traveler accurate matching scheduling model under reserved travel; an initial planned driving scheme is generated through a simulated annealing-genetic algorithm, and the initial planned driving scheme comprises a bus running timetable and a departure scheme; according to vehicle operation real-time data, bus operation delay caused by uncertainty of actual travel time is considered, and a speed regulation measure is adopted to reduce moment deviation between actual operation and a planned driving scheme. According to the invention, accurate matching scheduling and dynamic adjustment of the bus and the traveler in the reservation travel scene can be realized.

The invention discloses a traffic signal scheduling system and method based on road section congestion information, and relates to the technical field of traffic management and control. Comprising the following steps: S1, data acquisition and preprocessing: acquiring original traffic data of a road section in real time through a multi-source data acquisition unit, the original traffic data comprising traffic flow, vehicle speed, queuing length, occupancy and travel time; meanwhile, external environment data are obtained, wherein the external environment data comprise weather information, holiday and festival information and traffic event information; traffic data and external environment data are acquired in real time through the multi-source data acquisition unit, data quality is guaranteed through cleaning, denoising and standardized preprocessing, and the problem that data fusion of an existing system is insufficient is solved; by means of an adaptive weight learning model, historical traffic data, real-time data and environmental data are combined to dynamically adjust a congestion evaluation index weight, and the defect that a fixed or empirical weight is difficult to adapt to traffic dynamic changes is overcome.

The application discloses an elevator destination floor distribution method, comprising the following steps: step S101, setting a limit value; step S110, generating a new elevator call; step S111, counting the elevator calls; step S112, judging whether there is a car below the limit value; step S114, selecting a car with the elevator call registration number below the limit value as a primary selected car; step S115, judging whether a priority car selection rule is met; step S117, finding a car meeting the priority car selection rule and selecting the car meeting the priority car selection rule as a final selected car; if no car meeting the priority car selection rule is found, the primary selected car is selected as the final selected car. The candidate car selection rule provided by the application can distribute the destination floor registrations of similar floors to the same car as much as possible, and can further shorten the travel time of the elevator and improve the operation efficiency of the elevator group.

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.