557 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 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 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.

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 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 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 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 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 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.

The invention discloses a charging pile operation and maintenance optimization method and system based on intelligent prediction and scheduling, and belongs to the technical field of charging pile operation and maintenance. The method comprises the steps of designing a deep learning model, dividing a data set for training, and outputting a fault emergency degree; dividing candidate task groups, setting radiation range constraints of operation and maintenance sites, and combining the candidate task groups to perform priority ranking and task combination to obtain task groups; in combination with the state and skill of the operation and maintenance personnel, distributing a corresponding task group; calculating the passing time of the operation and maintenance route, optimizing the route based on the task group to obtain an optimized route, and combining the optimized route with the task group to obtain an operation and maintenance task; environment safety detection is carried out on the fault charging pile, and visual verification is carried out on the equipment state; defining an execution condition, judging whether the execution condition of the operation and maintenance task is met or not, and executing the operation and maintenance task when the execution condition is met; and if not, running the classification processing rule, carrying out time window replanning, and executing a conflict resolution strategy to obtain a new operation and maintenance task.

The invention discloses an urban rail transit peak period train departure interval optimization method based on multi-line cooperative scheduling, and relates to the technical field of rail transit operation management. The method comprises the following steps: firstly, constructing a network topology containing key transfer stations and a passenger flow OD matrix; establishing a passenger travel space-time network model to accurately describe a transfer waiting process; further constructing an optimization model with the goal of minimizing the total cost of the system, wherein the model comprehensively considers the passenger waiting time, the in-vehicle crowding degree, the operation cost and the transfer collaboration degree; and finally, designing a mixed intelligent algorithm combining Lagrangian relaxation and a meta-heuristic algorithm for solving, and generating a multi-line cooperative departure interval scheme. According to the method, the limitation of single-line independent scheduling is broken through, resource allocation can be optimized from the overall perspective of a line network, the total travel time of passengers and transfer station congestion are effectively reduced, and the overall transportation efficiency and service quality of a rail transit system in peak periods are remarkably improved.

The invention relates to the technical field of deep-sea mining vehicle path planning, and discloses a deep-sea mining vehicle full-coverage path planning method based on an improved bio-activation neural network, and the method comprises the following steps: (1) analyzing the climbing dynamics of a deep-sea mining vehicle, and determining the passing time of each deep-sea mining region; (2) establishing a deep-sea mining operation environment model based on the determined passing time of each region; (3) establishing a grid heuristic function for deep-sea mining coverage operation requirements; and (4) in the deep-sea mining operation process, improving an escape path planning algorithm for dead zone escape demands, optimizing a target search mechanism and a fitness function, and performing point-to-point intelligent path planning on the mining vehicle. According to the method, an improved biological activation neural network algorithm and an escape algorithm are combined, deep-sea mining operation can be autonomously, accurately and efficiently completed under the condition that various seabed obstacles exist, and safety, energy conservation and mining efficiency are guaranteed.

A method or system for dynamically optimizing order fulfillment wait times for shoppers using machine learning. The system identifies a shopper's current location via their client device. A trained machine learning model predicts: (i) a first wait time for receiving orders at the current location, (ii) a second wait time at an alternate location, and (iii) a travel time between the two locations. The model is trained using labeled data of shopper wait times, iteratively refining parameters to minimize prediction error. A combined wait time for the alternate location is computed by summing the predicted second wait time and the travel time. If the combined wait time is shorter than the wait time at the current location, the system suggests the alternate location to the shopper. Instructions are transmitted to the shopper's device to display a user interface with a map, the suggested route, and a recommendation indicator.

The invention relates to the technical field of landscape prediction, and discloses a high mountain cloud sea landscape prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the prediction basic data of a to-be-predicted day of a target region, and the prediction basic data comprises a relative humidity factor, a temperature condition factor, an atmospheric stability factor, a wind speed factor and a terrain uplift factor; and inputting the prediction basic data to the cloud sea landscape prediction model to obtain the cloud sea occurrence probability of the to-be-predicted day. According to the method, the application area is not limited, the construction cost is low, the prediction basic data of the to-be-predicted day in the target area can be continuously updated, passengers and photography enthusiasts can conveniently check the cloud sea occurrence probability in real time and arrange the travel time, the experience degree of the passengers is improved, and the scenic spot service quality is improved.

The invention relates to a distribution robust optimization-based library-crossing vehicle path planning method, which comprises the following steps of: obtaining statistical information of travel time based on road network data analysis of a distribution area; constructing a library-crossing vehicle path model under the uncertain travel time by taking the minimum comprehensive time cost as an objective function; a travel time uncertainty set is constructed based on a Wasserstein distance, and a library-crossing vehicle path distribution robust optimization model is established; and finally, solving the model by adopting a taboo search algorithm to obtain a robust optimal planning scheme of the over-warehouse distribution vehicle path. According to the method, distribution robust optimization is carried out on the warehouse-crossing distribution vehicle path, the feasibility and robustness of the warehouse-crossing vehicle distribution path scheme under the uncertain travel time are improved, and the method is of great significance in guaranteeing warehouse-crossing collaborative operation and achieving cost reduction and efficiency improvement of distribution.

Electric vehicles (EVs) for pick-up and delivery services using opportunity charging are routed in response to a task request for package or person pick-up and drop-off. A candidate EV for the task is determined based on a preliminary route analysis of routes between the pick-up and drop-off locations including an estimate of point-to-point travel time and electrical power consumption for each candidate EV. The candidate EVs are winnowed by determining which candidate EV is available during a time window of service that has sufficient state of charge to reach at least the pick-up location. Routing calculations are performed for each remaining candidate to develop an individual route between the pick-up location and the drop-off location and a predicted power consumption for the individual route. A candidate EV is assigned to service the task request that has sufficient state of charge, with minimal recharging delay, to meet the predicted power consumption for its individual route.

The invention discloses a bridge ship impact point positioning method and system based on shock wave time difference analysis, and the method comprises the steps: arranging a vibration sensor array on a plurality of piers in a non-navigable hole region of a bridge, and connecting the vibration sensor array with an edge calculation module; impact characteristics are extracted, and vibration acceleration is calculated; setting a dynamic threshold value, and calculating the coordinates of the impact point when the vibration acceleration peak value exceeds the dynamic threshold value; in the calculation process, double constraint conditions are constructed by using time-frequency joint similarity and time difference of impact characteristics, and correlation of other vibration sensors is carried out; constructing a positioning equation set special for the concrete structure bridge; and resolving a positioning equation set and outputting impact point coordinates. According to the system, a vibration sensor array is used for collecting vibration signals; the edge calculation module is used for extracting impact characteristics and calculating vibration acceleration and impact point coordinates of the vibration sensor; the central processing unit is used for outputting impact point coordinates and damage indexes. According to the method and the system, the impact point in the event that the ship impacts the bridge can be quickly and accurately positioned.

We provide a traffic management system, traffic management method, and traffic management program suitable for bus operations. [Solution] The operation management system includes: a setting unit that sets multiple location areas surrounded by multiple location information points in a continuous sequence in the direction of travel of the bus at the entrance and exit of a traffic facility where the bus is parked; an acquisition unit that acquires bus location information relating to the position of the bus running with a positioning sensor and bus time information relating to the time when the position is measured; a determination unit that determines whether the bus has passed through a location area based on the travel trajectory consisting of multiple bus positions recorded in the bus location information; and a recording unit that, if the determination unit determines that the bus has passed through a location area, records the bus's travel time based on the time recorded in the corresponding bus time information when the bus's position was measured.

Methods and apparatuses for providing an estimated time of arrival at a point of interest (POI) in a navigation system are described. According to one embodiment, the method includes collecting driving data and trajectory data, generating parking time data of the vehicles by deriving times required for the vehicles to park based on the trajectory data and integrating the times required for the vehicles to park, deriving travel times of the vehicles traversing links leading to an entrance to the parking lot based on the driving data, calculating the vehicles' entry wait times by aggregating the travel times, and generating entry wait time data by integrating the vehicles' entry wait times, and deriving estimated times of arrival of the vehicles by summing the times required for the vehicles to park and the entry wait times, and generating estimated arrival time data by integrating the vehicles' estimated times of arrival.

This processing time estimation method includes: allocating a plurality of tasks in an automated warehouse to each of a plurality of transport devices; generating a plurality of divided travel routes for each transport device by dividing, at a predetermined first time interval, an entire travel route during processing of the tasks for each transport device into N sections; and, for each of the N sections, executing input, to a trained model capable of outputting an estimated travel time for a divided travel route in a predetermined section for each transport device, of a divided travel route in an n-th section for each transport device, a map, and the first time interval.

The present application provides a traveling route prediction method, an electronic device, and a storage medium. The method comprises: acquiring a plurality of historical traveling trajectories of a target vehicle in historical traveling; filtering the plurality of historical traveling trajectories on the basis of a preset filtering rule to obtain target historical traveling trajectories; acquiring trip information of the target historical traveling trajectories; calculating route probabilities of respective target historical traveling trajectories having a same destination position and a same vehicle travel time period among the target historical traveling trajectories; establishing a habitual route database of the target vehicle on the basis of the route probabilities and the trip information; and upon detecting that the target vehicle is started, determining a first final predicted traveling route of the target vehicle on the basis of the habitual route database. In the present application, a future trip of a user can be predicted when the user does not use an automotive navigation system, thereby improving travel experience of the user.

In some implementations, a device may retrieve assignment data indicating territory assignments of a plurality of entities to a plurality of territories. The entities may have physical addresses in a plurality of serviced locations, and an entity may lack a territory assignment. The device may identify, for the serviced locations, respective travel times between the serviced locations and a location relating to a physical address for the entity. The device may identify a set of nearest entities in an order of travel proximity to the location according to the respective travel times. The device may monitor, during identifying of the set of nearest entities and in accordance with the territory assignments, respective counts of nearest entities for the plurality of territories, until a count of nearest entities for a territory satisfies a threshold. The device may generate updated assignment data to indicate an assignment of the entity to the territory.

The invention provides a collision early warning method, a vehicle and a storage medium. The method comprises the following steps: acquiring traffic light information of a signal light intersection and traffic data of the vehicle; according to the traffic data, target driving time is determined, and the target driving time comprises first driving time needed by a to-be-detected vehicle located behind the vehicle to arrive at the vehicle position of the vehicle and second driving time needed by the vehicle to pass through the signal lamp intersection; and based on the traffic light information, the first driving time and the second driving time, predicting a target vehicle colliding with the vehicle from the to-be-detected vehicles. According to the invention, the driving safety of the vehicle can be improved.

The embodiment of the invention provides a route planning method, system and device based on weather and user tendency and a vehicle, and belongs to the technical field of intelligent driving. The method comprises the steps that user tendency data, weather data and travel demand information are acquired, the user tendency data at least comprises a tendency driving route, tendency travel weather and tendency travel time which are associated, and the travel demand information comprises a departure place and a destination of travel; the current time is compared with the tending travel time, the weather data is compared with the tending travel weather, a matched target driving route is determined according to the travel demand information, and the target driving route is a certain tending driving route or a route generated by combining a plurality of tending driving routes; and outputting navigation information of the vehicle according to the target driving route. The invention aims to improve the accuracy of vehicle navigation route planning.