634 results about "Arrival time" patented technology

The invention provides a high-speed train timetable optimization and rescheduling method based on deep reinforcement learning, relates to the technical field of high-speed train scheduling and resource optimization, and aims to realize efficient and accurate high-speed train timetable optimization and rescheduling. Comprising the steps that a strategy network is constructed based on a Markov decision process model of high-speed rail scheduling, and the strategy network learns an action decision strategy of the Markov decision process model of the high-speed rail scheduling; inputting the current station state into the strategy network to obtain an action decision as a departure sequence of all trains at the current station; based on a train state transition model, simulating a train operation environment according to a departure sequence of all trains in the current station, and determining arrival time and departure time of all trains in the current station, which meet traffic safety constraints; according to the above steps, the arrival time and departure time of all trains in each station on the running route are iteratively calculated in sequence, and a train timetable is obtained.

The invention discloses a micro-seismic signal arrival time pickup method based on fuzzy clustering and variational mode decomposition. The method comprises the following steps: firstly, calculating characteristic functions of attribute characteristics such as a micro-seismic signal mean value, power and kurtosis and normalizing the characteristic functions to obtain a characteristic matrix, then primarily picking up a micro-seismic P-wave initial movement position and a first arrival moment through a fuzzy clustering algorithm, and extracting an effective time window by taking the first arrival moment as a reference point; a variational mode decomposition algorithm is utilized to decompose signals in a time window into K intrinsic mode function components, AIC function values of the components are calculated by means of an akaike information criterion algorithm, a minimum value point is picked up to serve as first arrival time, energy ratios of all the components are calculated, and final micro-seismic P-wave first arrival time is obtained through weighted calculation. The method effectively deals with the low signal-to-noise ratio environment of the underground coal mine, has higher pickup precision and reliability compared with a traditional pickup method, can provide accurate micro-seismic occurrence time and position information for mine safety early warning, and powerfully guarantees the safety production of the coal mine.

The invention discloses a container port berth and storage yard allocation combined planning operation method and system based on a cluster strategy, and the method comprises the steps: collecting the information of ships, container trucks and storage yards in a target time period, including the planned arrival time of the ships, the number and types of containers carried by the ships, the stockpiling state of the storage yards, and the planned arrival time of outer container trucks; the collected information is input into a pre-constructed multi-target mixed integer programming model, and an optimal programming strategy is calculated and comprises berth plans of all ships in a specified time period and loading and unloading operation plans of container batches; wherein the pre-established multi-target mixed integer programming model comprises three target functions, and the targets are the shortest ship berthing deviation expected turnover time, the minimum container transportation total distance between a berth and a storage yard container area and the minimum workload imbalance of the storage yard container area respectively; and executing ship and storage yard operation according to the optimal planning strategy. The overall operation efficiency of the wharf can be improved.

The embodiment of the invention provides a port logistics management and control method and system, and belongs to the technical field of port logistics management and control, and the method comprises the steps: predicting the arrival time of a ship and the required unloading duration through employing historical data and real-time information; according to the predicted ship arrival time, an optimal unloading berth is arranged, and a truck connection sequence is planned; synchronously regulating and controlling operation time windows of the ship and the truck based on the predicted interaction time of the ship and the truck; and optimizing the vehicle scheduling path and sequence in the harbor district according to the synchronous regulation and control result to improve the overall efficiency. Through the scheme of the embodiment of the invention, the problem of how to synchronously regulate and control the shipping date and the truck to improve the overall efficiency can be solved.

Techniques for determining an estimated time of arrival associated with a pickup location are described herein. For example, a customer can order a good or service for pickup at a pickup location. In some examples, order data can include a pickup location intent, such as a drive-through location, a pickup window, or a parking location. A location of a user computing device can be determined and can be input to a machine-learning model trained to determine estimated times of arrival for a particular location. In some examples, the machine-learning model can be trained with ground truth data specific to the location for which the model is to determine estimated times of arrival. The machine-learning model can output the estimated time of arrival and can update the time at any regular or irregular intervals, which can be used to prioritize an order in an order queue.

Techniques for managing pickup orders are discussed herein. A managing computing device can receive first and second pickup orders made at different time points associated with a first user. The pickup orders can be directed to the same store. The managing computing device can group the first and the second pickup orders as a batched order which can update a priority of the first or second pickup order. The managing computing device can receive location data from a computing device associated with the first user or a third party. The managing computing device can determine an estimated arrival time of the first user or the third party based on the location data. The managing computing device can send a message to a store computing device associated with the store, indicating the estimated arrival time and the batched order associated with the first user.

The invention provides a train operation adjusting method and device in a rain and snow mode. The method comprises the steps that a target running gear of a next whole-line train in the rain and snow mode is obtained; wherein the rain and snow mode comprises a plurality of driving gears, and different driving gears correspond to different train operation data; predicting an operation plan of the train according to the target driving gear based on a train operation adjustment model to obtain a target operation plan so as to guide the train to operate in a rain and snow mode; the train operation adjustment model is constructed by taking the arrival time of each train, the departure time of each train and the running gear of each train as state variables and taking the minimum passenger riding waiting time and the minimum train operation cost as objective functions; the train operation cost comprises train energy cost and train maintenance cost. According to the method, the driving efficiency and safety of the whole-line train in the rain and snow mode are improved.

The invention provides a ship lockage appointment scheduling optimization method, system, equipment and medium, and belongs to the field of shipping traffic scheduling, and the method comprises the steps: based on an arrival time prediction model, according to the planned arrival time, historical actual arrival time and ship attribute data of at least two appointment ships, obtaining a ship lockage appointment scheduling optimization model; generating a mean value and a standard deviation of the estimated arrival time corresponding to each reserved ship; based on the mean value and the standard deviation of the estimated arrival time, constructing a multi-target optimization model meeting a preset optimization target and a preset constraint condition; calculating corresponding scheduling reference time according to the mean value and the standard deviation of the estimated arrival time, and creating an initial scheduling scheme population according to the scheduling reference time; and based on a non-dominated sorting genetic algorithm and a variable neighborhood search algorithm, solving the multi-objective optimization model according to the initial scheduling scheme population to obtain a final scheduling scheme. According to the invention, the traffic jam of the hub is effectively relieved, and the lockage efficiency of the ship is improved.

The invention provides a high-speed rail emergency train number matching optimization method and system based on timeliness guarantee, and belongs to the field of logistics distribution scheme optimization. According to the method, the set business process comprises customer delivery, goods pickup and delivery, inter-station transportation and goods pickup and delivery; according to the order data and the rider data, the taking and delivering service time distribution of the two links of taking goods to the station and taking goods and delivering goods is constructed, and then the goods waiting time, the latest arrival time of the day and the goods arrival time meeting the maximum delay time are determined; taking the smaller value of the latest arrival time of the day and the cargo arrival time meeting the maximum delay time as the latest arrival time of the cargo; determining a train alternative set according to cargo time limit requirements and cargo OD stations; and based on the timeliness guarantee and the operation cost, constructing a high-speed rail emergency train number matching optimization model based on the timeliness guarantee, and solving the model to obtain an optimized train number matching result. According to the invention, the punctuality and reliability of transportation are improved, and the overall operation efficiency and economic benefits are improved.

The invention discloses a driver side navigation switching management method and system. The invention relates to the field of intelligent traffic and mobile application development. Passenger order information (such as new order generation and order state change) from a scheduling system is continuously monitored; acquiring the current navigation state (destination, route planning, driving state and the like) of the driver in real time; switching condition judgment is carried out; if the driver is in common navigation and receives a passenger order, calling a preset judgment table based on the order emergency degree and the predicted arrival time of the driver, and if judging that a switching condition is met, triggering a navigation switching process; according to the system, the navigation switching response time is compressed from the second level to the millisecond level through an active mechanism of order triggering and state sensing, and a driver can complete switching without manual operation. A map SDK asynchronous calling technology is adopted, parameter transmission and state cleaning are completed in the background, and the switching success rate is increased.

The invention relates to the technical field of intelligent prediction, and discloses an emergency resource reserve intelligent prediction system and method based on a graph neural network, and the system comprises a space-time diagram construction module which collects port, berth, channel segment, emergency warehouse and ship information, passing time window information and emergency information, and constructs an event flow heterogeneous space-time diagram; the backbone screening module is used for screening backbone sub-graphs and setting a relation feature conversion rule and a time feature representation rule; the graph memory updating module is used for updating node and edge features; the multi-task prediction module is used for outputting quantile demand prediction, reachability scores, transportation timeliness and safety stock threshold values; the consistency constraint training module is used for executing consistency constraint training; the inventory allocation decision-making module is used for generating allocation distribution quantity and outputting predicted arrival time; and the berth time window recharging module is used for generating a berth operation time window and recharging the actual arrival time. According to the invention, emergency resource demand prediction, inventory threshold calculation and allocation and berth operation linkage decision are realized.

A method for routing an autonomous vehicle from a start location to a target destination location via a set of road segments is provided. The method includes determining at least two candidate routes comprising a respective subset of the set of road segments. Each of the at least two candidate routes indicates how the autonomous vehicle shall travel from the start location, to arrive at the target destination location within a time interval of a target time of arrival. The method determines a flexibility metric for each of the at least two candidate routes indicative of a possible time adjustment when adjusting a speed of the autonomous vehicle. The method selects a route based on the determined flexibility metrics.

The invention discloses an operation and maintenance method of integrated energy equipment based on a large model and a related device. The method comprises the following steps: firstly, determining a plurality of first to-do tasks of comprehensive energy equipment operation and maintenance work, and obtaining task scheduling parameters of each task, including task deadline, current time, predicted arrival time from a current position to a task destination calculated based on real-time road traffic conditions, and task emergency degree; and calculating the priority score of each task according to the task scheduling parameters by using a pre-trained priority scoring model. And then sorting the tasks according to the priority scores, and performing operation and maintenance planning based on a sorting result to formulate an operation and maintenance plan of a plurality of to-be-handled tasks, so as to perform operation and maintenance on a plurality of first to-be-handled tasks based on the operation and maintenance plan. The priority score is calculated by using data and a model, human intervention is eliminated, and fair distribution of tasks is ensured. The evaluation accuracy is improved by combining task scheduling and predicted arrival time, the road condition is reflected, delay is avoided, and the execution efficiency is improved.

The invention discloses a time management optimization method and a display device for an ambulance, and belongs to the technical field of medical first aid management. The method comprises the steps of obtaining an exclusive data set and multi-dimensional dynamic data of a target patient in real time, calling a preset demand rule base, calculating the emergency degree of the target patient in combination with the exclusive data set, and performing emergency level division; constructing a transfer planning model, screening a resource-adaptive target hospital for a target patient, synchronously planning a global optimal driving path, calculating predicted arrival time, and generating an individual transfer plan; global summarization is carried out on target patients to be transferred to form a matrix to be transferred, resource adaptation verification is carried out on the basis of a target hospital, an actual target transfer set is generated, and the transfer process is monitored in real time to update the predicted arrival time in real time; and after the transfer is completed, obtaining the actual arrival time, calculating the predicted deviation value, marking the deviation reason, forming an optimized data set, shortening the transfer time efficiency, and reducing the treatment delay risk.

The invention discloses an online car-hailing order allocation method and system based on an artificial intelligence algorithm, and relates to the technical field of intelligent scheduling, and the method comprises the steps: obtaining to-be-allocated order data and driver data; determining a candidate driver set by adopting a dynamic search radius and a multi-state driver screening strategy, and calculating an estimated arrival time in combination with a real-time traffic condition; establishing a multi-factor order receiving willingness evaluation model, and calculating the order receiving probability of each order-driver combination; constructing a comprehensive matching degree evaluation model, calculating the matching degree of all order-driver combinations, and generating a matching degree matrix; processing the matching degree matrix by adopting a global optimization algorithm, and outputting an order-driver allocation result; and pushing an order to a driver in the order-driver allocation result, monitoring a response state, and performing reallocation processing on a non-response condition. Through the multi-factor order receiving willingness evaluation and the global optimization algorithm, the success rate and matching precision of order allocation are improved, the order waiting time is shortened, and the overall allocation efficiency is improved.

The invention relates to the technical field of server hardware fault diagnosis, in particular to an intelligent fault diagnosis method and system for a server mainboard and a storage medium. The method comprises the following steps: configuring monitoring parameters in a bus link controller of a server mainboard, and setting an undisturbed sampling window as a training sequence time period and an idle symbol time period in a bus physical layer data transmission process; periodically reading a link adaptive equalization parameter, an error count value and symbol arrival time through a bus link controller register, and simultaneously collecting a temperature parameter and a voltage parameter at the current moment; and combining the link adaptive equalization parameter, the error count value, the symbol arrival time, the temperature parameter and the voltage parameter at the same sampling moment as a signal quality vector at the moment. According to the method, two data transmission-free windows of the training sequence time period and the idle symbol time period are identified and utilized, so that the monitoring operation and the service data are staggered, and the sampling operation is ensured not to interfere with normal data transmission.

A mine earthquake positioning method based on an improved pigeon inspired optimization algorithm comprises the following steps: 1) preprocessing original mine earthquake waveform data by using ACF to obtain an ACF sequence; 2) performing first arrival time pickup on the processed data by using a long and short time window algorithm STA / LTA; (3) storing the station coordinates and the first arrival time picked in the step (2) into a file according to the format of station coordinates X, station coordinates Y, station coordinates Z and first arrival time; 4) reading the file generated in the step 3), and setting an upper boundary and a lower boundary of a search space; 5) selecting a combination of a time-of-arrival method and a variance function as a target function; and 6) using the improved pigeon inspired optimization algorithm FC-PIO to carry out seismic source positioning operation: minimizing the target function in the step 5) through the improved pigeon inspired optimization algorithm FC-PIO, finding an optimal solution, and obtaining the occurrence position of a mine earthquake event. Through the method, the mine earthquake positioning precision and efficiency can be remarkably improved in practical application, and the reliability of mine safety monitoring is ensured.

The invention provides a method and device for predicting bus arrival time, electronic equipment, a medium and a program product, and relates to the field of data processing, in particular to the fields of intelligent transportation, smart cities, map big data and the like. According to the implementation scheme, predetermined arrival time information and reference arrival time information of a target bus for a to-be-predicted shift are obtained, and the predetermined arrival time information comprises predetermined time when the target bus arrives at all stations in the to-be-predicted shift; the reference arrival time information comprises the real time when the target bus arrives at at least part of stations in all stations in a historical shift corresponding to the to-be-predicted shift; correcting the predetermined arrival time information based on the reference arrival time information to obtain predicted arrival time information for the to-be-predicted shift; and determining the time when the target bus arrives at the target station based on the predicted arrival time information and the information of the target station provided by the user.

The invention belongs to the technical field of port operation management, and provides a port project whole-process decision management system based on big data analysis, which comprises the steps of performing verification analysis on a plurality of historical predicted port arrival time through hypothetical moving windows, determining an optimal moving window by using a moving window averaging method and an Euclidean calculation model, and determining the port arrival time according to the optimal moving window; obtaining a model correction factor according to historical predicted time and actual time, obtaining predicted port arriving time, comparing the predicted port arriving time with to-be-loaded departure time, if the predicted port arriving time is not matched with the to-be-loaded departure time, rejecting original stable connection ships, reselecting the ships according to a connection matching degree, and performing re-evaluation until the ships with time dimension connection matching are found from a connection ship sequence, by optimizing a moving window and introducing a correction factor, the port arrival time prediction accuracy is improved, the problem that the port ship scheduling efficiency is effectively improved due to loading and unloading caused by time mismatching is avoided, and the problems of how to accurately predict the port arrival time of the stably connected ship and how to judge whether the port arrival time is matched with the to-be-loaded departure time or not are solved.

Systems and methods for predicting an estimated time of arrival for an equipment at a destination location are disclosed. The method includes inputting to a trained machine learning model a starting location, the destination location and at least one event data, generated in response to an event during a trip of the equipment via the railroad from the starting location to the destination location. The machine learning model is trained using a plurality of historical shipment route data. A particular historical shipment route data comprises at least a location and a corresponding timestamp. The method includes predicting, using the trained machine learning model, the estimated time of arrival of the equipment at the destination location.

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 logistics arrival time forecasting system and method based on multi-data fusion, relates to the technical field of logistics arrival time forecasting, and aims to solve the technical problem that the deviation between the forecasting result of an existing logistics arrival time forecasting system and the actual arrival time is large. The system comprises a data acquisition module, a path planning module, a time calculation module and an early warning and output module. The output end of the data acquisition module is connected with the path planning module and the time calculation module, and the data acquisition module is used for acquiring multi-source data. The multi-source data comprises historical logistics data, order and delivery information data, weather data, real-time road condition data, map data, transportation tool characteristic data, cargo special demand data, transportation tool connection hidden loss data and transportation tool collaborative delay association data; and the output end of the path planning module is connected with the time calculation module. The method has the advantage of improving the accuracy of the arrival time forecast in the multimodal transport scene.

The invention discloses an equipment distance measurement method, electronic equipment, a storage medium and a product, and relates to the technical field of equipment distance measurement, the disclosed equipment distance measurement method comprises the following steps: sending an ultrasonic signal to slave equipment, and receiving a response signal replied by the slave equipment; performing interference elimination processing on the response signal to obtain a pure response signal; and determining the arrival time of the signal direct path from the pure response signal, and determining the distance between the master device and the slave device according to the arrival time. According to the invention, the distance measurement precision in a multi-device distance measurement scene can be improved.

The invention discloses a ship arrival time segmented prediction method and system based on a complex network, and the method comprises the following steps: taking the AIS data, channel features and traffic environment features of a ship as feature data, calculating the importance scores of all feature data, taking the feature data of which the importance score is greater than a set importance threshold as important features; weighting the important features according to the importance scores to obtain a complexity score of each track point in the AIS data; traversing all the track points, if the difference value of the complexity scores of two adjacent track points is smaller than a set threshold value, dividing the two track points into the same leg, otherwise, dividing the two track points into different legs; and marking the important features according to a leg division result, training a prediction model by using the marked important features, and realizing segmented prediction of the arrival time of the ship by using the trained prediction model.

The invention belongs to the field of intelligent transportation, and discloses a bus lane sharing control method based on the optimal bus speed, which comprises the following steps: S1, bus position monitoring and planning speed acquisition; s2, predicting predicted arrival time and stop time; s3, calculating the time for closing the bus lane; s4, calculating the time of reopening the bus lane; and S5, sending an instruction to the lane signal lamp. According to the method, conflicts between the buses and the social vehicles are reduced through reasonable space-time resource dynamic allocation, so that the influence on the passing efficiency of the social vehicles is reduced while the priority passing of the buses is ensured. According to the method, the passing efficiency and the punctuality rate of the bus can be improved, the reliability of a bus system is improved, the safety and smoothness of an urban traffic system can be ensured, energy consumption and tail gas emission are reduced, and meanwhile the riding experience and satisfaction degree of passengers are improved.

The invention discloses a charging pile dynamic distribution method in a multi-forklift collaborative operation scene, and relates to the technical field of intelligent logistics dispatch, and the method comprises the steps: S1, based on a current traffic flow prediction result, generating the predicted arrival time of a forklift at each charging pile, and S2, according to the predicted arrival time, determining the charging pile according to the predicted arrival time. The method comprises the steps of S1, calculating the actual waiting duration and the corresponding priority weight of each forklift, S3, dynamically regulating and controlling the charging queuing sequence of the forklifts based on the priority weights, and S4, sending the regulated queuing sequence to a target forklift to guide the target forklift to complete charging pile distribution. According to the charging pile dynamic allocation method in the multi-forklift collaborative operation scene, the charging queuing sequence is regulated and controlled according to the current traffic flow prediction result, so that the problems of congestion and overlong waiting time are solved.

The invention discloses an inventory management method, and the method comprises a time-sharing arrival planning step which comprises the steps: obtaining an order demand of a manufacturer, and making a time-sharing arrival plan according to the order demand and corresponding supplier distribution; and the warehouse layout optimization step comprises the steps of obtaining warehouse-out frequency and order quantity information of each cargo in the warehouse, sorting and classifying the cargo according to the sales volume or the sales volume, dividing the warehouse space into a plurality of storage areas according to the classification, and placing the cargo in the storage area corresponding to the classification where the cargo is located. According to the inventory management method, the arrival time of the goods is reasonably distributed, so that the goods arrive at different time periods, and the pressure of the warehouse space is reduced. Through the step of optimizing the warehouse layout, the most suitable storage position of the goods is determined according to the goods order and the circulation frequency, and the overall operation efficiency is improved.

A method for configuring a service flow attribute, an apparatus, and a system are provided. A first network element receives a time window of time sensitive communication assistance container burst arrival time of a first service flow and obtains an offset of time sensitive communication assistance information burst arrival time of the first service flow based on the time window. Then, the first network element determines attribute information of the first service flow based on the offset and sends the attribute information to a second network element.

The invention discloses a communication method and device, and relates to the field of communication. The method comprises the following steps: receiving first configuration information from access network equipment, wherein the first configuration information is used for indicating a first time-frequency resource; when there is uplink data to be transmitted, and the position distance between the first position of the terminal device at the first moment and the second position corresponding to the service cell is less than a first distance threshold, and / or the signal strength of the service cell is greater than a first signal strength threshold, sending the uplink data on a first time-frequency resource; the first moment is the arrival moment of the uplink data or the first target time corresponding to the first time-frequency resource. According to the invention, the terminal equipment does not carry out random access, and uses the first time-frequency resource indicated by the access network equipment to send the uplink data, so that the signaling overhead and the transmission delay are reduced, and the uplink data capacity is enhanced. The first time-frequency resource is a shared common time-frequency resource for different terminal devices, the resource utilization rate is high, and the transmission capacity of the communication system can be enhanced.

The present invention relates to an intelligent berthing method for inland anchorages, comprising the following steps: S1: a ship sends a berth application and obtains the ship's current information; S2: using the ship information to predict the ship's arrival time at each available anchorage; S3: collecting information on all currently moored ships at the anchorage and allocating berths to incoming ships based on the current berthing situation at the anchorage; S4: after a ship has moored, checking whether it has moored at the designated location as required; S5: after receiving a departure information, collecting the ship's departure time for use in allocating berths to subsequent incoming ships; and S6: upon reaching the scheduled departure time, the ship performs the departure operation. This method can predict ship arrival times and intelligently allocate berths to incoming ships, thereby reducing anchorage management costs, improving anchorage operation efficiency, and enhancing the intelligent construction of anchorages.