CN111738550B - Travel guest-building method, device, equipment and storage medium based on dynamic programming - Google Patents
Travel guest-building method, device, equipment and storage medium based on dynamic programming Download PDFInfo
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Abstract
The invention relates to the field of artificial intelligence, is applied to the field of intelligent traffic, and discloses a method, a device, equipment and a storage medium for grouping passengers on the basis of dynamic planning, which are used for matching available vehicle resources and demand orders and improving the passenger grouping efficiency. The method comprises the following steps: acquiring a plurality of travel demand orders, wherein the travel demand orders are used for providing travel vehicles for users; processing the travel demand orders according to preset rules to obtain a target travel demand order set and target order numbers corresponding to each target travel demand order; acquiring vehicle information within a first preset time period range; traversing each target order number in the target travel demand order set to obtain an order number combination; calling a dynamic programming algorithm to traverse the order number combinations, and determining all order distribution combinations according to the number of stations and vehicle information; determining a plurality of site combinations to be matched according to all order distribution combinations and a Dijiestra algorithm; and obtaining the target site combination with the inter-site distance not exceeding the threshold value.
Description
Technical Field
The present invention relates to the field of machine learning, and in particular, to a method, apparatus, device, and storage medium for making a guest on a trip based on dynamic programming.
Background
Wisdom trip of internet utilizes cell-phone APP to utilize cell-phone developments to collect the trip demand, matches available transportation service, further reduces the trip expense, alleviates relevant traffic pollution and energy consumption problem. Currently, intelligent travel of the internet includes two steps, demand matching and path planning. In the demand matching stage, demand-transportation service matching is performed according to travel demands of individual or whole orders and the seat number of vehicles. In the aspect of path planning, according to the starting point and the ending point of a customer, factors such as travel time, passing distance and the like are considered, a route with the lowest time consumption is designed, a vehicle is guided to travel in a complex inter-city traffic environment, and the user experience of travel is improved.
At present, the number of seats of vehicles similar to products in the industry is small, and the number of people to be spliced is small. Because the number of seats is small, the calculation amount of the spelling list is not large, and the journey time is not limited. When the existing travel software performs customer matching, if the data of the earlier-stage orders is insufficient, the orders cannot be more efficiently matched through training a machine learning model, and the customer matching efficiency is low.
Disclosure of Invention
The invention provides a travel grouping method, device, equipment and storage medium based on dynamic programming, which are used for solving the problem of low grouping efficiency under the condition that the data of the number of orders in the earlier stage is insufficient and more efficient order matching can not be carried out on the orders through a training machine learning model.
A first aspect of an embodiment of the present invention provides a method for making a group of passengers on a trip based on dynamic planning, including: acquiring a plurality of travel demand orders, wherein the travel demand orders are used for providing travel vehicles for users, and the travel demand orders comprise departure places, departure stations, destinations, destination stations and riding moments; processing the travel demand orders according to a preset rule to obtain a target travel demand order set and target order numbers corresponding to each target travel demand order, wherein the riding time of the target travel demand order set is within a first preset time period range; acquiring vehicle information in the first preset time period range, wherein the vehicle information comprises the total number of seats of each vehicle to be distributed, the residual seat number of each vehicle to be distributed and the total number of vehicles of the vehicle to be distributed; traversing each target order number and the vehicle information in the target travel demand order set, and determining the number of boarding persons and alighting persons at all stations to obtain an order number combination; invoking a dynamic programming algorithm to traverse the order number combinations, and determining all order distribution combinations, wherein the order distribution combinations comprise a plurality of pre-distributed target order numbers; determining a plurality of site combinations to be matched according to all the order distribution combinations and a Di Jie St La algorithm, wherein the sum of the distances between combinable sites in the site combinations to be matched is shortest; and sequencing the plurality of station combinations to be matched to obtain a target station combination with the inter-station distance not exceeding a threshold value.
Optionally, in a first implementation manner of the first aspect of the embodiment of the present invention, the processing the plurality of travel demand orders according to a preset rule to obtain a target travel demand order set and a target order number corresponding to each target travel demand order, where a riding time of the target travel demand order set is within a first preset period range includes: analyzing the travel demand orders to obtain riding time of each user, wherein each user corresponds to one travel demand order; dividing the travel demand orders according to preset time intervals to generate a plurality of initial travel demand order sets, wherein the riding time ranges of the initial travel demand order sets are different; screening the plurality of initial travel demand order sets according to the departure station and the destination station to generate a plurality of transition travel demand order sets, wherein the departure station and the destination station in each transition travel demand order set are in the same city, and the destination station is also in the same city; selecting a target travel demand order set corresponding to a first preset period from the plurality of transition travel demand order sets, and determining a target order number corresponding to each travel demand order in the marked travel demand order set.
Optionally, in a second implementation manner of the first aspect of the embodiment of the present invention, the calling a dynamic programming algorithm traverses the order population combinations to determine all order allocation combinations, where the order allocation combinations include a plurality of pre-allocated target order numbers, and includes: traversing the order number combination, setting the meeting of the number of boarding points and the full rate requirement of the vehicle as a sub-problem, and obtaining a plurality of sub-problems; and calling a dynamic programming algorithm to sequentially solve a plurality of sub-problems to obtain a plurality of order allocation combinations, wherein the order allocation combinations comprise a plurality of pre-allocated target order numbers.
Optionally, in a third implementation manner of the first aspect of the embodiment of the present invention, traversing the order number combination sets a sub-problem that meets a number of boarding points requirement and a vehicle full rate requirement, to obtain a plurality of sub-problems, including: traversing the order number combination, and screening a plurality of candidate order combinations meeting the number of the boarding points in the order number combination, wherein the number of the boarding points is not more than three for departure places and destinations; and setting the vehicle full rate requirement as a sub-problem based on the plurality of candidate order combinations, and obtaining a plurality of sub-problems, wherein the vehicle full rate requirement is that the vehicle full rate of each vehicle is greater than or equal to the corresponding minimum full rate.
Optionally, in a fourth implementation manner of the first aspect of the embodiment of the present invention, the determining, according to the all order allocation combinations and the dijkstra algorithm, a plurality of combinations of sites to be matched, where a sum of distances between combinable sites in the combinations of sites to be matched is shortest includes: initializing an array route, a vertex set T and a vertex set U, wherein the array is used for storing the shortest distance from a source point s to each vertex, and the vertex set T is used for storing the vertex of the shortest path which is found; setting the path weight of the source point s to 0; if there is a side (s, e) that can be reached directly to the source point s, then set the array route [ e ] to d (s, e), and set the path length of all other vertices that cannot reach the source point s to infinity; selecting a minimum path value in the array route, wherein the minimum path value is the shortest path from a source point s to the nearest vertex corresponding to the minimum path value, and adding the nearest vertex into a preset table; judging whether the newly added nearest vertex can reach other vertexes or not, and judging whether the path length reaching other points through the newly added nearest vertex is shorter than the path length of the source point s directly reaching other points or not; if the vertex weights meet the conditions, updating the weights of the vertices in route; and continuing to find the minimum value in the vertex set U until all vertices in the route are contained in the preset table, generating a station combination to be matched, wherein the sum of the distances between combinable stations in the station combination to be matched is shortest.
Optionally, in a fifth implementation manner of the first aspect of the embodiment of the present invention, after the sorting the combinations of the stations to be matched, determining a target station combination with a distance between stations not exceeding a threshold, the method further includes: and forming a two-dimensional matrix by the distances between the head stations and the tail stations of all the departure places and the destination places in the target site combination.
Optionally, in a sixth implementation manner of the first aspect of the embodiment of the present invention, after the forming a two-dimensional matrix by using distances between the head stations and the tail stations of all departure places and destinations in the target station combination, the method further includes: and packaging the target order number and the operation time period corresponding to the target site combination, generating a push message, and sending the push message and the two-dimensional matrix to a terminal.
A second aspect of an embodiment of the present invention provides a travel group guest device based on dynamic planning, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a plurality of travel demand orders, the travel demand orders are used for providing travel vehicles for users, and the travel demand orders comprise departure places, departure stations, destinations, destination stations and riding moments; the processing module is used for processing the travel demand orders according to preset rules to obtain a target travel demand order set and target order numbers corresponding to each target travel demand order, and the riding time of the target travel demand order set is within a first preset time period range; the second acquisition module is used for acquiring vehicle information in the first preset time period range, wherein the vehicle information comprises the total number of seats of each vehicle to be distributed, the residual seat number of each vehicle to be distributed and the total number of vehicles of the vehicle to be distributed; the traversing module is used for traversing each target order number and the vehicle information in the target travel demand order set, determining the number of boarding persons and the number of alighting persons at all stations and obtaining an order number combination; the first determining module is used for calling a dynamic programming algorithm to traverse the order number combinations and determining all order distribution combinations, wherein the order distribution combinations comprise a plurality of pre-distributed target order numbers; the second determining module is used for determining a plurality of station combinations to be matched according to all the order distribution combinations and a Di Jie St-Lag algorithm, and the sum of the distances between combinable stations in the station combinations to be matched is shortest; and the sorting module is used for sorting the plurality of station combinations to be matched to obtain a target station combination with the inter-station distance not exceeding a threshold value.
Optionally, in a first implementation manner of the second aspect of the embodiment of the present invention, the processing module includes: the analysis unit is used for analyzing the travel demand orders to obtain riding time of each user, and each user corresponds to one travel demand order; the dividing unit is used for dividing the travel demand orders according to preset time intervals to generate a plurality of initial travel demand order sets, and the riding time ranges of the initial travel demand order sets are different; the screening unit is used for screening the plurality of initial travel demand order sets according to the departure station and the destination station to generate a plurality of transition travel demand order sets, wherein the departure station is in the same city in each transition travel demand order set, and the destination station is also in the same city; the selection determining unit is used for selecting a target travel demand order set corresponding to a first preset period from the plurality of transition travel demand order sets, and determining a target order number corresponding to each travel demand order in the marked travel demand order set.
Optionally, in a second implementation manner of the second aspect of the embodiment of the present invention, the first determining module includes: the traversing unit is used for traversing the order number combination, setting the requirement of meeting the number of the boarding points and the requirement of the vehicle full rate as one sub-problem, and obtaining a plurality of sub-problems; and the calling unit is used for calling the dynamic programming algorithm to sequentially solve the plurality of sub-problems to obtain a plurality of order allocation combinations, wherein the order allocation combinations comprise a plurality of pre-allocated target order numbers.
Optionally, in a third implementation manner of the second aspect of the embodiment of the present invention, the traversing unit is specifically configured to: traversing the order number combination, and screening a plurality of candidate order combinations meeting the number of the boarding points in the order number combination, wherein the number of the boarding points is not more than three for departure places and destinations; and setting the vehicle full rate requirement as a sub-problem based on the plurality of candidate order combinations, and obtaining a plurality of sub-problems, wherein the vehicle full rate requirement is that the vehicle full rate of each vehicle is greater than or equal to the corresponding minimum full rate.
Optionally, in a fourth implementation manner of the second aspect of the embodiment of the present invention, the second determining module is specifically configured to: initializing an array route, a vertex set T and a vertex set U, wherein the array is used for storing the shortest distance from a source point s to each vertex, and the vertex set T is used for storing the vertex of the shortest path which is found; setting the path weight of the source point s to 0; if there is a side (s, e) that can be reached directly to the source point s, then set the array route [ e ] to d (s, e), and set the path length of all other vertices that cannot reach the source point s to infinity; selecting a minimum path value in the array route, wherein the minimum path value is the shortest path from a source point s to the nearest vertex corresponding to the minimum path value, and adding the nearest vertex into a preset table; judging whether the newly added nearest vertex can reach other vertexes or not, and judging whether the path length reaching other points through the newly added nearest vertex is shorter than the path length of the source point s directly reaching other points or not; if the vertex weights meet the conditions, updating the weights of the vertices in route; and continuing to find the minimum value in the vertex set U until all vertices in the route are contained in the preset table, generating a station combination to be matched, wherein the sum of the distances between combinable stations in the station combination to be matched is shortest.
Optionally, in a fifth implementation manner of the second aspect of the embodiment of the present invention, the trip group guest device based on dynamic programming further includes: and the composition module is used for composing the distances between the head stations and the tail stations of all the departure places and the destination in the target station combination into a two-dimensional matrix.
Optionally, in a sixth implementation manner of the second aspect of the embodiment of the present invention, the trip group guest device based on dynamic programming further includes: and the generation and transmission module is used for packaging the target order number and the operation time period corresponding to the target site combination, generating a push message and transmitting the push message and the two-dimensional matrix to the terminal.
A third aspect of the embodiments of the present invention provides a trip guest device based on dynamic programming, a memory and at least one processor, where the memory stores instructions, and the memory and the at least one processor are interconnected by a line; and the at least one processor calls the instruction in the memory so that the trip group guest equipment based on the dynamic programming executes the trip group guest method based on the dynamic programming.
A fourth aspect of the embodiments of the present invention provides a computer readable storage medium storing a computer program, which when executed by a processor, implements the steps of the trip group guest method based on dynamic programming described in any one of the above embodiments.
In the technical scheme provided by the embodiment of the invention, a plurality of travel demand orders are acquired, wherein the travel demand orders are used for providing travel vehicles for users, and each travel demand order comprises a departure place, a departure station, a destination station and a riding time; processing the travel demand orders according to preset rules to obtain a target travel demand order set and target order numbers corresponding to each target travel demand order, wherein the riding time of the target travel demand order set is within a first preset period range; acquiring vehicle information in a first preset time period range, wherein the vehicle information comprises the total number of seats of each vehicle to be distributed, the residual seat number of each vehicle to be distributed and the total number of vehicles of the vehicles to be distributed; traversing each target order number and vehicle information in the target travel demand order set, and determining the number of boarding persons and alighting persons at all stations to obtain an order number combination; calling a dynamic programming algorithm to traverse the order number combinations, and determining all order distribution combinations, wherein each order distribution combination comprises a plurality of pre-distributed target order numbers; determining a plurality of site combinations to be matched according to all order distribution combinations and a Dijiestra algorithm, wherein the sum of the distances between combinable sites in the site combinations to be matched is shortest; and sequencing the plurality of station combinations to be matched to obtain the target station combination with the inter-station distance not exceeding the threshold value. According to the embodiment of the invention, the order combination meeting the conditions is determined through the dynamic programming algorithm, the target combination with the nearest site distance is determined through the Di Jie St algorithm, the matching of the riding requirement and the running vehicle is carried out according to the target combination, the available vehicle is quickly found, the available vehicle resource and the required order are matched, the journey time after the client gets on the vehicle is reduced, the distance is shortened, and the passenger organization efficiency is improved.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a method for grouping passengers on a trip based on dynamic programming in an embodiment of the present invention;
FIG. 2 is a schematic diagram of an embodiment of a dynamically planned travel guest device according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of another embodiment of a dynamically planned travel guest device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an embodiment of a trip guest device based on dynamic programming in an embodiment of the present invention.
Detailed Description
The invention provides a travel passenger organization method, device, equipment and storage medium based on dynamic programming, which are used for quickly finding available vehicles, matching available vehicle resources and demand orders, reducing the journey time of customers after boarding, shortening the distance and improving the passenger organization efficiency.
In order to enable those skilled in the art to better understand the present invention, embodiments of the present invention will be described below with reference to the accompanying drawings.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, a flow chart of a method for making a group of customers based on dynamic programming according to an embodiment of the present invention specifically includes:
101. the method comprises the steps of obtaining a plurality of travel demand orders, wherein the travel demand orders are used for providing travel vehicles for users, and each travel demand order comprises a departure place, a departure station, a destination station and a riding time.
The method comprises the steps that a server obtains a plurality of travel demand orders, wherein the travel demand orders are used for providing travel vehicles for users, and the travel demand orders comprise departure places, departure stations, destinations, destination stations and riding moments. Where the origin and destination are specific cities, e.g., guangzhou, shenzhen, etc. The departure site and the destination site are selected from a preset site list, and the user can only select the recorded site from the site list. For example, the site list may include a Guangzhou television station, a Guangzhou zoo, and a Guangzhou YueXiunan passenger station, where the user may select the Guangzhou television station as the departure site and the Guangzhou zoo as the destination site. The 12 pm of a certain day is selected as the riding time.
It should be noted that the site list may include sites in the same city or different cities, and the setting of the sites may be reasonably set in terms of landmark building, well-known scenic spots, bus sites, and the like, which is not limited in this specific embodiment.
It can be understood that the execution subject of the present invention may be a trip guest device based on dynamic planning, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
102. And processing the travel demand orders according to a preset rule to obtain a target travel demand order set and target order numbers corresponding to each target travel demand order, wherein the riding time of the target travel demand order set is within a first preset period range.
The specific process comprises the following steps: (1) The method comprises the steps that a server analyzes a plurality of travel demand orders to obtain riding time of each user, and each user corresponds to one travel demand order;
(2) The method comprises the steps that a server divides a plurality of travel demand orders according to preset time intervals, a plurality of initial travel demand order sets are generated, and riding time ranges of each initial travel demand order set are different;
for example, if the riding time of 2 orders is 9:00, the riding time of 3 orders is 9:30, the riding time of 1 order is 9:35, the riding time of 1 order is 9:45, the riding time of 1 order is 10:15, if the preset time interval is half an hour, 5 orders of 9:00-9:30 can be used as an initial travel demand order set A, 2 orders of 9:30-10:00 can be used as an initial travel demand order set B, and 1 order of 10:00-10:30 can be used as an initial travel demand order set C. If the preset time interval is 1 hour, 7 orders of 9:00-10:00 can be used as an initial travel demand order set D, and 1 order of 10:00-11:00 can be used as an initial travel demand order set E, and the method is not limited herein.
(3) The server screens the initial travel demand order sets according to the departure stations and the destination stations to generate transition travel demand order sets, wherein the departure stations in each transition travel demand order set are in the same city, and the destination stations are also in the same city;
if 4 orders in the initial travel demand order set a are started from Guangzhou, 1 order is started from Guangzhou and started to Shenzhen, and 1 order is started to Buddha, because the number of the orders to Buddha is small, 1 order to Buddha can be removed from the order set a, the removed orders are divided again, and the remaining 4 orders which are started to Shenzhen are used as the transitional travel demand order set F.
(4) The server selects a target travel demand order set corresponding to a first preset period from a plurality of transition travel demand order sets, and determines a target order number corresponding to each travel demand order in the target travel demand order set.
For example, when the first preset period is 9:00-9:30, the transitional travel demand order set F may be selected as the target travel demand order set, and order numbers corresponding to 4 orders in the transitional travel demand order set F may be determined. Wherein the first preset period is greater than or equal to the preset time interval.
103. And acquiring vehicle information in a first preset time period range, wherein the vehicle information comprises the total number of seats of each vehicle to be distributed, the residual seat number of each vehicle to be distributed and the total number of vehicles of the vehicle to be distributed.
The server obtains vehicle information within a first preset period, wherein the vehicle information comprises the total number of seats of each vehicle to be distributed, the residual seat number of each vehicle to be distributed and the total number of vehicles of the vehicle to be distributed. For example, in the range of 9:00-9:30, the vehicles to be dispensed include 5 vehicles, wherein 5 vehicles 3, 7 vehicles 1, and 11 vehicles 1; wherein 5 seats are left in each of the 5 cars, 3 seats are left in the 7 cars, and 6 seats are left in the 11 cars.
104. Traversing each target order number and vehicle information in the target travel demand order set, and determining the number of boarding persons and the number of alighting persons at all stations to obtain an order number combination.
For example, the order number may be a date plus a serial number, or may be set reasonably according to actual situations, so as to obtain the number of passengers on or off each site. Because the departure ground place and the departure place are fixed, only the number of guests at each station is different, the people on the buses and the people off the buses at all stations are traversed firstly, for example, 9:00 to 9:30, there are many stations on which guests get on, and there are many stations on which guests get off.
105. And (3) calling a dynamic programming algorithm to traverse the order number combinations, and determining all order distribution combinations, wherein the order distribution combinations comprise a plurality of pre-distributed target order numbers.
Specifically, the server traverses the order number combination, and sets the requirement of meeting the number of the boarding points and the requirement of the vehicle full rate as a sub-problem to obtain a plurality of sub-problems; the server calls a dynamic programming algorithm to sequentially solve a plurality of sub-problems to obtain a plurality of order distribution combinations, wherein the order distribution combinations comprise a plurality of pre-distributed target order numbers.
It will be appreciated that the solution of the previous order allocation combination provides useful information for the solution of the next order allocation combination.
It should be noted that once each order status is determined, it is not affected by decisions made after that status. That is, this order number combination process does not affect the previous state, but is only related to the current state. Each order satisfies the condition: 1) The principle that the departure site and the destination site are not more than three is adopted; 2) Each order allocation combination meets the minimum full rate requirement.
Optionally, the server traverses an order number combination, sets a meeting of a number of boarding points requirement and a vehicle full rate requirement as a sub-problem, and obtains a plurality of sub-problems, including:
The server traverses the order number combinations, and screens out a plurality of candidate order combinations meeting the number of the boarding points in the order number combinations, wherein the number of the boarding points is not more than three for departure places and destinations; the server sets the vehicle full rate requirement as a sub-problem based on the combination of the candidate orders to obtain a plurality of sub-problems, wherein the vehicle full rate requirement is that the vehicle full rate of each vehicle is larger than or equal to the corresponding minimum full rate.
106. Determining a plurality of site combinations to be matched according to all order distribution combinations and a Dijiestra algorithm, wherein the sum of the distances between combinable sites in the site combinations to be matched is shortest;
the server searches and solves the problem of single-source shortest paths of the weighted directed graph or the undirected graph according to breadth first, and finally obtains a shortest path tree by using a Dijiestra algorithm. The shortest path tree is used for calculating the shortest distance between stations, and a driver should walk to which station to connect with and then approach to which station to connect with; or first from which site to download and then to which site. The specific process comprises the following steps:
(1) The server initializes a array route, a vertex set T and a vertex set U, wherein the array is used for storing the shortest distance from a source point s to each vertex, and the vertex set T is used for storing the vertex of the shortest path which is found;
Initially, T is the set of vertices for which the shortest path has been calculated, and U is the set of vertices for which the shortest path has not been calculated. Each vertex in the U set is weighted (i.e., the path length from the source point to that point).
(2) The server sets the path weight of the source point s to 0;
the server sets the path weight of the source point S to 0, so t= { S (0) }, S (0) indicates that the shortest distance from the source point S to the source point S is 0, i.e. the route [ S ] value is 0, and the vertex set T includes only the source point S.
(3) If there is a side (s, e) that can be reached directly to the source point s, the server sets the array route [ e ] as d (s, e), and sets the path length of all other vertices that cannot reach the source point s as infinity route [ infinity ];
where E is the edge set and the other vertices are vertices that cannot be reached by the source point s. The path length is set to infinity, i.e. the weight is set to ≡.
(4) The server selects a minimum path value in the array route, wherein the minimum path value is the shortest path from a source point s to the nearest vertex corresponding to the minimum path value, and adds the nearest vertex to a preset table;
specifically, the nearest vertex is added to the vertex set T while the nearest vertex is removed from the vertex set U.
(5) The server judges whether the newly added nearest vertex can reach other vertexes or not, and judges whether the path length of reaching other points through the newly added nearest vertex is shorter than the path length of directly reaching other points by the source point s or not;
(6) If the vertex weight satisfies the condition, the server updates the weight of the vertex in route;
(7) And the server continues to find the minimum value in the vertex set U until all vertices in the route are contained in the preset table, a station combination to be matched is generated, and the sum of the distances between combinable stations in the station combination to be matched is shortest.
107. And sequencing the plurality of station combinations to be matched to obtain the target station combination with the inter-station distance not exceeding the threshold value.
The threshold value may be set according to actual situations, for example, the threshold value may be 15 minutes, the duration of 15 minutes is an initial value, the threshold value may be changed by a demander, or the project may be optimized according to actual data later, because urban traffic is changeable.
Optionally, after sorting the plurality of combinations of stations to be matched and determining the target station combinations with the inter-station distances not exceeding the threshold value, the method further includes:
and forming a two-dimensional matrix by the distances between the head stations and the tail stations of all the departure places and the destination places in the target site combination.
The method is to record the sites and the distances between the sites as a matrix, determine which site of the departure place is the last site of the departure place, and go to which site of the destination to put down the first batch of guests.
Optionally, after forming a two-dimensional matrix by using distances between the head stations and the tail stations of all the departure places and the destination in the target site combination, the method further includes:
and packaging the target order number and the operation time period corresponding to the target site combination, generating a push message, and sending the push message and the two-dimensional matrix to the terminal.
Specifically, the server determines the moment of reaching each station, obtains the operation time period, and determines the number of boarding and disembarking persons at each station at the same time, and sends the boarding and disembarking persons to the front end together. In this period, the front end includes a passenger end and a driver end, and the messages received by different ports are different, for example, the passenger end may receive a push message, the message indicates that the user gets on the vehicle, and the driver end may receive a two-dimensional matrix in addition to the push message, to obtain a driving route and the number of people getting on or off each station.
For example, order number, operation period, number of boarding persons and disembarking persons at each station, and time to each station may be differently exhibited according to the nature of the front end. The front end is an Application (APP) front end, and the front end User Interface (UI) uses information to design how to present to the user, and the user includes a passenger end (e.g. notifying the passenger of getting on the car from a point of view), a driver end (e.g. notifying the driver where to get on and off the car), and a carrier end (e.g. performing vehicle dispatching).
According to the embodiment of the invention, the order combination meeting the conditions is determined through the dynamic programming algorithm, the target combination with the nearest site distance is determined through the Di Jie St algorithm, the matching of the riding requirement and the running vehicle is carried out according to the target combination, the available vehicle is quickly found, the available vehicle resource and the required order are matched, the journey time after the client gets on the vehicle is reduced, the distance is shortened, and the passenger organization efficiency is improved.
The scheme can be applied to the intelligent traffic field, so that the construction of intelligent cities is promoted.
The method for grouping the passengers on a trip based on dynamic programming in the embodiment of the present invention is described above, and the device for grouping the passengers on a trip based on dynamic programming in the embodiment of the present invention is described below, referring to fig. 2, an embodiment of the device for grouping the passengers on a trip based on dynamic programming in the embodiment of the present invention includes:
a first obtaining module 201, configured to obtain a plurality of travel demand orders, where the travel demand orders are used to provide travel vehicles for users, and the travel demand orders include a departure place, a departure station, a destination station, and a riding time;
the processing module 202 is configured to process the plurality of travel demand orders according to a preset rule to obtain a target travel demand order set and a target order number corresponding to each target travel demand order, where a riding time of the target travel demand order set is within a first preset period range;
A second obtaining module 203, configured to obtain vehicle information within the first preset period, where the vehicle information includes a total number of seats of each vehicle to be allocated, a remaining number of seats of each vehicle to be allocated, and a total number of vehicles of the vehicle to be allocated;
the traversing module 204 is configured to traverse each target order number and the vehicle information in the target travel demand order set, determine the number of boarding persons and the number of alighting persons at all sites, and obtain an order number combination;
a first determining module 205, configured to invoke a dynamic programming algorithm to traverse the order number combinations and determine all order allocation combinations, where the order allocation combinations include a plurality of pre-allocated target order numbers;
a second determining module 206, configured to determine a plurality of combinations of sites to be matched according to the all order allocation combinations and the dijkstra algorithm, where a sum of distances between combinable sites in the combinations of sites to be matched is shortest;
and the ranking module 207 is configured to rank the plurality of station combinations to be matched to obtain a target station combination with a distance between stations not exceeding a threshold value.
According to the embodiment of the invention, the order combination meeting the conditions is determined through the dynamic programming algorithm, the target combination with the nearest site distance is determined through the Di Jie St algorithm, the matching of the riding requirement and the running vehicle is carried out according to the target combination, the available vehicle is quickly found, the available vehicle resource and the required order are matched, the journey time after the client gets on the vehicle is reduced, the distance is shortened, and the passenger organization efficiency is improved.
Referring to fig. 3, another embodiment of the trip passenger device based on dynamic planning in the embodiment of the present invention includes:
a first obtaining module 201, configured to obtain a plurality of travel demand orders, where the travel demand orders are used to provide travel vehicles for users, and the travel demand orders include a departure place, a departure station, a destination station, and a riding time;
the processing module 202 is configured to process the plurality of travel demand orders according to a preset rule to obtain a target travel demand order set and a target order number corresponding to each target travel demand order, where a riding time of the target travel demand order set is within a first preset period range;
a second obtaining module 203, configured to obtain vehicle information within the first preset period, where the vehicle information includes a total number of seats of each vehicle to be allocated, a remaining number of seats of each vehicle to be allocated, and a total number of vehicles of the vehicle to be allocated;
the traversing module 204 is configured to traverse each target order number and the vehicle information in the target travel demand order set, determine the number of boarding persons and the number of alighting persons at all sites, and obtain an order number combination;
a first determining module 205, configured to invoke a dynamic programming algorithm to traverse the order number combinations and determine all order allocation combinations, where the order allocation combinations include a plurality of pre-allocated target order numbers;
A second determining module 206, configured to determine a plurality of combinations of sites to be matched according to the all order allocation combinations and the dijkstra algorithm, where a sum of distances between combinable sites in the combinations of sites to be matched is shortest;
and the ranking module 207 is configured to rank the plurality of station combinations to be matched to obtain a target station combination with a distance between stations not exceeding a threshold value.
Optionally, the processing module 202 includes:
the parsing unit 2021 is configured to parse the plurality of travel demand orders to obtain a riding time of each user, where each user corresponds to one travel demand order;
the dividing unit 2022 is configured to divide the plurality of travel demand orders according to a preset time interval, generate a plurality of initial travel demand order sets, and each of the initial travel demand order sets has a different riding time range;
the screening unit 2023 is configured to screen the plurality of initial travel demand order sets according to a departure station and a destination station, generate a plurality of transitional travel demand order sets, and each of the transitional travel demand order sets has a departure station in the same city and a destination station in the same city;
the selection determining unit 2024 is configured to select a target travel demand order set corresponding to the first preset period from the plurality of transitional travel demand order sets, and determine a target order number corresponding to each travel demand order in the marked travel demand order set.
Optionally, the first determining module 205 includes:
the traversing unit 2051 is configured to traverse the combination of the number of people in the order, and set a sub-problem that meets the number of boarding points and the full rate requirement of the vehicle, so as to obtain a plurality of sub-problems;
and the calling unit 2052 is configured to call the dynamic programming algorithm to sequentially solve the plurality of sub-problems, so as to obtain a plurality of order allocation combinations, where the order allocation combinations include a plurality of pre-allocated target order numbers.
Optionally, the traversing unit 2051 is specifically configured to:
traversing the order number combination, and screening a plurality of candidate order combinations meeting the number of the boarding points in the order number combination, wherein the number of the boarding points is not more than three for departure places and destinations; and setting the vehicle full rate requirement as a sub-problem based on the plurality of candidate order combinations, and obtaining a plurality of sub-problems, wherein the vehicle full rate requirement is that the vehicle full rate of each vehicle is greater than or equal to the corresponding minimum full rate.
Optionally, the second determining module 206 is specifically configured to:
initializing an array route, a vertex set T and a vertex set U, wherein the array is used for storing the shortest distance from a source point s to each vertex, and the vertex set T is used for storing the vertex of the shortest path which is found; setting the path weight of the source point s to 0; if there is a side (s, e) that can be reached directly to the source point s, then set the array route [ e ] to d (s, e), and set the path length of all other vertices that cannot reach the source point s to infinity; selecting a minimum path value in the array route, wherein the minimum path value is the shortest path from a source point s to the nearest vertex corresponding to the minimum path value, and adding the nearest vertex into a preset table; judging whether the newly added nearest vertex can reach other vertexes or not, and judging whether the path length reaching other points through the newly added nearest vertex is shorter than the path length of the source point s directly reaching other points or not; if the vertex weights meet the conditions, updating the weights of the vertices in route; and continuing to find the minimum value in the vertex set U until all vertices in the route are contained in the preset table, generating a station combination to be matched, wherein the sum of the distances between combinable stations in the station combination to be matched is shortest.
Optionally, the trip group guest device based on dynamic planning further includes:
a composing module 208 is configured to compose a two-dimensional matrix of distances between the head stations and the tail stations of all the departure points and the destination points in the target station combination.
Optionally, the trip group guest device based on dynamic planning further includes:
and the generation and transmission module 209 is configured to package a target order number and an operation period corresponding to the target site combination, generate a push message, and transmit the push message and the two-dimensional matrix to a terminal.
According to the embodiment of the invention, the order combination meeting the conditions is determined through the dynamic programming algorithm, the target combination with the nearest site distance is determined through the Di Jie St algorithm, the matching of the riding requirement and the running vehicle is carried out according to the target combination, the available vehicle is quickly found, the available vehicle resource and the required order are matched, the journey time after the client gets on the vehicle is reduced, the distance is shortened, and the passenger organization efficiency is improved.
The trip group guest device based on dynamic programming in the embodiment of the present invention is described in detail from the point of view of the modularized functional entity in the above fig. 2 to 3, and the trip group guest device based on dynamic programming in the embodiment of the present invention is described in detail from the point of view of hardware processing in the following.
Fig. 4 is a schematic structural diagram of a dynamically planned travel group client device according to an embodiment of the present invention, where the dynamically planned travel group client device 400 may generate relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 410 (e.g., one or more processors) and a memory 420, and one or more storage media 430 (e.g., one or more mass storage devices) storing application 433 or data 432. Wherein memory 420 and storage medium 430 may be transitory or persistent storage. The program stored on the storage medium 430 may include one or more modules (not shown), each of which may include a series of instruction operations on the dynamically planned travel group guest device 400. Still further, the processor 410 may be configured to communicate with the storage medium 430 to execute a series of instruction operations in the storage medium 430 on the dynamically planned travel group guest device 400.
The dynamic programming-based travel group client device 400 may also include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input/output interfaces 460, and/or one or more operating systems 431, such as Windows Serve, mac OS X, unix, linux, freeBSD, and the like. Those skilled in the art will appreciate that the dynamic programming based travel group client device structure shown in fig. 4 does not constitute a limitation of the dynamic programming based travel group client device and may include more or fewer components than shown, or may combine certain components, or may be a different arrangement of components. The processor 410 may perform the functions of the first acquisition module 201, the processing module 202, the second acquisition module 203, the traversal module 204, the first determination module 205, the second determination module 206, the ranking module 207, and the composition module 208 in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, when the instructions run on a computer, cause the computer to perform the steps of the trip guest-building method based on dynamic programming.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The trip group guest method based on dynamic programming is characterized by comprising the following steps:
acquiring a plurality of travel demand orders, wherein the travel demand orders are used for providing travel vehicles for users, and the travel demand orders comprise departure places, departure stations, destinations, destination stations and riding moments;
processing the travel demand orders according to a preset rule to obtain a target travel demand order set and target order numbers corresponding to each target travel demand order, wherein the riding time of the target travel demand order set is within a first preset time period range;
the processing of the travel demand orders according to a preset rule to obtain a target travel demand order set and a target order number corresponding to each target travel demand order, wherein the riding time of the target travel demand order set is within a first preset time period range, and the processing comprises the following steps:
Analyzing the travel demand orders to obtain riding time of each user, wherein each user corresponds to one travel demand order;
dividing the travel demand orders according to preset time intervals to generate a plurality of initial travel demand order sets, wherein the riding time ranges of the initial travel demand order sets are different;
screening the plurality of initial travel demand order sets according to the departure station and the destination station to generate a plurality of transition travel demand order sets, wherein the departure station and the destination station in each transition travel demand order set are in the same city, and the destination station is also in the same city;
selecting a target travel demand order set corresponding to a first preset period from the plurality of transition travel demand order sets, and determining a target order number corresponding to each travel demand order in the marked travel demand order set;
acquiring vehicle information in the first preset time period range, wherein the vehicle information comprises the total number of seats of each vehicle to be distributed, the residual seat number of each vehicle to be distributed and the total number of vehicles of the vehicle to be distributed;
traversing each target order number and the vehicle information in the target travel demand order set, and determining the number of boarding persons and alighting persons at all stations to obtain an order number combination;
Invoking a dynamic programming algorithm to traverse the order number combinations, and determining all order distribution combinations, wherein the order distribution combinations comprise a plurality of pre-distributed target order numbers;
determining a plurality of site combinations to be matched according to all the order distribution combinations and a Di Jie St La algorithm, wherein the sum of the distances between combinable sites in the site combinations to be matched is shortest;
the method comprises the steps of determining a plurality of site combinations to be matched according to all the order distribution combinations and a Dijiestra algorithm, wherein the sum of the distances between combinable sites in the site combinations to be matched is shortest, and the method comprises the following steps:
initializing an array route, a vertex set T and a vertex set U, wherein the array is used for storing the shortest distance from a source point s to each vertex, and the vertex set T is used for storing the vertex of the shortest path which is found;
setting the path weight of the source point s to 0;
if there is a side (s, e) that can be reached directly to the source point s, then set the array route [ e ] to d (s, e), and set the path length of all other vertices that cannot reach the source point s to infinity;
selecting a minimum path value in the array route, wherein the minimum path value is the shortest path from a source point s to the nearest vertex corresponding to the minimum path value, and adding the nearest vertex into a preset table;
Judging whether the newly added nearest vertex can reach other vertexes or not, and judging whether the path length reaching other points through the newly added nearest vertex is shorter than the path length of the source point s directly reaching other points or not;
if the vertex weights meet the conditions, updating the weights of the vertices in route;
the minimum value is found in the vertex set U until all vertices in the route are contained in the preset table, a station combination to be matched is generated, and the sum of the distances between combinable stations in the station combination to be matched is shortest;
and sequencing the plurality of station combinations to be matched to obtain a target station combination with the inter-station distance not exceeding a threshold value.
2. The dynamic programming-based travel group guest method according to claim 1, wherein the invoking the dynamic programming algorithm traverses the order population combinations to determine all order allocation combinations including a plurality of pre-allocated target order numbers, comprising:
traversing the order number combination, setting the meeting of the number of boarding points and the full rate requirement of the vehicle as a sub-problem, and obtaining a plurality of sub-problems;
and calling a dynamic programming algorithm to sequentially solve a plurality of sub-problems to obtain a plurality of order allocation combinations, wherein the order allocation combinations comprise a plurality of pre-allocated target order numbers.
3. The method of claim 2, wherein traversing the order number combination sets meeting a number of boarding points requirement and a vehicle full rate requirement as one sub-problem, and obtaining a plurality of sub-problems comprises:
traversing the order number combination, and screening a plurality of candidate order combinations meeting the number of the boarding points in the order number combination, wherein the number of the boarding points is not more than three for departure places and destinations;
and setting the vehicle full rate requirement as a sub-problem based on the plurality of candidate order combinations, and obtaining a plurality of sub-problems, wherein the vehicle full rate requirement is that the vehicle full rate of each vehicle is greater than or equal to the corresponding minimum full rate.
4. A method of dynamically planning based on a trip group guest according to claims 1-3, characterized in that after said sorting of said plurality of combinations of stations to be matched, determining a combination of target stations for which the inter-station distance does not exceed a threshold value, further comprising:
and forming a two-dimensional matrix by the distances between the head stations and the tail stations of all the departure places and the destination places in the target site combination.
5. The method for dynamically planning based on claim 4, further comprising, after said combining the distances between the head stations and the tail stations of all the departure points and the destination points in the target station combination to form a two-dimensional matrix:
And packaging the target order number and the operation time period corresponding to the target site combination, generating a push message, and sending the push message and the two-dimensional matrix to a terminal.
6. A travel group guest device based on dynamic programming, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a plurality of travel demand orders, the travel demand orders are used for providing travel vehicles for users, and the travel demand orders comprise departure places, departure stations, destinations, destination stations and riding moments;
the processing module is used for processing the travel demand orders according to preset rules to obtain a target travel demand order set and target order numbers corresponding to each target travel demand order, and the riding time of the target travel demand order set is within a first preset time period range;
the processing of the travel demand orders according to a preset rule to obtain a target travel demand order set and a target order number corresponding to each target travel demand order, wherein the riding time of the target travel demand order set is within a first preset time period range, and the processing comprises the following steps:
analyzing the travel demand orders to obtain riding time of each user, wherein each user corresponds to one travel demand order;
Dividing the travel demand orders according to preset time intervals to generate a plurality of initial travel demand order sets, wherein the riding time ranges of the initial travel demand order sets are different;
screening the plurality of initial travel demand order sets according to the departure station and the destination station to generate a plurality of transition travel demand order sets, wherein the departure station and the destination station in each transition travel demand order set are in the same city, and the destination station is also in the same city;
selecting a target travel demand order set corresponding to a first preset period from the plurality of transition travel demand order sets, and determining a target order number corresponding to each travel demand order in the marked travel demand order set;
the second acquisition module is used for acquiring vehicle information in the first preset time period range, wherein the vehicle information comprises the total number of seats of each vehicle to be distributed, the residual seat number of each vehicle to be distributed and the total number of vehicles of the vehicle to be distributed;
the traversing module is used for traversing each target order number and the vehicle information in the target travel demand order set, determining the number of boarding persons and the number of alighting persons at all stations and obtaining an order number combination;
The first determining module is used for calling a dynamic programming algorithm to traverse the order number combinations and determining all order distribution combinations, wherein the order distribution combinations comprise a plurality of pre-distributed target order numbers;
the second determining module is used for determining a plurality of station combinations to be matched according to all the order distribution combinations and a Di Jie St-Lag algorithm, and the sum of the distances between combinable stations in the station combinations to be matched is shortest;
the method comprises the steps of determining a plurality of site combinations to be matched according to all the order distribution combinations and a Dijiestra algorithm, wherein the sum of the distances between combinable sites in the site combinations to be matched is shortest, and the method comprises the following steps:
initializing an array route, a vertex set T and a vertex set U, wherein the array is used for storing the shortest distance from a source point s to each vertex, and the vertex set T is used for storing the vertex of the shortest path which is found;
setting the path weight of the source point s to 0;
if there is a side (s, e) that can be reached directly to the source point s, then set the array route [ e ] to d (s, e), and set the path length of all other vertices that cannot reach the source point s to infinity;
selecting a minimum path value in the array route, wherein the minimum path value is the shortest path from a source point s to the nearest vertex corresponding to the minimum path value, and adding the nearest vertex into a preset table;
Judging whether the newly added nearest vertex can reach other vertexes or not, and judging whether the path length reaching other points through the newly added nearest vertex is shorter than the path length of the source point s directly reaching other points or not;
if the vertex weights meet the conditions, updating the weights of the vertices in route;
the minimum value is found in the vertex set U until all vertices in the route are contained in the preset table, a station combination to be matched is generated, and the sum of the distances between combinable stations in the station combination to be matched is shortest;
and the sorting module is used for sorting the plurality of station combinations to be matched to obtain a target station combination with the inter-station distance not exceeding a threshold value.
7. A dynamic programming-based travel group guest device, the dynamic programming-based travel group guest device comprising: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the dynamic programming-based travel group guest device to perform the dynamic programming-based travel group guest method of any one of claims 1-5.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which when executed by a processor implements the dynamic programming based method of live boarding of any one of claims 1-5.
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| CN110599300A (en) * | 2019-08-30 | 2019-12-20 | 上海钧正网络科技有限公司 | Order processing method and device, computer equipment and storage medium |
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| WO2021232586A1 (en) | 2021-11-25 |
| CN111738550A (en) | 2020-10-02 |
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