CN112418676A - Vehicle launching method and device, readable storage medium and electronic equipment - Google Patents
Vehicle launching method and device, readable storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a vehicle launching method, a vehicle launching device, a readable storage medium and electronic equipment. The embodiment of the invention obtains a first time and a first position; determining a set duration and real-time travel data within a set range according to the first time and the first position, clustering the position of the at least one trip point, and determining at least one clustering area; determining at least one vehicle release position according to the at least one clustering region, wherein each clustering region comprises at least one vehicle release position; performing path planning on the at least one vehicle release position, and determining a preliminary vehicle release route, wherein the preliminary vehicle release route comprises all the vehicle release positions; and adjusting the initial vehicle throwing route to determine the vehicle throwing route. By the method, the vehicle launching accuracy and the vehicle launching speed are improved.
Description
Technical Field
The invention relates to the field of vehicle sharing, in particular to a vehicle launching method, a vehicle launching device, a readable storage medium and electronic equipment.
Background
With the development of sharing economy, sharing devices are increased, and the lives of people are changed, such as sharing vehicles, sharing umbrellas and the like, wherein the sharing vehicles comprise sharing single vehicles, sharing electric vehicles and sharing automobiles; the shared vehicle greatly facilitates the traveling of people, and people can independently borrow and return the shared vehicle through the corresponding client side in a city where the shared vehicle is put in, so that the convenience degree of the occurrence is improved.
In the existing shared vehicle releasing process, the releasing position of a vehicle is mainly judged by depending on the experience of offline operation and maintenance personnel, when academic conferences, singing meetings, sports meetings, new commercial complex operation and other gathering activities occur in an area range, or the updating of public transport stations, the use of new living areas and the like can also influence the requirement condition of the vehicle in the area range, the vehicle is released by the experience of the offline operation and maintenance personnel, the releasing is possibly not timely, and the problems of the vehicle using requirements of users and the like cannot be solved.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a vehicle delivery method, a vehicle delivery device, a readable storage medium, and an electronic device, which improve accuracy of vehicle delivery and a speed of vehicle delivery.
In a first aspect, an embodiment of the present invention provides a method for vehicle delivery, where the method includes: acquiring a first time and a first position; determining a set time length and real-time travel data within a set range according to the first time and the first position, wherein the set time length is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one travel point position; clustering the position of the at least one trip point, and determining at least one clustering area; determining at least one vehicle release position according to the at least one clustering region, wherein each clustering region comprises at least one vehicle release position; performing path planning on the at least one vehicle release position, and determining a preliminary vehicle release route, wherein the preliminary vehicle release route comprises all the vehicle release positions; and adjusting the initial vehicle throwing route to determine the vehicle throwing route.
Preferably, the method further comprises: the acquiring the first time and the first position specifically includes: public opinion information is obtained; and analyzing the public opinion information according to a natural language processing NLP, and determining a first time and a first position, wherein the first time is the starting time of the occurrence of the gathering event, and the first position is the position of the occurrence of the gathering event.
Preferably, the acquiring public opinion information includes: and crawling a network data source to acquire the public opinion information, wherein the network data source comprises at least one of network news, comments, forums, blogs, microblogs and posts.
Preferably, the clustering the at least one travel point position specifically includes: clustering the at least one trip point location through density-based spatial clustering and noise application (DBSCAN).
Preferably, the path planning for the at least one vehicle launching location and the determining of the initial vehicle launching route specifically include: performing path planning on the at least one vehicle throwing position through an ant colony algorithm to determine an optimal path, wherein the optimal path is the path with the shortest distance between the vehicle throwing positions; and determining the optimal path as the preliminary vehicle delivery route.
Preferably, the adjusting the preliminary vehicle launching route to determine the vehicle launching route specifically includes: and adjusting the weight of the path between any two vehicle releasing positions in the preliminary vehicle releasing path according to the Hungarian algorithm, and determining the vehicle releasing path.
Preferably, the real-time itinerary data further includes POI points of interest.
Preferably, the method further comprises: and classifying the travel corresponding to the real-time travel data by adopting a decision tree model according to the travel point position of the real-time travel data, the POI, the area, the aggregation event and the occurrence time of the aggregation event, and determining the event type corresponding to the travel, wherein the event type comprises a long-term aggregation event or a short-term aggregation event.
Preferably, the method further comprises: in response to the event type being a long-term aggregate event, determining the vehicle delivery route as a long-term vehicle delivery route.
Preferably, the method further comprises: determining the vehicle delivery route as a short-term vehicle delivery route in response to the event type being a short-term aggregate event.
In a second aspect, an embodiment of the present invention provides a device for vehicle launching, including: an acquisition unit for acquiring a first time and a first position; a determining unit, configured to determine a set duration and real-time travel data within a set range according to the first time and the first position, where the set duration is a time of a set length after the first time, the set range is an area with the first position as a center, and the real-time travel data includes at least one travel point position; the clustering unit is used for clustering the position of the at least one trip point and determining at least one clustering area; the determining unit is further configured to determine at least one vehicle delivery location according to the at least one clustering region, where each clustering region includes at least one vehicle delivery location; the processing unit is used for planning a path of the at least one vehicle release position and determining a preliminary vehicle release route, wherein the preliminary vehicle release route comprises all the vehicle release positions; the processing unit is further used for adjusting the preliminary vehicle throwing route and determining the vehicle throwing route.
Preferably, the obtaining unit is specifically configured to: public opinion information is obtained; and analyzing the public opinion information according to Natural Language Processing (NLP), and determining a first time and a first position, wherein the first time is the starting time of the occurrence of the gathering event, and the first position is the position of the occurrence of the gathering event.
Preferably, the obtaining unit is further configured to: and crawling a network data source to acquire the public opinion information, wherein the network data source comprises at least one of network news, comments, forums, blogs, microblogs and posts.
Preferably, the clustering unit has a function for: clustering the at least one trip point location by density-based spatial clustering and noise application (DBSCAN).
Preferably, the processing unit has means for: performing path planning on the at least one vehicle launching position through an ant colony algorithm to determine an optimal path, wherein the optimal path is the path with the shortest distance between the vehicle launching positions; and determining the optimal path as the preliminary vehicle delivery route.
Preferably, the processing unit is further specifically configured to: and adjusting the weight of the path between any two vehicle release positions in the initial vehicle release path according to the Hungarian algorithm, and determining the vehicle release path.
Preferably, the real-time itinerary data further includes POI points of interest.
Preferably, the apparatus further comprises: the classification unit is configured to classify the trip corresponding to the real-time trip data by using a decision tree model according to the trip point position of the real-time trip data, the POI, the area, the aggregation event, and the occurrence time of the aggregation event, and determine an event type corresponding to the trip, where the event type includes a long-term aggregation event or a short-term aggregation event.
Preferably, the processing unit is further configured to: in response to the event type being a long-term aggregated event, determining the vehicle delivery route as a long-term vehicle delivery route.
Preferably, the processing unit is further configured to: determining the vehicle delivery route as a short-term vehicle delivery route in response to the event type being a short-term aggregate event.
In a third aspect, embodiments of the present invention provide a computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement a method as set forth in the first aspect or any one of the possibilities of the first aspect.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, the memory being configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to the first aspect or any one of the possibilities of the first aspect.
The embodiment of the invention obtains a first time and a first position; determining a set time length and real-time travel data within a set range according to the first time and the first position, wherein the set time length is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one trip point position; clustering the position of the at least one trip point, and determining at least one clustering area; determining at least one vehicle release position according to the at least one clustering region, wherein each clustering region comprises at least one vehicle release position; performing path planning on the at least one vehicle throwing position, and determining a preliminary vehicle throwing route, wherein the preliminary vehicle throwing route comprises all the vehicle throwing positions; and adjusting the initial vehicle throwing route to determine the vehicle throwing route. By the method, the vehicle releasing position where the vehicle needs to be released can be determined, the vehicle is released to the vehicle releasing position according to the determined vehicle releasing route, and the vehicle releasing accuracy and speed are improved.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a method of vehicle delivery in accordance with an embodiment of the present invention;
FIG. 2 is a map of a vehicle launch according to an embodiment of the present invention;
FIG. 3 is a map of a vehicle launch according to an embodiment of the present invention;
FIG. 4 is a map of a vehicle launch according to an embodiment of the present invention;
FIG. 5 is a map of a vehicle launch according to an embodiment of the present invention;
FIG. 6 is a flow chart of a method of vehicle delivery in accordance with an embodiment of the present invention;
FIG. 7 is a schematic view of a vehicle launch apparatus according to an embodiment of the present invention;
fig. 8 is a schematic diagram of an electronic device of an embodiment of the invention.
Detailed Description
The present disclosure is described below based on examples, but the present disclosure is not limited to only these examples. In the following detailed description of the present disclosure, certain specific details are set forth. It will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present disclosure.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the application, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present disclosure, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified.
Generally, in the prior art, in the releasing process of a shared vehicle, the releasing position of the vehicle is mainly determined by depending on experience of an offline operation and maintenance worker, for example, if the offline operation and maintenance worker determines that the demand of the vehicle in a certain area range is increased by experience, the vehicle is released in the area, but the experience of the offline operation and maintenance worker has certain limitation on releasing the vehicle, and the releasing is not timely or inaccurate; for example, when a short-term gathering activity of gathering activities such as academic conferences, concerts, sports meetings, new business complex operations and the like occurs in an area, the demand of vehicles may suddenly increase within a few days or a few hours, but the number of vehicle releases in the area is far from the demand of users; or the use of a new living area, the opening of a new subway station and a new bus station, the updating of public transport stations and the like can affect the demand condition of vehicles in the area for a long time; if the vehicle is thrown only by the experience of the offline operation and maintenance personnel, the new vehicle throwing position cannot be automatically excavated, the short-term gathering travel requirement cannot be met, the throwing may not be timely, and the problems of the vehicle using requirement and the like of the user cannot be solved.
The embodiment of the invention discloses a vehicle launching method, and FIG. 1 is a flow chart of the vehicle launching method in the embodiment of the invention. As shown in fig. 1, the method specifically comprises the following steps:
step S100, a first time and a first position are obtained.
In a possible implementation manner, the acquiring the first time and the first position specifically includes: public opinion information is obtained; analyzing the public opinion information according to Natural Language Processing (NLP), and determining a first time and a first position, wherein the first time is a starting time of occurrence of an aggregation event, and the first position is a position of occurrence of the aggregation event.
The public opinion information is description and reflection of public opinions, and the public opinion information refers to information, messages, voice messages, information, instructions, data and signals for objectively reflecting public opinion states and motion conditions thereof in the information motion process of collecting, sorting, analyzing, reporting, utilizing and feeding back the public opinion attitude. The public opinion information acquisition comprises: the public opinion information is obtained by crawling a network data source, wherein the network data source comprises at least one of network news, comments, forums, blogs, microblogs and posts, and the public opinion information source can also comprise other conditions, which is not described in detail in the embodiment of the invention.
For example, assuming that the acquired public opinion information is analyzed according to natural language processing, and assuming that the public opinion information is from a microblog, a large number of users publish information about art exhibitions in the sunward area 798 artistic area, and publish information that a shared vehicle is difficult to find at the position, and some users publish information that the start time of the art exhibitions is 2020, 8, 20 and the end time of the art exhibitions is 2020, 8, 25 and the like, by analyzing the public opinion information, the start time, i.e., the first time, of the art exhibitions is determined, and the first position of the art exhibitions is determined to be the sunward area 798 artistic area.
Step S101, determining a set time length and real-time travel data within a set range according to the first time and the first position, wherein the set time length is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one travel point position.
In a possible implementation manner, assuming that the real-time travel data is short-range travel data, for example, a travel within 2.5 kilometers (km) is a short range, a set duration is determined 1 hour after the first time, a region with the first position as a center and a radius of 5km is determined as a set range, and real-time travel data included in the set region within one hour after the first time is determined, specifically, the real-time travel data includes a plurality of travel points, where the travel points may also be referred to as boarding points of a user.
For example, as shown in fig. 2, within 1 hour after the first time, 50 trip points are determined in a circular area with a radius of 5km, then 50 trip points are determined, each trip point is labeled from 1 to 50, and in fig. 2, only the first 20 trip point labels are given, that is, trip point 1, trip point 2, trip point 3, trip point 4, trip point 5, trip point 6, trip point 7, trip point 8, trip point 9, trip point 10, trip point 11, trip point 12, trip point 13, trip point 14, trip point 15, trip point 16, trip point 17, trip point 18, trip point 19, and trip point 20, and other trip points are not repeated.
Step S102, clustering the position of the at least one trip point, and determining at least one clustering area.
In a possible implementation manner, the clustering the at least one travel point location specifically includes: clustering the at least one travel point location by Density-Based Spatial Clustering of Applications with Noise (DBSCAN).
Specifically, the DBSCAN algorithm is characterized by not depending on distance but depending on density, so that the defect that only spherical clusters can be found by the distance-based algorithm is overcome; the core idea of the DBSCAN algorithm is that starting from a certain core point, the DBSCAN algorithm is continuously expanded to a region with accessible density, so that a maximized region comprising the core point and boundary points is obtained, and the density of any two points in the region is connected; the DBSCAN algorithm does not need to appoint the number of clusters in advance during clustering, and the number of the final clusters is uncertain.
In the embodiment of the present invention, other clustering algorithms may also be used to cluster the trip points, which is not limited in the embodiment of the present invention.
For example, it is assumed that after the trip points in fig. 2 are clustered by the DBSCAN algorithm, 5 clustering regions are determined, specifically, as shown in fig. 3, the clustering regions are respectively a clustering region 1, a clustering region 2, a clustering region 3, a clustering region 4, and a clustering region 5, and since the clustering regions are generated by clustering the occurrence points, each clustering region includes a certain number of trip points. For example, the clustering area 1 includes a trip point 1, a trip point 2, a trip point 3, a trip point 4, a trip point 5, a trip point 6, a trip point 7, a trip point 8, a trip point 9, and a trip point 10, which are merely exemplary illustrations, and a specific clustering situation is determined according to an actual situation, which is not described in detail in the embodiment of the present invention.
Step S103, determining at least one vehicle launching position according to the at least one clustering region, wherein each clustering region comprises at least one vehicle launching position.
In a possible implementation manner, any position in the clustering area is used as a vehicle release position, wherein the vehicle release position may also be referred to as a vehicle release point, that is, a place where the operation and maintenance personnel release vehicles intensively; specifically, the central point of the cluster region may be determined as the vehicle release position of the region.
In a possible implementation manner, each clustering region may have one vehicle release position or a plurality of vehicle release positions, which is not limited in the embodiment of the present invention and is determined according to actual situations.
For example, as shown in fig. 3, there are a clustering area 1, a clustering area 2, a clustering area 3, a clustering area 4, and a clustering area 5, and 5 clustering areas, and if 1 vehicle drop position is determined in each area, there are 5 vehicle drop positions, specifically, as shown in fig. 4, 5 vehicle drop positions, specifically, the vehicle drop position of the clustering area 1 is a vehicle drop position a, the vehicle drop position of the clustering area 2 is a vehicle drop position B, the vehicle drop position of the clustering area 3 is a vehicle drop position C, the vehicle drop position of the clustering area 4 is a vehicle drop position D, and the vehicle drop position of the clustering area 5 is a vehicle drop position E.
And S104, planning a path of the at least one vehicle release position, and determining a preliminary vehicle release route, wherein the preliminary vehicle release route comprises all the vehicle release positions.
In a possible implementation manner, the path planning for the at least one vehicle release location and determining a preliminary vehicle release route specifically include: performing path planning on the at least one vehicle launching position through an Ant Colony Optimization (ACO) to determine an optimal path, wherein the optimal path is a path with the shortest distance between the vehicle launching positions; and determining the optimal path as the preliminary vehicle delivery route.
Specifically, the ant colony algorithm is also called ant algorithm, and is a probability type algorithm for finding an optimized path in a graph. The ant colony algorithm is a simulated evolution algorithm from the behavior of ants finding paths in the process of searching food. The principle of the ant colony algorithm is that ants leave a substance called pheromone in the moving process, and the number of the spread pheromone is less and less along with the moving distance, so that the concentration of the pheromone is the strongest at home or around food, the ants can select the direction according to the pheromone, and certainly, the thicker the pheromone is, the higher the probability of selection is, and the pheromone has certain volatilization. The motion process of ants can be simply summarized as follows: when no pheromone guides the surrounding, the motion of the ants has certain inertia and has certain probability to select other directions; when the pheromone is guided around, the motion direction is selected according to the density intensity probability of the pheromone; when finding food, ants leave the a pheromone relevant to the family, when finding the family, ants leave the b pheromone relevant to the food, and as the moving distance increases, the spread pheromone is less and less, and the pheromone can volatilize automatically along with the time lapse; for example, if there are two paths to food, one longer path a and one shorter path b, although ants are present on both paths a and b, and because path b is shorter than path a, the ants spend less time passing through path b, and as time goes on and pheromone volatilizes, the pheromone concentration on path b is gradually stronger than path a, at this time, because the concentration of path b is stronger than path a, more and more ants will select path b, and the concentration of path b will only be stronger and stronger. Over time, ants converge on path b, so that local optimality, i.e. the shortest path, can be skipped.
In the embodiment of the present invention, it is assumed that the vehicle delivery positions a, B, C, D and E are located on the way, an optimal path of the 5 vehicle delivery positions is shown in fig. 5, the order of the optimal path passing through the vehicle delivery positions is vehicle delivery position a-vehicle delivery position C-vehicle delivery position B-vehicle delivery position D-vehicle delivery position E, and a path between each two vehicle delivery positions is the shortest path between the two vehicle delivery positions.
And S105, adjusting the initial vehicle throwing route and determining the vehicle throwing route.
In a possible implementation manner, the adjusting the preliminary vehicle delivery route and determining the vehicle delivery route specifically include: and adjusting the weight of the path between any two vehicle releasing positions in the preliminary vehicle releasing path according to the Hungarian algorithm, and determining the vehicle releasing path.
Specifically, the hungarian algorithm is the most common algorithm for partial graph matching, the core of the algorithm is to search for an amplification path, the algorithm is an algorithm for obtaining maximum matching of bipartite graphs by using the amplification path, and due to the nature of the amplification path, matching edges in the amplification path are always one more than non-matching edges, so that if the matched edges in one amplification path are abandoned and the non-matching edges are selected as the matching edges, the number of matching is increased. The Hungarian algorithm is to continuously find an augmentation road, and if the augmentation road cannot be found, the maximum matching is achieved.
In the embodiment of the invention, assuming that the shortest path between the vehicle throwing position B and the vehicle throwing position D is not passable, other passable paths are determined according to the Hungarian algorithm, two other passable paths are assumed to be determined, and one of the paths is selected according to the weights of the two determined other passable paths, so that the final vehicle throwing path is determined.
In the embodiment of the invention, a first time and a first position of an aggregation event are firstly acquired, and then a set duration and real-time travel data within a set range are determined according to the first time and the first position, wherein the set duration is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one travel point position; clustering the at least one trip point position, determining at least one clustering area, and then determining at least one vehicle release position according to the at least one clustering area, wherein each clustering area comprises at least one vehicle release position; and finally, path planning is carried out on the at least one vehicle release position according to an ant colony algorithm, a primary vehicle release path is determined, wherein the primary vehicle release path comprises all the vehicle release positions, and the primary vehicle release path is adjusted according to a Hungarian algorithm to determine the vehicle release path. By the method, the time corresponding to the aggregation event and the position of the vehicle to be released in the range can be quickly determined, the optimal vehicle releasing route is planned, and the operation and maintenance personnel release the vehicles to the vehicle releasing positions on the vehicle releasing route one by one according to the optimal vehicle releasing route.
In a possible implementation mode, after the vehicle releasing route is determined, the releasing route is immediately sent to operation and maintenance personnel, and the operation and maintenance personnel release the vehicle according to the vehicle releasing route and the vehicle releasing position, so that the vehicle using requirements of users are met, and the vehicle using experience of the users is improved.
In a possible implementation manner, since the aggregate events can be divided into two types, one type is short-term aggregate events, and the other type is long-term aggregate events, wherein the short-term aggregate events include aggregate activities such as academic conferences, sunconcerts, sports meetings, new business complex employment and the like, that is, aggregate events occurring only in a period of time; the long-term aggregation events include the use of new living areas, the opening of new subway stations, the opening of new bus stations, i.e., such aggregation events that may continue to occur over a long period of time.
In a possible implementation manner, because duration of different aggregation events is different, a type of an aggregation event needs to be determined before vehicle launching is performed, specifically, a decision tree model is adopted to classify a trip corresponding to real-time trip data according to a trip point position, a point of interest (POI), an area, an aggregation event and occurrence time of the aggregation event, and an event type corresponding to the trip is determined, wherein the event type includes a long-term aggregation event or a short-term aggregation event; in an embodiment of the present invention, the process of the above-mentioned trip classification occurs after acquiring the implementation trip data.
The following is a description of a vehicle delivery method according to an embodiment, and specifically as shown in fig. 6, the method includes the following steps:
step S600, acquiring a first time and a first position;
step S601, determining a set time length and real-time travel data within a set range according to the first time and the first position, wherein the set time length is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one travel point position;
step S602, classifying the travel corresponding to the real-time travel data by adopting a decision tree model according to the travel point position, the point of interest (POI), the area, the aggregation event and the occurrence time of the aggregation event of the real-time travel data, and determining the event type corresponding to the travel.
Step S603, clustering the at least one trip point position, and determining at least one clustering area.
Step S604, determining at least one vehicle launching position according to the at least one clustering region, wherein each clustering region comprises at least one vehicle launching position.
Step S605, performing path planning on the at least one vehicle launching position according to an ant colony algorithm, and determining a preliminary vehicle launching path, wherein the preliminary vehicle launching path comprises all the vehicle launching positions.
And S606, adjusting the initial vehicle release route according to the Hungarian algorithm, and determining the vehicle release route.
In a possible implementation manner, the step S602 and the step S603 may be in a parallel logical relationship, or the step S602 may be processed after the step S603, which is not limited in the present invention.
In a possible implementation manner, the step S602 is to determine the vehicle delivery route as a long-term vehicle delivery route in response to the event type being a long-term aggregated event; or determining the vehicle delivery route as a short-term vehicle delivery route in response to the event type being a short-term aggregate event.
For example, if the event type is determined to be a long-term aggregation event, the determined vehicle delivery route is long-term valid, and the offline operation and maintenance personnel need to perform vehicle delivery and vehicle maintenance on the vehicle delivery position on the vehicle delivery route for a long time; if the event type is judged to be a short-term aggregation event, the determined vehicle delivery route is short-term effective, the offline operation and maintenance personnel only need to carry out vehicle delivery and vehicle maintenance on the vehicle delivery position on the vehicle delivery route in a short term (for example, within 3 days, within one week and the like), and after the short-term aggregation event is finished, the vehicles on the vehicle delivery route can be recovered and the like; the vehicle using requirements of the user can be met only in a short period.
In a possible implementation manner, after a vehicle delivery route of a short-term aggregation event is determined, rapid operation evaluation needs to be performed on travel demands of the short-term aggregation event, and if a profit degree determined by the evaluation meets a set condition, vehicle delivery is performed according to the determined vehicle delivery route; after the vehicle delivery route of the long-term aggregation event is determined, determining whether vehicle delivery can be carried out according to a policy of the location of the long-term aggregation event, and if the location allows vehicle delivery, carrying out vehicle delivery according to the determined vehicle delivery route;
in the embodiment of the invention, at least one trip point is determined according to the acquired real-time travel data, the position of the at least one trip point is clustered, and at least one clustering area is determined; according to the method, the travel demand of the user can be obtained in real time, the vehicle releasing route and the vehicle releasing position on the vehicle releasing route are determined according to the travel demand of the user, the travel demand of the user can be dynamically met, and the use experience of the user is improved.
Fig. 7 is a schematic diagram of a vehicle delivery apparatus according to an embodiment of the present invention. As shown in fig. 7, the apparatus of the present embodiment includes an acquisition unit 701, a determination unit 702, a clustering unit 703, and a processing unit 704.
The acquiring unit 701 is configured to acquire a first time and a first position; a determining unit 702, configured to determine a set duration and real-time travel data within a set range according to the first time and the first position, where the set duration is a time of a set length after the first time, the set range is an area with the first position as a center, and the real-time travel data includes at least one travel point position; a clustering unit 703, configured to cluster the at least one trip point position, and determine at least one clustering area; the determining unit 702 is further configured to determine at least one vehicle release location according to the at least one cluster region, where each cluster region includes at least one vehicle release location; a processing unit 704, configured to perform path planning on the at least one vehicle release location, and determine a preliminary vehicle release route, where the preliminary vehicle release route includes all the vehicle release locations; the processing unit 704 is further configured to adjust the preliminary vehicle release route, and determine a vehicle release route.
Further, the obtaining unit is specifically configured to: public opinion information is obtained; and analyzing the public opinion information according to a natural language processing NLP, and determining a first time and a first position, wherein the first time is the starting time of the occurrence of the gathering event, and the first position is the position of the occurrence of the gathering event.
Further, the obtaining unit is further configured to: and crawling a network data source to acquire the public opinion information, wherein the network data source comprises at least one of network news, comments, forums, blogs, microblogs and posts.
Further, the clustering unit has means for: clustering the at least one travel point position by applying DBSCAN based on spatial clustering and noise.
Further, the processing unit has means for: performing path planning on the at least one vehicle launching position through an ant colony algorithm to determine an optimal path, wherein the optimal path is a path with the shortest distance between the vehicle launching positions; and determining the optimal path as the initial vehicle launching route.
Further, the processing unit is specifically further configured to: and adjusting the weight of the path between any two vehicle release positions in the preliminary vehicle release path according to a Hungarian algorithm, and determining the vehicle release path.
Further, the real-time travel data also includes POI points of interest.
Further, the apparatus further comprises: the classification unit is configured to classify the trip corresponding to the real-time trip data by using a decision tree model according to the trip point position of the real-time trip data, the POI, the area, the aggregation event, and the occurrence time of the aggregation event, and determine an event type corresponding to the trip, where the event type includes a long-term aggregation event or a short-term aggregation event.
Further, the processing unit is further configured to: in response to the event type being a long-term aggregate event, determining the vehicle delivery route as a long-term vehicle delivery route.
Further, the processing unit is further configured to: determining the vehicle delivery route as a short-term vehicle delivery route in response to the event type being a short-term aggregate event.
Fig. 8 is a schematic diagram of an electronic device of an embodiment of the invention. In this embodiment, the electronic device is a server. It should be understood that other electronic devices, such as raspberry pies, are also possible. As shown in fig. 8, the electronic device: includes at least one processor 801; and a memory 802 communicatively coupled to the at least one processor 801; and a communication component 803 communicatively coupled to the scanning device, the communication component 803 receiving and transmitting data under control of the processor 801; wherein the memory 802 stores instructions executable by the at least one processor 801 to implement: acquiring a first time and a first position; determining a set time length and real-time travel data within a set range according to the first time and the first position, wherein the set time length is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one travel point position; clustering the position of the at least one trip point, and determining at least one clustering area; determining at least one vehicle release position according to the at least one clustering region, wherein each clustering region comprises at least one vehicle release position; performing path planning on the at least one vehicle release position, and determining a preliminary vehicle release route, wherein the preliminary vehicle release route comprises all the vehicle release positions; and adjusting the initial vehicle throwing route to determine the vehicle throwing route.
Further, the processor is specifically configured to perform: public opinion information is obtained; and analyzing the public opinion information according to a natural language processing NLP to determine a first time and a first position, wherein the first time is the starting time of the occurrence of the aggregation event, and the first position is the position of the occurrence of the aggregation event.
Further, the processor is specifically configured to perform: crawling a network data source to acquire the public opinion information, wherein the network data source comprises at least one of network news, comments, forums, blogs, microbo and posts.
Further, the processor is specifically configured to perform: clustering the at least one trip point location by density-based spatial clustering and noise application (DBSCAN).
Further, the processor is specifically configured to perform: performing path planning on the at least one vehicle launching position through an ant colony algorithm to determine an optimal path, wherein the optimal path is a path with the shortest distance between the vehicle launching positions; and determining the optimal path as the preliminary vehicle delivery route.
Further, the processor is specifically configured to perform: and adjusting the weight of the path between any two vehicle release positions in the preliminary vehicle release path according to a Hungarian algorithm, and determining the vehicle release path.
Further, the real-time travel data also includes POI points of interest.
Further, the processor is further configured to perform: and classifying the journey corresponding to the real-time journey data by adopting a decision tree model according to the position of the travel point of the real-time journey data, the POI, the area, the aggregation event and the occurrence time of the aggregation event, and determining the event type corresponding to the journey, wherein the event type comprises a long-term aggregation event or a short-term aggregation event.
Further, the processor is further configured to perform: in response to the event type being a long-term aggregation event, determining the vehicle delivery route as a long-term vehicle delivery route.
Further, the processor is further configured to perform: determining the vehicle delivery route as a short-term vehicle delivery route in response to the event type being a short-term aggregation event.
Specifically, the electronic device includes: one or more processors 801 and memory 802, fig. 8 illustrates one example of a processor 801. The processor 801 and the memory 802 may be connected by a bus or other means, and fig. 8 illustrates an example of a connection by a bus. Memory 802, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 801 executes various functional applications of the device and data processing by running nonvolatile software programs, instructions, and modules stored in the memory 802, so as to implement the vehicle delivery method described above.
The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 802 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 802 optionally includes memory located remotely from the processor 801, which may be connected to external devices via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 802, and when executed by the one or more processors 801, perform the vehicle delivery method of any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.
Embodiments of the present invention relate to a non-transitory storage medium storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as those skilled in the art can understand, all or part of the steps in the method for implementing the above embodiments may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, etc.) or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples for carrying out the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
Claims (22)
1. A method of vehicle delivery, the method comprising:
acquiring a first time and a first position;
determining a set time length and real-time travel data within a set range according to the first time and the first position, wherein the set time length is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one travel point position;
clustering the position of the at least one trip point, and determining at least one clustering area;
determining at least one vehicle release position according to the at least one clustering region, wherein each clustering region comprises at least one vehicle release position;
performing path planning on the at least one vehicle release position, and determining a preliminary vehicle release route, wherein the preliminary vehicle release route comprises all the vehicle release positions;
and adjusting the initial vehicle throwing route to determine the vehicle throwing route.
2. The method of claim 1, wherein the obtaining the first time and the first location specifically comprises:
public opinion information is obtained;
and analyzing the public opinion information according to a natural language processing NLP, and determining a first time and a first position, wherein the first time is the starting time of the occurrence of the gathering event, and the first position is the position of the occurrence of the gathering event.
3. The method of claim 2, wherein the obtaining public opinion information comprises:
and crawling a network data source to acquire the public opinion information, wherein the network data source comprises at least one of network news, comments, forums, blogs, microblogs and posts.
4. The method according to claim 1, wherein the clustering of the at least one travel point location specifically comprises:
clustering the at least one trip point location by density-based spatial clustering and noise application (DBSCAN).
5. The method according to claim 1, wherein the path planning for the at least one vehicle delivery location and the determining of the preliminary vehicle delivery route comprises:
performing path planning on the at least one vehicle launching position through an ant colony algorithm to determine an optimal path, wherein the optimal path is a path with the shortest distance between the vehicle launching positions;
and determining the optimal path as the preliminary vehicle delivery route.
6. The method according to claim 1, wherein the adjusting the preliminary vehicle delivery route to determine the vehicle delivery route comprises:
and adjusting the weight of the path between any two vehicle release positions in the preliminary vehicle release route according to a Hungarian algorithm, and determining the vehicle release route.
7. The method of claim 1, wherein the real-time trip data further comprises POI points of interest.
8. The method of claim 7, further comprising:
and classifying the itineraries corresponding to the real-time itinerary data by adopting a decision tree model according to the travel point position of the real-time itinerary data, the POI, the area, the aggregation event and the occurrence time of the aggregation event, and determining the event types corresponding to the itineraries, wherein the event types comprise long-term aggregation events or short-term aggregation events.
9. The method of claim 8, further comprising:
in response to the event type being a long-term aggregate event, determining the vehicle delivery route as a long-term vehicle delivery route.
10. The method of claim 8, further comprising:
determining the vehicle delivery route as a short-term vehicle delivery route in response to the event type being a short-term aggregate event.
11. A device for vehicle delivery, the device comprising:
an acquisition unit for acquiring a first time and a first position;
the determining unit is used for determining a set time length and real-time travel data within a set range according to the first time and the first position, wherein the set time length is a set length of time after the first time, the set range is an area with the first position as a center, and the real-time travel data comprises at least one travel point position;
the clustering unit is used for clustering the position of the at least one trip point and determining at least one clustering area;
the determining unit is further configured to determine at least one vehicle delivery location according to the at least one clustering region, where each clustering region includes at least one vehicle delivery location;
the processing unit is used for planning a path of the at least one vehicle release position and determining a preliminary vehicle release route, wherein the preliminary vehicle release route comprises all the vehicle release positions;
the processing unit is further used for adjusting the preliminary vehicle throwing route and determining the vehicle throwing route.
12. The apparatus of claim 11, wherein the obtaining unit is specifically configured to:
public opinion information is obtained;
and analyzing the public opinion information according to a natural language processing NLP, and determining a first time and a first position, wherein the first time is the starting time of the occurrence of the gathering event, and the first position is the position of the occurrence of the gathering event.
13. The apparatus of claim 12, wherein the obtaining unit is further configured to:
and crawling a network data source to acquire the public opinion information, wherein the network data source comprises at least one of network news, comments, forums, blogs, microblogs and posts.
14. The apparatus of claim 11, wherein the clustering unit has means for:
clustering the at least one trip point location by density-based spatial clustering and noise application (DBSCAN).
15. The apparatus of claim 11, wherein the processing unit is to:
performing path planning on the at least one vehicle launching position through an ant colony algorithm to determine an optimal path, wherein the optimal path is a path with the shortest distance between the vehicle launching positions;
and determining the optimal path as the preliminary vehicle delivery route.
16. The apparatus as recited in claim 11, wherein said processing unit is further specifically configured to:
and adjusting the weight of the path between any two vehicle release positions in the preliminary vehicle release route according to a Hungarian algorithm, and determining the vehicle release route.
17. The apparatus of claim 11, wherein the real-time trip data further comprises POI points of interest.
18. The apparatus of claim 17, further comprising:
the classification unit is configured to classify the trip corresponding to the real-time trip data by using a decision tree model according to the trip point position of the real-time trip data, the POI, the area, the aggregation event, and the occurrence time of the aggregation event, and determine an event type corresponding to the trip, where the event type includes a long-term aggregation event or a short-term aggregation event.
19. The apparatus as recited in claim 18, said processing unit to further:
in response to the event type being a long-term aggregate event, determining the vehicle delivery route as a long-term vehicle delivery route.
20. The apparatus as recited in claim 18, said processing unit to further:
determining the vehicle delivery route as a short-term vehicle delivery route in response to the event type being a short-term aggregate event.
21. A computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1-10.
22. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the steps of any of claims 1-10.
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