CN111080198B - Method, device, computer equipment and storage medium for generating vehicle logistics path - Google Patents
Method, device, computer equipment and storage medium for generating vehicle logistics path Download PDFInfo
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Abstract
The invention discloses a method, a device, computer equipment and a storage medium for generating a vehicle logistics path, which are used for acquiring vehicle quotient data and acquiring a vehicle quotient data set according to the vehicle quotient data; wherein the vendor data includes geographic location and number of vehicles; generating a cluster from the vehicle quotient data set according to a clustering algorithm; determining distribution weights according to the number of vehicles, determining a clustering center of the clustering cluster according to the distribution weights and the geographic position, and setting the clustering center as a vehicle self-lifting point; according to the geographic position and the vehicle self-lifting point, a vehicle logistics path passing through the vehicle self-lifting point is generated, so that a logistics distribution path is optimized, and the problems of high distribution cost and low efficiency of the vehicle logistics path are solved.
Description
Technical Field
The present disclosure relates to the field of logistics technologies, and in particular, to a method, an apparatus, a computer device, and a storage medium for generating a vehicle logistics path.
Background
With the development of the times, consumers can make shopping through various channels, and the shopping experience of users becomes important, and further the problem of logistics distribution paths becomes a focus of attention, in the related art, research on logistics distribution path methods still stays to be suitable for traditional retail modes, online and offline are not well converged to obtain good shopping experience, in the process of transporting vehicles, the traditional distribution modes are that vehicles are loaded in actual warehouses by trucks for high-speed freight, in the process of being distributed to the tail ends of physical stores, the actual warehouses often stay at high-speed ports and are replaced by trolleys for distribution, or the distribution modes are that all vehicles are sent to an automobile city, however, the coverage area of the mode is limited, the general standard is difficult to be reached, so that in order to reduce the whole transportation cost as much as possible, the distribution paths need to be optimized, the path optimization method in the related art has long searching time and is not suitable for new enterprises, the distribution paths of different physical stores are also easy to repeat, and the distribution cost of the same customer point is high, and the efficiency is low.
Aiming at the problems of high distribution cost and low efficiency of a vehicle logistics path in the related art, no effective solution is proposed at present.
Disclosure of Invention
Aiming at the problems of high distribution cost and low efficiency of a vehicle logistics path in the related art, the invention provides a method, a device, computer equipment and a storage medium for generating the vehicle logistics path.
According to one aspect of the present invention, there is provided a method of generating a vehicle logistics path, the method comprising:
acquiring vehicle quotient data, and acquiring a vehicle quotient data set according to the vehicle quotient data; wherein the vendor data includes geographic location and number of vehicles;
generating a cluster from the vehicle quotient data set according to a clustering algorithm; determining distribution weights according to the number of vehicles, determining a clustering center of the clustering cluster according to the distribution weights and the geographic position, and setting the clustering center as a vehicle self-lifting point;
and generating a vehicle logistics path passing through the vehicle self-lifting point according to the geographic position and the vehicle self-lifting point.
In one embodiment, the generating the cluster from the vendor data set according to a clustering algorithm, and determining a cluster center of the cluster according to the distribution weight and the geographic location, where the setting the cluster center as a vehicle self-lifting point includes:
selecting N pieces of vehicle quotient data in the vehicle quotient data set as initial clustering centers, distributing the vehicle quotient data to the initial clustering centers, and generating a first clustering cluster; wherein N is a positive integer;
determining a first mean value of the vehicle quotient data according to the distribution weight and the geographic position, and setting the first mean value as a first center point of the first clustering cluster;
distributing the vehicle quotient data in the first cluster to the first center point according to the geographic position under the condition that the first distance is larger than or equal to a preset threshold value, and generating a second cluster; the first distance is the distance between the first center point and the initial clustering center;
determining a second average value of the vehicle quotient data according to the distribution weight and the geographic position, and setting the second average value as a second center point of the second clustering cluster;
under the condition that the second distance is smaller than the preset threshold value, determining the second center point as the vehicle self-lifting point; the second distance is a distance between the second center point and the first center point.
In one embodiment, the determining a second average of the vendor data according to the distribution weight and the geographic location, and setting the second average as a second center point of the second cluster includes:
determining the vehicle quotient data which does not belong to the first cluster and the second cluster, and taking the vehicle quotient data as a reject point; and under the condition that the eliminating point is removed, determining the second center point according to the distribution weight and the geographic position.
In one embodiment, after determining that the second center point is the vehicle self-lifting point if the second distance is smaller than the preset threshold, the method includes:
repeatedly generating an Mth cluster according to the geographic position when the second distance is greater than or equal to the preset threshold value, and taking the Mth cluster center as the vehicle self-lifting point when the distance between the Mth cluster center and the Mth-1 cluster center is smaller than the preset threshold value; wherein M is a positive integer greater than 2.
In one embodiment, the determining the distribution weight according to the number of vehicles includes:
setting a delivery weight of a first vehicle number of the first vehicle manufacturer data as a first delivery weight, and setting a delivery weight of a second vehicle number of the second vehicle manufacturer data as a second delivery weight;
comparing the first vehicle number with the second vehicle number, the first delivery weight being greater than the second delivery weight in the case where the first vehicle number is greater than the second vehicle number.
In one embodiment, the generating a vehicle logistics path through the vehicle self-lifting point according to the geographic location and the vehicle self-lifting point comprises:
determining a first estimated total distance from the vehicle flow path; determining a second estimated total distance of the bus city to the vendor data based on the geographic location;
comparing the first predicted total distance to the second predicted total distance, and determining a first solution to be delivered from the high speed mouth to the vehicle self-lifting point if the first predicted total distance is less than or equal to the second predicted total distance;
in the event that the first projected total distance is greater than the second projected total distance, a second scenario of delivery from the high speed mouth to the bus city is determined.
In one embodiment, after the generating a vehicle logistics path through the vehicle self-lifting point according to the geographic position and the vehicle self-lifting point, the method includes:
according to the vehicle logistics path, sending vehicle taking information to a terminal; the vehicle taking information is used for indicating vehicle taking time and vehicle taking places.
According to another aspect of the present invention, there is provided an apparatus for generating a vehicle logistics path, the apparatus comprising:
the acquisition module is used for acquiring the vehicle quotient data and acquiring a vehicle quotient data set according to the vehicle quotient data; wherein the vendor data includes geographic location and number of vehicles;
the design module is used for generating a cluster from the vehicle quotient data set according to a clustering algorithm; the design module determines distribution weights according to the number of vehicles, determines a clustering center of the clustering cluster according to the distribution weights and the geographic position, and sets the clustering center as a vehicle self-lifting point;
and the generation module is used for generating a vehicle logistics path passing through the vehicle self-lifting point according to the geographic position and the vehicle self-lifting point.
In one embodiment, the design module is further configured to select N pieces of vendor data in the vendor data set as an initial cluster center, and allocate the vendor data to the initial cluster center to generate a first cluster; wherein N is a positive integer;
the design module determines a first mean value of the vehicle quotient data according to the distribution weight and the geographic position, and sets the first mean value as a first center point of the first clustering cluster;
the design module distributes the vehicle quotient data in the first cluster to the first center point according to the geographic position under the condition that the first distance is larger than or equal to a preset threshold value, and generates a second cluster; the first distance is the distance between the first center point and the initial clustering center;
the design module determines a second average value of the vehicle manufacturer data according to the distribution weight and the geographic position, and sets the second average value as a second center point of the second clustering cluster;
the design module determines the second center point as the vehicle self-lifting point under the condition that the second distance is smaller than the preset threshold value; the second distance is a distance between the second center point and the first center point.
In one embodiment, the apparatus further comprises a transmitting module;
the sending module is used for sending vehicle taking information to a terminal according to the vehicle logistics path; the vehicle taking information is used for indicating vehicle taking time and vehicle taking places.
According to another aspect of the present invention there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when the computer program is executed.
According to another aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
According to the invention, a method, a device, computer equipment and a storage medium for generating a vehicle logistics path are adopted to acquire vehicle quotient data, and a vehicle quotient data set is acquired according to the vehicle quotient data; wherein the vendor data includes geographic location and number of vehicles; generating a cluster from the vehicle quotient data set according to a clustering algorithm; determining distribution weights according to the number of vehicles, determining a clustering center of the clustering cluster according to the distribution weights and the geographic position, and setting the clustering center as a vehicle self-lifting point; according to the geographic position and the vehicle self-lifting point, a vehicle logistics path passing through the vehicle self-lifting point is generated, so that a logistics distribution path is optimized, and the problems of high distribution cost and low efficiency of the vehicle logistics path are solved.
Drawings
Fig. 1 is a schematic diagram of a vehicle logistics path generation application scenario according to an embodiment of the present invention;
FIG. 2 is a flow chart diagram of a method of generating a vehicle logistics path according to an embodiment of the present invention;
FIG. 3 is a second flowchart of a method for generating a vehicle logistics path according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a vehicle self-lifting point according to an embodiment of the present invention;
FIG. 5 is a flow chart III of a vehicle logistics path generation method in accordance with an embodiment of the present invention;
FIG. 6 is a flow chart diagram of a method of generating a vehicle logistics path in accordance with an embodiment of the present invention;
FIG. 7 is a flow chart fifth of a vehicle logistics path generation method in accordance with an embodiment of the present invention;
FIG. 8 is a block diagram of a vehicle flow path generating apparatus according to an embodiment of the present invention;
FIG. 9 is a block diagram II of a vehicle flow path generating device according to an embodiment of the present invention;
fig. 10 is a block diagram of the inside of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be noted that, the term "first\second\third" related to the embodiment of the present invention is merely to distinguish similar objects, and does not represent a specific order for the objects, and the "first\second\third" may exchange a specific order or sequence where allowed. It is to be understood that the "first/second/third" differentiated objects may be interchanged where appropriate, such that embodiments of the invention described herein may be implemented in sequences other than those illustrated or described herein.
In this embodiment, a method for generating a vehicle logistics path is provided, and fig. 1 is a schematic diagram of an application scenario of generating a vehicle logistics path according to an embodiment of the present invention, as shown in fig. 1, in the application scenario, a terminal 12 communicates with a server 14 through a network. The server 14 generates clusters from the vehicle quotient dataset according to a clustering algorithm; determining distribution weight according to the number of vehicles of the vehicle quotient data, determining a clustering center of the clustering cluster according to the distribution weight and the geographic position by the server 12, and setting the clustering center as a vehicle self-lifting point so as to generate a vehicle logistics path passing through the vehicle self-lifting point; the server 14 transmits pick-up information to the terminal 12 according to the vehicle logistics path, the pick-up information indicating a pick-up time and a pick-up place. The terminal 12 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 14 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
In this embodiment, a method for generating a vehicle logistics path is provided, fig. 2 is a flowchart of a method for generating a vehicle logistics path according to an embodiment of the present invention, as shown in fig. 2, and the method includes the following steps:
step S202, acquiring vehicle quotient data, and acquiring a vehicle quotient data set according to the vehicle quotient data; wherein the vendor data includes geographic location and number of vehicles; the geographic location may be obtained through an application program interface (Application Programming Interface, abbreviated as API) of the golgi or hundred degrees, the geographic location including the longitude and latitude of the vendor data; the number of vehicles is the number of vehicles that need to be distributed to each of the vehicle merchants.
Step S204, generating a cluster from the vehicle quotient data set according to a clustering algorithm; determining distribution weights according to the number of vehicles, determining a clustering center of the clustering cluster according to the distribution weights and the geographic position, and setting the clustering center as a vehicle self-lifting point; the larger the distribution weight of the vehicle manufacturer data is, the closer the cluster center is to the vehicle manufacturer data.
Step S206, generating a vehicle logistics path passing through the vehicle self-lifting point according to the geographic position and the vehicle self-lifting point; under the condition that a logistics transport means, such as a truck, can be provided with a plurality of automobiles, a plurality of automobile manufacturers can be continuously distributed, in the process of selecting a preferential path, the distances from each automobile manufacturer to the self-lifting point of the automobile are marked on a map by utilizing longitude and latitude, and if the distances from two adjacent automobile manufacturers are smaller than the average value of the distances from automobile manufacturer data to the self-lifting point of the automobile in the data set, the automobiles of the two automobile manufacturers can be distributed in the same truck at the same time, so that the fact that the logistics path of the automobile is not repeated is realized.
In the related art, the vehicle logistics distribution efficiency is lower, the vehicle manufacturer data set is obtained through the steps S202 to S206, the vehicle manufacturer data set is generated into a cluster according to a clustering algorithm, the vehicle self-lifting points are determined according to the distribution weight and the geographic position, and the vehicle logistics path is generated according to the vehicle self-lifting points, so that all distribution demand points can be effectively reclustered, and the logistics distribution resources are more reasonably and efficiently recombined, thereby solving the problems of high distribution cost and low efficiency of the vehicle logistics path, improving the service quality and improving the user experience.
In one embodiment, a method for generating a vehicle logistics path is provided, and fig. 3 is a flowchart two of a method for generating a vehicle logistics path according to an embodiment of the present invention, as shown in fig. 3, the method includes the following steps:
step S302, N pieces of vehicle quotient data in the vehicle quotient data set are selected as initial clustering centers, and the vehicle quotient data are distributed to the initial clustering centers to generate a first clustering cluster; wherein N is a positive integer; determining a first average value of the vehicle manufacturer data according to the distribution weight and the geographic position, and setting the first average value as a first center point of the first cluster;
the N initial cluster centers are vendor data randomly selected from the vendor data set, and distances from other vendor data in the vendor data set to the initial cluster center can be obtained according to a euclidean distance formula, as shown in formula 1:
and distributing the vehicle manufacturer data to an initial clustering center with the smallest distance from the vehicle manufacturer data according to the calculated distance d. Meanwhile, the average value is comprehensively calculated according to the distribution weight and the longitude and latitude, so that the larger the distribution weight of the vehicle quotient data is, the closer the clustering center is to the vehicle quotient data.
Step S304, distributing the vehicle quotient data in the first cluster to the first center point according to the geographic position under the condition that the first distance is larger than or equal to a preset threshold value, and generating a second cluster; the first distance is the distance between the first center point and the initial clustering center; determining a second average value of the vehicle manufacturer data according to the distribution weight and the geographic position, and setting the second average value as a second center point of the second cluster; if the first distance is greater than or equal to the preset threshold value, the distance between the new centroid and the original centroid is larger, and the algorithm still needs to be calculated in a recycling mode, namely, the clustering center of the cluster is calculated again according to the existing vehicle quotient data in the cluster; the preset threshold may be set to 5 km.
Step S306, determining the second center point as the vehicle self-lifting point under the condition that the second distance is smaller than the preset threshold value; wherein the second distance is the distance between the second center point and the first center point; if the second distance is smaller than a preset threshold value, the position change of the recalculated centroid is not large, the algorithm tends to converge, and therefore the center of the finally formed cluster is set as a vehicle self-lifting point;
fig. 4 is a schematic view of a vehicle self-lifting point according to an embodiment of the invention, as shown in fig. 4. And repeating the circulation algorithm until the clustering center is finally determined, and at the moment, under the condition of generating the reject points, rejecting the vehicle quotient data serving as the reject points, thereby determining the vehicle self-lifting points of the clustering center, avoiding the influence of a few vehicle quotient with more remote positions on the positions of the vehicle self-lifting points, and saving distribution resources.
Through the steps S302 to S306, N pieces of vendor data are selected as initial clustering centers, clusters are generated according to geographic positions, and vehicle self-lifting points are determined according to distribution weights and the geographic positions, so that one distribution resource is used for matching different distribution requirements, the problem of repeated distribution paths is solved, and the logistics distribution paths of vehicles are optimized.
In one embodiment, a method for generating a vehicle logistics path is provided, and fig. 5 is a flowchart III of a method for generating a vehicle logistics path according to an embodiment of the present invention, as shown in fig. 5, the method includes the following steps:
step S502, a distribution weight of a first vehicle number of first vehicle quotient data is set as a first distribution weight, and a distribution weight of a second vehicle number of second vehicle quotient data is set as a second distribution weight; the distribution weight may also be determined by the geographic location, for example, if the geographic location of the vendor data is in a scenic spot environment such as a park, the distribution weight of the vendor data is correspondingly reduced.
Step S504, comparing the first vehicle number with the second vehicle number, wherein the first delivery weight is greater than the second delivery weight in the case that the first vehicle number is greater than the second vehicle number; for example, in the case where the distribution weight ranges from 1 to 10, the first distribution weight is set to 5 and the second distribution weight is set to 3 in the case where the first vehicle number is 56 and the second vehicle number is 20; and distributing the distribution weight to the longitude and latitude of the data position of the vehicle manufacturer, thereby comprehensively calculating the mean value of the clustering center.
Through the steps S502 to S504, the distribution weight is set according to the number of vehicles, and the number of vehicles to be distributed is considered while the self-lifting point of the vehicles is determined according to the geographic position, so that the self-lifting point of the vehicles tends to approach to the vehicle manufacturer with large number of vehicles to be distributed, thereby saving distribution resources, improving distribution efficiency and further optimizing the design of the logistics path of the vehicles.
In one embodiment, a method for generating a vehicle logistics path is provided, fig. 6 is a flowchart of a method for generating a vehicle logistics path according to an embodiment of the present invention, as shown in fig. 6, the method includes the following steps:
step S602, determining a first expected total distance according to the vehicle logistics path; determining a second estimated total distance of the bus city to the bus vendor data based on the geographic location; the method comprises the steps of obtaining longitude and latitude of vehicle quotient data by map visualization; the second expected total distance from the bus city to the bus merchant may be defined as Σpi, where i is the number of bus merchants and p is the distance to the delivery point.
Step S604, comparing the first estimated total distance with the second estimated total distance, and determining a first scheme for delivering from the high-speed mouth to the self-lifting point of the vehicle in the case that the first estimated total distance is less than or equal to the second estimated total distance; if the value of Σpi is larger, the vehicle manufacturer data needing to be distributed is more, and the vehicle manufacturer can automatically go to the vehicle self-picking point closest to the vehicle manufacturer to pick up the vehicle.
Step S606, determining a second scenario for delivery from the high speed mouth to the bus city, in case the first predicted total distance is greater than the second predicted total distance; if the value of Σpi is small, the delivery vehicles can be uniformly delivered to the automobile city.
Through the steps S602 to S606, the actual scene of the vehicle logistics is comprehensively analyzed, and the optimal distribution scheme is selected according to the analysis result, so that the distribution resources are saved according to the actual situation, and the design of the vehicle logistics path is optimized.
In one embodiment, a method for generating a vehicle logistics path is provided, fig. 7 is a flowchart five of a method for generating a vehicle logistics path according to an embodiment of the present invention, as shown in fig. 7, the method includes the following steps:
step S702, according to the vehicle logistics path, sending vehicle taking information to a terminal; the vehicle taking information is used for indicating vehicle taking time and vehicle taking places; specifically, under the condition that a vehicle of a vehicle manufacturer is distributed to a vehicle self-lifting point where corresponding vehicle manufacturer data are located according to the vehicle logistics path, the vehicle manufacturer can receive vehicle-picking information sent by a server through a terminal, under the condition that the vehicle-picking time is the current time, the vehicle manufacturer goes to the vehicle self-lifting point appointed by the vehicle-picking place to pick up the vehicle, and if the identity of the vehicle manufacturer passes, the vehicle can be picked up; the identity verification comprises face authentication, fingerprint authentication or other authentication modes, so that the occurrence of false vehicle pick-up phenomenon is avoided.
Through the step S702, according to the vehicle logistics path, the vehicle taking information is sent to the terminal, so that the user is reminded of timely taking the vehicle, on-line management of vehicle logistics distribution is realized, and the distribution efficiency of the vehicle is further improved.
It should be understood that, although the steps in the flowcharts of fig. 2, 3, 5, 6, and 7 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps of fig. 2, 3, 5, 6, and 7 may include multiple sub-steps or phases that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or phases are performed necessarily occur sequentially, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or phases of other steps.
In this embodiment, there is provided a vehicle logistics path generation apparatus, fig. 8 is a block diagram of a vehicle logistics path generation apparatus according to an embodiment of the present invention, as shown in fig. 8, including an acquisition module 82, a design module 84, and a generation module 86;
the acquiring module 82 is configured to acquire vendor data, and acquire a vendor data set according to the vendor data; wherein the vendor data includes geographic location and number of vehicles;
the design module 84 is configured to generate a cluster from the vendor data set according to a clustering algorithm; the design module determines distribution weight according to the number of vehicles, determines a clustering center of the clustering cluster according to the distribution weight and the geographic position, and sets the clustering center as a vehicle self-lifting point;
the generating module 86 is configured to generate a vehicle logistics path through the vehicle self-lifting point according to the geographic location and the vehicle self-lifting point.
Through the above embodiment, the obtaining module 82 obtains the vehicle merchant data set, the design module 84 generates the cluster from the vehicle merchant data set according to the clustering algorithm, and determines the vehicle self-lifting point according to the distribution weight and the geographic position, and the generating module 86 generates the vehicle logistics path according to the vehicle self-lifting point, so that the reclustering can be effectively performed on all distribution demand points, and the logistics distribution resources are more reasonably and efficiently recombined, thereby solving the problems of high distribution cost and low efficiency of the vehicle logistics path, improving the service quality and improving the user experience.
In one embodiment, the design module 84 of the vehicle logistics path generation apparatus is further configured to select N pieces of vendor data in the vendor data set as an initial cluster center, assign the vendor data to the initial cluster center, and generate a first cluster; wherein N is a positive integer;
the design module 84 determines a first mean of the vendor data based on the distribution weights and the geographic locations, the first mean being set as a first center point of the first cluster;
the design module 84 assigns the vendor data to the first center point according to the geographic location to generate a second cluster if the first distance is greater than or equal to a preset threshold; the first distance is the distance between the first center point and the initial clustering center;
the design module 84 determines a second mean of the vendor data based on the distribution weights and the geographic locations, the second mean being set as a second center point of the second cluster;
the design module 84 determines the second center point as the vehicle self-lifting point if the second distance is less than the preset threshold; the second distance is a distance between the second center point and the first center point.
In one embodiment, the design module 84 of the vehicle logistics path generation apparatus is further configured to set a delivery weight of a first vehicle quantity of the first vehicle manufacturer data to a first delivery weight and a delivery weight of a second vehicle quantity of the second vehicle manufacturer data to a second delivery weight;
the design module 84 compares the first vehicle quantity to the second vehicle quantity, the first delivery weight being greater than the second delivery weight if the first vehicle quantity is greater than the second vehicle quantity.
In one embodiment, the generation module 86 of the vehicle flow path generation device is further configured to determine a first expected total distance from the vehicle flow path; the generation module 86 determines a second projected total distance of the bus city to the vendor data based on the geographic location;
the generation module 86 compares the first projected total distance to the second projected total distance and determines a first scenario for delivery from the high speed mouth to the vehicle self-service point if the first projected total distance is less than or equal to the second projected total distance;
the generation module 86 determines a second scenario for delivery from the high speed mouth to the bus city if the first expected total distance is greater than the second expected total distance.
In one embodiment, an apparatus for generating a vehicle logistics path is provided, and fig. 9 is a block diagram two of a vehicle logistics path generating apparatus according to an embodiment of the present invention, as shown in fig. 9, the apparatus further includes a sending module 92;
the sending module 92 is configured to send vehicle taking information to a terminal according to the vehicle logistics path; the vehicle taking information is used for indicating vehicle taking time and vehicle taking places.
The specific limitation of the vehicle physical distribution path generating device can be referred to the limitation of the vehicle physical distribution path generating method hereinabove, and will not be described herein. The respective modules in the above-described vehicle physical distribution path generating apparatus may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and fig. 10 is a block diagram of an interior of the computer device according to an embodiment of the present invention, as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data related to the generation of the vehicle logistics path. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a vehicle logistics path generation method.
It will be appreciated by those skilled in the art that the structure shown in fig. 10 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps in the vehicle logistics path generation method provided in the above embodiments when the processor executes the computer program.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the steps in the vehicle logistics path generation method provided in the above embodiments.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (7)
1. A method of vehicle logistics path generation, the method comprising:
acquiring vehicle quotient data, and acquiring a vehicle quotient data set according to the vehicle quotient data; wherein the vendor data includes geographic location and number of vehicles;
generating a cluster from the vehicle quotient data set according to a clustering algorithm; determining distribution weights according to the number of vehicles, determining a clustering center of the clustering cluster according to the distribution weights and the geographic positions, and setting the clustering center as a vehicle self-lifting point, wherein the method comprises the following steps:
selecting N pieces of vehicle quotient data in the vehicle quotient data set as initial clustering centers, distributing the vehicle quotient data to the initial clustering centers, and generating a first clustering cluster; wherein N is a positive integer;
determining a first mean value of the vehicle quotient data according to the distribution weight and the geographic position, and setting the first mean value as a first center point of the first clustering cluster;
distributing the vehicle quotient data in the first cluster to the first center point according to the geographic position under the condition that the first distance is larger than or equal to a preset threshold value, and generating a second cluster; the first distance is the distance between the first center point and the initial clustering center;
determining a second average value of the vehicle quotient data according to the distribution weight and the geographic position, and setting the second average value as a second center point of the second clustering cluster;
under the condition that the second distance is smaller than the preset threshold value, determining the second center point as the vehicle self-lifting point; wherein the second distance is the distance between the second center point and the first center point;
repeatedly generating an Mth cluster according to the geographic position when the second distance is greater than or equal to the preset threshold value, and taking the Mth cluster center as the vehicle self-lifting point when the distance between the Mth cluster center and the Mth-1 cluster center is smaller than the preset threshold value; wherein M is a positive integer greater than 2;
generating a vehicle logistics path passing through the vehicle self-lifting point according to the geographic position and the vehicle self-lifting point, comprising:
determining a first estimated total distance from the vehicle flow path; determining a second estimated total distance of the bus city to the vendor data based on the geographic location;
comparing the first predicted total distance to the second predicted total distance, and determining a first solution to be delivered from the high speed mouth to the vehicle self-lifting point if the first predicted total distance is less than or equal to the second predicted total distance;
in the event that the first projected total distance is greater than the second projected total distance, a second scenario of delivery from the high speed mouth to the bus city is determined.
2. The method of claim 1, wherein the determining a second average of the vendor data based on the delivery weights and the geographic locations, the setting the second average as a second center point of the second cluster comprises:
determining the vehicle quotient data which does not belong to the first cluster and the second cluster, and taking the vehicle quotient data as a reject point; and under the condition that the eliminating point is removed, determining the second center point according to the distribution weight and the geographic position.
3. The method of claim 1, wherein said determining a distribution weight based on said number of vehicles comprises:
setting a delivery weight of a first vehicle number of the first vehicle manufacturer data as a first delivery weight, and setting a delivery weight of a second vehicle number of the second vehicle manufacturer data as a second delivery weight;
comparing the first vehicle number with the second vehicle number, the first delivery weight being greater than the second delivery weight in the case where the first vehicle number is greater than the second vehicle number.
4. The method of claim 1, wherein after the generating a vehicle logistics path through the vehicle self-lifting point based on the geographic location and the vehicle self-lifting point, the method comprises:
according to the vehicle logistics path, sending vehicle taking information to a terminal; the vehicle taking information is used for indicating vehicle taking time and vehicle taking places.
5. An apparatus for vehicle logistics path generation, characterized by being applied to the method for vehicle logistics path generation as claimed in any one of claims 1 to 4, comprising:
the acquisition module is used for acquiring the vehicle quotient data and acquiring a vehicle quotient data set according to the vehicle quotient data; wherein the vendor data includes geographic location and number of vehicles;
the design module is used for generating a cluster from the vehicle quotient data set according to a clustering algorithm; the design module determines distribution weights according to the number of vehicles, determines a clustering center of the clustering cluster according to the distribution weights and the geographic position, and sets the clustering center as a vehicle self-lifting point;
and the generation module is used for generating a vehicle logistics path passing through the vehicle self-lifting point according to the geographic position and the vehicle self-lifting point.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 4 when the computer program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111798172B (en) * | 2020-05-26 | 2023-07-18 | 嘉兴亚航信息技术有限公司 | Dangerous chemical transportation method based on field-contraindicated horse racing concept |
| CN113837677A (en) * | 2020-06-24 | 2021-12-24 | 顺丰科技有限公司 | Method, device and equipment for determining logistics line generation strategy |
| CN115358686B (en) * | 2022-10-24 | 2023-02-03 | 深圳联友科技有限公司 | Method and device for optimizing complete vehicle logistics network and storage medium |
| CN116562598B (en) * | 2023-07-07 | 2023-09-19 | 成都花娃网络科技有限公司 | Distribution scheduling method, device and storage medium |
| CN118071212B (en) * | 2024-04-19 | 2024-06-25 | 江苏零浩网络科技有限公司 | Logistics efficiency index intelligent analysis and optimization method and system based on model |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201307005D0 (en) * | 2012-04-25 | 2013-05-29 | Xerox Corp | Method and system for automatically detecting anomalies at a traffic intersection |
| CN103473612A (en) * | 2013-09-06 | 2013-12-25 | 周伟华 | Site selection and transportation optimization method for super-large scale logistics distribution |
| CN104951850A (en) * | 2015-06-25 | 2015-09-30 | 广东工业大学 | Method for solving multiple-depot logistics transportation vehicle routing problem |
| CN105868843A (en) * | 2016-03-22 | 2016-08-17 | 南京邮电大学 | Route planning method oriented to goods delivery |
| CN106203739A (en) * | 2016-07-29 | 2016-12-07 | 广东工业大学 | A kind of method and system of multi-logistics center logistics transportation scheduling |
| CN107977739A (en) * | 2017-11-22 | 2018-05-01 | 深圳北斗应用技术研究院有限公司 | Optimization method, device and the equipment in logistics distribution path |
| CN108564211A (en) * | 2018-04-09 | 2018-09-21 | 无锡太湖学院 | Goods' transportation routing method and system for planning |
| WO2019095883A1 (en) * | 2017-11-20 | 2019-05-23 | 北京京东尚科信息技术有限公司 | Information generation method and apparatus |
| WO2019128355A1 (en) * | 2017-12-29 | 2019-07-04 | 北京京东尚科信息技术有限公司 | Method and device for determining accurate geographic location |
| CN109978075A (en) * | 2019-04-04 | 2019-07-05 | 江苏满运软件科技有限公司 | Vehicle dummy location information identifying method, device, electronic equipment, storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120323633A1 (en) * | 2011-06-16 | 2012-12-20 | International Business Machines Corporation | Supply chain management solution |
| EP3511878A1 (en) * | 2018-01-11 | 2019-07-17 | Tata Consultancy Services Limited | Systems and methods for scalable multi-vehicle task allocation |
-
2019
- 2019-11-29 CN CN201911199146.2A patent/CN111080198B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201307005D0 (en) * | 2012-04-25 | 2013-05-29 | Xerox Corp | Method and system for automatically detecting anomalies at a traffic intersection |
| CN103473612A (en) * | 2013-09-06 | 2013-12-25 | 周伟华 | Site selection and transportation optimization method for super-large scale logistics distribution |
| CN104951850A (en) * | 2015-06-25 | 2015-09-30 | 广东工业大学 | Method for solving multiple-depot logistics transportation vehicle routing problem |
| CN105868843A (en) * | 2016-03-22 | 2016-08-17 | 南京邮电大学 | Route planning method oriented to goods delivery |
| CN106203739A (en) * | 2016-07-29 | 2016-12-07 | 广东工业大学 | A kind of method and system of multi-logistics center logistics transportation scheduling |
| WO2019095883A1 (en) * | 2017-11-20 | 2019-05-23 | 北京京东尚科信息技术有限公司 | Information generation method and apparatus |
| CN107977739A (en) * | 2017-11-22 | 2018-05-01 | 深圳北斗应用技术研究院有限公司 | Optimization method, device and the equipment in logistics distribution path |
| WO2019128355A1 (en) * | 2017-12-29 | 2019-07-04 | 北京京东尚科信息技术有限公司 | Method and device for determining accurate geographic location |
| CN108564211A (en) * | 2018-04-09 | 2018-09-21 | 无锡太湖学院 | Goods' transportation routing method and system for planning |
| CN109978075A (en) * | 2019-04-04 | 2019-07-05 | 江苏满运软件科技有限公司 | Vehicle dummy location information identifying method, device, electronic equipment, storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 陈婵丽 ; 钟映 ; .基于改进K-means聚类方法的新零售物流配送路径优化.物流技术.2019,(第05期),全文. * |
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