CN113240218A - Logistics distribution planning method and system based on big data - Google Patents
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Abstract
The application provides a logistics distribution planning method based on big data, which comprises the following steps: acquiring weight information and a delivery address of each large commodity in a plurality of large commodities to be delivered by a current vehicle; planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity; screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths; calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route; and selecting a target distribution path from the plurality of distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected.
Description
Technical Field
The application relates to the technical field of logistics, in particular to a logistics distribution planning method and system based on big data.
Background
At present, in the field of logistics technology, during the process of distributing goods, a route is often planned based on road conditions and distribution addresses, and the weight of each goods is not considered, so that the protection of vehicles during the process of planning the route is insufficient.
In view of the above problems, no effective technical solution exists at present.
Disclosure of Invention
The embodiment of the application aims to provide a logistics distribution planning method and a logistics distribution planning system based on big data, which can realize smaller abrasion to a vehicle on the basis of ensuring less distance to be traveled, so that the service life of the vehicle is prolonged.
The embodiment of the application further provides a logistics distribution planning method based on big data, which comprises the following steps:
acquiring weight information and a delivery address of each large commodity in a plurality of large commodities to be delivered by a current vehicle;
planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address;
screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected;
calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route;
and selecting a target distribution path from the plurality of distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected.
Optionally, in the logistics distribution planning method based on big data according to the embodiment of the present application, the screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths includes:
screening a plurality of second distribution paths from the plurality of first distribution paths, wherein the distance of each second distribution path is less than that of the unselected first distribution paths;
and selecting a plurality of to-be-selected distribution paths from the plurality of second distribution paths, wherein the path repetition degree, the path value and the number of the traffic lights of each to-be-selected distribution path are all smaller than the path repetition degree, the path value and the number of the traffic lights of the first distribution path which is not selected.
Optionally, in the logistics distribution planning method based on big data according to the embodiment of the present application, the planning a plurality of first distribution paths according to the first address of the distribution station and the distribution address of each large item includes:
and inputting the first address of the distribution station and the distribution address of each large commodity into a first preset neural network model to obtain a plurality of first distribution paths.
Optionally, in the logistics distribution planning method based on big data according to the embodiment of the present application, the calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and a total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route includes:
acquiring the traffic light position of each traffic light of each to-be-selected distribution path and the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution path;
calculating the total weight value of the large commodities which are not delivered when vehicles are at the traffic lights on each distribution route to be selected according to the total weight value of the large commodities which are not delivered when the vehicles are at the traffic lights;
calculating a vehicle wear value at each traffic light position according to the total load weight value of the large commodities;
and calculating the total vehicle wear value of each distribution path to be selected according to the vehicle wear value of each traffic light of each distribution path to be selected and the corresponding probability value.
Optionally, in the logistics distribution planning method based on big data according to the embodiment of the application, the calculating a vehicle wear value at each traffic light position according to the total load weight value of the big goods includes:
and inputting the total load weight value of the large commodity and the corresponding vehicle model into a preset neural network model, thereby calculating to obtain the vehicle wear value of the vehicle at each traffic light position.
Optionally, in the logistics distribution planning method based on big data according to the embodiment of the present application, obtaining a probability value that a vehicle encounters a red light at each traffic light of each to-be-selected distribution route includes:
and calculating the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution route according to the traffic light turning rule at each traffic light.
Optionally, in the logistics distribution planning method based on big data according to the embodiment of the application, the selecting a target distribution route from the multiple distribution routes to be selected according to the total vehicle wear value of each distribution route to be selected includes:
selecting at least two third distribution paths with smaller total vehicle wear values from the plurality of distribution paths to be selected;
if the total wear difference value between the minimum vehicle total wear value in the at least two third distribution paths and the vehicle total wear values of other third distribution paths is larger than the total wear target preset value, taking the third distribution path with the minimum vehicle total wear value as a target distribution path;
and if the difference value of the distribution paths of any two third distribution paths in the at least two third distribution paths is smaller than the target preset value of the distribution path, selecting the third distribution path with the smallest route from the at least two third distribution paths as the target distribution path.
In a second aspect, an embodiment of the present application further provides a logistics distribution planning system based on big data, where the system includes: a memory and a processor, wherein the memory includes a program of a big data-based logistics distribution planning method, and the program of the big data-based logistics distribution planning method realizes the following steps when executed by the processor:
acquiring weight information and a delivery address of each large commodity in a plurality of large commodities to be delivered by a current vehicle;
planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address;
screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected;
calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route;
and selecting a target distribution path from the plurality of distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected.
Optionally, in the logistics distribution planning system based on big data according to the embodiment of the application, when executed by the processor, the program of the logistics distribution planning method based on big data further implements the following steps:
screening a plurality of second distribution paths from the plurality of first distribution paths, wherein the distance of each second distribution path is less than that of the unselected first distribution paths;
and selecting a plurality of to-be-selected distribution paths from the plurality of second distribution paths, wherein the path repetition degree, the path value and the number of the traffic lights of each to-be-selected distribution path are all smaller than the path repetition degree, the path value and the number of the traffic lights of the first distribution path which is not selected.
In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes a logistics distribution planning method program based on big data, and when the logistics distribution planning method program based on big data is executed by a processor, the steps of the logistics distribution planning method based on big data as described in any one of the above are implemented.
In view of the above, the logistics distribution planning method and system based on big data provided by the embodiment of the application obtain the weight information and the distribution address of each of the large commodities in the plurality of large commodities to be distributed by the current vehicle; planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address; screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected; calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route; selecting a target distribution path from the multiple distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected; the vehicle can be abraded less on the basis of ensuring less distance to be traveled, so that the service life of the vehicle is prolonged.
Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a logistics distribution planning method based on big data according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a logistics distribution planning system based on big data according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart illustrating a logistics distribution planning method based on big data according to some embodiments of the present application. The logistics distribution planning method based on big data is used for terminal equipment such as mobile phones and computers. The logistics distribution planning method based on big data comprises the following steps:
s101, acquiring weight information and a delivery address of each large commodity in a plurality of large commodities to be delivered by the current vehicle.
S102, planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address.
S103, screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of the traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected.
S104, calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route.
And S105, selecting a target distribution path from the multiple distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected.
In step S101, the weight information and the delivery address may be imported from a server. Wherein the large article may refer to an article having a weight exceeding a preset threshold, for example 20 kg. Such as refrigerators, washing machines, air conditioners, tables, computers, etc.
In step S102, the plurality of first distribution paths may be formed by a path generation method in the prior art. Of course, it is understood that path planning may be performed based on more road condition information and weather information, or a plurality of first distribution paths may be obtained by inputting the first address of the distribution station and the distribution address of each large commodity into a first preset neural network model. The first preset neural network model is a pre-trained neural network model.
In step S103, the multiple to-be-selected distribution paths to be screened may be screened by using a preset mathematical model, or screened by using a traversal comparison method. The number of screened candidate delivery paths may be based on the total number of first delivery paths, for example, if the total number is n, a first delivery path of one or 2 is selected as the candidate delivery path.
In step S104, since the larger the load is during braking, the more serious the wear of the vehicle is, and the service life of the vehicle is affected, a neural network model may be trained in advance, and the neural network model may be used to calculate a quantifiable vehicle wear value caused by braking once based on the model and the load of the vehicle. The total wear value of the vehicles of each path can be calculated by the wear values of the vehicles at the traffic lights.
In step S105, the to-be-selected delivery route with the minimum vehicle total wear value may be selected as the target delivery route, or the to-be-delivered route with the minimum vehicle total wear value and the shortest route may be used as the target delivery route.
In some embodiments, this step S103 may comprise the following sub-steps:
s1031, screening a plurality of second distribution paths from the plurality of first distribution paths, wherein the route of each second distribution path is smaller than the route of the unselected first distribution path;
s1032, selecting a plurality of to-be-selected distribution paths from the plurality of second distribution paths, wherein the path repetition degree, the route value and the traffic light quantity of each to-be-selected distribution path are all smaller than those of the first distribution paths which are not selected.
The route repetition degree is the proportion of the length of the local route which is taken twice or more than twice to the total route, that is, the route planning process needs to take as few loops as possible and less repeated sections.
In some embodiments, this step S104 may include the following sub-steps:
s1041, obtaining the traffic light position of each traffic light of each to-be-selected distribution path and the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution path;
s1042, calculating the total weight value of the undelivered large commodities of the vehicle at each traffic light on each to-be-selected distribution route according to the total weight value of the undelivered large commodities at each traffic light position;
s1043, calculating a vehicle wear value at each traffic light according to the total load weight value of the large commodities;
s1044, calculating a total vehicle wear value of each to-be-selected distribution path according to the vehicle wear values of the traffic lights of each to-be-selected distribution path and the corresponding probability values.
In some embodiments, this step S1043 may include: and inputting the total load weight value of the large commodity and the corresponding vehicle model into a preset neural network model, thereby calculating to obtain the vehicle wear value of the vehicle at each traffic light position. The neural network model is preset to be obtained by pre-training.
In some embodiments, S1041 may comprise: and calculating the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution route according to the traffic light turning rule at each traffic light. For example, in many places, an intersection has different time ratios of traffic lights in different directions according to the degree of busy traffic in different directions. The probability value that the vehicle encounters a red light at traffic light a may = the duration of the red light in the direction of progress of the candidate delivery route at the traffic light a divided by the sum of the duration of the red light and the duration of the green light.
In this step S1044, the vehicle total wear value W = p1W1+ p2W2+ … + Pnwn. Wherein Pn is the probability that the vehicle encounters a red light at a traffic light n on a route to be selected. wn is the vehicle wear value at the traffic light n.
In some embodiments, this step S105 may include the following sub-steps:
s1051, selecting at least two third distribution paths with smaller total vehicle wear values from the multiple to-be-selected distribution paths; s1052, if the total wear difference value between the minimum vehicle total wear value in the at least two third distribution paths and the vehicle total wear values of other third distribution paths is greater than the total wear target preset value, taking the third distribution path with the minimum vehicle total wear value as a target distribution path; and S1053, if the difference value of the distribution paths of any two third distribution paths in the at least two third distribution paths is smaller than the target preset value of the distribution path, selecting the third distribution path with the minimum path from the at least two third distribution paths as the target distribution path.
The preset value may be 0.05 of the minimum total wear value of the vehicle, but is not limited thereto.
In view of the above, in the logistics distribution planning method based on big data provided in the embodiment of the present application, the weight information and the distribution address of each of the large commodities in the large commodities to be distributed by the current vehicle are obtained; planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address; screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected; calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route; selecting a target distribution path from the multiple distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected; the vehicle can be abraded less on the basis of ensuring less distance to be traveled, so that the service life of the vehicle is prolonged.
According to the embodiment of the invention, the method further comprises the following steps:
carrying out descending sorting according to the load capacity of the single large commodities on each distribution path to be selected to obtain a sequence for distributing the load capacity of all the single large commodities;
obtaining a corresponding distribution route sequence according to all the single large commodities;
obtaining a route traffic light quantity sequence according to all the single large commodities distributed;
carrying out array transposition on the obtained load capacity sequence, the distance sequence and the quantity sequence of all the single large commodities for distribution and carrying out threshold value comparison on the obtained load capacity sequence, the distance sequence and the quantity sequence;
selecting a sequence with the largest difference value with a preset threshold value as a target sequence according to the reference sequence threshold value comparison result;
and taking the single target large commodity corresponding to the target sequence as the large commodity to be preferentially distributed in the distribution route to be selected.
It should be noted that, the order of delivering the single-piece goods is different for each delivery path to be selected due to different routes, the same delivery path to be selected is sorted according to the carrying capacity of all the large-piece goods to be delivered to obtain the carrying capacity sequence of all the large-piece goods, and obtains the corresponding delivery path sequence and route traffic light number sequence corresponding to each single-piece goods, the array transposition processing is performed according to the carrying capacity sequence, the path sequence and the traffic light number sequence of all the large-piece goods, and the threshold value comparison is performed with the reference sequence threshold value, the sequence with the largest difference value with the preset threshold value is selected as the target sequence, the single-piece target large-piece goods corresponding to the target sequence is taken as the prior delivery large-piece goods of the delivery path to be selected for delivery, and the single-piece large-piece goods generating large carrying capacity wear amount can be preferentially delivered while referring to the delivery path and route traffic light number, thereby reducing the amount of vehicle distribution gross payload wear.
According to the embodiment of the invention, the method further comprises the following steps:
selecting three single large commodity carrying weights { a, b and c } with the carrying weights arranged in the first three according to the carrying weights of all the single large commodities on each distribution path to be selected;
obtaining total probability values { X, Y, Z } of the corresponding distribution destinations passing through all traffic lights and encountering red lights according to the three single large commodities with the first three load ranks;
carrying out square multiplication according to the corresponding load capacity obtained by the three single large commodities with the first three load capacities and the total probability value of encountering red light to obtain corresponding commodity load-carrying abrasion parameter values { A, B and C } of the three single large commodities;
comparing the difference values of the load-carrying wear parameter values of the corresponding commodities of the three single large commodities with a target wear parameter preset value respectively;
and selecting the target single large commodity with the largest difference value with the target wear parameter preset value as a preferential selection distribution commodity of the to-be-selected distribution path.
It should be noted that, three single large commodities of the first three are obtained according to the loading capacity of all the single large commodities on each distribution route to be selected, the loading capacity { a, b, c } of the three single large commodities, and the total probability value { X, Y, Z } of all traffic lights encountering red lights, wherein the traffic lights pass by the three single large commodities to be distributed to the destination, carrying out matrix multiplication according to the load capacity of the three single large commodities and the total probability value of encountering red light to obtain corresponding load-carrying abrasion parameter values { A, B and C } of each single large commodity, and comparing the difference values with the target wear parameter preset values according to the obtained load wear parameter values { A, B, C }, selecting the single large commodity with the largest difference value as the preferred delivery commodity of the delivery path to be selected, the total abrasion of the load capacity of the vehicle can be reduced by calculating and definitely generating the monovalent large commodity with the maximum vehicle-mounted abrasion capacity as the preferential distribution;
wherein, the total probability value { X, Y, Z } of the three single-piece large commodities meeting the red light at all traffic lights corresponding to the distribution destination is:
the corresponding commodity load-carrying wear parameter values { A, B, C } of the three single large commodities are as follows:
wherein a, b and c are the respective carrying capacities of three single large commodities,
，
，
the probability value of red light is met at the ith, j and k traffic lights through which the three single large commodities respectively pass, and m, n and q are the m, n and q traffic lights through which the three single large commodities respectively pass.
Referring to fig. 2, an embodiment of the present application further provides a logistics distribution planning system based on big data, where the system includes: a memory 201 and a processor 202, wherein the memory 201 includes a program of a big data-based logistics distribution planning method, and when executed by the processor 202, the program of the big data-based logistics distribution planning method implements the following steps: acquiring weight information and a delivery address of each large commodity in a plurality of large commodities to be delivered by a current vehicle; planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address; screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected; calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route; and selecting a target distribution path from the plurality of distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected.
The weight information and the delivery address may be imported from a server. Wherein the large article may refer to an article having a weight exceeding a preset threshold, for example 20 kg. Such as refrigerators, washing machines, air conditioners, tables, computers, etc. Wherein, the plurality of first distribution paths can be formed by adopting a path generation method in the prior art. Of course, it is understood that path planning may be performed based on more road condition information and weather information, or a plurality of first distribution paths may be obtained by inputting the first address of the distribution station and the distribution address of each large commodity into a first preset neural network model. The first preset neural network model is a pre-trained neural network model. The selected multiple distribution paths can be screened by adopting a preset mathematical model or by adopting a traversing and comparing mode. The number of screened candidate delivery paths may be based on the total number of first delivery paths, for example, if the total number is n, a first delivery path of one or 2 is selected as the candidate delivery path.
Because the larger the load capacity is during braking, the more serious the wear of the vehicle is, and the service life of the vehicle is affected, therefore, a neural network model can be trained in advance, and the neural network model can be adopted to calculate a quantifiable vehicle wear value caused by one-time braking based on the model and the load capacity of the vehicle. The total wear value of the vehicles of each path can be calculated by the wear values of the vehicles at the traffic lights.
The distribution route to be selected with the minimum vehicle total wear value can be selected as the target distribution route, and the distribution route to be selected with the minimum vehicle total wear value and the shortest route can also be used as the target distribution route.
In some embodiments, the program of the big data based logistics distribution planning method, when executed by the processor 202, implements the following steps:
screening a plurality of second distribution paths from the plurality of first distribution paths, wherein the distance of each second distribution path is less than that of the unselected first distribution paths; and selecting a plurality of to-be-selected distribution paths from the plurality of second distribution paths, wherein the path repetition degree, the path value and the number of the traffic lights of each to-be-selected distribution path are all smaller than the path repetition degree, the path value and the number of the traffic lights of the first distribution path which is not selected. The route repetition degree is the proportion of the length of the local route which is taken twice or more than twice to the total route, that is, the route planning process needs to take as few loops as possible and less repeated sections.
In some embodiments, the program of the big data based logistics distribution planning method, when executed by the processor 202, implements the steps of: acquiring the traffic light position of each traffic light of each to-be-selected distribution path and the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution path; calculating the total weight value of the large commodities which are not delivered when vehicles are at the traffic lights on each to-be-selected delivery path according to the total weight value of the large commodities which are not delivered when the vehicles are at the traffic lights; calculating the vehicle wear value at each traffic light according to the total load weight value of the large commodities; and calculating the total vehicle wear value of each distribution path to be selected according to the vehicle wear value of each traffic light of each distribution path to be selected and the corresponding probability value.
In some embodiments, the program of the big data based logistics distribution planning method, when executed by the processor 202, implements the steps of: and inputting the total load weight value of the large commodity and the corresponding vehicle model into a preset neural network model, thereby calculating to obtain the vehicle wear value of the vehicle at each traffic light position. The neural network model is preset to be obtained by pre-training.
In some embodiments, the program of the big data based logistics distribution planning method, when executed by the processor 202, implements the steps of: and calculating the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution route according to the traffic light turning rule at each traffic light. For example, in many places, an intersection has different time ratios of traffic lights in different directions according to the degree of busy traffic in different directions. The probability value that the vehicle encounters a red light at traffic light a may = the duration of the red light in the direction of progress of the candidate delivery route at the traffic light a divided by the sum of the duration of the red light and the duration of the green light. Wherein the vehicle total wear value W = p1W1+ p2W2+ … + Pnwn. Wherein Pn is the probability that the vehicle encounters a red light at a traffic light n on a route to be selected. wn is the vehicle wear value at the traffic light n.
In some embodiments, the program of the big data based logistics distribution planning method, when executed by the processor 202, implements the steps of: selecting at least two third distribution paths with smaller total vehicle wear values from the plurality of distribution paths to be selected; if the total wear difference value between the minimum vehicle total wear value in the at least two third distribution paths and the vehicle total wear values of other third distribution paths is larger than the total wear target preset value, taking the third distribution path with the minimum vehicle total wear value as a target distribution path; and if the difference value of the distribution paths of any two third distribution paths in the at least two third distribution paths is smaller than the target preset value of the distribution path, selecting the third distribution path with the smallest route from the at least two third distribution paths as the target distribution path. The total wear target preset value may be 0.05 of the minimum vehicle total wear value, but is not limited thereto.
In view of the above, the logistics distribution planning system based on big data provided in the embodiment of the present application obtains the weight information and the distribution address of each of the large commodities in the large commodities to be distributed by the current vehicle; planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address; screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected; calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route; selecting a target distribution path from the multiple distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected; the vehicle can be abraded less on the basis of ensuring less distance to be traveled, so that the service life of the vehicle is prolonged.
According to the embodiment of the invention, the method further comprises the following steps:
carrying out descending sorting according to the load capacity of the single large commodities on each distribution path to be selected to obtain a sequence for distributing the load capacity of all the single large commodities;
obtaining a corresponding distribution route sequence according to all the single large commodities;
obtaining a route traffic light quantity sequence according to all the single large commodities distributed;
carrying out array transposition on the obtained load capacity sequence, the distance sequence and the quantity sequence of all the single large commodities for distribution and carrying out threshold value comparison on the obtained load capacity sequence, the distance sequence and the quantity sequence;
selecting a sequence with the largest difference value with a preset threshold value as a target sequence according to the reference sequence threshold value comparison result;
and taking the single target large commodity corresponding to the target sequence as the large commodity to be preferentially distributed in the distribution route to be selected.
It should be noted that, the order of delivering the single-piece goods is different for each delivery path to be selected due to different routes, the same delivery path to be selected is sorted according to the carrying capacity of all the large-piece goods to be delivered to obtain the carrying capacity sequence of all the large-piece goods, and obtains the corresponding delivery path sequence and route traffic light number sequence corresponding to each single-piece goods, the array transposition processing is performed according to the carrying capacity sequence, the path sequence and the traffic light number sequence of all the large-piece goods, and the threshold value comparison is performed with the reference sequence threshold value, the sequence with the largest difference value with the preset threshold value is selected as the target sequence, the single-piece target large-piece goods corresponding to the target sequence is taken as the prior delivery large-piece goods of the delivery path to be selected for delivery, and the single-piece large-piece goods generating large carrying capacity wear amount can be preferentially delivered while referring to the delivery path and route traffic light number, thereby reducing the amount of vehicle distribution gross payload wear.
According to the embodiment of the invention, the method further comprises the following steps:
selecting three single large commodity carrying weights { a, b and c } with the carrying weights arranged in the first three according to the carrying weights of all the single large commodities on each distribution path to be selected;
obtaining total probability values { X, Y, Z } of the corresponding distribution destinations passing through all traffic lights and encountering red lights according to the three single large commodities with the first three load ranks;
carrying out square multiplication according to the corresponding load capacity obtained by the three single large commodities with the first three load capacities and the total probability value of encountering red light to obtain corresponding commodity load-carrying abrasion parameter values { A, B and C } of the three single large commodities;
comparing the difference values of the load-carrying wear parameter values of the corresponding commodities of the three single large commodities with a target wear parameter preset value respectively;
and selecting the target single large commodity with the largest difference value with the target wear parameter preset value as a preferential selection distribution commodity of the to-be-selected distribution path.
It should be noted that, three single large commodities of the first three are obtained according to the loading capacity of all the single large commodities on each distribution route to be selected, the loading capacity { a, b, c } of the three single large commodities, and the total probability value { X, Y, Z } of all traffic lights encountering red lights, wherein the traffic lights pass by the three single large commodities to be distributed to the destination, carrying out matrix multiplication according to the load capacity of the three single large commodities and the total probability value of encountering red light to obtain corresponding load-carrying abrasion parameter values { A, B and C } of each single large commodity, and comparing the difference values with the target wear parameter preset values according to the obtained load wear parameter values { A, B, C }, selecting the single large commodity with the largest difference value as the preferred delivery commodity of the delivery path to be selected, the total abrasion of the load capacity of the vehicle can be reduced by calculating and definitely generating the monovalent large commodity with the maximum vehicle-mounted abrasion capacity as the preferential distribution;
wherein, the total probability value { X, Y, Z } of the three single-piece large commodities meeting the red light at all traffic lights corresponding to the distribution destination is:
the corresponding commodity load-carrying wear parameter values { A, B, C } of the three single large commodities are as follows:
wherein a, b and c are the respective carrying capacities of three single large commodities,
，
，
the probability value of red light is met at the ith, j and k traffic lights through which the three single large commodities respectively pass, and m, n and q are the m, n and q traffic lights through which the three single large commodities respectively pass.
The embodiment of the present application provides a storage medium, and when being executed by a processor, the computer program performs the method in any optional implementation manner of the above embodiment. The storage medium may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A logistics distribution planning method based on big data is characterized by comprising the following steps:
acquiring weight information and a delivery address of each large commodity in a plurality of large commodities to be delivered by a current vehicle;
planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address;
screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected;
calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route;
and selecting a target distribution path from the plurality of distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected.
2. The big data-based logistics distribution planning method according to claim 1, wherein the screening of the plurality of first distribution paths for a plurality of distribution paths to be selected comprises:
screening a plurality of second distribution paths from the plurality of first distribution paths, wherein the distance of each second distribution path is less than that of the unselected first distribution paths;
and selecting a plurality of to-be-selected distribution paths from the plurality of second distribution paths, wherein the path repetition degree, the path value and the number of the traffic lights of each to-be-selected distribution path are all smaller than the path repetition degree, the path value and the number of the traffic lights of the first distribution path which is not selected.
3. The logistics distribution planning method based on big data according to claim 1, wherein the planning of the plurality of first distribution paths according to the first address of the distribution station and the distribution address of each big good comprises:
and inputting the first address of the distribution station and the distribution address of each large commodity into a first preset neural network model to obtain a plurality of first distribution paths.
4. The logistics distribution planning method based on big data according to claim 1, wherein the calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each distribution route to be selected and the total weight value of the large goods which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each distribution route to be selected comprises:
acquiring the traffic light position of each traffic light of each to-be-selected distribution path and the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution path;
calculating the total weight value of the large commodities which are not delivered when vehicles are at the traffic lights on each distribution route to be selected according to the total weight value of the large commodities which are not delivered when the vehicles are at the traffic lights;
calculating a vehicle wear value at each traffic light position according to the total load weight value of the large commodities;
and calculating the total vehicle wear value of each distribution path to be selected according to the vehicle wear value of each traffic light of each distribution path to be selected and the corresponding probability value.
5. The big data based logistics distribution planning method of claim 4, wherein the calculating of the vehicle wear value at each traffic light position according to the big goods total load weight value comprises:
and inputting the total load weight value of the large commodity and the corresponding vehicle model into a preset neural network model, thereby calculating to obtain the vehicle wear value of the vehicle at each traffic light position.
6. The logistics distribution planning method based on big data according to claim 1, wherein the obtaining of the probability value that the vehicle encounters a red light at each traffic light of each to-be-selected distribution route comprises:
and calculating the probability value of the vehicles encountering the red light at each traffic light of each to-be-selected distribution route according to the traffic light turning rule at each traffic light.
7. The big data based logistics distribution planning method of claim 1, wherein the selecting a target distribution route from the plurality of distribution routes according to the total vehicle wear value of each distribution route to be selected comprises:
selecting at least two third distribution paths with smaller total vehicle wear values from the plurality of distribution paths to be selected;
if the total wear difference value between the minimum vehicle total wear value in the at least two third distribution paths and the vehicle total wear values of other third distribution paths is larger than the total wear target preset value, taking the third distribution path with the minimum vehicle total wear value as a target distribution path;
and if the difference value of the distribution paths of any two third distribution paths in the at least two third distribution paths is smaller than the target preset value of the distribution path, selecting the third distribution path with the smallest route from the at least two third distribution paths as the target distribution path.
8. A logistics distribution planning system based on big data is characterized in that the system comprises: a memory and a processor, wherein the memory includes a program of a big data-based logistics distribution planning method, and the program of the big data-based logistics distribution planning method realizes the following steps when executed by the processor:
acquiring weight information and a delivery address of each large commodity in a plurality of large commodities to be delivered by a current vehicle;
planning a plurality of first distribution paths according to a first address of a distribution station and a distribution address of each large commodity, wherein each first distribution path passes through each distribution address;
screening a plurality of to-be-selected distribution paths from the plurality of first distribution paths, wherein the distance value and the number of traffic lights of each to-be-selected distribution path are smaller than those of the first distribution paths which are not selected;
calculating a vehicle wear value corresponding to each traffic light position according to each traffic light position on each to-be-selected distribution route and the total weight value of the large commodities which are not delivered at each traffic light position, so as to obtain the total vehicle wear value of each to-be-selected distribution route;
and selecting a target distribution path from the plurality of distribution paths to be selected according to the total vehicle wear value of each distribution path to be selected.
9. The big-data based logistics distribution planning system of claim 8, wherein the program of the big-data based logistics distribution planning method when executed by the processor further implements the steps of:
screening a plurality of second distribution paths from the plurality of first distribution paths, wherein the distance of each second distribution path is less than that of the unselected first distribution paths;
and selecting a plurality of to-be-selected distribution paths from the plurality of second distribution paths, wherein the path repetition degree, the path value and the number of the traffic lights of each to-be-selected distribution path are all smaller than the path repetition degree, the path value and the number of the traffic lights of the first distribution path which is not selected.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a big data-based logistics distribution planning method program, and when the big data-based logistics distribution planning method program is executed by a processor, the steps of a big data-based logistics distribution planning method according to any one of claims 1 to 7 are implemented.
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