CN114563008A - Path planning method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a path planning method, a path planning device, a computer device and a storage medium. The method comprises the steps of responding to a path planning request, determining a planning object to be subjected to path planning and an article to be delivered, obtaining position information and load upper limit information of the planning object, determining the planning object corresponding to the article to be delivered according to the position information, determining the weight to be delivered of the planning object according to the weight of the article to be delivered and the planning object corresponding to the article to be delivered, performing distance minimization clustering on the planning object according to the weight to be delivered, the position information and the load upper limit information to obtain a clustering result, and performing path planning on each category in the clustering result to obtain a path planning result. The path planning problem is divided into cluster grouping for minimizing the inter-class distance based on the limitation of the vehicle load, and then a combined mode of path planning is carried out on each group, so that the efficient dispatching of the articles is realized based on the grouping and the serial connection in the group.
Description
Technical Field
The present application relates to the field of logistics technology, and in particular, to a path planning method, apparatus, computer device, and storage medium.
Background
In the current logistics scene, the adopted logistics distribution mode is generally that goods are sent from a transit, the goods in the transit are received through a network point, and then the goods are distributed to customers through the network point.
In order to improve the delivery timeliness and reduce the sorting pressure of goods at distribution points, the goods are directly sent to customers from the transfer, but in the mode, a large number of express delivery personnel are required to be directly sent for delivery, if the number of the delivery personnel is small, the goods need to be taken and delivered from the transfer to the transfer, the delivery time of the express delivery personnel is not only reduced in the process of the transfer, and the transfer transportation cost is increased. Under such conditions, the dispatch efficiency cannot be significantly improved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a path planning method, apparatus, computer device and storage medium capable of improving dispatch efficiency.
A path planning method comprises the following steps:
responding to the path planning request, and determining a planning object to be subjected to path planning and an article to be delivered;
acquiring position information and load upper limit information of a planning object, and determining the planning object corresponding to the article to be delivered according to the position information;
determining the weight of the planning object to be dispatched according to the weight of the item to be dispatched and the planning object corresponding to the item to be dispatched;
performing distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result;
and respectively carrying out path planning on each type in the clustering results to obtain path planning results.
In one embodiment, the distance minimization clustering is performed on the planning objects according to the weight to be dispatched, the position information and the upper limit load information, and the obtaining of the clustering result includes:
and clustering the planning objects by taking the minimum number of classes, the maximum total load and the minimum total distance as clustering targets according to the weight, the position information and the upper limit load information to be dispatched, so as to obtain a clustering result, wherein the total load is the total weight of all the classes of the articles to be dispatched, and the total distance is the total maximum distance in all the classes.
In one embodiment, based on the position information and the load upper limit information, clustering the planning objects by using the minimum number of classes, the maximum total load and the minimum total distance as clustering targets, and obtaining a clustering result comprises:
acquiring clustering constraint conditions, wherein the clustering constraint conditions comprise that the total weight of articles to be dispatched of a class is not more than the maximum load upper limit in the class, and the maximum distance in the class is not more than the preset maximum handover distance between planning objects;
based on clustering constraint conditions, according to the weight, the position information and the upper limit information of the load to be dispatched, and with the minimum number of classes, the maximum total load and the minimum total distance as clustering targets, clustering the planning objects to obtain clustering results.
In one embodiment, the total weight of the items to be dispatched of the class is the total weight of the items to be dispatched corresponding to the planning object in the same class within the preset time range.
In one embodiment, the path planning is performed on each of the clustering results, and obtaining the path planning result includes:
and taking the transit station as a path planning starting point, and performing serial path planning on each planning object in the same class to obtain a path planning result comprising the connection sequence of each planning object.
In one embodiment, the serial path planning includes any one of greedy algorithm, heuristic algorithm, and precision algorithm based implementation.
In one embodiment, the obtaining of the position information and the upper load limit information of the planning object includes:
acquiring historical dispatching information and vehicle load of a planning object;
determining load upper limit information according to the vehicle load capacity, and determining a dispatching central point according to a dispatching address in the historical dispatching information;
and taking the dispatch center point as the position information of the planning object.
A path planning apparatus, the apparatus comprising:
the request response module is used for responding to the path planning request and determining a planning object to be subjected to path planning and an article to be dispatched;
the information acquisition module is used for acquiring the position information and the load upper limit information of the planning object and determining the planning object corresponding to the article to be delivered according to the position information;
the dispatching weight determining module is used for determining the weight to be dispatched of the planning object according to the weight of the item to be dispatched and the planning object corresponding to the item to be dispatched;
the clustering module is used for carrying out distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit information of the load to obtain a clustering result;
and the path planning module is used for respectively planning paths of each type in the clustering results to obtain path planning results.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
responding to the path planning request, and determining a planning object to be subjected to path planning and an article to be delivered;
acquiring position information and load upper limit information of a planning object, and determining the planning object corresponding to the article to be delivered according to the position information;
determining the weight to be delivered of the planning object according to the weight of the item to be delivered and the planning object corresponding to the item to be delivered;
performing distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result;
and respectively carrying out path planning on each type in the clustering results to obtain path planning results.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
responding to the path planning request, and determining a planning object to be subjected to path planning and an article to be delivered;
acquiring position information and load upper limit information of a planning object, and determining the planning object corresponding to the article to be delivered according to the position information;
determining the weight to be delivered of the planning object according to the weight of the item to be delivered and the planning object corresponding to the item to be delivered;
performing distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result;
and respectively carrying out path planning on each type in the clustering results to obtain path planning results.
The path planning method, the device, the computer equipment and the storage medium determine a planning object to be subjected to path planning and an article to be dispatched based on a path planning request, acquire position information and load upper limit information of the planning object, determine the planning object corresponding to the article to be dispatched according to the position information, determine the weight to be dispatched of the planning object according to the weight of the article to be dispatched and the planning object corresponding to the article to be dispatched, perform distance minimization clustering on the planning object according to the weight to be dispatched, the position information and the load upper limit information to obtain a clustering result so as to ensure that the clustering grouping can meet the limitation requirement of the maximum load upper limit, realize the grouping of the planning objects, then perform path planning on each category in the clustering result respectively to obtain a path planning result, divide the path planning problem into inter-category distance minimization clustering grouping based on vehicle load limitation, each grouping is then routed in a manner to achieve efficient dispatch of the item based on the grouping and the intra-group concatenation.
Drawings
FIG. 1 is a diagram of an embodiment of a path planning method;
FIG. 2 is a schematic flow chart diagram of a path planning method in one embodiment;
FIG. 3 is a schematic flow chart diagram illustrating a path planning method according to another embodiment;
FIG. 4 is a schematic flow chart diagram illustrating a path planning method according to yet another embodiment;
FIG. 5 is a flow chart illustrating a path planning method according to another embodiment;
FIG. 6 is a schematic flow chart diagram illustrating a method for path planning in yet another embodiment;
FIG. 7 is a block diagram of a path planning apparatus according to an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The path planning method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The server 104 responds to the path planning request sent by the terminal 102, determines a planning object to be subjected to path planning and an article to be delivered, obtains position information and load upper limit information of the planning object, determines the planning object corresponding to the article to be delivered according to the position information, determines the weight to be delivered of the planning object according to the weight of the article to be delivered and the planning object corresponding to the article to be delivered, performs distance minimization clustering on the planning object according to the weight to be delivered, the position information and the load upper limit information to obtain clustering results, performs path planning on each category in the clustering results respectively to obtain path planning results and feeds the path planning results back to the terminal 102.
The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a path planning method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps 202 to 210.
The path planning request is a processing request for planning a movement path of a plurality of objects. The path planning request can be initiated by a manager of the management center based on a triggering operation of the terminal, or can be automatically triggered by the server according to a pre-matched triggering strategy.
Path planning refers to a strategy for constructing a path, and a path refers to a sequence point or a curve connecting a start point position and an end point position. The connection of the planning objects in sequence and movement position is realized by path planning.
In an embodiment, the path planning request may carry information of a planning object to be subjected to path planning and an item to be delivered, and the server may determine the planning object and the item to be delivered by information extraction. In other embodiments, the planning object and the information of the item to be dispatched may also be stored in a preconfigured manner, and after receiving the path planning request, the server stores the path according to the preconfigured information, and searches for and obtains the planning object to be subjected to the path planning and the information of the item to be dispatched.
The planning object may be an express delivery person who delivers an item, and the item to be delivered is an item that needs to be delivered to each customer in a transit, where different express delivery persons may pre-configure corresponding delivery areas, for example, if the delivery area of the express delivery person 1 is area a, and the area where the address of the recipient of the item to be delivered is located is also area a, the item is delivered by the express delivery person 1.
And 204, acquiring the position information and the load upper limit information of the planning object, and determining the planning object corresponding to the article to be delivered according to the position information.
The location information may specifically be location information of a region corresponding to the planning object. For example, if the courier 1 corresponds to the area a, the location information may be location information corresponding to the area a. Specifically, the location information may be location information of a center point of the area a, or may be location information corresponding to a certain location in the area a determined based on the historical dispatch information comprehensive evaluation. The determination mode of the position information can be adjusted according to actual needs, and in order to reduce data errors in the data processing process, the determination modes of the position information of different planning objects need to be consistent. For example, the central point is used as the position information or the historical dispatch information is used for determining the position information.
The upper load limit information is the maximum weight of the article that can be carried corresponding to the planned object. Specifically, the load upper limit information may determine the load upper limit information of the planning target based on the maximum load of the delivery vehicle of the courier. It can be understood that different couriers may correspond to delivery vehicles of different vehicle types, and thus, different plan objects may have different upper load limits.
Determining a planning object corresponding to the item to be delivered according to the position information means determining a corresponding relation between the item to be delivered and the planning object according to a delivery area corresponding to the planning object and a receiver address corresponding to the item to be delivered.
And step 206, determining the weight to be delivered of the planning object according to the weight of the item to be delivered and the planning object corresponding to the item to be delivered.
And determining the weight to be delivered of the planning object by accumulating the articles to be delivered corresponding to the planning object according to the weight.
In embodiments, the items to be dispatched may be divided on a periodic basis, such as on a half-day or one-day basis. In this case, the dispatch-required weight of the plan object is the weight of the item to be dispatched within a specified time period at the dispatch time.
The period of the division may also be divided based on the number of shifts of the courier, for example, if there are three shifts in a day, the delivery weight of the planning object is the weight of the item to be delivered in the delivery shift. It should be noted that the weight to be delivered should not be greater than the maximum weight of the delivery vehicle.
And 208, performing distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result.
The distance minimized clustering refers to clustering that aims to minimize the sum of maximum distances among various classes. The maximum distance between the classes refers to the distance between two express delivery personnel with the farthest distance in the same class. In the embodiment, the clustering process needs to meet the condition that the sum of the weights to be dispatched in the same class is not more than the upper limit of the load, and the distance minimization is realized on the basis of meeting the condition.
Clustering refers to a process of dividing a set of physical or abstract objects into a plurality of classes composed of similar objects, and the clustering result is a plurality of classes obtained by dividing a plurality of planning objects. It should be noted that each category obtained by clustering includes at least one planning object, and each planning object belongs to only one category.
And step 210, respectively performing path planning on each type in the clustering results to obtain path planning results.
The path planning refers to a path generation process for performing serial connection on planning objects in each class, the objects to be dispatched are dispatched in groups through grouped path planning, and the fast and efficient object dispatching is realized through the mode of serial connection in the groups.
The path planning method comprises the steps of determining a planning object to be subjected to path planning and an article to be dispatched based on a path planning request, obtaining position information and load upper limit information of the planning object, determining the planning object corresponding to the article to be dispatched according to the position information, determining the weight of the planning object to be dispatched according to the weight of the article to be dispatched and the planning object corresponding to the article to be dispatched, performing distance minimization clustering on the planning object according to the weight, the position information and the load upper limit information to obtain a clustering result so as to ensure that the clustering grouping can meet the limitation requirement of the maximum load upper limit, realizing the grouping of the planning objects, then respectively performing path planning on each category in the clustering result to obtain a path planning result, dividing the path planning problem into inter-category distance minimization clustering grouping based on vehicle load capacity limitation, each grouping is then routed in a manner to achieve efficient dispatch of the item based on the grouping and the intra-group concatenation.
In one embodiment, as shown in fig. 3, distance minimization clustering is performed on the planning objects according to the required delivery weight, the location information, and the upper limit load information, and a clustering result is obtained, step 208, which includes step 302.
And clustering the planning objects by taking the minimum number of classes, the maximum total load and the minimum total distance as clustering targets according to the weight, the position information and the upper limit load information to be dispatched to obtain a clustering result.
Wherein the total load is the total weight of all types of articles to be dispatched, and the total distance is the total maximum distance in all types.
The number of classes refers to the number of clustered classes, and the larger the number of classes, the more corresponding groups. Each group takes the transit as a starting point during path planning, so that the number of the planning objects directly butted with the transit can be reduced by reducing the number of the groups, the total dispatching distance of all the planning objects is reduced, and the dispatching efficiency of the goods is improved as a whole.
In one embodiment, as shown in fig. 4, based on the position information and the load upper limit information, clustering the planning objects with the minimum number of classes, the maximum total load, and the minimum total distance as clustering targets, and obtaining a clustering result includes step 302, which includes steps 402 to 404.
And step 404, clustering the planning objects based on the clustering constraint conditions according to the weight, the position information and the load upper limit information to be dispatched, and taking the minimum number of the classes, the maximum total load and the minimum total distance as clustering targets to obtain a clustering result.
The clustering constraint condition is a constraint condition which needs to be observed in the clustering process, the constraint refers to various limits of the decision scheme, the clustering constraint condition can limit clustering results in a mode of limiting the size relation between different items of data, the size relation between the different items of data and a fixed value and the like, and invalid clustering results are avoided.
For example, in a certain class, the planning objects include a dispatching person 1, a dispatching person 2 and a dispatching person 3, wherein the weight of the item to be dispatched of the dispatching person 1 is 200Kg, the upper limit of the load is 1000Kg, the weight of the item to be dispatched of the dispatching person 2 is 300Kg, the upper limit of the load is 1500Kg, the weight of the item to be dispatched of the dispatching person 3 is 400Kg, and the upper limit of the load is 2000Kg, then in the class, the total weight of the item to be dispatched of the class is 900Kg, and the upper limit of the load in the class is 1000 Kg.
The preset maximum handover distance refers to an allowable distance between two planning objects needing to perform the handover of the article. The maximum distance in a class refers to the largest distance in the distance between two same classes. Or, taking the planning objects in a certain class including the dispatching person 1, the dispatching person 2 and the dispatching person 3 as an example, the distance between the dispatching person 1 and the dispatching person 2 is 3km, the distance between the dispatching person 2 and the dispatching person 3 is 4km, the distance between the dispatching person 1 and the dispatching person 3 is 5km, and the maximum distance in the class is 5 km.
By limiting that the total weight of the articles to be dispatched of the classes is not more than the maximum load upper limit in the classes, and the maximum distance in the classes is not more than the preset maximum handover distance between the planning objects, the corresponding planning objects in each class can be ensured to meet the constraint conditions in the clustering result, and the maximization of the article dispatching efficiency can be conveniently realized after the path planning.
In one application example, the clustering process is implemented by a clustering model, and the input parameters of the clustering model are as follows:
express delivery personnel i day t need to send weight: w is ait
Distance from express delivery person i to express delivery person j: distij
Express delivery personnel i vehicle load restriction: c. Ci
Maximum number: m
Maximum distance: d
The decision variables include:
whether express delivery personnel i belong to the kth class: x is the number ofik(ascribed to 1, not ascribed to 0)
Whether the kth class contains express delivery personnel: y isk(including 1, not including O)
Kth maximum intra-class distance: sk
Class k minimum load: u shapek
Optimizing the target:
minimize number of classes:
maximum capacity of maximized class:
minimize maximum distance within class:
constraint conditions are as follows:
each courier belongs to only one class:
every class has express delivery personnel to belong to:
maximum weight limit for each class:
within each class spacing constraint:
xik*xjk*distij≤sk≤D*yk for i，j，k∈{1，2…，n}，i≠j
in order to load all required heavy goods by each vehicle returning to the transit station, it is critical to find a group capable of meeting the limit of the vehicle load, however, due to the different types of the vehicles of each express courier, the express courier directly returns to the transit station in a round-robin mode, and the minimum vehicle load of the express courier directly in a small group is used as the upper limit of the group load. In order to reduce the influence of the sequence of the serial connection points on the goods aging, the actual emergency situation is quickly responded, the distance between the connection points needs to be balanced, and the distance between every two express delivery personnel in the group is minimized. In addition, due to the fact that the express delivery personnel directly send different heavy goods quantity of each shift every day, although the problem can be solved by adopting the mean value of the weights of the heavy goods of the shifts of the express delivery personnel, the mean value model cannot accurately cope with the situation that the weights of the heavy goods of the express delivery personnel in the shifts are all larger than the mean value, and therefore a vehicle cannot finish all heavy goods in the group at one time. In this case, adding a time-dimension group capacity limit may enhance model robustness. Through the cluster analysis of the cluster model, the cluster model can be compatible with the differentiated load upper limit grouping of different vehicle types, the practical applicability of the model is improved, the robust modeling of the cluster model improves the performability of the model result, the situation that the total weight of the group exceeds the load upper limit of the vehicle due to the fluctuation of the dispatching weight of dispatching personnel is reduced, the maximum inter-class distance can be used as an optimization target, the influence of the sequence of the cluster points on the total mileage of the path is reduced, and the capability of the model grouping for dealing with the actual emergency (such as the line re-planning) is improved.
In one embodiment, as shown in fig. 5, path planning is performed on each of the clustering results, and the path planning result is obtained, step 210, which includes step 502.
And planning a route of the grouped planning objects, namely express delivery personnel, based on the positions of the express delivery personnel, starting from the transfer of the transfer vehicle, and sequentially connecting the express delivery personnel in the same group in series.
In one embodiment, the serial path planning includes any one of greedy algorithm, heuristic algorithm, and precision algorithm based implementation.
The greedy algorithm (also called greedy algorithm) is that when solving a problem, the selection which is the best in the current view is always made. That is, rather than considering the global optimum, the algorithm results in a locally optimal solution in some sense. Specifically, the greedy algorithm is characterized in that the greedy algorithm is performed step by step, and the optimal selection is often performed according to certain optimization measure on the basis of the current situation without considering various possible overall situations, so that a large amount of time which is consumed for finding the optimal solution and is required for exhausting all the possible solutions is saved. The greedy algorithm adopts a top-down mode, successive greedy selection is made through an iteration method, the problem is simplified into a sub-problem with a smaller scale every time greedy selection is made, and an optimal solution of the problem can be obtained through each greedy selection. In view of the fact that the distance between express delivery personnel in the group is minimized by the clustering model, a greedy algorithm can be directly adopted for path planning, namely, from the transition, the express delivery personnel closest to the current point are selected each time until all the express delivery personnel are connected in series.
A heuristic algorithm (heuristic algorithm) is proposed with respect to the optimization algorithm. An optimization algorithm for a problem finds the optimal solution for each instance of the problem. An algorithm based on an intuitive or empirical construct gives, at an acceptable cost (in terms of computation time and space), a feasible solution for each instance of the combinatorial optimization problem to be solved, the degree of deviation of which from the optimal solution is generally unpredictable. The path planning of the packet can also be performed by adopting a greedy algorithm. In addition, the precision algorithm is also an algorithm that can find an optimal solution.
In one embodiment, as shown in fig. 6, position information and upper limit loading information of a planning object are obtained, and according to the position information, the planning object corresponding to the item to be delivered is determined, i.e., step 204, which includes steps 602 to 608.
And step 604, determining the upper limit information of the load according to the vehicle load capacity, and determining a dispatching center point according to the dispatching address in the historical dispatching information.
And step 606, taking the dispatch center point as the position information of the planning object.
The historical delivery information refers to the item delivery information of the planning object, namely the express delivery personnel, within the past specified time range. The specified time range may be a month, a half month, or the like with the current time as a boundary, and the specific duration may be set as required. The item delivery information comprises a delivery range, a delivery address and a delivery quantity, and a delivery central point in the delivery range can be obtained by performing weighted analysis on the delivery address and the delivery quantity. For example, if the number of items addressed to the northeast direction of the area a in the area a is greater than the total number of items to be served, the serving center point will be biased to the northeast direction of the area after the weighted analysis. In other embodiments, the center point of the range corresponding to the area may also be used as the dispatch center point. By using the dispatch center point as the position information of the planning object, the position of the center point of the planning object is embodied.
It should be understood that, although the steps in the flowcharts related to the above embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in each flowchart related to the above embodiment may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
In one embodiment, as shown in fig. 7, there is provided a path planning apparatus, including: a request response module 702, an information acquisition module 704, a dispatch weight determination module 706, a clustering module 708, and a path planning module 710, wherein:
a request response module 702, configured to determine, in response to the path planning request, a planning object to be subjected to path planning and an item to be delivered.
And the information acquisition module 704 is configured to acquire the position information and the upper limit of load information of the planning object, and determine the planning object corresponding to the item to be delivered according to the position information.
The dispatch weight determining module 706 is configured to determine a to-be-dispatched weight of the planning object according to the weight of the item to be dispatched and the planning object corresponding to the item to be dispatched.
And the clustering module 708 is used for performing distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit information of the load to obtain a clustering result.
And the path planning module 710 is configured to perform path planning on each of the clustering results to obtain a path planning result.
In one embodiment, the clustering module is further configured to cluster the planning objects according to the required delivery weight, the position information and the upper limit load information, with the minimum number of classes, the maximum total load and the minimum total distance as clustering targets, to obtain a clustering result, where the total load is a total weight of the various types of articles to be delivered, and the total distance is a total maximum distance within each type.
In one embodiment, the clustering module is further configured to obtain a clustering constraint condition, where the clustering constraint condition includes that a total weight of the items to be dispatched of the class is not greater than an upper limit of a maximum load in the class, and a maximum distance in the class is not greater than a preset maximum handover distance between the planning objects; based on clustering constraint conditions, according to the weight, the position information and the upper limit information of the load to be dispatched, and with the minimum number of classes, the maximum total load and the minimum total distance as clustering targets, clustering the planning objects to obtain clustering results.
In one embodiment, the total weight of the items to be dispatched of the class is the total weight of the items to be dispatched corresponding to the planning object in the same class within the preset time range.
In one embodiment, the path planning module is further configured to perform serial path planning on the planning objects in the same class by using the transit as a path planning starting point, so as to obtain a path planning result including a connection sequence of the planning objects.
In one embodiment, the serial path planning includes any one of greedy algorithm, heuristic algorithm, and precision algorithm based implementation.
In one embodiment, the information obtaining module is further configured to obtain historical dispatch information and a vehicle capacity of the planning object; determining load upper limit information according to the vehicle load capacity, and determining a dispatching central point according to a dispatching address in the historical dispatching information; and taking the dispatch center point as the position information of the planning object.
The path planning device determines a planning object to be subjected to path planning and an article to be dispatched based on a path planning request, acquires position information and load upper limit information of the planning object, determines the planning object corresponding to the article to be dispatched according to the position information, determines the weight to be dispatched of the planning object according to the weight of the article to be dispatched and the planning object corresponding to the article to be dispatched, performs distance minimization clustering on the planning object according to the weight to be dispatched, the position information and the load upper limit information to obtain a clustering result so as to ensure that the clustering grouping can meet the limitation requirement of the maximum load upper limit, realizes the grouping of the planning objects, then performs path planning on each category in the clustering result respectively to obtain a path planning result, divides the path planning problem into inter-category distance minimization clustering grouping based on vehicle load limitation, each group is then routed in a manner to achieve efficient dispatch of the item based on the group and the intra-group concatenation.
For the specific definition of the path planning device, reference may be made to the above definition of the path planning method, which is not described herein again. The modules in the path planning apparatus may be wholly or partially implemented by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, and a network interface 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing path planning data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a path planning method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
responding to the path planning request, and determining a planning object to be subjected to path planning and an article to be delivered; acquiring position information and load upper limit information of a planning object, and determining the planning object corresponding to the article to be delivered according to the position information; determining the weight to be delivered of the planning object according to the weight of the item to be delivered and the planning object corresponding to the item to be delivered; performing distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result; and respectively carrying out path planning on each type in the clustering results to obtain path planning results.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and clustering the planning objects by taking the minimum number of classes, the maximum total load and the minimum total distance as clustering targets according to the weight, the position information and the upper limit load information to be dispatched, so as to obtain a clustering result, wherein the total load is the total weight of all the classes of the articles to be dispatched, and the total distance is the total maximum distance in all the classes.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring clustering constraint conditions, wherein the clustering constraint conditions comprise that the total weight of the articles to be dispatched of a class is not more than the maximum load upper limit in the class, and the maximum distance in the class is not more than the preset maximum handover distance between planning objects; based on clustering constraint conditions, according to the weight, the position information and the upper limit information of the load to be dispatched, and with the minimum number of classes, the maximum total load and the minimum total distance as clustering targets, clustering the planning objects to obtain clustering results.
In one embodiment, the total weight of the items to be dispatched of the class is the total weight of the items to be dispatched corresponding to the planning object in the same class within the preset time range.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and taking the transit station as a path planning starting point, and performing serial path planning on each planning object in the same class to obtain a path planning result comprising the connection sequence of each planning object.
In one embodiment, the serial path planning includes any one of greedy algorithm, heuristic algorithm, and precision algorithm based implementation.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring historical dispatching information and vehicle load capacity of a planning object; determining load upper limit information according to the vehicle load capacity, and determining a dispatching central point according to a dispatching address in the historical dispatching information; and taking the dispatch center point as the position information of the planning object.
The computer equipment for realizing the path planning method determines a planning object to be subjected to path planning and an article to be dispatched based on a path planning request, acquires position information and load upper limit information of the planning object, determines the planning object corresponding to the article to be dispatched according to the position information, determines the weight to be dispatched of the planning object according to the weight of the article to be dispatched and the planning object corresponding to the article to be dispatched, performs distance minimization clustering on the planning object according to the weight to be dispatched, the position information and the load upper limit information to obtain a clustering result so as to ensure that the clustering grouping can meet the limitation requirement of the maximum load upper limit, realizes the grouping of the planning objects, then performs path planning on each category in the clustering result respectively to obtain a path planning result, and divides the path planning problem into inter-category distance minimization clustering grouping based on vehicle load limitation, each grouping is then routed in a manner to achieve efficient dispatch of the item based on the grouping and the intra-group concatenation.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
responding to the path planning request, and determining a planning object to be subjected to path planning and an article to be dispatched; acquiring position information and load upper limit information of a planning object, and determining the planning object corresponding to the article to be delivered according to the position information; determining the weight to be delivered of the planning object according to the weight of the item to be delivered and the planning object corresponding to the item to be delivered; performing distance minimization clustering on the planning objects according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result; and respectively carrying out path planning on each type in the clustering results to obtain path planning results.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and clustering the planning objects by taking the minimum number of classes, the maximum total load and the minimum total distance as clustering targets according to the weight, the position information and the upper limit load information to be dispatched, so as to obtain a clustering result, wherein the total load is the total weight of all the classes of the articles to be dispatched, and the total distance is the total maximum distance in all the classes.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring clustering constraint conditions, wherein the clustering constraint conditions comprise that the total weight of articles to be dispatched of a class is not more than the maximum load upper limit in the class, and the maximum distance in the class is not more than the preset maximum handover distance between planning objects; based on clustering constraint conditions, according to the weight, the position information and the upper limit information of the load to be dispatched, and with the minimum number of classes, the maximum total load and the minimum total distance as clustering targets, clustering the planning objects to obtain clustering results.
In one embodiment, the total weight of the items to be dispatched of the class is the total weight of the items to be dispatched corresponding to the planning object in the same class within the preset time range.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and taking the transit station as a path planning starting point, and performing serial path planning on each planning object in the same class to obtain a path planning result comprising the connection sequence of each planning object.
In one embodiment, the serial path planning includes any one of greedy algorithm, heuristic algorithm, and precision algorithm based implementation.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring historical dispatching information and vehicle load capacity of a planning object; determining load upper limit information according to the vehicle load capacity, and determining a dispatching central point according to a dispatching address in the historical dispatching information; and taking the dispatch center point as the position information of the planning object.
The computer-readable storage medium for implementing the path planning method determines a planning object to be subjected to path planning and an article to be dispatched based on a path planning request, acquires position information and upper limit load information of the planning object, determines the planning object corresponding to the article to be dispatched according to the position information, determines the weight to be dispatched of the planning object according to the weight of the article to be dispatched and the planning object corresponding to the article to be dispatched, performs distance minimization clustering on the planning object according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result so as to ensure that the clustering grouping can meet the limitation requirement of the maximum upper limit load, realizes the grouping of the planning objects, then performs path planning on each category in the clustering result respectively to obtain a path planning result, and divides the path planning problem into inter-category distance minimization clustering grouping based on the limitation of vehicle load capacity, each grouping is then routed in a manner to achieve efficient dispatch of the item based on the grouping and the intra-group concatenation.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of path planning, the method comprising:
responding to the path planning request, and determining a planning object to be subjected to path planning and an article to be delivered;
acquiring position information and load upper limit information of the planning object, and determining the planning object corresponding to the article to be delivered according to the position information;
determining the weight to be delivered of the planning object according to the weight of the item to be delivered and the planning object corresponding to the item to be delivered;
performing distance minimization clustering on the planning object according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result;
and respectively carrying out path planning on each type in the clustering results to obtain path planning results.
2. The method according to claim 1, wherein the performing distance minimization clustering on the planning objects according to the to-be-dispatched weight, the position information, and the upper load limit information to obtain a clustering result comprises:
and clustering the planning objects by taking the minimum number of classes, the maximum total load and the minimum total distance as clustering targets according to the weight to be dispatched, the position information and the upper limit load information to obtain a clustering result, wherein the total load is the total weight of all classes of the articles to be dispatched, and the total distance is the total maximum distance in all classes.
3. The method according to claim 2, wherein the clustering the planning objects with the minimum number of classes, the maximum total load and the minimum total distance as clustering targets based on the position information and the load upper limit information to obtain a clustering result comprises:
acquiring clustering constraint conditions, wherein the clustering constraint conditions comprise that the total weight of articles to be dispatched of a class is not more than the maximum load upper limit in the class, and the maximum distance in the class is not more than the preset maximum handover distance between planning objects;
based on the clustering constraint condition, clustering the planning objects according to the weight to be dispatched, the position information and the load upper limit information by taking the minimum number of classes, the maximum total load and the minimum total distance as clustering targets to obtain a clustering result.
4. The method of claim 3, wherein the total weight of the items to be dispatched of the class is the total weight of the items to be dispatched corresponding to the planning object in the same class within a preset time range.
5. The method according to claim 1, wherein the performing path planning on each of the clustering results respectively to obtain a path planning result comprises:
and taking the transit station as a path planning starting point, and performing serial path planning on each planning object in the same class to obtain a path planning result comprising the connection sequence of each planning object.
6. The method of claim 5, wherein the concatenated path planning comprises any one of greedy algorithm, heuristic algorithm, and precision algorithm based implementation.
7. The method of claim 1, wherein obtaining location information and upper load limit information for the planning object comprises:
acquiring historical dispatching information and vehicle load capacity of a planning object;
determining load upper limit information according to the vehicle load capacity, and determining a dispatching central point according to a dispatching address in the historical dispatching information;
and taking the dispatch center point as the position information of the planning object.
8. A path planning apparatus, the apparatus comprising:
the request response module is used for responding to the path planning request and determining a planning object to be subjected to path planning and an article to be dispatched;
the information acquisition module is used for acquiring the position information and the load upper limit information of the planning object and determining the planning object corresponding to the article to be delivered according to the position information;
the dispatch weight determining module is used for determining the weight to be dispatched of the planning object according to the weight of the item to be dispatched and the planning object corresponding to the item to be dispatched;
the clustering module is used for performing distance minimization clustering on the planning object according to the weight to be dispatched, the position information and the load upper limit information to obtain a clustering result;
and the path planning module is used for respectively planning paths of each type in the clustering results to obtain path planning results.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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