CN113537863A - Route planning method, route planning device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a route planning method, a route planning device, a computer device and a storage medium. The method comprises the following steps: acquiring transportation order data, wherein the transportation order data comprises transportation date data and target address data; acquiring an order data set according to the transportation date data and the target address data; acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm; and planning a fixed route according to the high-frequency demand point set to obtain the fixed high-frequency route. According to the method and the system, which transportation places are the high-frequency demand points corresponding to the orders are determined through transportation order data, then the fixed route is planned based on the determined high-frequency demand points, and compared with the fixed route based on the administrative region, the fixed route is more fit with the existing transportation orders, and the transportation efficiency can be effectively improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a route planning method and apparatus, a computer device, and a storage medium.
Background
With the development of computer and artificial intelligence technologies, route planning technologies have emerged, which are one of the main research contents of sports planning. The movement planning consists of route planning and trajectory planning, sequence points or curves connecting the starting position and the end position are called routes, and the strategy for forming the routes is called route planning. In a small-batch multi-point distribution transportation scene, the transportation cost, the driving distance and the distribution timeliness are core indexes. Therefore, scientific planning needs to be performed on the distribution points of each route in series in the transportation scene.
In the current small-batch multipoint distribution route distribution scene, a common route planning method mainly lays out fixed administrative area routes as a main route, and distribution routes are obtained by connecting administrative areas in series.
However, in practical applications, the transportation efficiency of such distribution routes obtained by connecting administrative areas in series is low.
Disclosure of Invention
In view of the above, it is necessary to provide a route planning method, a route planning apparatus, a computer device, and a storage medium, which can effectively improve the transportation efficiency.
A method of route planning, the method comprising:
acquiring transportation order data, wherein the transportation order data comprises transportation date data and target address data;
acquiring an order data set according to the transportation date data and the target address data;
acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and planning a fixed route according to the high-frequency demand point set to obtain a fixed high-frequency route.
In one embodiment, the obtaining the order data set according to the transportation date data and the destination address data includes:
clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point;
and acquiring an order data set according to the transportation date data and the demand point.
In one embodiment, the obtaining, by an Apriori algorithm, the high-frequency demand point set corresponding to the order data set includes:
scanning the order data set, and taking the occurring demand points as an initial frequent item set;
mining the initial frequent item set through an Apriori algorithm according to a preset support threshold to obtain a target frequent item set;
and acquiring high-frequency demand points in the demand points according to the target frequent item set.
In one embodiment, the performing fixed route planning according to the high-frequency demand point set includes:
obtaining route starting address data;
constructing a VRP (varied Routing protocol, multi-loop transportation) Problem model according to the route starting address data and target address data corresponding to the demand points in the high-frequency demand point set;
and solving the VRP problem model to obtain a fixed high-frequency route.
In one embodiment, the constructing a VRP problem model according to the route starting address data and the target address data corresponding to the demand points in the high-frequency demand point set includes:
constructing a transportation distance matrix according to the distance from the route starting address data to the target address data corresponding to the demand point;
acquiring a route string point number constraint and a route distance constraint;
and solving the transportation distance matrix according to the route cluster point number constraint and the route distance constraint to form a calculation model by taking the shortest total distance as a target, and constructing a VRP problem model.
In one embodiment, after the fixed route planning is performed according to the high-frequency demand point set and a fixed high-frequency route is obtained, the method further includes:
determining demand points which are not passed by the fixed high-frequency route;
and constructing a single-point straight hair route according to the geographical position corresponding to the demand point which is not passed by the fixed high-frequency route.
A route planning apparatus, the apparatus comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring transportation order data which comprises transportation date data and target address data;
the collection construction module is used for acquiring an order data collection according to the transportation date data and the target address data;
the association analysis module is used for acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and the route planning module is used for planning a fixed route according to the high-frequency demand point set to obtain a fixed high-frequency route.
In one embodiment, the set construction module is configured to:
clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point;
and acquiring an order data set according to the transportation date data and the demand point.
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:
acquiring transportation order data, wherein the transportation order data comprises transportation date data and target address data;
acquiring an order data set according to the transportation date data and the target address data;
acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and planning a fixed route according to the high-frequency demand point set to obtain a fixed high-frequency route.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring transportation order data, wherein the transportation order data comprises transportation date data and target address data;
acquiring an order data set according to the transportation date data and the target address data;
acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and planning a fixed route according to the high-frequency demand point set to obtain a fixed high-frequency route.
According to the route planning method, the route planning device, the computer equipment and the storage medium, the transportation order data are obtained, and the transportation order data comprise transportation date data and target address data; acquiring an order data set according to the transportation date data and the target address data; acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm; and planning a fixed route according to the high-frequency demand point set to obtain the fixed high-frequency route. According to the method and the system, which transportation places are the high-frequency demand points corresponding to the orders are determined through transportation order data, then the fixed route is planned based on the determined high-frequency demand points, and compared with the fixed route based on the administrative region, the fixed route is more fit with the existing transportation orders, and the transportation efficiency can be effectively improved.
Drawings
FIG. 1 is a diagram of an exemplary routing method;
FIG. 2 is a flow diagram illustrating a method of route planning in one embodiment;
FIG. 3 is a schematic sub-flow chart of step 203 of FIG. 2 in one embodiment;
FIG. 4 is a schematic sub-flow chart illustrating step 205 of FIG. 2 according to one embodiment;
FIG. 5 is a schematic sub-flow chart of step 207 of FIG. 2 in one embodiment;
FIG. 6 is a block diagram of a route planning device according to an embodiment;
FIG. 7 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 route planning method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the route planning server 104 via a network. When a user needs to plan a future batch of transportation orders to ensure that multi-point transportation corresponding to the orders can be performed and transportation efficiency is ensured, the user may submit corresponding transportation order data to the route planning server 104 by the terminal 102 to request the route planning server 104 to perform corresponding route planning. The route planning server 104 acquires transportation order data, which includes transportation date data and destination address data; acquiring an order data set according to the transportation date data and the target address data; acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm; and planning a fixed route according to the high-frequency demand point set to obtain the fixed high-frequency route. . The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the route planning 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 route planning method is provided, which is illustrated by applying the method to the route planning server 104 in fig. 1, and includes the following steps:
The transportation order data refers to data corresponding to an order submitted by a customer for transporting an article to be transported from an initial address to a target address. The shipping date data is the time point corresponding to the shipment of the item, and the destination address data is the destination address corresponding to the shipment of the item. For example, a shipping order may be that it is desired to ship item A from an initial shipping warehouse to destination address B on day X, month Y. The shipping order data may also contain information such as the type or quantity of the item to be shipped.
Specifically, when a user receives a large batch of transportation order data, some fixed routes of transportation can be planned through the route planning of the application, so that the transportation efficiency of the transportation order is improved. Wherein the user may submit the corresponding order data to the routing server 104 for subsequent routing by the routing server 104. The order data submitted by the user includes transportation date data corresponding to transportation and basic data of route planning such as destination address data of transportation.
The order data set refers to a transportation order set divided according to the transportation date. The step of obtaining the order data set according to the transportation date data and the destination address data specifically means that transportation orders in the same transportation date are divided into the same set according to the transportation date, if there are transportation tasks of respectively transporting the item to three locations A, B and D at 2018/1/1, the order data set corresponding to 2018/1/1 is (a, B, D), and if there are transportation tasks of respectively transporting the item to four locations A, B, D and F at 2018/1/2, the order data set corresponding to 2018/1/2 is (a, B, D, F).
Specifically, after receiving the order data, the route planning server 104 constructs a corresponding order data set according to the transportation date data and the destination address data in the order data, so as to abstract the order data, thereby improving the comprehensive efficiency of the route planning process.
And step 205, acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm.
The Apriori algorithm is an association rule mining-based algorithm. The method uses an iterative method of searching layer by layer to find out the relation of item sets in a database to form a rule, and the process of the method consists of connection and pruning. The concept of a set of terms in the algorithm is a set of terms. The set of K terms is a set of K terms. The frequency of occurrence of a set of items is the number of transactions that contain the set of items, referred to as the frequency of the set of items. If a certain item set meets the minimum support, it is called a frequent item set. Association rules: for representing associations implicit within the data. For example, the goods are required to be transported to the site A within a certain date and are also required to be transported to the site B. In the application, the obtained order data set is mined according to the corresponding association rule through an Apriori algorithm, the determined frequent item set is a high-frequency demand point set corresponding to the transportation order data, and the high-frequency demand points correspond to part of the transportation target addresses.
Specifically, the route planning server 104 carries an association rule mining model constructed based on an Apriori algorithm, and after the order data set is obtained, the route planning server 104 inputs the order data set into the association rule mining model, mines association rules in the order data set through the Apriori algorithm, and obtains a corresponding high-frequency demand point set.
And step 207, planning a fixed route according to the high-frequency demand point set to obtain a fixed high-frequency route.
The fixed high-frequency route is a fixed route for carrying out transportation operation on a transportation date corresponding to the transportation date data, and the fixed high-frequency route comprises a plurality of different transportation destination addresses. The truck driver can take the fixed high-frequency route as a transportation route, and transport the goods to be transported corresponding to the transportation order data from the initial address to the target address. The fixed route planning refers to a process of obtaining a required fixed high-frequency route based on planning of a determined high-frequency demand point, preset route string point number limitation, preset route distance limitation and the like.
Specifically, after determining the high-frequency demand point, the route planning server 104 may perform corresponding path planning to determine a fixed high-frequency route, and when completing a corresponding transportation order through the fixed high-frequency route, the transportation efficiency may be effectively improved. Specifically, in one embodiment, a corresponding VRP model may be constructed based on high-frequency demand points that need to be connected in series, distances between the high-frequency demand points, route string point number limitation, route distance limitation, and the like, and then the VRP model is solved to obtain a corresponding fixed high-frequency route.
According to the route planning method, transportation order data are obtained, and the transportation order data comprise transportation date data and target address data; acquiring an order data set according to the transportation date data and the target address data; acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm; and planning a fixed route according to the high-frequency demand point set to obtain the fixed high-frequency route. According to the method and the system, which transportation places are the high-frequency demand points corresponding to the orders are determined through transportation order data, then the fixed route is planned based on the determined high-frequency demand points, and compared with the fixed route based on the administrative region, the fixed route is more fit with the existing transportation orders, and the transportation efficiency can be effectively improved.
In one embodiment, as shown in FIG. 3, step 203 comprises:
and step 302, clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point.
And step 304, acquiring an order data set according to the transportation date data and the demand points.
The geographic location may specifically include longitude and latitude data corresponding to the target address, and the clustering specifically refers to clustering target address data with similar distances together. The clustered target addresses correspond to a demand point, the geographic position corresponding to the demand point is the central point of the cluster, and the target addresses which cannot be clustered can be independently regarded as the demand point. The order data set is a set constructed from all demand points within the same shipping date. In one embodiment, the target address data may be clustered by a DBSCAN (Density-Based Clustering of Applications with Noise) Clustering algorithm. In another embodiment, the target address data may then be clustered by Kmeans clustering.
Specifically, since the distances of some transportation target locations are close, in order to reduce the calculation amount in the subsequent calculation process, the target addresses in the transportation order data may be clustered according to the geographic positions corresponding to the target address data, so that the number of the target addresses involved in the calculation process is reduced, and the calculation efficiency of the high-frequency demand point calculation and the planning efficiency of the fixed high-frequency route are improved. In the embodiment, the demand points are obtained by clustering the target address data, so that the effects of reducing the calculated amount and improving the route planning efficiency can be achieved.
In one embodiment, as shown in FIG. 4, step 205 comprises:
And step 403, mining an initial frequent item set through an Apriori algorithm according to a preset support threshold value to obtain a target frequent item set.
And 405, acquiring high-frequency demand points in the demand points according to the target frequent item set.
The initial frequent item set is a set including all demand points, which includes all the demand points that occur, including high-frequency demand points and non-high-frequency demand points. The degree of support specifically refers to how likely the association rule is to occur. In the application, the user can set the minimum support threshold according to the number of routes to be planned and the relevance strength of each demand point between the routes to be ensured. For the same transportation order data, the higher the preset support threshold is, the fewer the obtained high-frequency demand points are, the number of routes can be correspondingly reduced, and the stronger the relevance of each demand point among the routes is. The lower the preset support threshold is, the more high-frequency demand points are obtained, the number of routes is increased, and the relevance between the demand points among the routes is relatively weaker. The target frequent item set is a target set obtained based on the initial frequent item set, and elements in the set are high-frequency demand points in the demand points.
Specifically, the routing server 104 may scan all order data sets, taking the emerging demand points as an initial set of frequent items. And then mining an initial frequent item set through an Apriori algorithm according to a preset support threshold value specified by a user to obtain a target frequent item set. And finally, acquiring high-frequency demand points in the demand points according to the target frequent item set. As a specific embodiment, the route planning method of the present application is specifically used for planning a fixed high-frequency route for a transportation route corresponding to order data in a 4-day time window, and relates to that target demand points include five regions D1, D2, D3, D4, and D5, and in the first day, goods need to be transported to three demand points D1, D3, and D4. In the next day, the cargo needs to be transported to three demand points D2, D3, D5. In the third day, cargo needs to be transported to four demand points D1, D2, D3, D5. During the fourth day, the cargo needs to be transported to two demand points D2, D5. The preset minimum support threshold is 50%. Then four order data sets are { D1, D3, D4, D5}, { D2, D3, D5}, { D1, D2, D3, D5}, and { D2, D5}, respectively, and then the initial set of frequent items is { D1, D2, D3, D4, D5}, where the four order data sets include two of data D1, 50% support for D1, and 75% support for D2. The degree of support of D3 was 75%. The degree of support of D4 was 25%. The degree of support of D5 was 75%. Wherein the support of D4 is below a preset minimum support threshold of 50%. Its corresponding demand point D4 is pruned. Then the frequent item set corresponding to the initial frequent item set is { D1, D2, D3, D5}, and then a candidate frequent binomial set is generated by linking elements in the frequent binomial set, specifically including six sets of data of { D1, D2}, { D1, D3}, { D1, D5}, { D2, D3}, { D2, D5}, { D3, D5}, and then their support degrees are respectively obtained, which are 25%, 50%, 25%, 50%, 75%, and 50%. The support degrees of { D1, D2} and { D1, D5} are lower than 50%, pruning is performed, the real frequent binomial set is four groups of data of { D1, D3}, { D2, D3}, { D2, D5}, and { D3, D5}, and then linking is performed to generate three groups of data of corresponding candidate frequent trinomial sets, specifically { D1, D2, D3}, { D1, D3, D5} and { D2, D3, D5}, the support degrees of which are respectively 25%, 25% and 50%, the obtained frequent trinomial sets are only one group of { D2, D3, D5}, when the number of the group of the frequent item set is 0 or 1, mining is finished, and the finally obtained frequent item set is the target frequent item set. The demand points D2, D3 and D5 in the target frequent item set, namely the frequent three item set, are high-frequency demand points mined by an Apriori algorithm. In the embodiment, the high-frequency demand point in the demand point is determined through an Apriori algorithm, and then a corresponding fixed high-frequency route can be constructed based on the high-frequency demand point, so that the route planning efficiency is improved.
As shown in fig. 5, step 207 specifically includes:
And step 506, solving a VRP problem model to obtain a fixed high-frequency route.
The route starting address may specifically refer to a shipping starting location corresponding to the transportation order. The VRP problem, namely the multi-loop transportation problem, is that a proper route is designed for demand points of a series of customers, so that vehicles pass through the routes in order, and certain optimization goals, such as shortest mileage, least cost, shortest time, shortest fleet scale and high vehicle utilization rate, are achieved under the condition that certain constraint conditions, such as cargo demand, delivery time, vehicle load capacity limit, mileage limit, time limit and the like, are met.
Specifically, after determining which points are high-frequency demand points, the route planning server 104 constructs a corresponding VRP problem model by adding some constraints in the actual transportation process, and then based on the route starting address data and the target address data corresponding to the demand points in the high-frequency demand point set, and determines a fixed high-frequency route by solving the VRP problem model. For example, in a specific embodiment, the shortest total target distance may be used as a target, and a corresponding maximum string point number constraint, a single-line maximum distance constraint, a traffic constraint, and the like are added to construct a corresponding VRP problem model, and then the fixed high-frequency route is constructed by solving the VRP problem model. In the embodiment, the fixed high-frequency route is obtained by constructing the VRP problem model and then solving the VRP problem model, so that the route planning efficiency is improved.
In one embodiment, step 504 includes: constructing a transportation distance matrix according to the distance from the route starting address data to the target address data corresponding to the demand point; acquiring a route string point number constraint and a route distance constraint; and (4) solving a transport distance matrix according to the route cluster point number constraint and the route distance constraint to construct a VRP problem model.
The route string point number constraint refers to the number of high-frequency demand points which can be passed by a fixed high-frequency route, and the route distance constraint refers to the distance from the starting point of the fixed high-frequency route to the last demand point of the route. Since the actual transportation process of the goods is restricted by the loading capacity of the vehicle and the mileage of the vehicle, the restrictions can be added to construct a corresponding VRP problem model.
Specifically, the distance from the route starting address data to the target address data corresponding to the demand point can be firstly used, then a corresponding transport distance matrix is constructed, and then the route string point number constraint and the route distance constraint are obtained. And solving a transport distance matrix by adding route cluster point number constraint and route distance constraint to construct a VRP problem model by taking the shortest total distance of the high-frequency demand points in series as a target. Then, the target of route planning, a plurality of fixed high-frequency routes, can be obtained by solving the VRP problem model. In the embodiment, the practicability of the planned fixed high-frequency route can be effectively improved by adding corresponding route constraints in the VRP problem model.
In one embodiment, step 207 comprises:
and determining the demand points which are not passed by the fixed high-frequency route.
And constructing a single-point straight hair route according to the geographical position corresponding to the demand point which is not passed by the fixed high-frequency route.
The single-point hair-straightening route is a fixed route directly from an initial address to a target address, and since the fixed high-frequency route can only bring high-frequency demand points into a road and does not pay special attention to non-high-frequency demand points, route planning of the non-high-frequency demand points can be realized by constructing the single-point hair-straightening route. All transport orders are then completed through the fixed high frequency route and the constructed single point straight-forward route.
Specifically, the demand points involved in the fixed high-frequency route can be determined firstly, then the demand points are obtained based on clustering, which demand points are searched and determined to be the demand points which do not pass through the fixed high-frequency route, and then the transportation management of the demand points which do not pass through the fixed high-frequency route is realized through the single-point straight route. In the embodiment, the path management of the demand points which are not passed by the fixed high-frequency route is realized by constructing the single-point straight route, so that the coverage of the path management can be effectively improved, and the transportation efficiency is improved.
It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order 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 some of the steps in fig. 2-5 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 6, there is provided a route planning apparatus including: a data acquisition module 601, a set construction module 603, an association analysis module 605, and a route planning module 607, wherein:
the data obtaining module 601 is configured to obtain transportation order data, where the transportation order data includes transportation date data and destination address data.
The set building module 603 is configured to obtain an order data set according to the transportation date data and the destination address data.
And the association analysis module 605 is configured to obtain a high-frequency demand point set corresponding to the order data set through an Apriori algorithm.
And the route planning module 607 is configured to perform fixed route planning according to the high-frequency demand point set, and obtain a fixed high-frequency route.
In one embodiment, the set construction module 603 is specifically configured to: clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point; and acquiring an order data set according to the transportation date data and the demand points.
In one embodiment, the association analysis module 605 is specifically configured to: scanning an order data set, and taking the appeared demand points as an initial frequent item set; mining an initial frequent item set through an Apriori algorithm according to a preset support threshold to obtain a target frequent item set; and acquiring high-frequency demand points in the demand points according to the target frequent item set.
In one embodiment, the route planning module 607 is specifically configured to: obtaining route starting address data; constructing a VRP problem model according to the route initial address data and target address data corresponding to the demand points in the high-frequency demand point set; and solving the VRP problem model to obtain a fixed high-frequency route.
In one embodiment, the route planning module 607 is further operable to: constructing a transportation distance matrix according to the distance from the route starting address data to the target address data corresponding to the demand point; acquiring a route string point number constraint and a route distance constraint; and (4) solving a transport distance matrix according to the route cluster point number constraint and the route distance constraint to construct a VRP problem model.
In one embodiment, the system further comprises a single point hair straightening routing module for: determining demand points which are not passed by the fixed high-frequency route; and constructing a single-point straight hair route according to the geographical position corresponding to the demand point which is not passed by the fixed high-frequency route.
For the specific definition of the route planning device, reference may be made to the above definition of the route planning method, which is not described herein again. The modules in the route planning device can be wholly or partially implemented by software, hardware and 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, the internal structure of which may be as shown in fig. 7. 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 route 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 route planning method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 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:
acquiring transportation order data, wherein the transportation order data comprises transportation date data and target address data;
acquiring an order data set according to the transportation date data and the target address data;
acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and planning a fixed route according to the high-frequency demand point set to obtain the fixed high-frequency route.
In one embodiment, the processor, when executing the computer program, further performs the steps of: clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point; and acquiring an order data set according to the transportation date data and the demand points.
In one embodiment, the processor, when executing the computer program, further performs the steps of: scanning an order data set, and taking the appeared demand points as an initial frequent item set; mining an initial frequent item set through an Apriori algorithm according to a preset support threshold to obtain a target frequent item set; and acquiring high-frequency demand points in the demand points according to the target frequent item set.
In one embodiment, the processor, when executing the computer program, further performs the steps of: obtaining route starting address data; constructing a VRP problem model according to the route initial address data and target address data corresponding to the demand points in the high-frequency demand point set; and solving the VRP problem model to obtain a fixed high-frequency route.
In one embodiment, the processor, when executing the computer program, further performs the steps of: constructing a transportation distance matrix according to the distance from the route starting address data to the target address data corresponding to the demand point; acquiring a route string point number constraint and a route distance constraint; and (4) solving a transport distance matrix according to the route cluster point number constraint and the route distance constraint to construct a VRP problem model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining demand points which are not passed by the fixed high-frequency route; and constructing a single-point straight hair route according to the geographical position corresponding to the demand point which is not passed by the fixed high-frequency route.
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:
acquiring transportation order data, wherein the transportation order data comprises transportation date data and target address data;
acquiring an order data set according to the transportation date data and the target address data;
acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and planning a fixed route according to the high-frequency demand point set to obtain the fixed high-frequency route.
In one embodiment, the computer program when executed by the processor further performs the steps of: clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point; and acquiring an order data set according to the transportation date data and the demand points.
In one embodiment, the computer program when executed by the processor further performs the steps of: scanning an order data set, and taking the appeared demand points as an initial frequent item set; mining an initial frequent item set through an Apriori algorithm according to a preset support threshold to obtain a target frequent item set; and acquiring high-frequency demand points in the demand points according to the target frequent item set.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining route starting address data; constructing a VRP problem model according to the route initial address data and target address data corresponding to the demand points in the high-frequency demand point set; and solving the VRP problem model to obtain a fixed high-frequency route.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing a transportation distance matrix according to the distance from the route starting address data to the target address data corresponding to the demand point; acquiring a route string point number constraint and a route distance constraint; and (4) solving a transport distance matrix according to the route cluster point number constraint and the route distance constraint to construct a VRP problem model.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining demand points which are not passed by the fixed high-frequency route; and constructing a single-point straight hair route according to the geographical position corresponding to the demand point which is not passed by the fixed high-frequency route.
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 related to 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.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification 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, which falls 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 route planning, the method comprising:
acquiring transportation order data, wherein the transportation order data comprises transportation date data and target address data;
acquiring an order data set according to the transportation date data and the target address data;
acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and planning a fixed route according to the high-frequency demand point set to obtain a fixed high-frequency route.
2. The method of claim 1, wherein obtaining the set of order data based on the ship date data and the destination address data comprises:
clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point;
and acquiring an order data set according to the transportation date data and the demand point.
3. The method according to claim 2, wherein the obtaining the high-frequency demand point set corresponding to the order data set by Apriori algorithm comprises:
scanning the order data set, and taking the occurring demand points as an initial frequent item set;
mining the initial frequent item set through an Apriori algorithm according to a preset support threshold to obtain a target frequent item set;
and acquiring high-frequency demand points in the demand points according to the target frequent item set.
4. The method of claim 2, wherein the fixed route planning according to the set of high frequency demand points, obtaining a fixed high frequency route comprises:
obtaining route starting address data;
constructing a VRP problem model according to the route starting address data and target address data corresponding to the demand points in the high-frequency demand point set;
and solving the VRP problem model to obtain a fixed high-frequency route.
5. The method of claim 4, wherein constructing a VRP problem model based on the route start address data and target address data corresponding to demand points in the set of high frequency demand points comprises:
constructing a transportation distance matrix according to the distance from the route starting address data to the target address data corresponding to the demand point;
acquiring a route string point number constraint and a route distance constraint;
and solving the transportation distance matrix according to the route cluster point number constraint and the route distance constraint to form a calculation model by taking the shortest total distance as a target, and constructing a VRP problem model.
6. The method according to claim 2, wherein the fixed route planning according to the high frequency demand point set further comprises, after obtaining a fixed high frequency route:
determining demand points which are not passed by the fixed high-frequency route;
and constructing a single-point straight hair route according to the geographical position corresponding to the demand point which is not passed by the fixed high-frequency route.
7. A route planning apparatus, characterized in that the apparatus comprises:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring transportation order data which comprises transportation date data and target address data;
the collection construction module is used for acquiring an order data collection according to the transportation date data and the target address data;
the association analysis module is used for acquiring a high-frequency demand point set corresponding to the order data set through an Apriori algorithm;
and the route planning module is used for planning a fixed route according to the high-frequency demand point set to obtain a fixed high-frequency route.
8. The apparatus of claim 7, wherein the set construction module is configured to:
clustering the target address data according to the geographic position corresponding to the target address data to obtain a demand point;
and acquiring an order data set according to the transportation date data and the demand point.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
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 6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140058763A1 (en) * | 2012-07-24 | 2014-02-27 | Deloitte Development Llc | Fraud detection methods and systems |
| CN105043400A (en) * | 2015-06-30 | 2015-11-11 | 百度在线网络技术（北京）有限公司 | Route planning method and device |
| CN108960946A (en) * | 2017-05-19 | 2018-12-07 | 北京京东尚科信息技术有限公司 | Order display methods and device |
| CN109003028A (en) * | 2018-07-17 | 2018-12-14 | 北京百度网讯科技有限公司 | Method and apparatus for dividing logistics region |
| CN109697523A (en) * | 2017-10-23 | 2019-04-30 | 顺丰科技有限公司 | The method, system and equipment for sending part path are received in optimization |
| CN110348679A (en) * | 2019-06-03 | 2019-10-18 | 菜鸟智能物流控股有限公司 | Logistics processing method and device, electronic equipment and storage medium |
| US20190325382A1 (en) * | 2018-04-20 | 2019-10-24 | United States Postal Service | Use of geospatial coordinate systems for tracking item delivery |
-
2020
- 2020-04-17 CN CN202010305627.3A patent/CN113537863A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140058763A1 (en) * | 2012-07-24 | 2014-02-27 | Deloitte Development Llc | Fraud detection methods and systems |
| CN105043400A (en) * | 2015-06-30 | 2015-11-11 | 百度在线网络技术（北京）有限公司 | Route planning method and device |
| CN108960946A (en) * | 2017-05-19 | 2018-12-07 | 北京京东尚科信息技术有限公司 | Order display methods and device |
| CN109697523A (en) * | 2017-10-23 | 2019-04-30 | 顺丰科技有限公司 | The method, system and equipment for sending part path are received in optimization |
| US20190325382A1 (en) * | 2018-04-20 | 2019-10-24 | United States Postal Service | Use of geospatial coordinate systems for tracking item delivery |
| CN109003028A (en) * | 2018-07-17 | 2018-12-14 | 北京百度网讯科技有限公司 | Method and apparatus for dividing logistics region |
| CN110348679A (en) * | 2019-06-03 | 2019-10-18 | 菜鸟智能物流控股有限公司 | Logistics processing method and device, electronic equipment and storage medium |
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