CN111612400A - Distribution time length prediction method and device - Google Patents
Distribution time length prediction method and device Download PDFInfo
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Abstract
The present application relates to the field of big data processing technologies, and in particular, to a method and an apparatus for predicting delivery duration. According to the method and the device, after a takeout order requested by a target user is received, the position of a rider is obtained when the rider receives the order, a merchant address and the address of the target user are extracted from the takeout order, the parking position of the rider is determined according to a plurality of historical orders containing the user address, a plurality of sub-routes in a distribution route are determined according to the position of the rider when the rider receives the order, the merchant address, the user address and the parking position, the distribution time of each sub-route is predicted, and the distribution time of the rider between the position when the rider receives the order and the user address is further predicted. According to the method and the device, the whole distribution route is divided into a plurality of sub-routes, wherein the sub-routes comprise the sub-routes from the parking position to the user address, the distribution time length of each sub-route is predicted respectively, the whole distribution time length is predicted, and the accuracy of the predicted distribution time length can be improved.
Description
Technical Field
The present application relates to the field of big data processing technologies, and in particular, to a method and an apparatus for predicting delivery duration.
Background
With the rapid development of the internet, the application based on the internet industry brings great convenience to people, the development of the take-out industry enables people to obtain needed articles without going out of home, however, in the take-out industry, the logistics instant delivery is one of the very important business scenes, and how to improve the service quality of users and improve the precision of predicting the delivery duration of riders is a more serious problem.
In the prior art, in order to prompt the delivery time of takeaway of a user, a merchant roughly predicts the approximate delivery time of takeaway according to the delivery range and the preparation time of takeaway, so that the actual delivery time of a rider is greatly different from the predicted delivery time, and the service quality of the user is reduced.
Disclosure of Invention
In view of this, an object of the embodiments of the present application is to provide a method and an apparatus for predicting delivery duration, in which after a takeaway order requested by a target user is received, a position of a rider when the rider receives the order is acquired, a merchant address and an address of the target user are extracted from the takeaway order, a parking position of the rider is determined according to a plurality of historical orders including the user address, a plurality of sub-routes in a delivery route are determined according to the position of the rider when the rider receives the order, the merchant address, the user address and the parking position, the delivery duration of each sub-route is predicted, and then the delivery duration between the position of the rider when the rider reaches the user address is predicted. According to the method and the device, the whole distribution route is divided into a plurality of sub-routes, wherein the sub-routes comprise the sub-routes from the parking position to the user address, the distribution time length of each sub-route is predicted respectively, the whole distribution time length is predicted, and the accuracy of the predicted distribution time length can be improved.
Mainly comprises the following aspects:
in a first aspect, an embodiment of the present application provides a method for predicting delivery duration, where the method for predicting delivery duration includes:
after receiving a takeout order requested by a target user, acquiring the position of a rider carrying the takeout order when taking the order, and extracting a merchant address of a merchant receiving the takeout order and a user address of the target user from the takeout order;
determining the parking position of the rider according to a plurality of historical takeout orders containing the user address; the parking position is a position when the vehicle parks in a preset range of the user address;
determining a plurality of sub-routes in a distribution route according to the position of the rider when the rider receives the order, the merchant address, the user address and the parking position;
predicting the distribution time length of each sub-route in the plurality of sub-routes according to the determined plurality of sub-routes in the distribution routes;
and predicting the delivery time length between the rider and the user address from the position when the rider receives the order based on the predicted delivery time length of each of the plurality of sub-routes.
In one possible embodiment, the determining the parking position of the rider based on a plurality of historical take-out orders including the user address comprises:
obtaining a plurality of parking positions from a plurality of historical takeaway orders containing the user address;
counting, for each of the plurality of parking positions, a number of occurrences of each parking position;
and selecting the parking position with the largest occurrence frequency from the plurality of parking positions, and determining the parking position as the parking position of the rider.
In one possible embodiment, for each historical take-away order, the predictive method further comprises determining a parking location in each historical take-away order according to the steps of:
according to the historical rider speed in the historical takeout order, in a preset range of the user address, converting the speed of the historical rider from riding speed to walking speed, and determining the position as a first position;
converting the speed of the historical rider from walking speed to riding speed within a preset range of the user address, and determining the position as a second position;
and determining a parking position in the historical takeaway order according to the first position and the second position.
In one possible implementation, the plurality of sub-processes includes:
a sub-route for the rider to reach the merchant address, a sub-route for the rider from the merchant address to the parking location, a sub-route for the rider from the parking location to the user address.
In a possible embodiment, the prediction method further comprises predicting a delivery duration of a sub-journey of the rider to reach the merchant address according to the following steps:
determining a first distance from the position of the rider to the address of the merchant according to the position of the rider when taking the order and the address of the merchant;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the delivery time length of the sub-route of the rider reaching the address of the merchant according to the quotient value between the first route and the average riding speed of the rider.
In one possible embodiment, the prediction method further comprises predicting a delivery duration of the rider for a sub-journey from the merchant address to the parking location according to the following steps:
determining a second distance from the merchant address to the parking position according to the merchant address and the parking position;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the distribution time length of the sub-distance from the address of the merchant to the parking position of the rider according to the quotient value between the second distance and the average riding speed of the rider.
In one possible embodiment, the prediction method further comprises predicting a delivery duration of a sub-journey of the rider from the parking position to the user address according to the following steps:
determining a third route from the parking position to the user address according to the parking position and the user address;
obtaining a historical parking position and a historical user address in each historical takeout order from a plurality of historical takeout orders comprising the rider, obtaining the distance from the historical parking position to the historical user address in the historical takeout order and the corresponding time, and calculating the average walking speed of the rider according to the plurality of distances in the plurality of historical takeout orders comprising the rider and the corresponding time of each distance;
and predicting the delivery time length of the rider from the parking position to the sub-route of the user address according to the quotient value between the third route and the average walking speed.
In one possible embodiment, the predicting a delivery time period between the rider arriving at the user address from a position at the time of order taking based on predicting a delivery time period for each of the plurality of sub-ranges includes:
and predicting the delivery time length from the position of the rider to the user address when the rider receives the order according to the predicted delivery time length of each sub-journey and the current weather information.
In a second aspect, an embodiment of the present application further provides a device for predicting delivery duration, where the device for predicting delivery duration includes:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring the position of a rider carrying a takeout order when the rider receives the takeout order after receiving the takeout order requested by a target user, and extracting the merchant address of a merchant receiving the takeout order and the user address of the target user from the takeout order;
a first determining module, configured to determine a parking position of the rider according to a plurality of historical take-out orders including the user address; the parking position is a position when the vehicle parks in a preset range of the user address;
the second determining module is used for determining a plurality of sub-routes in the distribution route according to the position of the rider when the rider receives the order, the merchant address, the user address and the parking position;
the first prediction module is used for predicting the distribution time length of each sub-route in the plurality of sub-routes according to the determined plurality of sub-routes in the distribution routes;
and the second prediction module is used for predicting the delivery time length between the rider arriving at the user address from the order receiving position based on the predicted delivery time length of each of the plurality of sub-routes.
In one possible embodiment, the first determination module is configured to determine the parking position of the rider according to the following steps:
obtaining a plurality of parking positions from a plurality of historical takeaway orders containing the user address;
counting, for each of the plurality of parking positions, a number of occurrences of each parking position;
and selecting the parking position with the largest occurrence frequency from the plurality of parking positions, and determining the parking position as the parking position of the rider.
In one possible embodiment, the first determining module is further configured to determine a parking position in each historical take-away order according to the following steps:
according to the historical rider speed in the historical takeout order, in a preset range of the user address, converting the speed of the historical rider from riding speed to walking speed, and determining the position as a first position;
converting the speed of the historical rider from walking speed to riding speed within a preset range of the user address, and determining the position as a second position;
and determining a parking position in the historical takeaway order according to the first position and the second position.
In one possible implementation, the plurality of sub-processes includes:
a sub-route for the rider to reach the merchant address, a sub-route for the rider from the merchant address to the parking location, a sub-route for the rider from the parking location to the user address.
In a possible embodiment, the first prediction module is configured to predict a delivery duration of a sub-route of the rider to the merchant address according to the following steps:
determining a first distance from the position of the rider to the address of the merchant according to the position of the rider when taking the order and the address of the merchant;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the delivery time length of the sub-route of the rider reaching the address of the merchant according to the quotient value between the first route and the average riding speed of the rider.
In one possible embodiment, the first prediction module is configured to predict a delivery duration of the rider for a sub-journey from the merchant address to the parking location, according to the following steps:
determining a second distance from the merchant address to the parking position according to the merchant address and the parking position;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the distribution time length of the sub-distance from the address of the merchant to the parking position of the rider according to the quotient value between the second distance and the average riding speed of the rider.
In one possible embodiment, the first prediction module is configured to predict a delivery duration of a sublay of the rider from the parking position to the user address according to the following steps:
determining a third route from the parking position to the user address according to the parking position and the user address;
obtaining a historical parking position and a historical user address in each historical takeout order from a plurality of historical takeout orders comprising the rider, obtaining the distance from the historical parking position to the historical user address in the historical takeout order and the corresponding time, and calculating the average walking speed of the rider according to the plurality of distances in the plurality of historical takeout orders comprising the rider and the corresponding time of each distance;
and predicting the delivery time length of the rider from the parking position to the sub-route of the user address according to the quotient value between the third route and the average walking speed.
In a possible implementation manner, the second prediction module is specifically configured to:
and predicting the delivery time length from the position of the rider to the user address when the rider receives the order according to the predicted delivery time length of each sub-journey and the current weather information.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device runs, the processor and the memory communicate with each other through the bus, and the machine-readable instructions are executed by the processor to perform the steps of the method for predicting a delivery duration described in the first aspect or any one of the possible embodiments of the first aspect.
In a fourth aspect, this embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the step of predicting the delivery duration described in the first aspect or any one of the possible implementation manners of the first aspect.
In the embodiment of the application, the whole distribution route is segmented, the sub-routes from the parking position to the user address are included, the distribution time length of each sub-route is respectively predicted, the whole distribution time length is further predicted, and the accuracy of the predicted distribution time length can be improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart illustrating a method for predicting delivery duration according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating a delivery sub-route provided by an embodiment of the present application;
fig. 3 is a schematic structural diagram illustrating a device for predicting delivery duration according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and that steps without logical context may be performed in reverse order or concurrently. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that, before the present application is proposed, in the prior art, in order to prompt the delivery time for the takeout of the user, the merchant roughly predicts the approximate time for the takeout delivery according to the delivery range and the preparation time for the takeout, which causes the actual delivery time of the rider to be much different from the predicted delivery time, and reduces the service quality of the user.
In order to solve the problems, the whole distribution route is divided into a plurality of sub routes, wherein the sub routes comprise the sub routes from the parking position to the user address, the distribution time length of each sub route is respectively predicted, the whole distribution time length is further predicted, and the accuracy of the predicted distribution time length can be improved.
For the convenience of understanding of the present application, the technical solutions provided in the present application will be described in detail below with reference to specific embodiments.
Fig. 1 is a flowchart of a method for predicting delivery duration according to an embodiment of the present disclosure. The method for predicting the delivery duration comprises the following steps:
s101: after receiving a takeout order requested by a target user, acquiring the position of a rider carrying the takeout order when taking the order, and extracting a merchant address of a merchant receiving the takeout order and a user address of the target user from the takeout order.
In this step, after receiving a takeout order requested by a target user, randomly selecting one rider from a plurality of riders waiting for delivery and delivering the takeout order, and obtaining a position of the rider when taking the order, and extracting takeout merchant information, including a merchant name, a merchant address and the like, predetermined by the target user from the takeout order generated by the target user, and extracting a user address of the target user generating the takeout order.
S102: determining the parking position of the rider according to a plurality of historical takeout orders containing the user address; the parking position is a position when the vehicle parks in a preset range of the user address.
In the step, a plurality of historical takeout orders with user addresses are screened out from a large number of historical takeout orders, and a rider is determined to be close to the user address from the plurality of historical takeout orders with the user addresses, so that the rider can select to park and walk to a parking position of the user address.
It should be noted that, in real life, riders can not reach the user address provided by the target user by riding an electric vehicle due to the limitation of cell entrance guard or the reason of several floors, but select an appropriate position to park near the user address of the target user, where the appropriate position may be a parking lot or a cell door near the user address, and then walk to the user address provided by the target user, so that the fact that the riders are near the user address and park at the user address is fully considered, and the accuracy of predicting the whole delivery time of the riders is improved.
It should be noted that the parking position of the rider is obtained through a large number of historical take-out orders related to the user address, that is, the parking position associated with the user address of the target user is stored in the historical take-out orders.
S103: and determining a plurality of sub-routes in the distribution route according to the position of the rider when taking the order, the merchant address, the user address and the parking position.
In the step, according to the obtained position of the rider when taking the order, the address of the merchant extracted from the takeaway order, the user address and the determined parking position associated with the user address, a plurality of sub-routes from the position of the rider when taking the order to the whole distribution route from the position of the rider to the user address are determined.
The plurality of sub-routes can comprise a position from the rider to a merchant address, the merchant address to a parking position and the parking position to a user address of a target user when the rider receives a receipt, and can also comprise a sub-route for finding the merchant address, a sub-route for finding the user address of the target user and the like.
S104: and predicting the distribution time length of each sub-route in the plurality of sub-routes according to the determined plurality of sub-routes in the distribution route.
In the step, according to a plurality of sub-routes from the order receiving position to the whole distribution route of the address of the user, the average speed of the rider in each sub-route is counted, and the distribution time length of each sub-route is predicted in sequence.
S105: and predicting the delivery time length between the rider and the user address from the position when the rider receives the order based on the predicted delivery time length of each of the plurality of sub-routes.
In this step, based on the predicted delivery duration of each sub-route in the plurality of sub-routes from the order receiving position to the user address, the total delivery duration of the rider can be predicted by summing the delivery durations of each sub-route.
In the embodiment of the application, after a takeout order requested by a target user is received, the position of a rider is obtained when the rider receives the order, a merchant address and the address of the target user are extracted from the takeout order, the parking position of the rider is determined according to a plurality of historical orders containing the user address, a plurality of sub-routes in a delivery route are determined according to the position, the merchant address, the user address and the parking position of the rider when the rider receives the order, the delivery duration of each sub-route is predicted, and the delivery duration of the rider from the position when the rider receives the order to the user address is further predicted. According to the method and the device, the whole distribution route is divided into a plurality of sub-routes, wherein the sub-routes comprise the sub-routes from the parking position to the user address, the distribution time length of each sub-route is predicted respectively, the whole distribution time length is predicted, and the accuracy of the predicted distribution time length can be improved.
In one possible embodiment, the determining the parking position of the rider according to the plurality of historical takeaway orders including the user address of the target user in S102 includes the following steps:
step (1): obtaining a plurality of parking locations from a plurality of historical take-away orders including the user address.
In this step, a plurality of history takeout orders including the user address are screened out from a large number of history takeout orders, and a plurality of parking positions near the user address of the target user are obtained from the plurality of history takeout orders.
It should be noted that, screening out a plurality of historical takeout orders including the user address not only screens out the historical takeout orders of the user address provided by the target user, but also screens out the historical takeout orders corresponding to the user address in the same unit or the same office building and the like as the user address.
Step (2): counting, for each of the plurality of parking positions, a number of occurrences of each parking position.
In this step, the number of times of occurrence of each parking position is counted by acquiring a plurality of parking positions.
In one example, there are 3 parking locations, namely parking location a, parking location B and parking location C, near the user address of the target user obtained from 10 historical takeout orders, so that the number of occurrences of each parking location is specifically counted, that is, parking location a occurs 6 times, parking location B occurs 3 times, and parking location C occurs 1 time.
And (3): and selecting the parking position with the largest occurrence frequency from the plurality of parking positions, and determining the parking position as the parking position of the rider.
In the step, according to the occurrence frequency of each parking position, the parking position corresponding to the maximum occurrence frequency is selected and determined as the parking position of the rider near the user address in the current distribution.
In one possible embodiment, for each historical take-away order, the predictive method further comprises determining a parking location in each historical take-away order according to the steps of:
step (I): and according to the historical rider speed in the historical takeout order, converting the speed of the historical rider from the riding speed to the walking speed within the preset range of the user address, and determining the position as a first position.
In the step, in the process of delivering takeout of the current history corresponding to the history takeout order, the speed and the position of the history rider are monitored in real time through a mobile phone of the current history rider, and when the speed of the history rider is within a preset range of a user address, the position of a critical point of uniform walking speed is determined as a first position from uniform riding speed.
The preset range of the user address refers to a preset range parameter, and the preset range is a relatively small value, can be 2 km square circle, 1 km square circle and the like, and is used for representing the position near the user address.
Step (II): and in the preset range of the user address, converting the speed of the historical rider from the walking speed to the riding speed, and determining the position as a second position.
In the step, after the historical rider finishes delivering the current historical delivery takeout corresponding to the historical takeout order, the speed and the position of the historical rider are monitored in real time through a mobile phone of the historical rider, the speed of the historical rider is converted from the constant walking speed to the position of a uniform riding speed critical point, and the position is determined to be a second position.
Step (three): and determining a parking position in the historical takeaway order according to the first position and the second position.
In the step, according to the position where the historical rider is near the user address and the riding speed is changed into the walking speed, and the position where the historical rider leaves the user address and is near the user address and is converted into the riding speed from the walking speed after the historical rider finishes distribution, the two positions are the same position and are the parking position of the rider near the user address, and the parking position is determined through the two positions.
It should be noted that the first position and the second position are selected together to determine the parking position because in real life, the rider often runs to the user address after parking in order to shorten the actual delivery time, and thus, for the parking position, the change in the speed of the rider is not obvious, and thus the first position and the second position are used together to determine the parking position.
It should be further noted that when a historical takeout order occurs, the speed and the position of the historical rider are obtained in real time through the mobile phone of the current historical rider, the first position and the second position are determined by monitoring the speed change and the position of the historical rider, and the parking position determined through the first position and the second position is stored in the historical takeout order.
In one possible implementation, the plurality of sub-processes includes:
a sub-route for the rider to reach the merchant address, a sub-route for the rider from the merchant address to the parking location, a sub-route for the rider from the parking location to the user address.
In the step, a plurality of sub-routes from the position when the rider receives the order to the user address in the whole distribution route are determined according to the obtained position when the rider receives the order, the address of a merchant, the parking position and the user address of the target user, wherein the sub-routes comprise the position when the rider receives the order to the address of the merchant, the address when the rider receives the order to the parking position from the address of the merchant and the user address when the rider walks from the parking position to the target user.
In an example, referring to fig. 2, fig. 2 is a schematic diagram illustrating a delivery sub-route provided in an embodiment of the present application, where determining a plurality of sub-routes in the entire delivery route includes: the sub-route 1 is a route from a position when a rider receives a receipt to an address of a merchant, the sub-route 2 is a route from the address of the merchant to a parking position, the sub-route 3 is a route from the parking position to a user address of a target user, the whole distribution route is divided into a plurality of sub-routes in detail, distribution time of each sub-route is predicted according to the characteristics of each sub-route, the whole distribution time is further predicted, and accuracy of predicting the distribution time is improved.
In one possible implementation, in S104, the prediction method further includes predicting a delivery duration of a sub-route of the rider to the merchant address according to the following steps:
step (a 1): and determining a first distance from the position of the rider to the address of the merchant according to the position of the rider when taking the order and the address of the merchant.
In the step, the distance from the position of the rider to the address of the merchant is determined according to the position of the rider when the rider receives the order and the address of the merchant.
It should be noted that, in determining the distance between the rider from the order receiving position to the merchant address, multiple routes may be determined by using a map carried by the server, and an optimal route may be determined according to the current road condition, and then the distance corresponding to the optimal route is determined.
Step (a 2): and obtaining a plurality of riding routes of the rider and riding time corresponding to each riding route from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the riding routes and the riding time corresponding to each riding route.
In the step, a plurality of historical takeout orders containing the rider are screened out from a large number of historical takeout orders, a plurality of riding paths of the rider and riding time corresponding to each riding path are obtained from the plurality of historical takeout orders containing the rider, and the average riding speed of the rider is calculated according to the plurality of riding paths and the riding time corresponding to each riding path.
Step (a 3): and predicting the delivery time length of the sub-route of the rider reaching the address of the merchant according to the quotient value between the first route and the average riding speed of the rider.
In the step, according to the determined first distance and the average riding speed of the rider, the first distance is divided by the average riding speed of the rider, and the delivery time corresponding to the first distance can be predicted.
It should be noted that, in the actual distribution process, there may be situations where traffic lights or roads are congested, and according to the average waiting time for waiting for the traffic lights and the average waiting time for road congestion, increasing the distribution time of the first journey by a certain time may improve the accuracy of predicting the distribution time length.
In one possible embodiment, in S104, the prediction method further comprises predicting a delivery duration of the rider for a sub-journey from the merchant address to the parking location according to the following steps:
step (b 1): and determining a second distance from the merchant address to the parking position according to the merchant address and the parking position.
In the step, the distance from the address of the merchant to the parking position of the rider is determined according to the address of the merchant and the parking position.
It should be noted that, in determining the distance from the address of the merchant to the parking position, a plurality of routes may be determined by using a map carried by the server, and an optimal route may be determined according to the current road condition, and then the distance corresponding to the optimal route may be determined.
Step (b 2): and obtaining a plurality of riding routes of the rider and riding time corresponding to each riding route from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the riding routes and the riding time corresponding to each riding route.
In the step, a plurality of historical takeout orders containing the rider are screened out from a large number of historical takeout orders, a plurality of riding paths of the rider and riding time corresponding to each riding path are obtained from the plurality of historical takeout orders containing the rider, and the average riding speed of the rider is calculated according to the plurality of riding paths and the riding time corresponding to each riding path.
Step (b 3): and predicting the delivery time length of the sub-distance from the address of the merchant to the parking position of the rider according to the second distance and the average riding speed of the rider.
In the step, according to the determined second distance and the average riding speed of the rider, the second distance is divided by the average riding speed of the rider, and the delivery time corresponding to the first distance can be predicted.
It should be noted that, in the actual distribution process, there may be situations of waiting for the merchant to prepare for takeout, and situations of traffic lights or road congestion, and according to the average time of waiting for the merchant to prepare for takeout, the average time of waiting for the traffic lights, and the average waiting time of road congestion, the accuracy of predicting the distribution time length may be improved by appropriately adding a certain time to the distribution time of the second route according to the actual situation.
In one possible embodiment, in S104, the prediction method further comprises predicting a delivery duration of the rider for a sub-trip from the parking position to the user address according to the following steps:
step (c 1): and determining a third distance from the parking position to the user address according to the parking position and the user address.
In the step, the distance from the parking position to the user address of the rider is determined according to the parking position and the user address.
Step (c 2): and obtaining a historical parking position and a historical user address in each historical takeout order from a plurality of historical takeout orders comprising the rider, obtaining the distance from the historical parking position to the historical user address in the historical takeout order and the corresponding time, and calculating the average walking speed of the rider according to the plurality of distances in the plurality of historical takeout orders comprising the rider and the corresponding time of each distance.
In this step, a plurality of historical takeout orders including the rider are screened out from a large number of historical takeout orders, a historical parking position in each historical takeout order and an address of a historical user in the historical takeout order are obtained from the plurality of historical takeout orders including the rider, and a distance from the historical parking position in each historical takeout order to the address of the historical user in the historical takeout order and a corresponding time are obtained, so that the average walking speed of the rider can be calculated according to the distance from the obtained plurality of historical parking positions to the address of the historical user in the same historical takeout order and the corresponding time of each distance.
Step (c 3): and predicting the delivery time length of the rider from the parking position to the sub-route of the user address according to the quotient value between the third route and the average walking speed.
In this step, the delivery time corresponding to the third route can be predicted by dividing the third route by the average walking speed of the rider according to the determined third route and the average walking speed of the rider.
It should be noted that, in the actual delivery process, there may be a case of waiting for the target user to receive takeaway, a case of waiting for the elevator, or a case of searching for the target user unit building, and according to the average time of waiting for the target user, the average time of waiting for the elevator, and the average time of searching for the target user unit building, adding a certain time to the delivery time of the third journey may improve the accuracy of predicting the delivery time length.
In one possible embodiment, in S104, the predicting a delivery time period between the rider arriving at the user address from a position at the time of order pickup based on predicting a delivery time period for each of the plurality of sub-routes includes:
and predicting the delivery time length from the position of the rider to the user address when the rider receives the order according to the predicted delivery time length of each sub-journey and the current weather information.
In this step, after the delivery time of each sub-route is predicted, the current weather conditions, such as severe weather conditions in rainy days, snow days, or windy days, are considered to be more difficult than the normal weather conditions in the delivery process, so that in the severe weather in rainy days, snow days, or windy days, the accuracy of predicting the delivery time length can be improved by properly increasing the predicted delivery time.
Based on the same application concept, a device for predicting delivery duration corresponding to the method for predicting delivery duration provided in the foregoing embodiment is also provided in the embodiments of the present application.
Referring to fig. 3, a schematic structural diagram of a device 300 for predicting delivery duration provided in the embodiment of the present application is shown, where as shown in fig. 3, the device 300 for predicting delivery duration provided in the embodiment of the present application includes:
an obtaining module 310, configured to obtain, after receiving a takeout order requested by a target user, a position of a rider carrying the takeout order when taking the order, and extract, from the takeout order, a merchant address of a merchant that receives the takeout order and a user address of the target user;
a first determining module 320, configured to determine a parking position of the rider according to a plurality of historical take-out orders including the user address; the parking position is a position when the vehicle parks in a preset range of the user address;
a second determining module 330, configured to determine a plurality of sub-routes in the distribution route according to the position of the rider when taking an order, the merchant address, the user address, and the parking position;
the first prediction module 340 is configured to predict, according to a plurality of sub routes in the determined distribution route, a distribution time length of each sub route in the plurality of sub routes;
a second prediction module 350, configured to predict, based on the predicted delivery duration of each of the plurality of sub-routes, a delivery duration between when the rider arrives at the user address from the position at which the order is taken.
According to the method, after a takeaway order requested by a target user is received, the position of a rider during order taking is obtained through an obtaining module 310, a merchant address and an address of the target user are extracted from the takeaway order, the parking position of the rider is determined through a first determining module 320 according to a plurality of historical orders containing the user address, a plurality of sub-routes in a delivery route are determined according to a second determining module 330 according to the position of the rider during order taking, the merchant address, the user address and the parking position, the delivery duration of each sub-route is predicted through a first predicting module 340, and the delivery duration of the rider between the position of the rider during order taking and the user address is predicted through a second predicting module 350. According to the method and the device, the whole distribution route is divided into a plurality of sub-routes, wherein the sub-routes comprise the sub-routes from the parking position to the user address, the distribution time length of each sub-route is predicted respectively, the whole distribution time length is predicted, and the accuracy of the predicted distribution time length can be improved.
In one possible embodiment, the first determining module 320 is configured to determine the parking position of the rider according to the following steps:
obtaining a plurality of parking positions from a plurality of historical takeaway orders containing the user address;
counting, for each of the plurality of parking positions, a number of occurrences of each parking position;
and selecting the parking position with the largest occurrence frequency from the plurality of parking positions, and determining the parking position as the parking position of the rider.
In one possible implementation, the first determining module 320 is further configured to determine a parking position in each historical take-away order according to the following steps:
according to the historical rider speed in the historical takeout order, in a preset range of the user address, converting the speed of the historical rider from riding speed to walking speed, and determining the position as a first position;
converting the speed of the historical rider from walking speed to riding speed within a preset range of the user address, and determining the position as a second position;
and determining a parking position in the historical takeaway order according to the first position and the second position.
In one possible implementation, the plurality of sub-processes includes:
a sub-route for the rider to reach the merchant address, a sub-route for the rider from the merchant address to the parking location, a sub-route for the rider from the parking location to the user address.
In a possible implementation, the first prediction module 340 is configured to predict a delivery duration of a sub-route of the rider to the merchant address according to the following steps:
determining a first distance from the position of the rider to the address of the merchant according to the position of the rider when taking the order and the address of the merchant;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the delivery time length of the sub-route of the rider reaching the address of the merchant according to the quotient value between the first route and the average riding speed of the rider.
In a possible embodiment, the first prediction module 340 is configured to predict a delivery duration of the rider for a sub-journey from the merchant address to the parking location according to the following steps:
determining a second distance from the merchant address to the parking position according to the merchant address and the parking position;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the distribution time length of the sub-distance from the address of the merchant to the parking position of the rider according to the quotient value between the second distance and the average riding speed of the rider.
In a possible embodiment, the first prediction module 340 is configured to predict a delivery duration of a sub-journey of the rider from the parking position to the user address according to the following steps:
determining a third route from the parking position to the user address according to the parking position and the user address;
obtaining a historical parking position and a historical user address in each historical takeout order from a plurality of historical takeout orders comprising the rider, obtaining the distance from the historical parking position to the historical user address in the historical takeout order and the corresponding time, and calculating the average walking speed of the rider according to the plurality of distances in the plurality of historical takeout orders comprising the rider and the corresponding time of each distance;
and predicting the delivery time length of the rider from the parking position to the sub-route of the user address according to the quotient value between the third route and the average walking speed.
In a possible implementation manner, the second prediction module 350 is specifically configured to:
and predicting the delivery time length from the position of the rider to the user address when the rider receives the order according to the predicted delivery time length of each sub-journey and the current weather information.
Based on the same application concept, referring to fig. 4, a schematic structural diagram of an electronic device 400 provided in the embodiment of the present application includes: a processor 410, a memory 420 and a bus 430, wherein the memory 420 stores machine-readable instructions executable by the processor 410, when the electronic device 400 is operated, the processor 410 communicates with the memory 420 through the bus 430, and the machine-readable instructions are executed by the processor 410 to perform the steps of the method for predicting the delivery duration according to any of the embodiments.
In particular, the machine readable instructions, when executed by the processor 410, may perform the following:
after receiving a takeout order requested by a target user, acquiring the position of a rider carrying the takeout order when taking the order, and extracting a merchant address of a merchant receiving the takeout order and a user address of the target user from the takeout order;
determining the parking position of the rider according to a plurality of historical takeout orders containing the user address; the parking position is a position when the vehicle parks in a preset range of the user address;
determining a plurality of sub-routes in a distribution route according to the position of the rider when the rider receives the order, the merchant address, the user address and the parking position;
predicting the distribution time length of each sub-route in the plurality of sub-routes according to the determined plurality of sub-routes in the distribution routes;
and predicting the delivery time length between the rider and the user address from the position when the rider receives the order based on the predicted delivery time length of each of the plurality of sub-routes.
In the embodiment of the application, after a takeout order requested by a target user is received, the position of a rider is obtained when the rider receives the order, a merchant address and the address of the target user are extracted from the takeout order, the parking position of the rider is determined according to a plurality of historical orders containing the user address, a plurality of sub-routes in a distribution route are determined according to the position, the merchant address, the user address and the parking position of the rider when the rider receives the order, the distribution time length of each sub-route is predicted, and the distribution time length from the position when the rider receives the order to the user address is further predicted. According to the method and the device, the whole distribution route is divided into a plurality of sub-routes, wherein the sub-routes comprise the sub-routes from the parking position to the user address, the distribution time length of each sub-route is predicted respectively, the whole distribution time length is predicted, and the accuracy of the predicted distribution time length can be improved.
Based on the same application concept, embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for predicting a delivery duration provided in the foregoing embodiments are performed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (11)
1. A method for predicting delivery duration, the method comprising:
after receiving a takeout order requested by a target user, acquiring the position of a rider carrying the takeout order when taking the order, and extracting a merchant address of a merchant receiving the takeout order and a user address of the target user from the takeout order;
determining the parking position of the rider according to a plurality of historical takeout orders containing the user address; the parking position is a position when the vehicle parks in a preset range of the user address;
determining a plurality of sub-routes in a distribution route according to the position of the rider when the rider receives the order, the merchant address, the user address and the parking position;
predicting the distribution time length of each sub-route in the plurality of sub-routes according to the determined plurality of sub-routes in the distribution routes;
and predicting the delivery time length between the rider and the user address from the position when the rider receives the order based on the predicted delivery time length of each of the plurality of sub-routes.
2. The method of predicting according to claim 1, wherein said determining a parking location of said rider based on a plurality of historical take-away orders including said user address comprises:
obtaining a plurality of parking positions from a plurality of historical takeaway orders containing the user address;
counting, for each of the plurality of parking positions, a number of occurrences of each parking position;
and selecting the parking position with the largest occurrence frequency from the plurality of parking positions, and determining the parking position as the parking position of the rider.
3. The prediction method of claim 2, wherein for each historical take-away order, the prediction method further comprises determining a parking location in each historical take-away order according to the steps of:
according to the historical rider speed in the historical takeout order, in a preset range of the user address, converting the speed of the historical rider from riding speed to walking speed, and determining the position as a first position;
converting the speed of the historical rider from walking speed to riding speed within a preset range of the user address, and determining the position as a second position;
and determining a parking position in the historical takeaway order according to the first position and the second position.
4. The prediction method of claim 1, wherein the plurality of sub-ranges comprises:
a sub-route for the rider to reach the merchant address, a sub-route for the rider from the merchant address to the parking location, a sub-route for the rider from the parking location to the user address.
5. The prediction method of claim 1, further comprising predicting a delivery duration for a sub-route for the rider to reach the merchant address according to the following steps:
determining a first distance from the position of the rider to the address of the merchant according to the position of the rider when taking the order and the address of the merchant;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the delivery time length of the sub-route of the rider reaching the address of the merchant according to the quotient value between the first route and the average riding speed of the rider.
6. The prediction method according to claim 1, further comprising predicting a delivery duration of the rider for a sub-trip from the merchant address to the parking location according to the steps of:
determining a second distance from the merchant address to the parking position according to the merchant address and the parking position;
obtaining a plurality of riding paths of the rider and riding time corresponding to each riding path from a plurality of historical takeout orders containing the rider, and calculating the average riding speed of the rider through the plurality of riding paths and the riding time corresponding to each riding path;
and predicting the distribution time length of the sub-distance from the address of the merchant to the parking position of the rider according to the quotient value between the second distance and the average riding speed of the rider.
7. The prediction method according to claim 1, further comprising predicting a delivery duration of the rider for a sub-trip from the parking position to the user address according to the following steps:
determining a third route from the parking position to the user address according to the parking position and the user address;
obtaining a historical parking position and a historical user address in each historical takeout order from a plurality of historical takeout orders comprising the rider, obtaining the distance from the historical parking position to the historical user address in the historical takeout order and the corresponding time, and calculating the average walking speed of the rider according to the plurality of distances in the plurality of historical takeout orders comprising the rider and the corresponding time of each distance;
and predicting the delivery time length of the rider from the parking position to the sub-route of the user address according to the quotient value between the third route and the average walking speed.
8. The prediction method according to claim 1, wherein the predicting of the delivery time period between the rider arriving at the user address from the position at the time of order taking based on the prediction of the delivery time period for each of the plurality of sub-routes comprises:
and predicting the delivery time length from the position of the rider to the user address when the rider receives the order according to the predicted delivery time length of each sub-journey and the current weather information.
9. An apparatus for predicting a delivery duration, the apparatus comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring the position of a rider carrying a takeout order when the rider receives the takeout order after receiving the takeout order requested by a target user, and extracting the merchant address of a merchant receiving the takeout order and the user address of the target user from the takeout order;
a first determining module, configured to determine a parking position of the rider according to a plurality of historical take-out orders including the user address; the parking position is a position when the vehicle parks in a preset range of the user address;
the second determining module is used for determining a plurality of sub-routes in the distribution route according to the position of the rider when the rider receives the order, the merchant address, the user address and the parking position;
the first prediction module is used for predicting the distribution time length of each sub-route in the plurality of sub-routes according to the determined plurality of sub-routes in the distribution routes;
and the second prediction module is used for predicting the delivery time length between the rider arriving at the user address from the order receiving position based on the predicted delivery time length of each of the plurality of sub-routes.
10. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the method of predicting a delivery duration according to any one of claims 1 to 8.
11. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, performs a method of predicting a delivery duration according to any one of claims 1 to 8.
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| CN202010440605.8A CN111612400A (en) | 2020-05-22 | 2020-05-22 | Distribution time length prediction method and device |
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| CN112381491A (en) * | 2020-12-07 | 2021-02-19 | 拉扎斯网络科技（上海）有限公司 | Method and device for obtaining delivery resource order receiving duration and electronic equipment |
| CN112583921A (en) * | 2020-12-16 | 2021-03-30 | 熊震 | Intelligent residential area access control system based on big data and method thereof |
| CN113469610A (en) * | 2021-05-28 | 2021-10-01 | 南京邮电大学 | Shortest path optimization method based on rider average meal time |
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| CN114330797A (en) * | 2020-09-27 | 2022-04-12 | 北京三快在线科技有限公司 | Distribution time length prediction method and device, storage medium and electronic equipment |
| CN114372834A (en) * | 2022-03-21 | 2022-04-19 | 广州宜推网络科技有限公司 | E-commerce platform management system and method based on big data |
| CN116596170A (en) * | 2023-07-18 | 2023-08-15 | 合肥城市云数据中心股份有限公司 | Intelligent prediction method for delivery time based on space-time attention mechanism |
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- 2020-05-22 CN CN202010440605.8A patent/CN111612400A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113762838A (en) * | 2020-09-11 | 2021-12-07 | 北京京东振世信息技术有限公司 | Aging determination method, device, server and storage medium |
| CN114330797A (en) * | 2020-09-27 | 2022-04-12 | 北京三快在线科技有限公司 | Distribution time length prediction method and device, storage medium and electronic equipment |
| CN112381491A (en) * | 2020-12-07 | 2021-02-19 | 拉扎斯网络科技（上海）有限公司 | Method and device for obtaining delivery resource order receiving duration and electronic equipment |
| CN112381491B (en) * | 2020-12-07 | 2024-01-19 | 拉扎斯网络科技（上海）有限公司 | Method and device for obtaining delivery resource order receiving time length and electronic equipment |
| CN112583921A (en) * | 2020-12-16 | 2021-03-30 | 熊震 | Intelligent residential area access control system based on big data and method thereof |
| CN113469610A (en) * | 2021-05-28 | 2021-10-01 | 南京邮电大学 | Shortest path optimization method based on rider average meal time |
| CN113469610B (en) * | 2021-05-28 | 2023-06-02 | 南京邮电大学 | Shortest path optimization method based on average dinner time of rider |
| CN114372834A (en) * | 2022-03-21 | 2022-04-19 | 广州宜推网络科技有限公司 | E-commerce platform management system and method based on big data |
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| CN116596170A (en) * | 2023-07-18 | 2023-08-15 | 合肥城市云数据中心股份有限公司 | Intelligent prediction method for delivery time based on space-time attention mechanism |
| CN116596170B (en) * | 2023-07-18 | 2023-09-22 | 合肥城市云数据中心股份有限公司 | Intelligent prediction method for delivery time based on space-time attention mechanism |
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