WO2021017609A1 - Determination of estimated time of arrival - Google Patents

Abstract

A method for determining the estimated time of arrival, relating to the technical field of computers. The method for determining the estimated time of arrival comprises: obtaining the order characteristics of the current order, the distributor characteristics of each distributor, and the accepted order characteristics of the distributor (110); estimating, by means of a pre-trained time-of-arrival estimation model, the on-time arrival probability and the transport capacity coefficient of each distributor for the current order at each preset time of arrival on the basis of the order characteristics, the distributor characteristics, and the accepted order characteristics (120); and determining the estimated time of arrival of the current order according to the obtained transport capacity and requirement data, the on-time arrival probability at each preset time of arrival, and the transport capacity coefficient (130). Since the estimated time is dynamically adjusted in combination with specific service goals, the estimated time output can balance user experience and distribution efficiency.

Description

Determine the estimated delivery time

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office, the application number is 201910696204.6, and the invention title is "Method, Apparatus, Electronic Equipment and Storage Medium for Determining Estimated Delivery Time" on July 30, 2019, and its entire contents Incorporated in this application by reference.

Technical field

This application relates to the field of computer technology, especially to determining the estimated delivery time.

Background technique

With the development of take-out and delivery services, ETA (Estimated Time of Arrival) is estimated to be able to provide take-out and delivery services to shippers (such as takeaway riders) and delivery objects (such as ordering users) The reference time improves the user experience to a certain extent. The ETA estimation method in the prior art usually adopts a combination of logistic regression model and fusion model. For example, input the sample data to be estimated into the logistic regression model to obtain output A, and at the same time according to the prediction method of the sample data Set the attribute value, input the estimated sample data into the corresponding attribute correlation model to obtain the output B, and then perform the weighted fusion of the results of A and B, and then calculate the fusion time according to the rules to make the final output .

Summary of the invention

In the first aspect, the embodiments of the present application provide a method for determining the estimated delivery time, including:

Obtain the air waybill characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the load waybill of the dispatcher;

Based on the characteristics of the waybill, the shipper and the characteristic of the baggage waybill, the pre-trained delivery time estimation model is used to estimate the punctual delivery of the current waybill by each of the shippers at each preset delivery time Probability and capacity capacity coefficient; wherein the capacity capacity coefficient represents the ability index of the dispatcher to carry at least one other waybill when delivering the current waybill;

Determine the expected delivery time of the current waybill according to the acquired capacity and demand data, the on-time delivery probability of each of the preset delivery times, and the capacity coefficient.

In the second aspect, an embodiment of the present application provides a device for determining an estimated delivery time, including:

The characteristic acquisition module is used to acquire the characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the carrying waybill of the dispatcher;

The multi-point punctual delivery probability estimation module is used to estimate the delivery time estimation model for each of the distributors based on the characteristics of the waybill, the characteristics of the dispatcher, and the characteristics of the baggage waybill. The on-time delivery probability and capacity coefficient of the waybill at each preset delivery time; wherein the capacity capacity coefficient represents the ability index of the shipper to be able to carry at least one other waybill when delivering the current waybill;

The estimated delivery time selection output module is used to determine the estimated delivery time of the current waybill based on the acquired capacity and demand data, the on-time delivery probability of each preset delivery time, and the capacity coefficient.

In the third aspect, an embodiment of the present application also discloses an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor executes the computer program when the computer program is executed. The method for determining the estimated delivery time described in the embodiment of the application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method for determining the estimated delivery time disclosed in the embodiment of the present application are disclosed.

The method for determining the estimated delivery time disclosed in the embodiments of the present application is obtained by obtaining the characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the dispatcher’s carrying waybill; then, based on the characteristics of the waybill and the dispatcher And the characteristics of the baggage waybill, through the pre-trained delivery time estimation model, predict the on-time delivery probability and the capacity coefficient of each of the distributors for the current waybill at each preset delivery time; where The capacity coefficient indicates the ability of the shipper to carry at least one other waybill when delivering the current waybill; finally, according to the obtained capacity and demand data, the on-time delivery probability of each preset delivery time and the The capacity coefficient is used to determine the estimated delivery time of the current waybill. Since the estimated time is dynamically adjusted in combination with specific business goals, the output estimated time can balance user experience and delivery efficiency.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present application more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some of the present application. Embodiments, for those of ordinary skill in the art, without creative labor, other drawings can be obtained from these drawings.

Figure 1 is a flow chart of the method for determining the estimated delivery time in the first embodiment of the present application;

FIG. 2 is a schematic diagram of a network model structure adopted in Embodiment 1 of the present application;

3 is a schematic diagram of the corresponding relationship between the preset delivery time and the on-time delivery probability of the model output in Embodiment 1 of the present application;

FIG. 4 is one of the schematic diagrams of the apparatus for determining the estimated delivery time according to the second embodiment of the present application;

FIG. 5 is the second structural diagram of the device for determining the estimated delivery time in the second embodiment of the present application;

Fig. 6 shows a block diagram of an electronic device for executing the method according to the present application; and,

Fig. 7 shows a storage unit for holding or carrying program codes for implementing the method according to the present application.

Specific embodiment

The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of this application.

Example one

An embodiment of the present application discloses a method for determining an estimated delivery time. As shown in FIG. 1, the method includes: step 110 to step 130.

Step 110: Obtain the characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the carrying waybill of the dispatcher.

The method for determining the estimated delivery time disclosed in the embodiments of the present application is applicable to multiple application fields in the distribution industry such as takeaway and logistics, so as to solve the problem of estimating the delivery time of the waybill. In this embodiment, in order to facilitate readers to understand the technical solution, the specific technical solution of the method for determining the estimated delivery time disclosed in this application will be illustrated in combination with application scenarios in the food delivery field.

The delivery person described in the embodiment of the application may be a takeaway delivery person, that is, a rider, or a delivery robot. In the following description of the embodiment of the application, the delivery person is a rider as an example to explain the specific technical solution; The waybill described in the application embodiment corresponds to the delivery task of one waybill.

In the specific implementation process, when the system generates a new waybill to be delivered, the current waybill can be generated according to the delivery task of the waybill, and the information of the current waybill includes at least geographic location information, such as the delivery start position and the delivery end position; It can also include information such as the price of the waybill.

In the embodiment of this application, the time required for the current waybill to be delivered by different riders is determined according to the information of the current waybill and the relevant information of each rider in the system. Finally, the current waybill is further determined according to the target of the delivery task to which or which the current waybill is suitable for Rider delivery, and determine the estimated delivery time when delivered by the corresponding rider.

During specific implementation, firstly, the current waybill characteristics are acquired based on the current waybill information, and the waybill characteristics include any one or more of the waybill price, delivery distance, and geographic location. Generally, the waybill information has a fixed format, and by analyzing the waybill information of the current waybill according to the fixed format, the characteristics of the current waybill can be obtained.

In the embodiment of the present application, the relevant information of the dispatcher includes at least the information of the dispatcher himself. In the embodiment of the present application, the information of the dispatcher himself is expressed through the characteristics of the dispatcher. The characteristics of the dispatcher described in the embodiments of the present application include any one or more of dispatcher level, dispatcher's dispatching ability, and dispatcher's dispatch type.

Taking the delivery person as an example of the rider, the characteristics of the rider include any one or more of the rider level, the rider's delivery ability, and the rider's delivery type. Among them, the characteristics of rider level, rider delivery ability, rider delivery type, etc. are determined by the system through analysis and calculation based on rider's historical delivery data, user comments, rider registration information and other data, and can be obtained through the system interface. The technical means for determining the characteristics of the rider level, the rider's delivery ability, the rider's delivery type and the like through analysis and calculation refer to the prior art, and will not be repeated in the embodiments of this application. Among them, the type of rider delivery refers to the type of rider who belongs to urban agency, Meituan self-operated, and crowdsourced delivery.

In other embodiments of the present application, usually when a certain shipper is carrying one or more waybills, the relevant information of the shipper also includes the information of the waybill carried by the shipper. In the embodiment of the present application, the information of the air waybill carried by the dispatcher is expressed by the characteristic of the air waybill carried by the dispatcher. In some embodiments of the present application, the characteristics of the air waybill carried by the shipper include: the air waybill characteristics of the air waybill other than the current waybill carried by the shipper; or, the characteristics of the air waybill carried by the shipper include: the The characteristics of all waybills carried by the shipper. Still taking the delivery person as the rider as an example, the characteristics of the waybill carried by the rider may include the characteristics of part of the waybill or all the waybills other than the current waybill carried by the rider. For the air waybill characteristics of each waybill carried by the rider, please refer to the aforementioned specific method of obtaining the air waybill characteristics of the current waybill, which will not be repeated here.

For example, for a takeaway waybill delivered from location A to location B, its characteristics can be expressed as F ₁ {(lng1,lat1),(lng2,lat2),value,Distance}, where (lng1,lat1) means take Meal location coordinates, (lng2, lat2) represents the delivery location coordinates, value represents the price of the waybill, and Distance represents the delivery distance. If there are N online riders in the system, the rider characteristics of each rider can be obtained separately, then the rider characteristics of rider n can be expressed as F ₂ {Level _n ,ability _n ,type _n }, where Level _n represents the level of rider n ,Ability _n represents the delivery capability of rider n, type _n represents the delivery type of rider n, N and n are natural numbers greater than 1, and n≤N. Furthermore, if a rider n is carrying two waybills, they are represented as order1 and order2 respectively. Among them, the waybill feature of the waybill order1 is represented as: order1_F1

{(lng1 _order1 ,lat1 _order1 ), (lng2 _order1 ,lat2 _order1 ),value _order1 ,Distance _order1 },

The waybill feature of order2 is expressed as: order2_F1

{(lng1 _order2 ,lat1 _order2 ),(lng2 _order2 ,lat2 _order2 ),value _order2 ,Distance _order2 }, then the characteristics of rider n's _baggage waybill can be expressed as F3{order1_F1,order1_F2}. The above-mentioned feature in the embodiment of the application is a vector representation obtained after encoding.

According to this method, for each rider, a set of feature vectors made up of the current waybill characteristics, the rider characteristics of the rider, and the rider's carrying waybill characteristics can be obtained for each rider. For example, the spliced characteristics of rider n can be expressed as F _n {F _n1 ,F _n2 ,F _n3 }.

Step 120, based on the characteristics of the waybill, the characteristics of the shipper, and the characteristics of the baggage waybill, through a pre-trained delivery time estimation model, it is estimated that each of the distributors will respond to the current waybill at each preset delivery time. Probability of on-time delivery and capacity factor.

Wherein, the capacity coefficient represents the ability index of the shipper to be able to carry at least one other waybill when delivering the current waybill.

In the embodiment of the present application, the pre-trained delivery time estimation model is used to estimate the on-time delivery probability and the capacity coefficient corresponding to each delivery time when each distributor delivers the current waybill. Wherein, the on-time delivery probability is used to indicate the probability of on-time delivery within a specified series of delivery times. The specified series of delivery times can be determined according to specific business requirements, or can be determined according to the delivery distance and delivery time data in the historical waybill data. For example, according to historical waybill data, the longest delivery time is 120 minutes, and the shortest delivery time is 0 minutes, then the specified series of delivery times can be 0 minutes, 1 minute, 2 minutes, ... 120 minutes, Alternatively, the specified series of delivery time may be a series of delivery time points such as 0 minutes, 5 minutes, 10 minutes, ... 120 minutes. The delivery time mentioned in the embodiments of the present application refers to the time required by the rider from the start position to the end position of the waybill, for example, the time required from the takeaway rider picking up the meal to the delivery.

In the embodiment of the present application, based on the characteristics of the waybill, the shipper, and the characteristic of the baggage waybill, the pre-trained delivery time estimation model is used to estimate that each of the dispatchers will have the current waybill in each forecast. Before the step of setting the on-time delivery probability and the capacity coefficient of the delivery time, it also includes: determining the characteristics of each waybill corresponding to each waybill according to the historical waybill data, the characteristics of the dispatcher of the dispatcher who delivered the waybill, and the dispatcher The piggyback air waybill feature is used as the sample data of the training sample corresponding to the corresponding air waybill; and, the sample label of the training sample corresponding to each waybill is determined according to the pickup time of each waybill in the historical air waybill data, the sample label It includes the on-time delivery probability label and the capacity coefficient label corresponding to the preset delivery time; training a delivery time estimation model based on the training sample, wherein the delivery time estimation model is a multi-class model.

During specific implementation, training samples are constructed based on historical air waybill data to train the delivery time estimation model. For example, for each historical takeaway delivery record, determine the pickup location, delivery location, and waybill price information of the waybill, and encode the waybill feature vector corresponding to the waybill record; at the same time, determine the record of the takeaway delivery record The delivery rider is further obtained, and the rider grade, rider delivery ability, and rider type information of the delivery rider are obtained and encoded to obtain the rider feature vector corresponding to the waybill record.

In some other embodiments of the present application, it is also possible to further determine the waybill information of other waybills carried by the rider who delivered the waybill at the same time based on the takeaway delivery record, that is, the waybills of other takeaway waybills that were delivered at the same time when the rider delivered the waybill. information. Usually, a certain rider will carry one or more other waybills at the same time, and the waybills carried by the rider at the same time often overlap partly on the delivery route and can share a certain part of the delivery distance. The waybill information of each waybill carried by the rider includes: pick-up location, delivery location, and waybill price information. Then, the above-mentioned characteristic information of each waybill is separately coded and spliced to obtain the characteristic vector of the rider's carrying waybill.

Next, take the aforementioned characteristics of the waybill, rider, and baggage waybill corresponding to the takeaway delivery record as sample data of the training sample generated by the takeaway record. According to this method, the sample data of the training samples generated by each historical takeaway delivery record can be determined.

Then, determine the sample label corresponding to each sample data.

The delivery time estimation model trained in the embodiment of the application is a multi-class model, and the task of the multi-class model is to predict the classification results of a piece of test data similar to sample data corresponding to multiple categories. Specific to the delivery time estimation model, the goal is to input data consisting of the air waybill characteristics, rider characteristics (ie, shipper characteristics), and carrying air waybill characteristics, and the delivery time estimation model will output multiple corresponding classification results , That is, output the on-time delivery probability of each preset delivery time and the capacity coefficient corresponding to the on-time delivery probability.

Therefore, it is necessary to further determine the on-time delivery probability of each take-out delivery record corresponding to each preset delivery time and the capacity coefficient corresponding to the on-time delivery probability as the sample label of the corresponding training sample. In the embodiment of the present application, the sample label consists of two parts, which are respectively recorded as the on-time delivery probability label and the capacity coefficient label. Among them, the on-time delivery probability label is used to indicate the on-time delivery probability of the training sample at each preset delivery time, and the capacity coefficient label is used to indicate that the rider corresponding to the training sample is delivering the waybill corresponding to the training sample The ability to carry other air waybills.

In some embodiments of the present application, the step of determining the sample label of the training sample corresponding to each waybill according to the delivery time of each waybill in the historical waybill data includes: for each waybill, according to the The delivery time of the waybill determines the value of the on-time delivery probability label of the training sample corresponding to the waybill, wherein the on-time delivery probability label is used to indicate the on-time delivery of the waybill at each preset delivery time Probability. Specifically, in the training samples of the delivery time estimation model, the on-time delivery probability label is used to indicate the classification results corresponding to each preset delivery time point.

Take the delivery time from 1 minute to 120 minutes as an example. Assuming that every minute the time changes, there is a delivery time, that is, the delivery time includes 120 delivery times from 1 minute to 120 minutes, and each of them is delivered When the time corresponds to a category predicted by the delivery time estimation model, the sample label is the classification result of on-time delivery probability corresponding to 120 delivery times. The delivery status of a certain waybill at each preset delivery time can be used as the on-time delivery probability label of the training sample corresponding to the waybill. That is, if at a certain point in time, the waybill has been delivered, the classification result of the corresponding category at that time point is 1; and the classification result corresponding to other delivery times is 0.

For example, if the delivery time of a historical waybill is 45 minutes, that is, the historical waybill has been delivered within 45 minutes of delivery time, that is, the probability of on-time delivery is 100%, then the training sample corresponding to the historical waybill will be delivered on time The classification result of the probability label corresponding to the 45th category can be set to 1, and the other classification results can be set to 0. That is, for this waybill, the on-time delivery probability tag corresponding to the preset delivery time of 45 minutes can be set to 1, and the on-time delivery probability tag corresponding to the preset delivery time of non-45 minutes can be set to 0 .

In the embodiment of the present application, it is also necessary to determine the capacity coefficient corresponding to each piece of historical waybill data at each preset delivery time according to each piece of historical waybill data.

In some embodiments of the application, the step of determining the sample label of the training sample corresponding to each waybill according to the delivery time of each waybill in the historical waybill data further includes: according to the delivery time of each waybill The delivery distance of all the waybills carried by the shipper of the waybill mentioned in the route, the delivery distance of the said waybill is divided, and each segment of the delivery distance corresponds to a segment time; according to the corresponding description of each delivery distance The segment time and the number of waybills sharing the corresponding segment's delivery route determine the delivery time of a single waybill sharing the segment's delivery route; for each waybill sharing this segment's delivery route, according to the delivery route of the waybill The sum of the delivery time of the single air waybill corresponding to each segment of the delivery route and the sum of the segment time lengths of each segment of the delivery route of the air waybill are used to determine the capacity coefficient corresponding to the air waybill; The capacity coefficient corresponding to the waybill is used as the capacity coefficient label of the training sample corresponding to the waybill.

Still taking the delivery time from 1 minute to 120 minutes as an example, assuming that every minute the time changes, there is a delivery time, that is, the delivery time includes 120 time points from 1 minute to 120 minutes, then each delivery time Corresponding to a capacity coefficient, each waybill data will correspond to 120 capacity capacity coefficients, and the capacity capacity coefficient corresponding to each delivery time constitutes the capacity capacity coefficient label of a training sample.

The following combines a piece of specific historical waybill data of rider A to illustrate the technical solution of determining the capacity coefficient label based on historical waybill data.

A specific historical air waybill data of rider A is as follows: take waybill 1 at 9:19:00; take waybill 2 after 380 seconds (ie 9:25:20); after another 900 seconds (ie 9:40:20) Send waybill 1; after another 250 seconds (ie 9:44:30), take waybill 3; after another 130 seconds (ie, 9:46:40), send waybill 2, and take waybill 4 at the same time; after another 770 seconds (Ie 9:59:30) send waybill 3; after 890 seconds (ie 10:14:20) send waybill 4. From this historical waybill data, it can be concluded that rider A is carrying 4 waybills at the same time. In the specific implementation, the delivery route of this historical waybill data can be divided into multiple delivery routes according to the time of each collection and delivery of the waybill. After the aforementioned historical air waybill data is divided, there will be 6 delivery routes, and the segment durations corresponding to each delivery route are: 380 seconds, 900 seconds, 250 seconds, 130 seconds, 770 seconds and 890 seconds.

The delivery route of each waybill may include one segment or multiple consecutive segments. For example, the delivery distance of the waybill 1 includes the first segment (380 seconds) and the second segment (900 seconds) of the delivery distance. Further, the delivery time of a single air waybill sharing the delivery distance is determined according to the segment time corresponding to each segment of the delivery distance included in the delivery distance of each waybill and the number of airway bills sharing the delivery distance. For example, the quotient of the segment duration corresponding to each segment of the delivery route and the number of waybills sharing the corresponding segment's delivery route is determined as the delivery duration of a single airway bill sharing the segment's delivery route. Take the delivery route of waybill 1 as an example. In the first segment of the delivery route, rider A only carries the waybill 1, and the delivery time of the waybill 1 corresponding to the first segment of delivery route is 380 seconds; the second segment of delivery route In, rider A is carrying waybill 1 and waybill 2, and the delivery time of waybill 1 and waybill 2 corresponding to the second segment of the delivery route are 900 seconds/2=450 seconds, respectively. According to this method, it is possible to determine the delivery time of a single air waybill corresponding to each section of the delivery distance included in the delivery distance of each waybill.

Next, for a certain waybill, the sum of the delivery time of a single waybill corresponding to each segment of the delivery route of the waybill can be shared with the segment time corresponding to each segment of the corresponding segment of the delivery route The ratio of the sum is used as the capacity coefficient corresponding to the waybill. Still taking the waybill 1 as an example, the delivery time of a single waybill corresponding to the first segment of the delivery route included in the waybill 1 is 380 seconds and the delivery time of a single waybill corresponding to the second segment of the delivery route included in the waybill 1 is 450 seconds The time obtained by the summation is 830 seconds, and the sum of the segment durations of the first segment and the second segment of the journey is 380 seconds + 900 seconds = 1280 seconds, and the capacity coefficient of the waybill 1 is 830/1280 = 0.64843750. You can determine the capacity coefficient corresponding to the delivery distance corresponding to the waybill 1 (that is, the delivery distance corresponding to the first 1280 seconds, the delivery distance corresponding to the first 380 seconds and the delivery distance corresponding to the 381th to the 1280th second) Is 0.64843750. According to this method, the capacity coefficient of the delivery route corresponding to each waybill can be determined.

Finally, for each waybill, the capacity coefficient corresponding to the waybill is used as the capacity capacity coefficient label of the training sample corresponding to the waybill. For example, for the aforementioned delivery route, the capacity coefficient of the waybill 1 is both 0.64843750. According to the foregoing method, the historical air waybill data is the data that has actually occurred, so the capacity coefficient of each delivery time of a certain waybill corresponding to the delivery route can be accurately obtained. In the implementation of this application, in order to improve the training efficiency of the model, the capacity coefficient corresponding to the final delivery time of the historical waybill is calculated, and the capacity coefficients of other preset delivery time points are determined by generalization of the model.

After determining a number of training samples based on historical waybill data, the delivery time estimation model is further trained based on the training samples.

The delivery time estimation model described in the embodiment of the application adopts a multi-target network structure, for example, a classification neural network with two targets as shown in FIG. 2 is adopted, and one of the targets is to output the classification result of the preset delivery time , Another goal is to output the capacity coefficient corresponding to each preset delivery time. Corresponding to different output targets of the classification neural network, the loss functions of the two network branches, the on-time delivery probability loss function 210 and the capacity coefficient loss function 220 are different.

As shown in FIG. 2, the delivery time estimation model further includes: a feature fusion layer 230, which is used to combine the first feature vector output by the waybill feature vectorization module 240 and the shipper feature vectorization module The second feature vector output by 250 is fused to obtain a fusion feature vector of the first preset length, and then the fusion feature vector is input to the loss functions 210 and 220, respectively, to obtain different classification results.

Wherein, the air waybill feature vectorization module 240 is further configured to integrate any one or more of the air waybill price, delivery distance, and geographic location input into the delivery time estimation model into a second forecast. Let the length of the first feature vector. The shipper feature vectorization module 250 is used to fuse the shipper feature of the shipper who delivers the current waybill with the shipper's piggy-backed waybill feature to obtain a second feature vector with a third preset length.

As shown in FIG. 2, the shipper feature vectorization module 250 further includes: a shipper feature fusion sub-module 2501, a shipper feature vectorization sub-module 2502, and a preset number of piggy-back waybill feature vectorization sub-modules 2503. Wherein, the dispatcher feature vectorization sub-module 2502 is used to fuse any one or more of the characteristic vectors of the dispatcher level, the dispatcher's dispatching ability, and the dispatcher's dispatch type into a dispatcher characteristic of the fourth preset length Vector; each of the piggybacked waybill feature vectorization sub-module 2503 is used to fuse the vector of the waybill feature of a certain waybill carried by the shipper into a first feature vector of a second preset length; the shipper feature fusion The sub-module 2501 is used for fusing the fourth preset length of the shipper feature vector and a preset number of the first feature vector output by the baggage waybill feature vectorization sub-module 2503 into a third preset length of the second feature vector.

In the training process of the delivery time estimation model, the loss function and the parameters of each feature fusion module and feature fusion sub-module are continuously adjusted through methods such as back propagation to obtain the optimal model parameters and complete the model training. The specific training process of the delivery time estimation model refers to the prior art, and the specific training process of the model is not limited in the embodiment of this application.

In other embodiments of the present application, the structure of the delivery time estimation model can also be in other forms, for example, the delivery feature fusion submodule 2501 is deleted, and the parameters of the delivery feature vectorization module 250 are adjusted by The shipper feature vectorization module 250 merges the shipper feature vector of the fourth preset length and the preset number of the first feature vector output by the baggage waybill feature vectorization submodule 2503 into a third preset length The second feature vector.

Of course, in other embodiments of the present application, a specific network structure can also be used to integrate the features of the waybill, the shipper, and the shipper’s baggage waybill, which can also solve the technical problem to be solved by this application and achieve the same The technical effects are not listed one by one in the embodiments of this application.

Taking the delivery person as the rider for example, after the delivery time estimation model training is completed, when estimating the on-time delivery probability of the current waybill, the characteristics of the current waybill, the characteristics of rider A, and the burden of rider A The air waybill features are input to the delivery time estimation model, and the delivery time estimation model will output the on-time delivery probability of rider A at each preset delivery time when the current waybill is delivered by rider A, and rider A's delivery office State the capacity coefficient of the current air waybill at each preset delivery time. In the same way, it is possible to estimate the on-time delivery probability and capacity coefficient of each rider at each preset delivery time when delivering the current waybill.

During the implementation of this application, in order to match the actual delivery status of the air waybill and refine the adjustment of the delivery time, by processing the on-time delivery probability corresponding to the preset delivery time output by the delivery time estimation model, each forecast is obtained. Set the probability of on-time delivery for a period of time before the delivery time.

So far, the on-time delivery probability and capacity coefficient of the current waybills within different delivery times have been determined when different riders deliver the current waybill. Among them, the corresponding table of the estimated rider, the preset delivery time and the on-time delivery probability is shown in Figure 3. In Figure 3, the first column on the left represents each preset delivery time; starting from the second column on the left, the data in each column identifies the punctual delivery within different delivery time periods when the corresponding rider delivers the current waybill Probability; starting from the second row from top to bottom, the data in each row represents the on-time delivery probability of different riders in the delivery time period on the left. For example, the value 99% in the bottom left corner of Figure 3 means that within 90 minutes, the probability that rider A will deliver the current waybill on time is 99%; the probability that rider A will deliver the current waybill on time in each time period is the leftmost column .

Step 130: Determine the expected delivery time of the current waybill according to the acquired capacity and demand data, the on-time delivery probability of each of the preset delivery times, and the capacity coefficient.

In some embodiments of the present application, the determination of the estimated delivery time of the current waybill based on the acquired capacity and demand data, the on-time delivery probability of each of the preset delivery times, and the capacity capacity coefficient includes : Determine the ratio of demand to capacity; if the ratio meets the preset ratio condition, determine the estimated delivery time of the current waybill according to the on-time delivery probability; if the ratio does not meet the preset ratio condition, then The capacity coefficient determines the estimated delivery time of the current waybill.

The role of the estimated delivery time is to adjust the balance between user experience and delivery efficiency. With sufficient capacity, reducing the expected delivery time can improve user experience. When the capacity is tight, extending the estimated delivery time can increase the capacity of the entire system. In the implementation of this application, firstly, the demand and capacity are defined to obtain the relationship data between demand and capacity, and then the system is guided to select the appropriate estimated delivery time for output, which effectively balances the delivery efficiency of the user experience and makes the output expected delivery Explainable time is stronger.

In some embodiments of the present application, the capacity and demand data of the distribution system can be obtained, and then the estimated time adjustment is made based on the obtained capacity and demand data. For example, the system defines a requirement as: the sum of the number of waybills within a specified geographic area, and defines a capacity as: the number of deliverers (such as the number of riders) that meet the conditions within the specified geographic area. Since demand and transportation capacity are different in size and magnitude, it is necessary to normalize the demand and transportation capacity into dimensionless numbers. In some embodiments of the present application, a normalization method in the prior art (such as a maximum-minimum method) may be used to normalize the demand and the capacity, and then calculate the ratio of the demand and the capacity. Furthermore, an appropriate estimated delivery time can be selected according to the ratio of demand and capacity.

In the foregoing steps, it has been determined that when each rider takes the current waybill, the on-time delivery probability at each preset delivery time and the capacity coefficient corresponding to each preset delivery time have been determined, and then based on the obtained capacity and demand data Choose a more appropriate estimated delivery time for output.

In some embodiments of the present application, if the ratio satisfies a preset ratio condition, determining the estimated delivery time of the current waybill according to the on-time delivery probability further includes: if the ratio satisfies the preset ratio Condition, the shortest delivery time corresponding to the on-time delivery probability meeting the preset probability condition is determined as the estimated delivery time of the current waybill. If the ratio does not meet the preset ratio condition, determining the estimated delivery time of the current waybill according to the capacity coefficient, further comprising: if the ratio does not meet the preset ratio condition, determining the closest to all The delivery time corresponding to the capacity coefficient of the ratio is used as the estimated delivery time of the current waybill.

To meet the preset ratio condition, the ratio of demand to capacity is less than or equal to the preset coefficient threshold, and the preset probability condition is that the on-time delivery probability is greater than 95%. Illustrate specific technical solutions for adjusting the estimated time of output based on the relationship between demand and capacity . Among them, the preset coefficient threshold can be determined according to offline operation experience, for example, set to 80%. When the calculated demand and capacity ratio meets the preset ratio condition (such as less than 85%), it means that the current capacity is surplus and there are more riders to choose from. The shortest delivery time required when the probability of on-time delivery is greater than 95% will be indicated. As the estimated delivery time of the current waybill, the rider information corresponding to the shortest delivery time is also output. In other embodiments of the present application, it is also possible to select the delivery time corresponding to the nearest rider as the estimated delivery time of the current waybill in the rider corresponding to the on-time delivery probability meeting the preset probability condition, At the same time, the rider information of the rider with the closest distance is output.

When the capacity is sufficient, under the premise of satisfying the user experience, the delivery time and the allocation of riders can be dynamically adjusted, which can further improve the delivery efficiency of the system.

When the calculated demand and capacity ratio does not meet the preset ratio condition (such as greater than 85%), it indicates that the current capacity is insufficient. For example, if all the riders are in full-load delivery, then the capacity coefficient corresponding to the closest ratio is selected. The delivery time is used as the estimated delivery time of the current waybill, and the rider information corresponding to the capacity coefficient closest to the above ratio is output. In other embodiments of the present application, when the calculated ratio between demand and capacity does not meet the preset ratio condition, the delivery time corresponding to the minimum value of the capacity coefficient greater than the above ratio can also be selected as the The estimated delivery time of the current waybill, and output the rider information corresponding to the minimum capacity coefficient greater than the above ratio.

The capacity coefficient reflects the rider’s ability to carry the air waybill at a certain time, and the ratio of capacity to demand reflects the distribution demand of the distribution system. When capacity is insufficient, the rider whose capacity coefficient matches the ratio of capacity and demand is selected for order distribution , Follow the law of consistency of the distribution pressure between the rider and the distribution system, so that the distribution system can operate stably, and at the same time, maximize the user experience.

The method for determining the estimated delivery time disclosed in the embodiments of the present application is to obtain the current waybill characteristics, the rider characteristics of each rider, and the rider's carrying waybill characteristics; then, based on the waybill characteristics, the rider characteristics, and the carrying The characteristics of the air waybill, through the pre-trained delivery time estimation model, estimate the on-time delivery probability and capacity coefficient of each rider for the current air waybill at each preset delivery time; wherein, the capacity capacity coefficient represents The rider is able to carry at least one capability index of other waybills when delivering the current waybill; finally, it is determined according to the obtained capacity and demand data, the on-time delivery probability of each preset delivery time, and the capacity coefficient Since the estimated delivery time of the current waybill is dynamically adjusted in combination with specific business goals, the output estimated time can balance user experience and delivery efficiency. In the prior art, when determining the estimated delivery time, at least there is a defect that the determined estimated delivery time cannot balance the balance between user experience and delivery efficiency. The method for determining the estimated delivery time disclosed in the embodiments of the present application is based on a universal delivery time estimation model suitable for multiple scenarios, and based on demand and capacity in real time, it is determined whether to take care of user experience or prefer delivery efficiency, and then , To further select the estimated delivery time of the model and the corresponding rider, so that the output estimated delivery time has a certain ability to explain the business.

On the other hand, due to the subsequent selection of the estimated time of the model based on capacity and demand, the model adopts abstract waybill characteristics, rider characteristics, and rider’s carrying waybill characteristics to deliver on time for different riders at each preset delivery time. The probability of arrival is not limited to the specific scenarios of the waybill, and there is no need to retrain the delivery time estimation model for different scenarios, which improves the efficiency of model training and reduces development costs.

On the other hand, this application adopts a multi-classification model, and the delivery time estimation model outputs on-time delivery probabilities of multiple preset delivery times, providing a more fine-grained selection range for subsequent adjustment strategies, which can improve the expected delivery of output The accuracy of time.

Example two

This embodiment discloses a device for determining the estimated delivery time. As shown in FIG. 4, the device includes:

The characteristic acquisition module 410 is used to acquire the characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the carrying waybill of the dispatcher;

The multi-point punctual delivery probability estimation module 420 is used to estimate the delivery time estimation model for each of the distributors based on the characteristics of the waybill, the characteristics of the shipper, and the characteristics of the baggage waybill. The on-time delivery probability and capacity coefficient of the current waybill at each preset delivery time; wherein the capacity capacity coefficient represents the ability index of the shipper to be able to carry at least one other waybill when delivering the current waybill;

The estimated delivery time selection output module 430 is configured to determine the estimated delivery time of the current waybill based on the acquired capacity and demand data, the on-time delivery probability of each preset delivery time, and the capacity coefficient.

In some embodiments of the present application, the estimated delivery time selection output module 430 is further configured to:

Determine the ratio of demand to capacity;

If the ratio satisfies the preset ratio condition, the estimated delivery time of the current waybill is determined according to the on-time delivery probability;

If the ratio does not meet the preset ratio condition, the estimated delivery time of the current waybill is determined according to the capacity coefficient.

Further, if the ratio satisfies a preset ratio condition, the step of determining the expected delivery time of the current waybill according to the on-time delivery probability includes:

If the ratio satisfies the preset ratio condition, the shortest delivery time corresponding to the on-time delivery probability that satisfies the preset probability condition is determined as the estimated delivery time of the current waybill.

Further, if the ratio does not satisfy a preset ratio condition, the step of determining the estimated delivery time of the current waybill according to the capacity coefficient includes:

If the ratio does not meet the preset ratio condition, the delivery time corresponding to the capacity coefficient closest to the ratio is determined as the estimated delivery time of the current waybill.

In some embodiments of the present application, as shown in FIG. 5, the device further includes:

The training sample generating module 440 is used to determine the characteristics of the waybill corresponding to each waybill, the characteristics of the shipper who delivered the waybill, and the characteristics of the baggage waybill of the distributor according to the historical waybill data, as the training sample corresponding to the corresponding waybill And, according to the delivery time of each waybill in the historical waybill data, determine the sample label of each training sample corresponding to the waybill, the sample label including the punctuality corresponding to the preset delivery time Delivery probability label and capacity coefficient label;

The estimation model training module 450 is configured to train a delivery time estimation model based on the training samples, wherein the delivery time estimation model is a multi-classification model.

In some embodiments of the present application, the step of determining the sample label of the training sample corresponding to each waybill according to the delivery time of each waybill in the historical waybill data includes:

For each waybill, the value of the on-time delivery probability label of the training sample corresponding to the waybill is determined according to the delivery time of the waybill, where the on-time delivery probability label is used to indicate that the waybill is in each pre-order. Set the probability of on-time delivery of the delivery time.

In some embodiments of the present application, the step of determining the sample label of the training sample corresponding to each of the waybills according to the delivery time of each waybill in the historical waybill data further includes:

According to the delivery distance of all the waybills carried by the shipper of the waybill in the delivery distance of each waybill, segment the delivery distance of the waybill, and each delivery distance corresponds to a segment time;

Determine the delivery time of a single air waybill sharing that segment of the delivery distance according to the segment duration corresponding to each segment of the delivery distance and the number of waybills sharing the corresponding segment of the delivery distance;

For each waybill sharing this section of the delivery route, the sum of the delivery time of the individual waybill corresponding to each section of the delivery route of the waybill and each section of the delivery route of the waybill The sum of the segment durations of the journey to determine the capacity coefficient corresponding to the waybill;

The capacity coefficient corresponding to the waybill is used as the capacity capacity coefficient label of the training sample corresponding to the waybill.

In some embodiments of the present application, the characteristics of the waybill include any one or more of the price of the waybill, the delivery distance, and the geographic location; the characteristics of the dispatcher include: the rank of the dispatcher, the dispatching ability of the dispatcher, and the dispatcher Any one or more of the delivery types; the characteristics of the waybill carried by the dispatcher include: the characteristics of the waybill other than the current waybill carried by the dispatcher.

The device for determining the expected delivery time disclosed in the embodiments of the present application is used to implement the steps of the method for determining the expected delivery time described in the first embodiment of the present application. For the specific implementation of each module of the device, refer to the corresponding steps. I won't repeat it here.

The device for determining the estimated delivery time disclosed in the embodiments of the present application obtains the characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the load-bearing waybill of the dispatcher; then, based on the characteristics of the waybill and the dispatcher And the characteristics of the baggage waybill, through the pre-trained delivery time estimation model, predict the on-time delivery probability and the capacity coefficient of each of the distributors for the current waybill at each preset delivery time; where The capacity coefficient indicates the ability of the shipper to carry at least one other waybill when delivering the current waybill; finally, according to the obtained capacity and demand data, the on-time delivery probability of each preset delivery time and the The capacity coefficient is used to determine the estimated delivery time of the current waybill. Since the estimated time is dynamically adjusted in combination with specific business goals, the output estimated time can balance user experience and delivery efficiency. Based on a universal delivery time estimation model suitable for multiple scenarios, it is determined in real time whether to take care of user experience or prefer delivery efficiency based on demand and capacity, and then further select the estimated delivery time of the model and the corresponding dispatcher , So that the estimated delivery time output has a certain ability to explain the business.

On the other hand, due to the subsequent selection of the estimated time of the model based on the capacity and demand, the model adopts the abstract waybill feature, the shipper's feature, and the shipper’s baggage waybill feature for different shippers to preset delivery times at each The on-time delivery probability is not limited to the specific scenarios of the waybill, and there is no need to retrain the delivery time estimation model for different scenarios, which improves the efficiency of model training and reduces development costs.

On the other hand, this application adopts a multi-classification model, and the delivery time estimation model outputs on-time delivery probabilities of multiple preset delivery times, providing a more fine-grained selection range for subsequent adjustment strategies, which can improve the expected delivery of output The accuracy of time.

Correspondingly, this application also discloses an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor. When the processor executes the computer program, the implementation is as in this application. The method for determining the estimated delivery time of the first embodiment. The electronic device may be a PC, a mobile terminal, a personal digital assistant, a tablet computer, etc.

The application also discloses a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the method for determining the estimated delivery time as described in the first embodiment of the application are realized.

The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment.

The method and device for determining the estimated delivery time provided by the present application are described in detail above. Specific examples are used in this article to explain the principles and implementation of the present application. The description of the above embodiments is only used to help understand the present application. The method of application and its core idea; meanwhile, for those skilled in the art, according to the idea of this application, there will be changes in the specific implementation and scope of application. In summary, the content of this specification should not be understood It is a restriction on this application.

The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the objectives of the solutions of the embodiments. Those of ordinary skill in the art can understand and implement it without creative work.

Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions can be embodied in the form of software products, which can be stored in computer-readable storage media, such as ROM/RAM, magnetic A disc, an optical disc, etc., include a number of instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiment.

Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the electronic device according to the embodiments of the present application. In an embodiment of the present application, it may be implemented as a device or device program (for example, a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for realizing the present application may be stored on a computer-readable medium, or may have the form of one or more signals. Such signals can be downloaded from Internet websites, or provided on carrier signals, or provided in any other form.

For example, FIG. 6 shows an electronic device that can implement the method according to the present application. The electronic device traditionally includes a processor 620 and a computer program product in the form of a memory 610 or a computer-readable medium. The memory 610 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), EPROM, hard disk, or ROM. The memory 610 has a storage space 6101 for executing the program code 6102 of any method step in the above method. For example, the storage space 6101 for program codes may include various program codes 6102 for implementing various steps in the above method. These program codes can be read out from or written into one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. Such a computer program product is usually a portable or fixed storage unit as described with reference to FIG. 7. The storage unit may have storage segments, storage spaces, etc. arranged similarly to the storage 620 in the electronic device of FIG. 6. The program code can be compressed in an appropriate form, for example. Generally, the storage unit includes computer-readable code 6102', that is, code that can be read by a processor such as 610. When the computer-readable code runs on an electronic device, it causes the electronic device to perform the determination described above. The steps in the method of estimated delivery time.

The “one embodiment”, “an embodiment” or “one or more embodiments” referred to herein means that a specific feature, structure, or characteristic described in combination with the embodiment is included in at least one embodiment of the present application. In addition, please note that the word examples "in one embodiment" herein do not necessarily all refer to the same embodiment.

In the instructions provided here, a lot of specific details are explained. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and technologies are not shown in detail, so as not to obscure the understanding of this specification.

In the claims, any reference signs placed between parentheses should not be constructed as a limitation to the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of multiple such elements. The application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims enumerating several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. do not indicate any order. These words can be interpreted as names.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application, not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions recorded in the foregoing embodiments are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims (11)

1. A method of determining the estimated delivery time, including:

   Obtaining the characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the carrying waybill of the dispatcher;

   Based on the characteristics of the waybill, the shipper and the characteristic of the baggage waybill, the pre-trained delivery time estimation model is used to estimate the punctual delivery of the current waybill by each of the shippers at each preset delivery time Probability and capacity capacity coefficient; wherein the capacity capacity coefficient represents the ability index of the dispatcher to carry at least one other waybill when delivering the current waybill;

   Determine the expected delivery time of the current waybill according to the acquired capacity and demand data, the on-time delivery probability of each of the preset delivery times, and the capacity coefficient.
2. The method according to claim 1, wherein the estimated delivery time of the current waybill is determined based on the acquired capacity and demand data, the on-time delivery probability of each of the preset delivery times, and the capacity coefficient The steps include:

   Determine the ratio of demand and capacity according to the obtained capacity and demand data;

   If the ratio satisfies the preset ratio condition, the estimated delivery time of the current waybill is determined according to the on-time delivery probability;

   If the ratio does not meet the preset ratio condition, the estimated delivery time of the current waybill is determined according to the capacity coefficient.
3. The method according to claim 2, wherein if the ratio satisfies a preset ratio condition, the step of determining the estimated delivery time of the current waybill according to the on-time delivery probability comprises:

   If the ratio satisfies the preset ratio condition, the shortest delivery time corresponding to the on-time delivery probability that satisfies the preset probability condition is determined as the estimated delivery time of the current waybill.
4. The method according to claim 2, wherein if the ratio does not meet a preset ratio condition, the step of determining the estimated delivery time of the current waybill according to the capacity coefficient comprises:

   If the ratio does not meet the preset ratio condition, the delivery time corresponding to the capacity coefficient closest to the ratio is determined as the estimated delivery time of the current waybill.
5. According to the method according to any one of claims 1 to 4, the delivery time estimation model is trained in the following manner:

   Determine the characteristics of the waybill corresponding to each waybill, the characteristics of the shipper who delivered the waybill, and the characteristics of the baggage waybill of the distributor according to the historical waybill data, as the sample data of the training sample corresponding to the corresponding waybill; and The delivery time of each waybill in the historical waybill data determines the sample label of the training sample corresponding to each said waybill, and the sample label includes the on-time delivery probability label and the capacity coefficient corresponding to the preset delivery time label;

   Train a delivery time estimation model based on the training samples.
6. The method according to claim 5, wherein the step of determining the sample label of the training sample corresponding to each of the waybills according to the delivery time of each waybill in the historical waybill data comprises:

   For each waybill, the value of the on-time delivery probability label of the training sample corresponding to the waybill is determined according to the delivery time of the waybill, where the on-time delivery probability label is used to indicate that the waybill is in each pre-order. Set the probability of on-time delivery of the delivery time.
7. The method according to claim 5 or 6, wherein the step of determining the sample label of the training sample corresponding to each of the waybills according to the delivery time of each waybill in the historical waybill data further comprises:

   According to the delivery distance of all the waybills carried by the shipper of the waybill in the delivery distance of each waybill, segment the delivery distance of the waybill, and each delivery distance corresponds to a segment time;

   Determine the delivery time of a single air waybill sharing that segment of the delivery distance according to the segment duration corresponding to each segment of the delivery distance and the number of waybills sharing the corresponding segment of the delivery distance;

   For each waybill sharing this section of the delivery route, the sum of the delivery time of the individual waybill corresponding to each section of the delivery route of the waybill and each section of the delivery route of the waybill The sum of the segment durations of the journey to determine the capacity coefficient corresponding to the waybill;

   The capacity coefficient corresponding to the waybill is used as the capacity capacity coefficient label of the training sample corresponding to the waybill.
8. A device for determining the estimated delivery time, including:

   The characteristic acquisition module is used to acquire the characteristics of the current waybill, the characteristics of the dispatcher of each dispatcher, and the characteristics of the carrying waybill of the dispatcher;

   The multi-point punctual delivery probability estimation module is used to estimate the delivery time estimation model for each of the distributors based on the characteristics of the waybill, the characteristics of the dispatcher, and the characteristics of the baggage waybill. The on-time delivery probability and capacity coefficient of the waybill at each preset delivery time; wherein the capacity capacity coefficient represents the ability index of the shipper to be able to carry at least one other waybill when delivering the current waybill;

   The estimated delivery time selection output module is used to determine the estimated delivery time of the current waybill based on the acquired capacity and demand data, the on-time delivery probability of each preset delivery time, and the capacity coefficient.
9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor implements any one of claims 1 to 7 when the computer program is executed The method of determining the estimated delivery time.
10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining an estimated delivery time according to any one of claims 1 to 7.
11. A computer program, comprising computer-readable code, which when the computer-readable code runs on an electronic device, causes the electronic device to execute the determination of the estimated delivery time according to any one of claims 1 to 7 Methods.

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