CN112801401A

CN112801401A - Method and device for determining time information of route

Info

Publication number: CN112801401A
Application number: CN202110179170.0A
Authority: CN
Inventors: 王中伟; 张兴斌; 邓克捷
Original assignee: Beijing Orange Heart Infinite Technology Development Co ltd
Current assignee: Beijing Orange Heart Infinite Technology Development Co ltd
Priority date: 2021-02-09
Filing date: 2021-02-09
Publication date: 2021-05-14

Abstract

According to an embodiment of the present disclosure, a method, an apparatus, a device, a storage medium, and a program product for determining time information of a route are provided. The method proposed herein comprises: dividing the target route into a plurality of segments based on a plurality of stopping positions in the target route; determining a segment navigation time for each segment based on an expected start time for each segment of the plurality of segments, the expected start time being a predetermined time or determined based on a segment navigation time for a previous segment; and determining time information for the target route based on the segment navigation time for each segment, the time information indicating at least one of a total navigation time for the target route or an expected arrival time for at least one of the stop locations. In this way, the influence of different starting times on the navigation time can be taken into account, so that more accurate time information of the route can be determined.

Description

Method and device for determining time information of route

Technical Field

Embodiments of the present disclosure relate generally to the field of computer technology, and more particularly, relate to a method, apparatus, device, storage medium, and program product for determining time information of a route.

Background

With the development of the era, route planning has become a fundamental technical problem concerned by many industries. For example, navigation applications require planning of a transit route for people from an origin to a destination point. Logistics distribution requires planning of distribution routes for multiple distribution points.

In the course of route planning, it is common to rely on time information between different candidate routes to select a more optimal route. For example, one may desire to select the route with the shortest transit time as the navigation route. Therefore, how to more accurately determine the time information of the route becomes the intersection point of current interest.

Disclosure of Invention

According to some embodiments of the present disclosure, a scheme of determining time information of a route is provided.

In a first aspect of the disclosure, a method of determining time information for a route is provided. The method comprises the following steps: dividing the target route into a plurality of segments based on a plurality of stopping positions in the target route; determining a segment navigation time for each segment based on an expected start time for each segment of the plurality of segments, the expected start time being a predetermined time or determined based on a segment navigation time for a previous segment; and determining time information for the target route based on the segment navigation time for each segment, the time information indicating at least one of a total navigation time for the target route or an expected arrival time for at least one of the stop locations.

In a second aspect of the disclosure, an apparatus for determining a navigation time is provided. The device includes: a segment dividing module configured to divide the target route into a plurality of segments based on a plurality of stopping positions in the target route; a first determining module configured to determine a segment navigation time for each segment based on an expected start time of each segment of the plurality of segments, the expected start time being a predetermined time or determined based on a segment navigation time of a previous segment; and a second determination module configured to determine time information for the target route, the time information indicating at least one of a total navigation time for the target route or an expected arrival time for the at least one stopping location.

In a third aspect of the present disclosure, there is provided an electronic device comprising one or more processors and memory for storing computer-executable instructions for execution by the one or more processors to implement a method according to the first aspect of the present disclosure.

In a fourth aspect of the present disclosure, a computer-readable storage medium is provided having computer-executable instructions stored thereon, wherein the computer-executable instructions, when executed by a processor, implement a method according to the first aspect of the present disclosure.

In a fifth aspect of the present disclosure, a computer program product is provided comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to the first aspect of the present disclosure.

According to the embodiments of the present disclosure, the embodiments of the present disclosure determine the corresponding segment navigation time based on the start time of each segment in the route, thereby enabling more accurate determination of the time information of the route.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:

FIG. 1 illustrates a block diagram of an example environment in which embodiments of the present disclosure can be implemented;

FIG. 2 illustrates a flow diagram of a process of determining time information for a road in accordance with some embodiments of the present disclosure;

fig. 3A and 3B illustrate schematic diagrams of a process of determining temporal information according to some embodiments of the present disclosure;

FIG. 4 illustrates a schematic diagram of constructing a candidate time set, according to some embodiments of the present disclosure;

FIG. 5A illustrates a schematic diagram of local sampling according to some embodiments of the present disclosure;

FIG. 5B illustrates a local fully connected graph determined by local sampling according to some embodiments of the present disclosure;

FIG. 6A shows a schematic diagram of local sampling according to further embodiments of the present disclosure;

FIG. 6B illustrates a partial full connectivity graph determined by partial sampling according to further embodiments of the present disclosure;

FIG. 7 illustrates a block diagram of an apparatus to determine a navigation time in accordance with some embodiments of the present disclosure; and

FIG. 8 illustrates a block diagram of an electronic device in which one or more embodiments of the disclosure may be implemented.

Detailed Description

Some example embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As discussed above, in the course of route planning, time information between different candidate routes is often relied upon to select a more optimal route. For example, one may desire to obtain time information about the total navigation time of a route.

Some conventional approaches generally only consider determining such a total navigation time based on the traffic state of each road segment at the time of departure from the starting point. However, during the course of the vehicle traveling along the route, the traffic conditions of the subsequent route may change, which results in a large difference between the time required for actual travel and the previously estimated total navigation time. This will greatly affect the user experience.

Furthermore, in scenarios such as the distribution of goods, such navigation time estimation errors will also affect the normal distribution of goods, possibly resulting in an increase in user dissatisfaction.

In view of this, embodiments of the present disclosure propose a scheme for determining time information of a route. In this scheme, first, the target route is divided into a plurality of segments based on a plurality of stop positions in the target route. Then, a segment navigation time for each segment is determined based on an expected start time for each segment of the plurality of segments, the expected start time being a predetermined time instant or determined based on a segment navigation time of a previous segment. The segmented navigation time of each segment can be used to determine time information for the target route, wherein the time information indicates at least one of a total navigation time for the target route or an expected arrival time for at least one stop location.

According to such an aspect, the embodiments of the present disclosure determine the corresponding segment navigation time based on the start time of each segment in the route, thereby enabling more accurate determination of the time information of the route.

Some example embodiments of the disclosure will now be described with continued reference to the accompanying drawings.

Example Environment

FIG. 1 illustrates a block diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. As shown in fig. 1, the environment 100 includes a first computing device 110 configured to generate time information 120 for a target route 150. In some implementations, the first computing device 110 may be any suitable type of electronic device. Illustratively, the first computing device 110 may be a server for planning a delivery path.

In some implementations, as shown in FIG. 1, target route 150 includes a plurality of stop locations, e.g., start point 132, and a plurality of waypoints 140-1 through 140-5 (referred to individually or collectively as waypoints 140). In the context of logistics distribution, the origin 132 may represent a warehouse or a loading location, etc.

In some implementations, the plurality of approach points 140 may be indicated by coordinate values to correspond to specific locations in the real world where goods are to be delivered. Alternatively, a plurality of waypoints 140 may also be represented by POIs, for example.

For example, in a community group-buying scenario, the delivery location 140 may represent a location corresponding to a "group leader" of the order, and the group-buying application needs to arrange the vehicle to deliver the corresponding goods to multiple "group leaders". As another example, in the context of courier logistics, delivery location 140 may also represent, for example, a location where couriers or logistics need to be delivered.

In a navigation scenario, the starting point 132 may represent a starting position of navigation, the emphasis point 134 may represent a destination position of navigation, and the multiple waypoints 140 may represent a position where a stop is needed.

As shown in fig. 1, the first computing device 110 may determine the time information 120 for the target route 150. In some implementations, the time information 120 may include, for example, a total navigation time 122 for the target route 150. Additionally or alternatively, the time information 120 may also include, for example, an expected arrival time 124 of at least one stopping location in the target route 150.

The specific process of determining the time information 120 of the target route 150 will be described in detail below in conjunction with fig. 2.

Example procedure

A process of determining time information of a route according to an embodiment of the present disclosure will be described in detail below with reference to fig. 2. Fig. 2 shows a schematic diagram of a process 200 of determining time information for a route, according to some embodiments of the present disclosure. For ease of discussion, the process of determining time information for a route is discussed with reference to FIG. 1. Process 200 may be performed, for example, at first computing device 110 shown in fig. 1. It should be understood that process 200 may also include blocks not shown and/or may omit blocks shown. The scope of the present disclosure is not limited in this respect.

As shown in FIG. 2, at block 202, first computing device 110 divides the target route into a plurality of segments 155 based on a plurality of stopping locations in target route 150.

Illustratively, as shown by target route 150 in FIG. 1, first computing device 110 may divide target route 150 into a plurality of segments 155-1, 155-2, 155-3, 155-4, and 155-5 based on, for example, a plurality of waypoints 140.

At block 204, the first computing device 110 determines a segment navigation time for each of the plurality of segments 155 based on an expected start time for each segment, where the expected start time is a predetermined time or determined based on the segment navigation time of the previous segment.

The process of determining a segment navigation time will be described below with reference to fig. 3A, which illustrates a schematic diagram 300A of determining time information for a route, according to some embodiments of the present disclosure.

As shown in FIG. 3A, for a first segment 155-1 of the plurality of segments 155, the first computing device 110 may determine an expected start time for segment 155-1, e.g., based on an expected departure time from the origin 132. In the example of fig. 3A, first computing device 110 may determine, for example, that the expected time of the distributor leaving origin 132 is 7:00 AM.

Accordingly, the first computing device 110 can determine the navigation time for segment 155-1 based on the expected start time of segment 155-1. In some implementations, first computing device 110 may, for example, facilitate a map service to determine that at 7:00AM, the navigation time for segment 155-1 is 10 minutes.

In still other implementations, the first computing device 110 can also determine the navigation time of the segment 155-1 from a plurality of sets of candidate navigation times that are expected to be built, for example. In some implementations, multiple sets of candidate navigation times may be constructed, for example, for different time ranges.

Illustratively, sets of candidate navigation times corresponding to (7:00-7:30AM), (7:30-8:00AM), (8:00-8:30AM), (8:30-9:00AM), (9:00-9:30AM), (9:30-10:00AM), (10:00-10:30AM), and (10:30-11:00AM), respectively, which respectively include navigation times required from one stay position to another stay position at corresponding time periods, may be previously constructed. The specific construction process of the navigation time set will be described in detail below with reference to fig. 4 to 6, and will not be described in detail here.

In some implementations, the first computing device 110 can determine a target set of navigation times from the plurality of candidate sets of navigation times that correspond to the expected start time, where the target set of navigation times indicates at least navigation times of the plurality of segments within the respective time range.

Taking segment 155-1 as an example, the first computing device 110 may determine a set of candidate navigation times corresponding to a time range (7:00-7:30AM) from a plurality of sets of candidate navigation times as the set of target navigation times, for example.

Additionally, the first computing device 110 may determine a segmented navigation time based on the set of target navigation times. Illustratively, the navigation time that the first computing device 110 may query from the target set of navigation times to segment 155-1 is 10 minutes.

It should be understood that the first computing device 110 may iteratively determine the navigation times for the plurality of segments 155. As shown in FIG. 3A, for segment 155-2, first computing device 110 may first determine that the start time of segment 155-2 is 7:10AM, for example, from the start time of segment 150-1 and the navigation time (10 minutes), and determine that the navigation time of segment 155-2 is 30 minutes using a map service or from a target set of navigation times corresponding to 7:10AM, based on the manner discussed above.

In some implementations, the start time of segment 155-2 may also take into account, for example, the expected dwell time at dwell point 140-1. For example, in a logistics delivery scenario, the first computing device 110 may determine the expected residence time based on the goods that need to be delivered at the stop point 140-1. Accordingly, first computing device 110 may determine the start time of segment 155-2 based on the start time of segment 155-1, the navigation time, and the expected dwell time at dwell point 140-1.

Based on a similar process, the first computing device 110 determines the navigation time of segment 155-3 to be 20 minutes based on, for example, a start time of 7:40AM for segment 155-3 and based on a map service or a target set of navigation times corresponding to 7:10AM (e.g., a set of candidate navigation times corresponding to a time range (7:30-8:00 AM)). Similarly, the first computing device 110 may also determine that the navigation time for segment 155-4 is 20 minutes and the navigation time for segment 155-5 is 18 minutes.

At block 206, the first computing device 110 determines time information for the target route 150 based on the segment navigation time for each segment 155, the time information indicating at least one of a total navigation time for the target route 150 or an expected arrival time for a plurality of stop locations.

In some implementations, the first computing device 110 may determine that the total navigation time of the target route 150 is 98 minutes, for example, from the sum of the segment navigation times of the segments 155.

In still other implementations, the first computing device 110 may determine the expected arrival time for each dwell location, for example, from the segment navigation time for each segment 155: the expected arrival time for dwell point 140-1 is 7:10AM, the expected arrival time for dwell point 140-2 is 7:40AM, the expected arrival time for dwell point 140-3 is 8:00AM, the expected arrival time for dwell point 140-4 is 8:20AM, and the expected arrival time for dwell point 140-5 is 8:38 AM.

Based on the methods discussed above, embodiments of the present disclosure can account for different segments of a route being driven through at different time periods. By determining the segment navigation time of each segment in the corresponding time phase, the embodiment of the disclosure can more accurately determine the time information of the route, thereby providing better support for navigation or logistics distribution.

In some implementations, as discussed above, the plurality of stop locations includes a plurality of delivery locations at which the goods are to be delivered.

In some implementations, the first computing device 110 may determine whether the target route (also referred to as a delivery route) satisfies a predetermined delivery time constraint based on the time information 120. The delivery time constraint includes at least one of: a first time constraint associated with a total navigation time, or a second time constraint associated with an expected arrival time. If it is determined that the target route does not satisfy the delivery time constraint, the first computing device 110 may adjust the target route.

For convenience of comparison with the route shown in fig. 3A, the process of adjusting the delivery route will be described below with reference to fig. 3B, which shows a schematic diagram 300B of determining time information of the route according to some embodiments of the present disclosure.

As shown in fig. 3B, the first computing device 110 may determine that the total navigation duration for the delivery route is 125 minutes. In some implementations, the first computing device 110 may determine, for example, that the total navigation duration for the delivery route exceeds a predetermined threshold, thereby determining that the delivery route needs to be adjusted.

In some implementations, the first computing device 110 may determine, for example, that the expected arrival time of the stop point 140-4 (i.e., the delivery location) is 9:05 AM. If the second time constraint indicates that the consignee corresponding to stop point 140-4 requires the shipment to be received 9 o' clock ago, then the computing device 110 may determine that the time information for the target route does not satisfy the second time constraint.

In some implementations, the first computing device 110 may adjust a delivery order of the plurality of delivery locations in the target route. For example, the first computing device 110 may adjust the delivery route shown in fig. 3B to the delivery route shown in fig. 3A, thereby increasing the efficiency of the delivery.

In some implementations, the first computing device 110 may also, for example, adjust a plurality of delivery locations included in the target route. For example, the first computing device 110 may remove one or more of the plurality of delivery locations to satisfy the time constraint. In some implementations, the removed one or more delivery locations may be scheduled for delivery to other delivery parties, for example.

As another example, the first computing device 110 may also add one or more delivery locations, for example, or swap one or more delivery locations with a plurality of delivery locations for which other delivery parties are responsible, thereby increasing the efficiency of the delivery.

In this manner, embodiments of the present disclosure may generate more reasonable delivery routes. Illustratively, the first computing device 110 may frame the delivery route of fig. 3B over the delivery route shown in fig. 3A, thereby increasing the efficiency of the cargo delivery. Comparing fig. 3A and 3B, it can be seen that the stay point 140-2 to the stay point 140-3 is, for example, the out-of-town direction, which requires 20 minutes of navigation time at the early peak, and the stay point 140-3 to the stay point 140-2 is the process direction, which requires 45 minutes of navigation time at the early peak.

By taking into account the segment navigation time of each segment, embodiments of the present disclosure may generate a more efficient delivery route.

In some implementations, when determining that the target route is a delivery route that meets the time constraint, the first computing device 110 may also determine an expected delivery time associated with a target delivery location of the plurality of delivery locations. For example, taking FIG. 3A as an example, the first computing device 110 may determine that the expected delivery time for the stop point 140-5 is 8:38 AM. Accordingly, the computing device 110 may provide information regarding the expected delivery time to the terminal devices associated with the target delivery locations. For example, the computing device 110 may send information that the expected delivery time of the goods is 8 o' clock 38am to the terminal device of the "consignee" corresponding to the stop point 140-5 to enable the "consignee" to prepare ahead of time.

Construction of candidate navigation time sets

A process of constructing a plurality of sets of candidate navigation times used for determining the segmented navigation times above according to an embodiment of the present disclosure will be described below with reference to fig. 4.

FIG. 4 illustrates a diagram 400 of constructing multiple sets of candidate navigation times according to some embodiments of the present disclosure. As shown in fig. 4, a ring computing device 420 may acquire a set of locations 410. In some implementations, the collection construction device 420 can be any suitable type of electronic device. Illustratively, the collection construction device 420 may be the same or a different computing device as the first computing device 110 in FIG. 1.

In some implementations, the set of locations 410 may include a plurality of stop locations in the target route. In some implementations, a set of locations 410 may be indicated by coordinate values to correspond to locations in the real world. Illustratively, the location 410 may represent a location where goods are to be shipped. For example, in a community group buying scenario, location 410 may represent a location corresponding to a "group leader" of a mosaic, and the group buying application needs to arrange for vehicles to dispatch the corresponding goods to multiple "group leaders". As another example, in the context of courier logistics, location 410 may also represent, for example, a location where couriers or logistics need to be delivered.

In some implementations, the computing device 420 needs to determine the navigation time between each of the set of locations 410 for the needs of scheduling vehicles and planning routes. However, as mentioned above, in an actual scenario, the number of locations 410 may be large, making it difficult for the computing device 420 to determine the full navigation time by invoking the map service 430.

According to embodiments of the present disclosure, the computing device 420 may utilize the mapping service 130 to construct the set of sampled navigation times 440 based on the manner of sampling. As shown in fig. 4, the set of sampled navigation times 440 may include navigation times 445 between a pair of locations determined from the sampling from the set of locations 410. It should be understood that such a navigation time 445 may include a two-way navigation time between two locations (i.e., both a navigation time from a first location to a second location and a navigation time from a second location to a first location). Alternatively, such a navigation time 445 may also include only one-way navigation times between two locations.

It is noted that there may be a large difference in navigation times for the same route in different time ranges. For example, the navigation times of the same road segment may differ by several times during peak hours and off-peak hours.

To improve the accuracy of the navigation time, the second computing device 420 may construct a plurality of sampled navigation time sets 440 corresponding to different time ranges. Illustratively, the second computing device 420 may determine multiple time ranges as desired. For example, the second computing device 420 may divide the time range by one period of half an hour, and accordingly, from 7:00AM to 11:00AM may be divided into time ranges T1, T2, T3, T4, T5, T6, T7, T8, exemplified by T1 and T8, where T1 represents the time ranges 7:00AM to 7:30AM and T8 represents 10:30AM to 11:00 AM. Accordingly, the second computing device 420 may construct 8 sets of sampled navigation times corresponding to the above 8 time ranges. It should be understood that such a division is merely exemplary, and any other suitable division may be employed.

In some implementations, the plurality of sets of sampled navigation times 440 can be represented by a data structure, such as a directed graph. As will be described in detail below, the sampling used by the present disclosure may be based on local sampling and connectivity sampling, thereby ensuring that the directed graph constructed based on the sampling is globally connected and has sufficient local detail. Furthermore, embodiments of the present disclosure can significantly reduce the amount of calls to the map service 430, thereby reducing network overhead, as compared to conventionally determining the total navigation time based on the map service 430 to construct a full communication graph between all locations.

As shown in fig. 4, when, for example, a navigation time between location a and location B is not determined based on sampling, the computing device 420 may estimate a target navigation time between location a and location B based on other associated navigation times 445. It should be understood that although in fig. 4 the target navigation time is only shown as comprising a one-way navigation time, the target navigation time may also comprise a navigation time from location B to location a, for example.

As shown in fig. 4, the second computing device 420 may construct a plurality of candidate navigation time sets 450 based on the target navigation time and the plurality of sample candidate times 440. The plurality of sets of candidate navigation times 450 may correspond to different time ranges and indicate navigation times for each two locations in the set of locations 410 within the respective time range.

Process for determining target navigation time

A process of determining a target navigation time according to an embodiment of the present disclosure will be described below. For convenience of description, the process of determining the target navigation time will be described below by taking the set of sampling navigation times 440 corresponding to one time range as an example.

In some implementations, the computing device 420 may determine a set of sampled navigation times 440 associated with a set of locations 410 based on the mapping service 430. The set of sampled navigation times 440 includes navigation times 445 associated with pairs of sample locations determined based at least on local sampling and connectivity sampling for a set of locations. Local sampling is used to determine the proximity associated with a location in the set of locations 410 to construct a sampling location pair, and connectivity sampling constructs a sampling location pair based on sequential selection of the set of locations 410.

In some implementations, the local sampling may be performed based on a geographic grid. A process of local sampling based on a geographic grid will be described below with reference to fig. 5A and 5B. Fig. 5A illustrates a schematic diagram 500A of local sampling according to some embodiments of the present disclosure.

As shown in fig. 5, for a target location 520 in the set of locations 410, the computing device 420 may determine a target geographic grid 515 corresponding to the target location.

In some implementations, the different coordinate regions may be divided into a plurality of geographic grids 510 having a predetermined size. Such a geographic grid 510 may have different shapes, and may be generally square or regular hexagonal. Accordingly, for the target location 520, the second computing device 420 may determine a corresponding target geographic grid based on its coordinates.

Additionally, the second computing device 420 may determine a proximity location associated with the target location based on a proximity geographic grid in proximity to the target geographic grid 515.

In some implementations, the second computing device 420 may, for example, determine a geographic grid positionally adjacent to the target geographic grid 515 as the neighboring geographic grid. Taking fig. 5A as an example, the second computing device 420 may determine, for example, 6 geographic grids surrounding the target geographic grid 515 as neighboring geographic grids and determine the locations 525 included in these neighboring geographic grids as neighboring locations associated with the target location 520. Instead, the location 530 will not be determined as a proximity location associated with the target location 520.

In some implementations, to ensure sufficiency of local sampling, the second computing device 420 may, for example, sequentially traverse the multi-tiered grid of the target geographic grid 515 for local sampling. Specifically, the second computing device 420 may first obtain the neighboring 6 geographic grids of the target geographic grid 515 and obtain the locations included therein as the proximity locations.

If the number of proximate locations does not exceed the predetermined threshold, the second computing device 420 may further obtain locations in 12 geographic grids that are outside of the 6 geographic grids as proximate locations.

In some implementations, to reduce the amount of calls to the mapping service 150, the second computing device 420 may iteratively perform such traversal until the obtained proximity location reaches a predetermined number threshold.

In some implementations, to avoid the obtained proximate location being too far from the target location 520, the second computing device 420 may also define the number of layers of the traversed geographic grid such that the grid distance of the obtained proximate geographic grid from the target geographic grid is less than a predetermined distance threshold, where the grid distance may indicate a distance between center points of the geographic grid. Illustratively, the second computing device 420 may terminate after traversing the 2-tier geographic grid regardless of the number of acquired proximate locations.

In some implementations, the second computing device 420 may also consider both traversal termination conditions at the same time and terminate the traversal process when one of the traversal termination conditions is satisfied.

In some implementations, the second computing device 420 can construct a sampling location pair based on the target location 520 and the determined proximity location 525. In some implementations, the second computing device 420 may, for example, construct at least one sampling location pair corresponding to each two of the target location 520 and the proximity location 525.

In some implementations, the second computing device 420 may perform the above geographic grid-based local sampling process on some or all of the set of locations 110. It should be appreciated that when the navigation times for two locations have been determined based on a previous sampling process, the navigation times for the two locations will not be repeated in subsequent sampling processes.

As shown in fig. 5B, the second computing device 420 may construct a local full connectivity graph 500B based on the target location 520 and the determined proximity location 525. Each vertex in the all-unicom graph 500B represents a target location 520 or an adjacent location 525 and each edge (one-way or two-way) represents a navigation time (one-way navigation time or two-way navigation time) between two locations determined based on the map service 150.

In some implementations, the local sampling may be performed based on distance. A process of local sampling based on distance will be described below with reference to fig. 6A and 6B. Fig. 6A illustrates a schematic diagram 600A of local sampling according to some embodiments of the present disclosure.

In some implementations, for a target location 610 in the set of locations 410, the second computing device 420 may determine distances of other locations in the set of locations 410 from the target location 610.

Additionally, the second computing device 420 may determine a predetermined number of nearby locations 620 from a set of locations based on the ranking of distances. Illustratively, the second computing device 420 may select, for example, the 20 locations that are closest to the target bit 610 as the proximity locations 620.

Alternatively, the second computing device 420 may determine, for example, a location for which the distance is less than a predetermined distance threshold as the proximity location 620.

In some implementations, the second computing device 420 may construct a sampling location pair based on the target location 610 and the determined proximity location 620. In some implementations, the second computing device 420 may, for example, construct at least one sampling location pair corresponding to each two of the target location 610 and the proximate location 620.

In some implementations, the second computing device 420 may perform the above distance-based local sampling process on some or all of the set of locations 410. It should be appreciated that when the navigation times for two locations have been determined based on a previous sampling process, the navigation times for the two locations will not be repeated in subsequent sampling processes.

As shown in fig. 4B, the second computing device 420 may construct a local full connectivity graph 600B based on the target location 620 and the determined proximity location 620. Each vertex in the all-unicom graph 600B represents a target location 610 or an adjacent location 620, and each edge (one-way or two-way) represents a navigation time (one-way navigation time or two-way navigation time) between two locations determined based on the map service 430.

In some implementations, where some locations are relatively isolated, the geographic grid-based local sampling may not result in sufficient pairs of sampled locations, and the second computing device 420 may also perform both geographic grid-based local sampling and distance-based location sampling on some or all of the set of locations 410, thereby ensuring local connectivity at a single location.

In some implementations, to ensure that the determined directed graph is globally connected based on the sampling locations, and that a path between two locations can always be found in the directed graph, the second computing device 420 also needs to perform connectivity sampling for a set of locations 410.

In some implementations, the second computing device 420 may perform connectivity sampling based on a random sequential selection of the set of locations 410. In particular, the second computing device 420 may select a first location from the set of locations 410 and iteratively perform the following process until the set of locations 410 are all selected: selecting a second location from the remaining locations in the set of locations that are not selected; constructing a sampling position pair based on the first position and the second position; and determining the second location as the new first location.

For example, when 100 locations are included in a group of locations, the second computing device 420 may first randomly select one location from the 100 locations as a starting location and then select a subsequent location from the remaining 99 locations and construct a sampling location pair based on the starting location and the subsequent location. Subsequently, the second computing device 420 may continue to randomly pick a location from the remaining 98 locations and construct a sample location pair with the previously selected location until all 100 locations are selected.

In some implementations, if the determined navigation time is a bidirectional navigation time based on the sample position pair, the second computing device 420 can ensure that the directed graph constructed based on the sequential selection is connected.

In some implementations, if the determined navigation time based on the sampling position pair is a one-way navigation time, the second computing device 420 can also construct a sampling position pair based on the last selected position and the start position, thereby constructing a one-way closed loop, and the directed graph that can be constructed is connected.

In some implementations, the second computing device 420 may, for example, perform multiple sequential-selection-based connectivity sampling, thereby enriching global connectivity.

In some implementations, the second computing device 420 can also perform, for example, random sampling for a set of locations to determine pairs of sample locations, where the random sampling is used to randomly construct pairs of sample locations based on the set of locations. Such random sampling is also referred to as monte carlo random sampling, and in particular, the second computing device 420 may randomly select a pair of locations from a set of locations and determine the pair of locations as a pair of sampled locations. It should be appreciated that the second computing device 420 may perform multiple random samplings to enhance the connectivity of the constructed directed graph.

It can be seen that based on the sampling process discussed above, embodiments of the present disclosure are on the order of o (N) with the map service invoked, where N represents the number of locations in a set of locations. This is in contrast to conventional O (N)²) Compared with the calling magnitude, the calling pressure and the network overhead for the map service are greatly reduced.

In some implementations, if the set of sampled navigation times 440 does not include a target navigation time between a pair of target locations, the second computing device 420 may determine the target navigation time based on a plurality of associated navigation times 445 in the set of sampled navigation times 440.

In some implementations, the second computing device 420 can determine a plurality of associated navigation times associated with a pair of target locations from the set of sampled navigation times based on a shortest path algorithm. As discussed above, the second computing device 420 may construct a directed graph based on the set of sampled navigation times 440. Accordingly, the second computing device 420 may utilize any suitable shortest path algorithm to determine a shortest path for a pair of destination locations in the directed graph and obtain a plurality of associated navigation times indicated by a plurality of edges included in the shortest path.

Additionally, the second computing device 420 may determine a target navigation time based on the determined plurality of associated navigation times. For example, when the target navigation time from location a to location B is not included in the set of sampled navigation times 140, the second computing device 420 may determine that the shortest path from location a to location B is "location a > location C > location B" and that the navigation time from location a to location C is 1 minute 30 seconds and the navigation time from location C to location B is 30 seconds, then the second computing device 420 may determine the target navigation time from location a to location B as the sum of the two navigation times from location a to location C and location C to location B.

In this manner, embodiments of the present disclosure can construct a sampled set of navigation times through local sampling and connectivity sampling, and determine navigation times between other locations based on the sampled set of navigation times, which may reduce the amount of calls to map services.

In some implementations, the second computing device 420 may also construct a plurality of sets of candidate navigation times 450 based on the plurality of sets of sampled navigation times 440 and the target navigation time. The set of candidate navigation times 450 may include navigation times between each two locations in the set of locations 410. Specifically, the second computing device 420 may, for example, complement a non-fully connected graph constructed based on the set of sampled navigation times 440 into a fully connected graph, i.e., with edge connections between each vertex in the graph.

Example apparatus and devices

Fig. 7 illustrates a schematic block diagram of an apparatus 700 for determining time information for a route, according to some embodiments of the present disclosure. The apparatus 700 may be implemented as or included in the first computing device 110, the second computing device 420, or other devices that implement the above processes of the present disclosure.

As shown in fig. 7, the apparatus 700 includes: a segment dividing module 710 configured to divide the target route into a plurality of segments based on the plurality of stopping locations in the target route. The apparatus 700 further includes a first determining module 720 configured to determine a segment navigation time for each of the plurality of segments based on an expected start time for each segment, the expected start time being a predetermined time or determined based on a segment navigation time of a previous segment. The apparatus 700 further comprises a second determining module 730 configured to determine time information of the target route based on the segmented navigation time of each segment, the time information indicating at least one of a total navigation time of the target route or an expected arrival time of at least one stop location.

In some implementations, the first determining module 720 includes: a target time set determination module configured to determine a target navigation time set corresponding to an expected start time from a plurality of candidate navigation time sets, the target navigation time set indicating at least navigation times of the plurality of segments within respective time ranges; and an aggregate query module configured to determine a segmented navigation time based on the set of target navigation times.

In some implementations, the apparatus 700 further includes: a first construction module configured to construct, based on a mapping service, a plurality of sets of sampled navigation times associated with a set of locations including a plurality of dwell locations, the plurality of sets of sampled navigation times being associated with different time ranges, the sets of sampled navigation times including navigation times associated with a plurality of pairs of sampled locations, the plurality of pairs of sampled locations being determined based at least on local sampling and connectivity sampling for the set of locations, the local sampling being used to determine proximate locations associated with locations in the set of locations to construct pairs of sampled locations, the connectivity sampling constructing pairs of sampled locations based on sequential selection of the set of locations; and a second construction module configured to construct a plurality of sets of candidate navigation times based on the plurality of sets of sampled navigation times.

In some implementations, the pairs of sampling locations are also determined based on random sampling for a set of locations, the random sampling used to randomly construct pairs of sampling locations based on the set of locations.

In some implementations, the apparatus 700 further includes a first sampling module configured to perform local sampling based on: determining, for a target location in a set of locations, a target geographic grid corresponding to the target location; determining a proximity location associated with the target location based on a proximity geographic grid in proximity to the target geographic grid; and constructing a pair of sampling locations based on the target location and the proximity location.

In some implementations, the number of proximate locations is less than a number threshold.

In some implementations, a grid distance of the neighboring geographic grid from the target geographic grid is less than a distance threshold, the grid distance indicating a distance between center points of the geographic grids.

In some implementations, the geographic grid is square or regular hexagonal.

In some implementations, the apparatus 700 further includes a second sampling module configured to perform local sampling based on: for a target location in a set of locations, determining distances of other locations in the set of locations from the target location; determining a predetermined number of nearby locations from a set of locations based on the ranking of distances; and constructing a pair of sampling locations based on the target location and the proximity location.

In some implementations, the first sampling module or the second sampling module further includes: a location pair construction module configured to construct at least one sampling location pair corresponding to each two of the target location and the nearby location.

In some implementations, the apparatus 700 further includes a third sampling module configured to perform connectivity sampling based on: selecting a first location from a set of locations; and iteratively performing the following process until a set of locations is selected: selecting a second location from the remaining locations in the set of locations that are not selected; constructing a sampling position pair based on the first position and the second position; and determining the second location as the new first location.

In some implementations, connectivity sampling is performed at least twice.

In some implementations, the second building module includes: a shortest path computation module configured to, for a set of sampled navigation times of a plurality of sets of sampled navigation times: determining a target navigation time based on the sampled navigation time set by using a most-segment path algorithm if the target navigation time between a pair of target positions is not indicated by the sampled navigation time set; and a third construction module configured to construct a set of candidate navigation times corresponding to the set of sampled navigation times based on the target navigation time.

In some implementations, the plurality of stop locations includes a plurality of delivery locations at which the goods are to be delivered.

In some implementations, the apparatus 700 further includes: a constraint judging module configured to determine whether the target route satisfies a predetermined delivery time constraint based on the time information, the delivery time constraint including at least one of: a first time constraint associated with a total navigation time, or a second time constraint associated with an expected arrival time; and a route adjustment module configured to adjust the target route in response to determining that the target route does not satisfy the delivery time constraint.

In some implementations, the route adjustment module includes: a first adjustment module configured to adjust a delivery order of the plurality of delivery locations in the target route; or a second adjustment module configured to adjust a plurality of delivery locations included in the target route.

In some implementations, the apparatus 700 further includes: a time determination module configured to determine an expected delivery time associated with a target delivery location of a plurality of delivery locations; and a providing module configured to provide information about the expected delivery time to a terminal device associated with the target delivery location.

Fig. 8 illustrates a block diagram that shows an electronic device 800 in which one or more embodiments of the disclosure may be implemented. It should be understood that the electronic device 800 illustrated in FIG. 8 is merely exemplary and should not be construed as limiting in any way the functionality and scope of the embodiments described herein. The electronic device 800 shown in fig. 8 may be included in or implemented as the first computing device 110 in fig. 1, the second computing device 420 in fig. 4, or other devices that implement the above processes of the present disclosure.

As shown in fig. 8, electronic device 800 is in the form of a general purpose computing device. The electronic device 800 may also be any type of computing device or server. The components of electronic device 800 may include, but are not limited to, one or more processors or processing units 810, memory 820, storage device 830, one or more communication units 840, one or more input devices 850, and one or more output devices 860. The processing unit 810 may be a real or virtual processor and can perform various processes according to programs stored in the memory 820. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capabilities of the electronic device 800.

Electronic device 800 typically includes a number of computer storage media. Such media may be any available media that is accessible by electronic device 800 and includes, but is not limited to, volatile and non-volatile media, removable and non-removable media. The memory 820 may be volatile memory (e.g., registers, cache, Random Access Memory (RAM)), non-volatile memory (e.g., Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory), or some combination thereof. The storage device 830 may be a removable or non-removable medium and may include a machine-readable medium, such as a flash drive, a magnetic disk, or any other medium that may be capable of being used to store information and/or data (e.g., map data) and that may be accessed within the electronic device 800.

The electronic device 800 may further include additional removable/non-removable, volatile/nonvolatile storage media. Although not shown in FIG. 8, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 820 may include a computer program product 825 having one or more program modules configured to perform the various methods or acts of the various embodiments of the disclosure.

Communication unit 840 enables communication with other computing devices over a communication medium. Additionally, the functionality of the components of the electronic device 800 may be implemented in a single computing cluster or multiple computing machines, which are capable of communicating over a communications connection. Thus, the electronic device 800 may operate in a networked environment using logical connections to one or more other servers, network Personal Computers (PCs), or another network node.

The input device 850 may be one or more input devices such as a mouse, keyboard, trackball, or the like. The output device(s) 860 may be one or more output devices such as a display, speakers, printer, or the like. Electronic device 800 may also communicate with one or more external devices (not shown), such as storage devices, display devices, etc., communication with one or more devices that enable a user to interact with electronic device 800, or communication with any devices (e.g., network cards, modems, etc.) that enable electronic device 800 to communicate with one or more other computing devices via communication unit 840, as desired. Such communication may be performed via input/output (I/O) interfaces (not shown).

According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions or a program are stored, wherein the computer-executable instructions or the program are executed by a processor to implement the above-described method or function. The computer-readable storage medium may include a non-transitory computer-readable medium. According to an exemplary implementation of the present disclosure, there is also provided a computer program product comprising computer executable instructions or a program, which are executed by a processor to implement the above described method or function. The computer program product may be tangibly embodied on a non-transitory computer-readable medium.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus, devices and computer program products implemented in accordance with the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions or programs.

These computer-executable instructions or programs may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-executable instructions or programs may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer-executable instructions or programs may be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer-implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various implementations of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing has described implementations of the present disclosure, and the above description is illustrative, not exhaustive, and not limited to the implementations disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terminology used herein was chosen in order to best explain the principles of various implementations, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand various implementations disclosed herein.

Example implementation

TS 1. a method of determining time information for a route, comprising:

dividing the target route into a plurality of segments based on a plurality of stopping positions in the target route;

determining a segment navigation time for each segment based on an expected start time for each segment of the plurality of segments, the expected start time being a predetermined time or determined based on a segment navigation time for a previous segment; and

based on the segment navigation time of each segment, time information of the target route is determined, the time information indicating at least one of a total navigation time of the target route or an expected arrival time of at least one stopping location.

TS 2. the method according to TS 1, wherein determining the segment navigation time for each segment comprises:

determining a target set of navigation times corresponding to an expected start time from a plurality of candidate sets of navigation times, the target set of navigation times being indicative of at least navigation times of the plurality of segments within respective time ranges; and

based on the set of target navigation times, a segmented navigation time is determined.

TS 3. the method according to TS 2, further comprising:

building, based on a mapping service, a plurality of sets of sampled navigation times associated with a set of locations including a plurality of dwell locations, the plurality of sets of sampled navigation times associated with different time ranges, the sets of sampled navigation times including navigation times associated with a plurality of pairs of sampled locations, the plurality of pairs of sampled locations determined based at least on local sampling and connectivity sampling for the set of locations, the local sampling used to determine proximate locations associated with locations in the set of locations to build pairs of sampled locations, the connectivity sampling used to build pairs of sampled locations based on sequential selection of the set of locations; and

a plurality of candidate navigation time sets are constructed based on the plurality of sampled navigation time sets.

4. The method of TS 3, wherein the plurality of pairs of sample locations are further determined based on random sampling for a set of locations, the random sampling used to randomly construct pairs of sample locations based on the set of locations.

TS 5. the method according to TS 3, further comprising performing local sampling based on:

for a target location in a set of locations,

determining a target geographic grid corresponding to the target location;

determining a proximity location associated with the target location based on a proximity geographic grid in proximity to the target geographic grid; and

based on the target location and the proximity location, a pair of sampling locations is constructed.

TS 6. the method according to TS 5, wherein the number of adjacent positions is less than the number threshold.

TS 7. the method according to TS 5, wherein a grid distance of the neighboring geographic grid from the target geographic grid is less than a distance threshold, the grid distance indicating a distance between center points of the geographic grids.

TS 8. the method according to TS 5, wherein the geographical grid is square or regular hexagonal.

TS 9. the method according to TS 3, further comprising performing local sampling based on:

for a target location in a set of locations,

determining distances of other locations in the set of locations from the target location;

determining a predetermined number of nearby locations from a set of locations based on the ranking of distances; and

TS 10. the method according to TS 5 or 9, wherein constructing the pair of sample locations based on the target location and the proximity location comprises:

at least one sampling location pair corresponding to each two of the target location and the proximate location is constructed.

TS 11. the method according to TS 3, further comprising performing connectivity sampling based on:

selecting a first location from a set of locations; and

the following process is iteratively performed until a set of positions is selected:

selecting a second location from the remaining locations in the set of locations that are not selected;

constructing a sampling position pair based on the first position and the second position; and

the second location is determined as the new first location.

TS 12. the method according to TS 11, wherein connectivity sampling is performed at least twice.

TS 13. the method of TS 3, wherein constructing a plurality of sets of candidate navigation times based on a plurality of sets of sampled navigation times comprises:

for a set of sampled navigation times of a plurality of sets of sampled navigation times:

if the target navigation time between the pair of target positions is not indicated by the sampling navigation time set, determining the target navigation time based on the sampling navigation time set by using a shortest path algorithm; and

based on the target navigation time, a set of candidate navigation times corresponding to the set of sampled navigation times is constructed.

TS 14. the method according to TS 1, wherein the plurality of stop locations comprises a plurality of delivery locations at which goods are to be delivered.

TS 15. the method according to TS 14, further comprising:

determining, based on the time information, whether the target route satisfies a predetermined delivery time constraint, the delivery time constraint including at least one of: a first time constraint associated with a total navigation time, or a second time constraint associated with an expected arrival time; and

in response to determining that the target route does not satisfy the delivery time constraint, the target route is adjusted.

TS 16. the method according to TS 15, wherein adjusting the target route comprises:

adjusting the delivery sequence of the plurality of delivery positions in the target route; or

A plurality of delivery locations included in the target route are adjusted.

TS 17. the method according to TS 14, further comprising:

determining an expected delivery time associated with a target delivery location of a plurality of delivery locations; and

information regarding the expected delivery time is provided to the terminal device associated with the target delivery location.

TS 18. an apparatus for determining time information for a route, comprising:

a segment dividing module configured to divide the target route into a plurality of segments based on a plurality of stopping positions in the target route;

a first determining module configured to determine a segment navigation time for each segment based on an expected start time of each segment of the plurality of segments, the expected start time being a predetermined time or determined based on a segment navigation time of a previous segment; and

a second determination module configured to determine time information for the target route based on the segment navigation time for each segment, the time information indicating at least one of a total navigation time for the target route or an expected arrival time for the plurality of stop locations.

TS 19. an electronic device, comprising:

a memory and a processor;

wherein the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement a method according to any one of TS 1 to 17.

TS 20. a computer readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executable by a processor to implement a method according to any one of TS 1 to 17.

TS 21. a computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement a method according to any one of TS 1 to 17.

Claims

1. A method of determining time information for a route, comprising:

determining a segment navigation time for each of the plurality of segments based on an expected start time for the each segment, the expected start time being a predetermined time instant or determined based on the segment navigation time for a previous segment; and

determining time information for the target route based on the segment navigation time for each segment, the time information indicating at least one of a total navigation time for the target route or an expected arrival time for at least one stop location.

2. The method of claim 1, wherein determining a segment navigation time for the each segment comprises:

determining a target set of navigation times corresponding to the expected start time from a plurality of candidate sets of navigation times, the target set of navigation times being indicative of at least navigation times of the plurality of segments within respective time ranges; and

determining the segmented navigation time based on the set of target navigation times.

3. The method of claim 2, further comprising:

building, based on a mapping service, a plurality of sets of sampled navigational time associated with a set of locations including the plurality of dwell locations, the plurality of sets of sampled navigational time associated with different time ranges, the sets of sampled navigational time including navigational times associated with a plurality of pairs of sampled locations, the plurality of pairs of sampled locations determined based at least on local sampling for the set of locations and connectivity sampling, the local sampling used to determine proximate locations associated with locations in the set of locations to build pairs of sampled locations, the connectivity sampling used to build pairs of sampled locations based on sequential selection of the set of locations; and

constructing the plurality of candidate sets of navigation times based on the plurality of sets of sampled navigation times.

4. The method of claim 1, wherein the plurality of stop locations comprises a plurality of delivery locations at which goods are to be delivered.

5. The method of claim 4, further comprising:

determining, based on the time information, whether the target route satisfies a predetermined delivery time constraint, the delivery time constraint including at least one of: a first time constraint associated with the total navigation time or a second time constraint associated with the expected arrival time; and

adjusting the target route in response to determining that the target route does not satisfy the delivery time constraint.

6. The method of claim 5, wherein adjusting the target route comprises:

adjusting a delivery order of the plurality of delivery locations in the target route; or

Adjusting the plurality of delivery locations included in the target route.

7. An apparatus for determining time information for a route, comprising:

a first determining module configured to determine a segment navigation time for each of the plurality of segments based on an expected start time of the each segment, the expected start time being a predetermined time instant or determined based on the segment navigation time of a previous segment; and

a second determination module configured to determine time information for the target route based on the segment navigation time for each segment, the time information indicating at least one of a total navigation time for the target route or an expected arrival time for at least one stop location.

8. An electronic device, comprising:

a memory and a processor;

wherein the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1 to 6.

9. A computer readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method of any one of claims 1 to 6.

10. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method of any one of claims 1 to 6.