CN112507047B - Optimal ordered path query method based on interest point preference - Google Patents
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- G06F16/22—Indexing; Data structures therefor; Storage structures
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Abstract
The invention provides an optimal ordered path query method based on interest point preference, which can efficiently solve the problem of optimal ordered path query of users under different interest point preference. The method comprises reference point inverted indexing and a cyclic optimal sub-path algorithm. Firstly, selecting a reference point for a road network, embedding nodes into the interest point by using the reference point to obtain a corresponding distance vector, and constructing a reference point inverted index. The circularly optimal sub-path search results are then used. The loop optimal sub-path algorithm comprises two stages of filtering and updating, wherein the filtering stage rapidly obtains a guide path through the optimal sub-path algorithm based on dynamic programming, and then the updating stage performs path expansion according to the guide path. The filter-update process will loop until the best ordered path that meets the user's query needs is found.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to an optimal ordered path query method based on interest point preference.
Background
With the development of smart phones and other devices and the wide application of big data, interest points in urban road networks are usually scored, and the interest points with higher scores are often better in consumption quality. Meanwhile, users continuously put forward more personalized path inquiry requirements, such as inquiring a path from s to d optimal, sequentially passing through restaurants, cinema and gas stations, and providing scores of the accessed restaurants, cinema and gas stations of not less than 4.0, 4.5 and 4.3 respectively. Satisfying such a query of a user is an optimal ordered path query method based on interest point preferences.
The optimal ordered path was first proposed by american scholars, followed by a series of research efforts to study the optimal ordered path. However, these works assume that the user has the same preference for points of interest of the same category in the road network, i.e. the score for all points of interest is the same. In fact, the user's preferences for different points of interest of the same category are different, which are reflected in different scores of the points of interest itself, the higher the score of the point of interest, the better its quality of service and consumption experience. Users are generally more inclined to access points of interest that are more scored.
Although the best ordered path queries have received attention and extensive research in the database field, there is currently no efficient method for best ordered path queries based on point of interest preferences. In the prior art, students research the optimal ordered path query of any sequence constraint, and Voronoi diagram is used for solving the problem of the optimal path query. None of the existing methods can be directly used for the scene of the optimal ordered path query based on the interest point preference. Firstly, because of the lack of efficient indexes, the path expansion process needs high calculation cost, and secondly, the current expansion mode based on Dijksra or a is low in efficiency, and the information provided by the query cannot be well utilized for pruning.
The patent specification with the application number of 201910349704.2 discloses a meta-path-based interest point recommendation method and a related device. Compared with the traditional interest point recommendation method, the interest point recommendation method has the advantages that the consideration is more comprehensive, the recommendation success rate of the interest points is effectively improved, and the data sparseness problem is solved. However, this patent fails to implement the filter-update process to loop until the optimal ordered path that meets the user's query needs is found.
Disclosure of Invention
The invention provides an optimal ordered path query method based on interest point preference, which can efficiently solve the problem of optimal ordered path query of users under different interest point preference.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
an optimal ordered path query method based on interest point preference comprises the following steps:
s1: the maximum reference point quantity f is transmitted, and a greedy merging algorithm is used for selecting reference points in the road network;
s2: node embedding is carried out on each interest point in the road network according to the selected reference point to obtain a corresponding distance vector, and a reference point inverted index is constructed;
s3: the filtering stage of the circulation optimal sub-path is carried out by using an optimal sub-path algorithm, the maximum value of the differences between corresponding elements of the distance vector between interest points is used as a distance lower boundary, the query is divided into a plurality of sub-problems, the optimal sub-path of each sub-problem is calculated by using a dynamic programming method, and finally a guide path and the cost thereof are obtained;
s4: and in the updating stage of the circulation optimal sub-path, the algorithm expands the path according to the interest point sequence of the guide path, obtains the shortest distance between the interest points, and updates the distance lower bound of the corresponding interest point pair. The interest points in all the guide paths are accumulated on the distances to obtain the actual cost of the feasible paths;
s5: the filtering stage and the updating stage of the optimal sub-path are alternately circulated, the actual cost of the feasible path obtained in the updating stage in each circulation is compared with the guiding path cost of the filtering stage, and when the actual cost of the feasible path is equal to the guiding path cost, the algorithm returns the feasible path as the optimal solution and stops.
Further, the specific process of the step S1 is:
the greedy merging algorithm selects a road network node capable of accurately estimating the distance of the most interest points to the distance as a reference point each time until the current reference pointThe number of the interest points is greater than or equal to f or the distances of all the interest point pairs can be accurately estimated by the current reference point. Wherein, a certain reference point can accurately estimate the interest point pair p 1 ，p 2 Meaning that the lower bound of the distance obtained by embedding the reference point is equal to p 1 ，p 2 Is a minimum distance of (2).
Further, the specific process of step S2 is as follows:
firstly, node embedding is carried out, and the process is as follows: the shortest distance from each interest point in the road network to all the reference points is calculated, and the result is stored in a vector form and is used as a distance vector V of the interest point. Then constructing the reference point inverted index, wherein the process is as follows: the reference point inverted index is composed of key value pairs < interest point category ID, interest point entity set S >, wherein each interest point entity in interest point entity set S includes an ID of the interest point, a score of the interest point and a distance vector V of the interest point.
Further, the specific process of step S3 is as follows:
when the optimal sub-path is solved by using dynamic programming, the distance vector of the corresponding interest point pair is obtained by using the inverted index of the reference point, the distance lower bound of the corresponding interest point pair is calculated, and the optimal sub-path is calculated by using the distance lower bound. The state transition equation of dynamic programming is as follows:
wherein the method comprises the steps ofMeans calculate interest point +.>Is defined by a lower distance bound. OS [ i, j]To be at the interest point p j S is the end point, s is the starting point, and the optimal paths of the first i interest point categories are accessed successively.
Further, the specific process of step S4 is as follows:
the actual shortest path between the interest point pairs is calculated by the interest point sequence corresponding to the guiding path obtained in the filtering stage, the interest point pairs are stored in the form of key value pairs < interest point pair IDs and distances >, and the distance lower bound of the corresponding interest point pairs is updated to be the actual shortest distance between the interest point pairs.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method comprises reference point inverted index and cyclic optimal sub-path algorithm. Firstly, selecting a reference point for a road network, embedding nodes into the interest point by using the reference point to obtain a corresponding distance vector, and constructing a reference point inverted index. The circularly optimal sub-path search results are then used. The loop optimal sub-path algorithm comprises two stages of filtering and updating, wherein the filtering stage rapidly obtains a guide path through the optimal sub-path algorithm based on dynamic programming, and then the updating stage performs path expansion according to the guide path. The filter-update process will loop until the best ordered path that meets the user's query needs is found.
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FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a diagram illustrating an exemplary operation of the method of the present invention;
fig. 3 is an example of a road network in an embodiment of the invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
We use the road network shown in fig. 2 to make a specific example of an optimal ordered path query based on the preference of points of interest for the present method.
In this example, definition 1 (road network G) road network G (VS, E, P) includes one node set VS and one directed weighted edge set E and a point of interest set P. Each point of interest in P belongs to one or more categories and each point of interest has a score ranging from 0 to 100.
Definition 2 (path R) a path r= { v 1 ，...，v n A sequence of nodes/points of interest, where the path between adjacent nodes/points of interest is the shortest path. The distance of the path is the sum of the shortest distances of all adjacent sequences.
Definition 3 (optimal ordered path of scoring constraints) given a starting position v s And end position v d And a class-score threshold pair ct= {<c 1 ，t 1 >，...，<c k ，t k >If a certain path R is from v s Starting, sequentially accessing c 1 ，...，c k And the score of the interest points is not lower than t respectively 1 ，...，t k And path R is the shortest in length, then this path R is referred to as the best ordered path of the scoring constraint.
Definition 4 (optimal ordered path query based on point of interest preference) in road network G, the user gives a starting position v s And end position v d And a class-score threshold pair ct= {<c 1 ，t 1 >，...，<c k ，t k >Queries that return an optimally ordered path with a scoring constraint are referred to as point-of-interest biased red-based optimally ordered path queries.
As shown in fig. 1-2, there are 17 nodes and 12 points of interest belonging to 4 categories respectively,the score representing the first point of interest belonging to the supermarket class is 80. The user gives the starting position v of the query 1 And end position v 16 And the class score threshold constraint is ct= {<c S ，70>，<c 2 ，90>，<c 3 ，50>，<c 4 ，90>}. And the maximum number of reference points for the road network is set to 3.
First, a reference point index is constructed for the road network. According to the descending order of the numbers of interest points which can be covered by each node, three nodes which can make the number of interest points covered be maximum are sequentially selected as reference points, and the three reference points selected in the road network are v 7 ，v 16 ，v 0 Together, pairs of points of interest can be covered 132. Then, node embedding is carried out on each interest point, and the shortest distance from the interest point to each reference point is calculated to obtain a distance vector, such as the interest pointIs v= {63, 72, 115}. Finally, constructing a reference point inverted index, which comprises the following specific processes: reference point inverted index is formed by key value pairs<Point of interest class ID, point of interest entity set S>Composition, wherein each point of interest entity in the set of point of interest entities S comprises an ID of the point of interest, a score of the point of interest and a distance vector V of the point of interest. For example, the interest point category is c H The interest point inverted index of (2) is +.>
Then executing the filtering stage of the circular optimal sub-path, firstly using the index to obtain the interest point category corresponding to the query, filtering the interest points with the scores smaller than the threshold according to the scoring threshold of the query, and then using the maximum value of the differences between the corresponding elements of the distance vectors of the interest point pairs as the distance lower boundary and dynamically planning to obtain the guiding pathAnd the cost of the pilot path is 85. Then calculating the shortest distance according to the interest point/node sequence corresponding to the guiding path to obtain a feasible path +.>And the cost of a viable path is 85. Since the cost of the feasible path is equal to the cost of the guided pathThus algorithm returns +.>And terminates.
Example 2
FIG. 3 is another example of a road network in which, as shown in FIG. 3 (a), a point of interest p 1 ，p 2 Belongs to category c 1 Point of interest p 3 ，p 4 ，p 5 Belonging to category 2. All points of interest score 100. User given query starting position v s Termination position v d And the class score threshold constraint is ct= {<c 1 ，70>，<c 2 ，90>}. The weight of the dotted line edge in the graph is the lower distance bound between the corresponding interest point pairs after node embedding is carried out on the graph by using the proper reference points. This example focuses on the execution flow of the loop optimal sub-path.
In the filtering stage of the circulation optimal sub-path, a distance lower bound and dynamic programming calculation are used to obtain a guiding path R p ＝{v s ，p 1 ，p 3 ，v d Cost 50, as shown in fig. 3 (b). Then, expansion is performed according to the guide path in the update phase, and (v s ，p 1 )，(p 1 ，p 3 ) And (p) 3 ，v d ) And updating the corresponding distance lower bound to obtain a feasible path R f ＝{v s ，p 1 ，p 3 ，v d Cost of 95 as shown in fig. 3 (c). Then filtering the optimal sub-path again, and calculating to obtain a guiding path R by using the latest distance lower bound and dynamic programming p ＝{v s ，p 1 ，p 4 ，v d And its path cost is 75 as shown in fig. 3 (d). Then enter an update phase, based on the pair of paths (p 1 ，p 4 )，(p 4 ，v d ) Calculates the shortest distance of the path R and updates the corresponding distance lower bound to obtain a feasible path R f ＝{v s ，p 1 ，p 4 ，v d Cost 80, as shown in fig. 3 (e). Then, filtering the optimal sub-pathA segment to obtain a guide path R p ＝{v s ，p 1 ，p 4 ，v d The cost is 80, which is the same as the cost of the current feasible path, so the algorithm stops and returns the optimal solution R f ＝{v s ，p 1 ，p 4 ，v d }。
The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
The same or similar reference numerals correspond to the same or similar components;
the positional relationship depicted in the drawings is for illustrative purposes only and is not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (9)
1. The optimal ordered path query method based on the interest point preference is characterized by comprising the following steps of:
s1: the maximum reference point quantity f is transmitted, and a greedy merging algorithm is used for selecting reference points in the road network;
s2: node embedding is carried out on each interest point in the road network according to the selected reference point to obtain a corresponding distance vector, and a reference point inverted index is constructed;
s3: the filtering stage of the circulation optimal sub-path is carried out by using an optimal sub-path algorithm, the query is divided into a plurality of sub-problems by taking the maximum value of the differences between corresponding elements of the distance vector between interest points as a distance lower boundary, and the optimal sub-path of each sub-problem is calculated by using a dynamic programming method, so that a guiding path and guiding path cost are finally obtained;
the specific process of the step S3 is as follows:
when the optimal sub-path is solved by using dynamic programming, the distance vector of the corresponding interest point pair is obtained by using the inverted index of the reference point to calculate the distance lower bound, and the optimal sub-path is calculated by using the distance lower bound;
s4: in the updating stage of the circulation optimal sub-path, the algorithm expands the path according to the interest point sequence of the guiding path, obtains the shortest distance between the interest points, and updates the distance lower boundary of the corresponding interest point pair; the interest points in all the guide paths are accumulated on the distances to obtain the actual cost of the feasible paths;
s5: the filtering stage and the updating stage of the optimal sub-path are alternately circulated, and when the actual cost of the feasible path is equal to the cost of the guiding path, the algorithm returns the feasible path as the optimal solution and stops.
2. The method according to claim 1, wherein in step S5, the actual cost of the feasible paths obtained in the update phase in each cycle is compared with the guide path cost of the filtering phase.
3. The method for querying the optimal ordered path based on the preference of the points of interest according to claim 2, wherein the specific process of step S1 is:
the greedy merging algorithm selects a road network node capable of accurately estimating the distance of the most interest point pairs as a reference point every time until the number of the current reference points is more than or equal to f or the distances of all the interest point pairs can be accurately estimated by the current reference points.
4. The method according to claim 3, wherein in the step S1, the reference point accurately estimates the distance between the pair of interest points, that is, the lower bound of the distance between the pair of interest points is equal to the shortest distance between the pair of interest points.
5. The method for optimal ordered path query based on point of interest preferences as claimed in claim 4, wherein said process of step S2 comprises:
node embedding is carried out: the shortest distance from each interest point in the road network to all the reference points is calculated, and the result is stored in a vector form and is used as a distance vector V of the interest point.
6. The method of claim 5, wherein the step S2 further comprises:
and (3) constructing a reference point inverted index: the reference point inverted index is composed of key value pairs < interest point category ID, interest point entity set S >, wherein each interest point entity in interest point entity set S includes an ID of the interest point, a score of the interest point and a distance vector V of the interest point.
7. The optimal ordered path query method based on interest point preference as claimed in claim 6, wherein the state transition equation of the dynamic programming in step S3 is:
wherein the method comprises the steps ofMeans calculate interest point +.>Is the lower distance bound of OS [ i, j ]]To be at the interest point p j S is the end point, s is the starting point, and the optimal paths of the first i interest point categories are accessed successively.
8. The method for searching for the optimal ordered path based on the preference of the points of interest as set forth in claim 7, wherein the specific procedure of step S4 is as follows: and calculating the actual shortest path between the interest point pairs according to the interest point sequence corresponding to the guide path obtained in the filtering stage.
9. The optimal ordered path query method based on interest point preference as claimed in claim 8, wherein the key value pair < interest point pair ID, distance > is stored and the distance lower bound of the corresponding interest point pair is updated to the actual shortest distance between them.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008666A (en) * | 2014-05-08 | 2014-08-27 | 中山大学 | Direction sign laying method oriented to interest points |
| KR101625957B1 (en) * | 2015-03-09 | 2016-05-31 | 경북대학교 산학협력단 | A system and method to process moving range k-nearest neighbor queries in road networks |
| CN110083767A (en) * | 2019-04-28 | 2019-08-02 | 广东工业大学 | A kind of point of interest recommended method and relevant apparatus based on first path |
| WO2019200752A1 (en) * | 2018-04-17 | 2019-10-24 | 平安科技（深圳）有限公司 | Semantic understanding-based point of interest query method, device and computing apparatus |
| CN110906942A (en) * | 2018-09-14 | 2020-03-24 | 上海擎感智能科技有限公司 | POI point reminding navigation method, system, storage medium and equipment |
-
2020
- 2020-06-16 CN CN202010545171.8A patent/CN112507047B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104008666A (en) * | 2014-05-08 | 2014-08-27 | 中山大学 | Direction sign laying method oriented to interest points |
| KR101625957B1 (en) * | 2015-03-09 | 2016-05-31 | 경북대학교 산학협력단 | A system and method to process moving range k-nearest neighbor queries in road networks |
| WO2019200752A1 (en) * | 2018-04-17 | 2019-10-24 | 平安科技（深圳）有限公司 | Semantic understanding-based point of interest query method, device and computing apparatus |
| CN110906942A (en) * | 2018-09-14 | 2020-03-24 | 上海擎感智能科技有限公司 | POI point reminding navigation method, system, storage medium and equipment |
| CN110083767A (en) * | 2019-04-28 | 2019-08-02 | 广东工业大学 | A kind of point of interest recommended method and relevant apparatus based on first path |
Non-Patent Citations (1)
| Title |
|---|
| "On personalized and sequenced route planning";Jian Dai 等;《World Wide Web》;第19卷;全文 * |
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