CN112507047A - Optimal ordered path query method based on interest point preference - Google Patents
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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 a user under different interest point preferences. The method comprises the steps of inverted index of reference points and a circular optimal sub-path algorithm. Firstly, selecting reference points for a road network, performing node embedding on interest points by using the reference points to obtain corresponding distance vectors, and constructing a reverse index of the reference points. The results are then searched using the circular optimal subpath. The loop optimal sub-path algorithm comprises two stages of filtering and updating, wherein the filtering stage is used for quickly obtaining a guide path through the optimal sub-path algorithm based on dynamic planning, and then path expansion is carried out according to the guide path in the updating stage. The filter-update process will loop until the optimal ordered path is found that satisfies the user query requirements.
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 devices such as smart phones and the like and the wide application of big data, points of interest in urban road networks generally have scores, and points of interest with higher scores generally have better consumption quality. Meanwhile, the user continuously puts forward more personalized path query requirements, for example, a path from s to d is queried to reach the optimal path, the optimal path passes through a restaurant, a cinema and a gas station in sequence, and the scores of the restaurant, the cinema and the gas station which are specified to be visited are respectively not less than 4.0, 4.5 and 4.3. The query satisfying the user is an optimal ordered path query method based on the interest point preference.
The optimal ordered path was first proposed by american scholars, and then a series of research works were conducted on the optimal ordered path. However, these works assume that users have the same preference for points of interest of the same category in the road network, i.e. all points of interest have the same score. In fact, the user's preferences for different points of interest of the same category are different, and the preferences are reflected in different scores of the points of interest themselves, and the higher the score of the points of interest, the better the service quality and the consumption experience of the points of interest. Users typically prefer to access points of interest with higher scores.
Although the optimal ordered path query has gained attention and extensive research in the database field, there is currently no efficient method for optimal ordered path query based on interest point preference. In the prior art, a learner researches an optimal ordered path query of any sequence constraint, and a Voronoi diagram is used for solving the optimal path query problem. At present, the existing methods cannot be directly used for the scene of optimal ordered path query based on interest point preference. Firstly, due to the lack of efficient indexes, the path expansion process needs high calculation cost, and secondly, the efficiency of the current expansion mode based on Dijksra or A is low, and the information provided by query cannot be well utilized for pruning.
The patent specification with the application number of 201910349704.2 discloses an interest point recommendation method based on a meta-path and a related device. Compared with the traditional interest point recommendation method, the method is more comprehensive in consideration, effectively improves the recommendation success rate of the interest points and solves the problem of data sparsity. However, the process of filtering-updating that this patent does not implement will loop until the optimal ordered path is found that satisfies the user's query requirements.
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 a user under different interest point preferences.
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: introducing the maximum number f of reference points, and selecting the reference points in the road network by using a greedy merging algorithm;
s2: performing node embedding on each interest point in the road network according to the selected reference point to obtain a corresponding distance vector, and constructing a reference point inverted index;
s3: the filtering stage of the circular optimal sub-path is carried out by using an optimal sub-path algorithm, the maximum value of the difference between corresponding elements of the distance vector between the interest points is used as a lower distance 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 planning method, and finally a guide path and the cost of the guide path are obtained;
s4: and circulating the updating stage of the optimal sub-path, and performing path expansion by an algorithm according to the interest point sequence of the guide path to obtain the shortest distance between the interest points and update the lower distance bound of the corresponding interest point pair. Accumulating the distances of the interest points in all the guide paths to obtain the actual cost of the feasible path;
s5: and alternately circulating the filtering stage and the updating stage of the optimal sub-path, comparing the actual cost of the feasible path obtained in the updating stage with the cost of the guide path in the filtering stage in each circulation, and returning and stopping the feasible path as the optimal solution by the algorithm when the actual cost of the feasible path is equal to the cost of the guide path.
Further, the specific process of step S1 is:
and selecting a road network node capable of accurately estimating the distance of the most interest point pairs as a reference point each time by the greedy merging algorithm 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. Wherein, a certain reference point can accurately estimate the interest point p1，p2Means that the lower bound of the distance embedded from the reference point is equal to p1，p2The shortest distance of (c).
Further, the specific process of step S2 is:
firstly, node embedding is carried out, and the process is as follows: and calculating the shortest distance between each interest point in the road network and all reference points, and storing the result in a vector form as a distance vector V of the interest point. And then constructing a reference point inverted index, wherein the process comprises the following steps: the reference point inverted index is composed of a key-value pair < interest point category ID, interest point entity set S >, wherein each interest point entity in the interest point entity set S includes the ID of the interest point, the score of the interest point and the 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 is calculated, and the optimal sub-path is calculated by using the distance lower bound. The state transition equation of the dynamic programming is as follows:
wherein
Points of interest calculation
Is lower bound of distance. OS [ i, j ]]To be interested in point pjAnd (4) taking s as a starting point as a terminal point, and successively accessing the optimal paths of the previous i interest point categories.
Further, the specific process of step S4 is as follows:
and calculating the actual shortest path between the interest point pairs by using the interest point sequence corresponding to the guide path obtained in the filtering stage, storing the actual shortest path in a key value pair (the interest point pair ID, the distance) form, and updating the lower bound of the distance of the corresponding interest point pair to the actual shortest distance between the interest point pair ID and the distance.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method comprises the reference point inverted index and the circular optimal sub-path algorithm. Firstly, selecting reference points for a road network, performing node embedding on interest points by using the reference points to obtain corresponding distance vectors, and constructing a reverse index of the reference points. The results are then searched using the circular optimal subpath. The loop optimal sub-path algorithm comprises two stages of filtering and updating, wherein the filtering stage is used for quickly obtaining a guide path through the optimal sub-path algorithm based on dynamic planning, and then path expansion is carried out according to the guide path in the updating stage. The filter-update process will loop until the optimal ordered path is found that satisfies the user query requirements.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a diagram illustrating an exemplary operation of the method of the present invention;
fig. 3 is a road network example in the embodiment of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present 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 optimal ordered path query example based on interest point preference for the method.
In this example, definition 1 (road network G) road network G (VS, E, P) includes a node set VS and a set of directed weighted edges E and a set of points of interest 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) one path R ═ v1，...，vnThe is 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 (scoring constrained optimal ordered path) given a starting position vsAnd the end position vdAnd a category-score threshold pair CT ═ great face<c1，t1>，...，<ck，tk>If a certain path R is from vsStarting, accessing c sequentially1，...，ckAnd the scores of the interest points are respectively not less than t1，...，tkAnd the length of the path R is shortest, then this path R is called the score-constrained optimal ordered path.
Definition 4 (optimal ordered path query based on interest point preference) in a routing network G, a user gives a starting position vsAnd the end position vdAnd a category-score threshold pair CT ═ great face<c1，t1>，...，<ck，tk>And returning a query of the optimal ordered path with the scoring constraint, which is called as the optimal ordered path query based on the reddening of the interest points.
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. Starting position v of user given inquiry1And the end position v16And the category score threshold is constrained to CT ═ great face<cS，70>，<c2，90>，<c3，50>，<c4，90>}. And the maximum number of reference points of 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 number of the interest point pairs which can be covered by each node, sequentially selecting three nodes which can enable the covered interest point number to be maximum as reference points, wherein v is the three reference points selected in the road network7，v16，v0The pair of interest points can be covered 132 in total. Then, each interest point is embedded into a node, and the shortest distance between each interest point and each reference point is calculated to obtain a distance vector, such as the interest point
Is given as V ═ 63, 72, 115. And finally, constructing a reference point inverted index, wherein the specific process is as follows: inverted indexing of reference points by key-value pairs<Interest point category ID, interest point entity set S>And each interest point entity in the interest point entity set S comprises the ID of the interest point, the score of the interest point and the distance vector V of the interest point. E.g. point of interest category cHThe interest point is inversely indexed as
Then executing the loop optimizerIn the path filtering stage, the index is used to obtain the interest point category corresponding to the query, the interest points with the score smaller than the threshold are filtered according to the score threshold of the query, and then the maximum value of the difference between the corresponding elements of the distance vectors of the interest point pairs is used as the lower distance bound and the guide path is obtained through dynamic planning
And the cost of the boot path is 85. Then, the shortest distance is calculated according to the interest point/node sequence corresponding to the guide path to obtain a feasible path
And the cost of a feasible path is 85. Since the cost of the feasible path is equal to the cost of the boot path, the algorithm returns
And terminates.
Example 2
FIG. 3 is another example of a road network in which points of interest p are shown in FIG. 3(a)1，p2Belong to class c1Point of interest p3，p4，p5Belonging to class 2. All the interest points are scored as 100. the user gives the initial position v of the querysAt the end position vdAnd the category score threshold is constrained to CT ═ great face<c1，70>，<c2，90>}. The weight of the dashed line edge in the graph is the lower bound of the distance between the corresponding interest point pairs after the node embedding is performed on the graph by using a proper reference point. This example focuses on the execution flow of the loop optimization sub-path.
In the filtering stage of the circular optimal sub-path, the guiding path R is obtained by using distance lower bound and dynamic programming calculationp＝{vs，p1，p3，vdAt a cost of 50, as shown in fig. 3 (b). Then, in the update stage, the calculation is carried out according to the expansion of the guide path to calculate (v)s，p1)，(p1，p3) And (p)3，vd) And updating the corresponding lower distance bound to obtainTo feasible path Rf＝{vs，p1，p3，vdAt a cost of 95, as shown in fig. 3 (c). Then, the filtering stage of the circular optimal sub-path is carried out again, and the guiding path R is obtained by using the latest distance lower bound and dynamic planning calculationp＝{vs，p1，p4，vdIts path cost is 75, as shown in fig. 3 (d). Then entering an update phase according to the guiding path pair (p)1，p4)，(p4，vd) The shortest distance is calculated and the corresponding lower distance boundary is updated to obtain a feasible path Rf＝{vs，p1，p4，vdAt a cost of 80, as shown in fig. 3 (e). Then, the filtering stage of the circular optimal sub-path is carried out to obtain a guide path Rp＝{vs，p1，p4，vdAt a cost of 80, the same as the current feasible path, so the algorithm stops and returns the optimal solution Rf＝{vs，p1，p4，vd}。
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
The same or similar reference numerals correspond to the same or similar parts;
the positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. An optimal ordered path query method based on interest point preference is characterized by comprising the following steps:
s1: introducing the maximum number f of reference points, and selecting the reference points in the road network by using a greedy merging algorithm;
s2: performing node embedding on each interest point in the road network according to the selected reference point to obtain a corresponding distance vector, and constructing a reference point inverted index;
s3: the filtering stage of the circular optimal sub-path is carried out by using an optimal sub-path algorithm, the maximum value of the difference between corresponding elements of the distance vector between the interest points is used as a lower distance 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 planning method, and finally a guide path and the cost of the guide path are obtained;
s4: and circulating the updating stage of the optimal sub-path, and performing path expansion by an algorithm according to the interest point sequence of the guide path to obtain the shortest distance between the interest points and update the lower distance bound of the corresponding interest point pair. Accumulating the distances of the interest points in all the guide paths to obtain the actual cost of the feasible path;
s5: and alternately circulating the filtering stage and the updating stage of the circular optimal sub-path, and when the actual cost of the feasible path is equal to the cost of the guide path, returning and stopping the feasible path as the optimal solution by the algorithm.
2. The method of claim 1, wherein in step S5, the actual cost of feasible paths obtained in the update stage in each loop is compared with the cost of the guide path in the filter stage.
3. The method for querying an optimal ordered path based on point of interest preference according to claim 2, wherein the specific process of the step S1 is as follows:
and selecting a road network node capable of accurately estimating the distance of the most interest point pairs as a reference point each time by the greedy merging algorithm 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 for querying an optimal ordered path according to the interest point preference as claimed in claim 3, wherein in the step S1, the interest point pair p is accurately estimated by referring to the points1,p2Means that the lower bound of the distance embedded from the reference point is equal to p1,p2The shortest distance of (c).
5. The method for querying an optimal ordered path based on point of interest preference according to claim 4, wherein the process of step S2 comprises:
carrying out node embedding: and calculating the shortest distance between each interest point in the road network and all reference points, and storing the result in a vector form as a distance vector V of the interest point.
6. The method for querying an optimal ordered path based on point of interest preference according to claim 5, wherein the process of step S2 further comprises:
constructing a reference point inverted index: the reference point inverted index is composed of a key-value pair < interest point category ID, interest point entity set S >, wherein each interest point entity in the interest point entity set S includes the ID of the interest point, the score of the interest point and the distance vector V of the interest point.
7. The method for querying an optimal ordered path based on point of interest preference according to claim 6, wherein 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, the distance lower bound is calculated, and the optimal sub-path is calculated by using the distance lower bound.
8. The method of claim 7, wherein the dynamically planned state transition equation in step S3 is:
wherein
Points of interest calculation
Lower bound of distance, OS [ i, j ]]To be interested in point pjAnd (4) taking s as a starting point as a terminal point, and successively accessing the optimal paths of the previous i interest point categories.
9. The method for querying an optimal ordered path based on point of interest preference according to claim 8, wherein the specific process of the 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.
10. The point-of-interest preference based optimal ordered path query method of claim 9, wherein the key value pair < point-of-interest pair ID, distance > is saved, and the distance lower bound of the corresponding point-of-interest pair is updated to the actual shortest distance between them.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113361788A (en) * | 2021-06-16 | 2021-09-07 | 东南大学 | Path planning method for multi-type service demand under urban environment |
| CN114279457A (en) * | 2021-12-23 | 2022-04-05 | 中南民族大学 | Path planning method, device, equipment and readable storage medium |
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 |
|---|
| JIAN DAI 等: ""On personalized and sequenced route planning"", 《WORLD WIDE WEB》, vol. 19 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113361788A (en) * | 2021-06-16 | 2021-09-07 | 东南大学 | Path planning method for multi-type service demand under urban environment |
| CN113361788B (en) * | 2021-06-16 | 2022-11-01 | 东南大学 | Path planning method for multi-type service demand under urban environment |
| CN114279457A (en) * | 2021-12-23 | 2022-04-05 | 中南民族大学 | Path planning method, device, equipment and readable storage medium |
| CN114279457B (en) * | 2021-12-23 | 2023-10-03 | 中南民族大学 | Path planning method, device, equipment and readable storage medium |
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