CN114780875A - Dynamic group travel planning query method - Google Patents
Dynamic group travel planning query method Download PDFInfo
- Publication number
- CN114780875A CN114780875A CN202210710687.2A CN202210710687A CN114780875A CN 114780875 A CN114780875 A CN 114780875A CN 202210710687 A CN202210710687 A CN 202210710687A CN 114780875 A CN114780875 A CN 114780875A
- Authority
- CN
- China
- Prior art keywords
- interest
- conditional
- interest points
- group
- query
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/14—Travel agencies
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a dynamic group travel planning query method, which comprises the following steps: acquiring a data set and query data; the data set includes points of interest; the interest points comprise text information and time information; constructing a comprehensive index structure based on the interest points, the inverted index and the R tree; performing pruning processing on the comprehensive index structure based on the query data, the text information and the time information to obtain a first set; pruning the search space of the data set based on the query data and the data set to obtain a second set; sorting the conditional interest points contained in the first set and the second set; calculating the candidate path distance of each group member in the query data based on the condition interest points arranged in sequence, and further obtaining a total travel distance, wherein the total travel distance comprises a plurality of the candidate path distances; and sequencing the total travel distances, wherein the minimum total travel distance is the optimal travel path. Through comprehensive index structure and pruning processing, effective interest points are extracted, and query efficiency is high.
Description
Technical Field
The invention relates to the technical field of space inquiry, in particular to a dynamic group travel planning inquiry method.
Background
The rapid development of mobile networks, positioning technologies, and wireless communication technologies has led to widespread use of location-based services in everyday life. With the application field of the intelligent mobile terminal becoming wider and wider, the fusion of the geographic position and the text data becomes more and more common, so that a large number of spatial text objects become available, and more places such as restaurants, movie theaters and shopping centers are added with text description information related to the geographic position. Meanwhile, performing a spatial text query on an object containing spatial information and text information has attracted extensive attention by researchers and the industry. The spatial keyword query uses a geographic position and a plurality of keywords as input parameters, and uses a plurality of data objects with highest spatial similarity and text similarity as results to return, but the spatial keyword query only considering the spatial similarity and the text similarity sometimes cannot meet the special requirements of users. For example, a group of users may want to query a restaurant that can serve both Chinese and Western food at ten o' clock in the evening, but most restaurants will have been suspended from service at that time, and if a conventional query algorithm is used, the returned results will likely not be desirable to the user.
Today, planning optimal trips to meet their requirements for people scattered in different places has become a considerable problem. The derived path planning problem not only comprises different types of interest point queries which the user needs to visit, but also plans an optimal path based on the interest points meeting the constraint. Such application scenarios are rather common in life, for example, users at different starting points want to eat together, watch a movie at six night, and then return to their respective homes, respectively, and a path with the minimum cost to meet these constraints needs to be planned. The path planning problem is closely related to the life of people, and thus has received the general attention of researchers. However, existing research efforts do not take into account dynamic changes in members, i.e., new users join or old members leave in the middle. The dynamic change of the members more accords with the requirement of real application, so the invention firstly provides the problem of dynamic group journey planning so as to plan the optimal journey meeting the requirements of all users.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defects in the prior art, and to provide a dynamic group travel plan query method.
The invention provides a dynamic group travel planning query method, which comprises the following steps:
acquiring a data set and query data; the data set includes points of interest; the interest points comprise text information and time information; the query data comprises group members, a starting position and a target position;
constructing a comprehensive index structure based on the interest points, the inverted index and the R tree;
pruning the comprehensive index structure based on the query data, the text information and the time information to obtain a first set;
pruning the search space of the data set based on the query data and the data set to obtain a second set;
sorting the conditional interest points contained in the first set and the second set; the conditional interest points are interest points meeting constraint conditions;
calculating the candidate path distance of each group member in the query data according to the initial position and the target position and based on the condition interest points arranged in sequence, and further obtaining a plurality of total travel distances; and sequencing the total travel distances, wherein the minimum total travel distance is the optimal travel path.
Preferably, the interest points further comprise interest point ids, the structure of the R tree is used as the basis of the comprehensive index structure, and the interest point ids are stored in leaf nodes of the comprehensive index structure; the non-leaf nodes of the comprehensive index structure are used for storing pointers pointing to child nodes of the comprehensive index structure; and associating all nodes of the comprehensive index structure with the inverted index to obtain the comprehensive index structure.
Preferably, the inverted index includes a text inverted index and a time inverted index.
Preferably, the query data further includes text information of the current query and time information of the current query.
Preferably, the interest points meeting the constraint condition are the interest points meeting the text information of the current query and the time information of the current query.
Preferably, the process of obtaining the first set is:
initializing a first priority queue and a first set; the first priority queue is used for storing leaf nodes of the comprehensive index structure, and the first set is used for storing conditional interest points;
positioning an area where the query data is located;
traversing child nodes of the root node from top to bottom from the root node of the comprehensive index structure; judging whether the child nodes meet the current query text information and the current query time information, and if not, continuing traversing;
if yes, judging whether the child node is a leaf node, and if not, continuing traversing; if yes, storing child nodes in the first priority queue; until the traversal is finished;
judging whether the child nodes in the first priority queue meet the current query text information and the current query time information; if not, giving up; if yes, adding child nodes in a first priority queue to the first set; at this time, the child nodes in the first priority queue are marked as conditional interest points.
Preferably, the process of obtaining the second set is:
positioning an area where query data is located, and defining a group of elliptical areas;
initializing a second priority queue, a second set, an elliptical first focus set and an elliptical second focus set; the second priority queue is used for storing a group of elliptical areas, the second set is used for storing conditional interest points, the first focus set of the ellipses is used for storing first focuses of the elliptical areas, and the second focus set of the ellipses is used for storing second focuses of the elliptical areas;
using a first centroid tracking function:
calculating a first focus of each elliptical area, and adding the first focus to the elliptical first focus set; wherein, the first and the second end of the pipe are connected with each other,Csa first focus of the elliptical region is indicated,nindicating the number of group members that are present,iis shown asiThe members of the group are,S i indicating group membershipu i The starting position of (a);
using a second centroid tracking function:
calculating a second focus of each elliptical area, and adding the second focus to an elliptical second focus set; wherein, the first and the second end of the pipe are connected with each other,C d a second focal point of the elliptical region is represented,nindicating the number of group members that are present,idenotes the firstiThe members of the group are,d i indicating group membershipu i The target position of (a);
when the first focus set and the second focus set of the ellipse are empty, repeating the above process;
when the first focus set and the second focus set of the ellipse are not empty, calculating each ellipse area by adopting an ellipse tracking function;
calculating the intersection of a plurality of elliptical areas by adopting an intersection tracking function, wherein the intersection is an area;
and adding the interest points in the intersection into the second set, wherein the interest points in the intersection are conditional interest points.
Preferably, the process of obtaining the optimal travel path is as follows:
initializing a third set, a candidate path set, an array and an optimal path; the third set is used for storing conditional interest points in the first set and the second set; the candidate path set is used for storing the candidate path distance of the group member; the array is used for storing the total travel distance;
when the third set is not empty, sorting the conditional interest points according to the currently inquired text information, and inserting the conditional interest points into the candidate path set;
grouping the conditional interest points based on the sorted conditional interest points; calculating the candidate path distance of each group member according to the initial position and the target position to obtain the candidate path distances of a plurality of group members; storing the candidate path distances of a plurality of group members into a candidate path set;
respectively obtaining a plurality of total travel distances according to the sorted conditional interest points and the candidate path distances of the group members; storing a plurality of total travel distances into an array;
sorting the plurality of total travel distances in an array; the minimum total travel distance is the optimal path for travel.
Preferably, the calculation formula for obtaining the candidate path distance of the group member is:
wherein the content of the first and second substances,PD i denotes the firstiCandidate path distances for the group members;dist(s i ,o i 1) Indicating group membershipu i Starting position ofS i To the first conditional point of interesto i 1The Euclidean distance of (c);dist(o i j,o i j+1) Indicating group membershipu i In the first placejA conditional point of interesto i jTo the firstj+1 conditional points of interesto i j+1The Euclidean distance of (c);dist(o i m ,d i ) Is shown asmA conditional point of interesto i m To group membersu i Target position ofd i The Euclidean distance of (c);o i 1、o i j、o i j+1、o i m respectively represent the firsti1 st, second of group member passingjPerson to be examined andj+1, secondmA conditional point of interest;mrepresenting the number of conditional points of interest.
Preferably, the calculation formula for obtaining the total travel distance is as follows:
wherein the content of the first and second substances,TDthe total distance of travel is represented as,nindicating the number of group members,iis shown asiCandidate path distances for the group members.
The technical scheme of the invention has the following advantages: by integrating the index structure and pruning, effective interest points are extracted, the query efficiency is high, the required memory overhead is low, and the feasibility is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a dynamic group travel plan query method in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a comprehensive index structure in the practice of the present invention;
FIG. 3 is a schematic diagram of the inverted text index of the structure diagram of FIG. 2;
FIG. 4 is a schematic diagram of the inverted time index of the structure diagram of FIG. 2;
FIG. 5 is a graph comparing algorithm execution times in the practice of the present invention;
FIG. 6 is a graph comparing input and output of an algorithm in accordance with an embodiment of the present invention;
FIG. 7a is a graph comparing the impact of current query keywords on California road network data in the United states of America in accordance with the present invention;
FIG. 7b is a graph comparing the impact of current query keywords on Orientberg road network data in Germany in accordance with an embodiment of the present invention;
FIG. 8a is a graph comparing the impact of group size on California road network data in U.S. under the implementation of the present invention;
FIG. 8b is a graph showing the effect of the group size under the road network data of Orldn castle, Germany in the practice of the present invention;
FIG. 9a is a graph comparing the effect of major axis size of ellipses under California road network data in the United states of America in the practice of the present invention;
fig. 9b is a graph comparing the effect of the size of the major axis of the ellipse on the road network data in the oerdenberg, germany, in the practice of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Furthermore, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present embodiment provides a dynamic group travel plan query method, which includes the steps of:
acquiring a data set and query data; the data set includes points of interest; the interest points comprise text information and time information; the query data comprises group members, an initial position, text information of current query of a target position and time information of current query;
constructing a comprehensive index structure based on the interest points, the inverted index and the R tree;
the interest points also comprise interest point ids; as shown in fig. 2, the structure of the R tree is used as the basis of the comprehensive index structure, and the interest point id is stored as a leaf node of the comprehensive index structure; the non-leaf nodes of the comprehensive index structure are used for storing pointers pointing to child nodes of the comprehensive index structure; and associating all nodes of the comprehensive index structure with the inverted index to obtain the comprehensive index structure. The inverted index includes a text inverted index and a time inverted index. In FIG. 2R 2、R 5、R 6The inverted text indexes associated with the nodes are as shown in fig. 3, and sequentially from left to right:R 2a text inverted index associated with the point,R 5The text reverse index associated with the node,R 6The node-associated text inverted index; in FIG. 2R 2、R 5、R 6The time reverse index associated with the node is shown in fig. 4, and sequentially from left to right:R 2time reverse index of point association,R 5Time inverted index of node association,R 6Time-reversed index of node association.
Pruning the comprehensive index structure based on the query data, the text information and the time information to obtain a first set;
specifically, the process of obtaining the first set is as follows:
initializing a first priority queue and a first set; the first priority queue is used for storing leaf nodes of the comprehensive index structure, and the first set is used for storing conditional interest points;
positioning an area where the query data is located;
traversing child nodes of the root node from top to bottom from the root node of the comprehensive index structure; judging whether the child nodes meet the current query text information and the current query time information, and if not, continuing traversing;
if yes, judging whether the child node is a leaf node, and if not, continuing traversing; if yes, storing the child nodes in the first priority queue; until the traversal is finished;
judging whether the child nodes in the first priority queue meet the current query text information and the current query time information; if not, giving up; if yes, adding child nodes in a first priority queue to the first set; at this time, the child nodes in the first priority queue are marked as conditional interest points.
Pruning the search space of the data set based on the query data and the data set to obtain a second set;
specifically, the process of obtaining the second set is as follows:
positioning an area where the query data is located, and defining a group of elliptical areas;
initializing a second priority queue, a second set, an ellipse first focus set and an ellipse second focus set; the second priority queue is used for storing a group of elliptical areas, the second set is used for storing conditional interest points, the first focus set of the ellipse is used for storing a first focus of the elliptical areas, and the second focus set of the ellipse is used for storing a second focus of the elliptical areas;
using a first centroid tracking function:
calculating a first focus of each elliptical area, and adding the first focus to the first focus set of the ellipses; wherein, the first and the second end of the pipe are connected with each other,Csa first focus of the elliptical region is indicated,nindicating the number of group members that are present,idenotes the firstiThe members of the group are,S i indicating group membershipu i The starting position of (a);
using a second centroid tracking function:
calculating a second focus of each elliptical area, and adding the second focus to an elliptical second focus set; wherein, the first and the second end of the pipe are connected with each other,C d a second focus of the elliptical region is indicated,nindicating the number of group members that are present,iis shown asiThe members of the group are,d i indicating group membershipu i The target position of (a);
when the first focus set and the second focus set of the ellipse are empty, repeating the above process;
when the first focus set and the second focus set of the ellipse are not empty, calculating each ellipse area by adopting an ellipse tracking function;
calculating the intersection of a plurality of elliptical areas by adopting an intersection tracking function, wherein the intersection is an area;
and adding the interest points in the intersection into the second set, wherein the interest points in the intersection are the conditional interest points.
Sorting the conditional interest points contained in the first set and the second set; the conditional interest points are interest points meeting constraint conditions;
calculating the candidate path distance of each group member in the query data according to the initial position and the target position and based on the condition interest points arranged in sequence, and further obtaining a plurality of total travel distances; and sequencing the total travel distances, wherein the minimum total travel distance is the optimal travel path.
Specifically, the process of obtaining the optimal travel path includes:
initializing a third set, a candidate path set, an array and an optimal path; the third set is used for storing conditional interest points in the first set and the second set; the candidate path set is used for storing candidate path distances of the group members; the array is used for storing the total travel distance;
when the third set is not empty, sorting the conditional interest points according to the currently inquired text information, and inserting the conditional interest points into the candidate path set;
grouping the conditional interest points based on the sorted conditional interest points; calculating the candidate path distance of each group member according to the initial position and the target position to obtain the candidate path distances of a plurality of group members; storing the candidate path distances of a plurality of group members into a candidate path set;
respectively obtaining a plurality of total travel distances according to the sorted conditional interest points and the candidate path distances of the group members; storing the total travel distances into an array;
sorting the plurality of total travel distances in an array; the minimum total travel distance is the optimal path for travel.
Further, the calculation formula for obtaining the candidate path distance of the group member is:
wherein, the first and the second end of the pipe are connected with each other,PD i is shown asiCandidate path distances for the group members;dist(s i ,o i 1) Indicating group membershipu i Starting position ofS i To the first conditional point of interesto i 1The Euclidean distance of (c);dist(o i j,o i j+1) Indicating group membershipu i In the first placejA conditional point of interesto i jTo the firstj+1 conditional points of interesto i j+1The Euclidean distance of (c);dist(o i m ,d i ) Denotes the firstmA conditional point of interesto i m To group memberu i Target position ofd i The Euclidean distance of (c);o i 1、o i j、o i j+1、o i m respectively represent the firsti1 st, second of group member passingjPerson to be examined andj+1, the firstmA conditional point of interest;mrepresenting the number of conditional points of interest.
Further, the calculation formula for obtaining the total travel distance is as follows:
wherein the content of the first and second substances,TDthe total distance of travel is indicated by,nindicating the number of group members,idenotes the firstiCandidate path distances for the group members.
In this example, the algorithm was evaluated in Euclidean data space using 2 real road network datasets (California, CA; Olderburgh, Germany):
table 1 is a american california road network data table;
from table 1, real road network data of california in the united states can be known;
table 2 is a road network data table of the aldenberg germany;
from table 2, the real road network data of orldenberg, germany can be seen.
In this embodiment, the dynamic group travel query method based on time awareness (bestd) provided in this embodiment and the NaiveDGTP algorithm are compared and analyzed in a dynamic group travel planning query task process.
As shown in fig. 5 and fig. 6, it can be seen that the execution time comparison and the input-output comparison graphs of the two algorithms processed under the CA and the OLD data sets. The BestTD algorithm is superior to the NaiveDGTP algorithm in both execution time and input and output.
As shown in fig. 7a and 7b, as the number of the current query text messages increases, the execution processing time of both algorithms increases, but the execution time of the BestTD algorithm is much less than that of the naivettp algorithm. This is because as the number of query keywords increases, the search range of the query becomes larger, and the number of points of interest increases. However, compared with the NaiveDGTP algorithm, the BestTD algorithm is high in efficiency, and can quickly query objects meeting conditions according to current query text information. Therefore, it can be seen that under the influence of the number of the current query keywords, the query performance of the BestTD algorithm is better than that of the NaiveDGTP algorithm.
As shown in fig. 8a and 8b, the execution time of both BestTD and NavieDGTP algorithms increases as the group size increases. Since an increase in the group size will result in a greater number of path computations, resulting in a longer processing time. It can be seen that bestttd requires much less processing time than NavieDGTP.
As shown in fig. 9a and 9b, the execution time of both methods increases as the size of the major axis of the ellipse increases. This is because the larger the ellipse major axis, the larger the search area, the more data sets are queried, and thus the longer the execution time required. It is observed in experiments that the BestTD algorithm is superior to the NaiveDGTP algorithm. For the navidgtp algorithm, it does not have any pruning strategy to reduce the search space, but instead uses the entire data space to compute the best group tour, resulting in long execution time. The BestTD algorithm adopts an ellipse attribute to refine a search space, so the execution time of the BestTD algorithm is far lower than that of the NaiveDGTP algorithm.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. 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. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for dynamic group travel plan query, comprising the steps of:
acquiring a data set and query data; the data set includes points of interest; the interest points comprise text information and time information; the query data comprises group members, a starting position and a target position;
constructing a comprehensive index structure based on the interest points, the inverted index and the R tree;
pruning the comprehensive index structure based on the query data, the text information and the time information to obtain a first set;
pruning the search space of the data set based on the query data and the data set to obtain a second set;
sorting conditional interest points contained in the first set and the second set; the conditional interest points are interest points meeting constraint conditions;
calculating the candidate path distance of each group member in the query data according to the starting position and the target position and based on the condition interest points arranged in sequence, and further obtaining a total travel distance, wherein the total travel distance comprises a plurality of travel distances; and sequencing the total travel distances, wherein the minimum total travel distance is the optimal travel path.
2. The method as claimed in claim 1, wherein the interest points further include an interest point id, and the structure of the R tree is used as a basis of the comprehensive index structure, and the interest point id is stored in a leaf node of the comprehensive index structure; the non-leaf nodes of the comprehensive index structure are used for storing pointers pointing to child nodes of the comprehensive index structure; and associating all nodes of the comprehensive index structure with the inverted index to obtain the comprehensive index structure.
3. The method of claim 2, wherein the inverted index comprises an inverted text index and an inverted time index.
4. The method of claim 2, wherein the query data further includes text information of the current query and time information of the current query.
5. The method of claim 4, wherein the points of interest that meet the constraint are points of interest that satisfy the text information of the current query and the time information of the current query.
6. The method of claim 5, wherein the first set is obtained by:
initializing a first priority queue and a first set; the first priority queue is used for storing leaf nodes of a comprehensive index structure, and the first set is used for storing the conditional interest points;
positioning an area where the query data is located;
traversing child nodes of the root node from top to bottom from the root node of the comprehensive index structure; judging whether the child nodes meet the text information of the current query and the time information of the current query or not, and if not, continuing traversing;
if yes, judging whether the child nodes are leaf nodes, and if not, continuing traversing; if yes, storing the child nodes into the first priority queue; until the traversal is finished;
judging whether child nodes in the first priority queue meet the text information of current query and the time information of current query or not; if not, abandoning; if so, adding child nodes in a first priority queue to the first set; at this time, the child nodes in the first priority queue are marked as the conditional interest points.
7. The method of claim 6, wherein the second set is obtained by:
positioning an area where query data is located, and defining a group of elliptical areas;
initializing a second priority queue, a second set, an elliptical first focus set and an elliptical second focus set; the second priority queue is used for storing a group of elliptical areas, the second set is used for storing the conditional interest points, the first focus set of the ellipses is used for storing the first focuses of the elliptical areas, and the second focus set of the ellipses is used for storing the second focuses of the elliptical areas;
using a first centroid tracking function:
calculating a first focus of each elliptical area, and adding the first focus to the elliptical first focus set; wherein, the first and the second end of the pipe are connected with each other,Csa first focus of the elliptical region is indicated,nrepresenting composition of matterThe number of the members is such that,idenotes the firstiThe members of the group are,S i indicating group membershipu i The starting position of (a);
using a second centroid tracking function:
calculating a second focus of each elliptical area, and adding the second focus to the elliptical second focus set; wherein, the first and the second end of the pipe are connected with each other,C d a second focal point of the elliptical region is represented,nindicating the number of group members that are present,iis shown asiThe members of the group are,d i indicating group membershipu i The target position of (a);
repeating the above process when the first focus set of ellipses and the second focus set of ellipses are empty;
when the first focus set and the second focus set of the ellipse are not empty, calculating each elliptical area by adopting an elliptical tracking function;
calculating the intersection of the plurality of elliptical areas by adopting an intersection tracking function, wherein the intersection is an area;
and adding the interest points in the intersection into the second set, wherein the interest points in the intersection are conditional interest points.
8. The method of claim 5, wherein the optimal path for the trip is obtained by:
initializing a third set, a candidate path set, an array and an optimal path; the third set is used for storing conditional interest points in the first set and the second set; the candidate path set is used for storing candidate path distances of group members; the array is used for storing the total travel distance;
when the third set is not empty, sorting the conditional interest points according to the text information of the current query, and inserting the conditional interest points into the candidate path set;
grouping based on the sorted conditional interest points; calculating the candidate path distance of each group member according to the starting position and the target position to obtain the candidate path distances of a plurality of group members; storing the candidate path distances of a plurality of group members to the candidate path set;
respectively obtaining a plurality of total travel distances according to the sorted conditional interest points and the candidate path distances of the group members; storing a plurality of said total travel distances to said array;
sorting the plurality of total travel distances in an array; the minimum total travel distance is the optimal travel path.
9. The method of claim 8, wherein the candidate path distances for the group members are calculated by the formula:
wherein the content of the first and second substances,PD i is shown asiCandidate path distances for the group members;dist(s i ,o i 1) Indicating group membershipu i Starting position ofS i To the first conditional point of interesto i 1The Euclidean distance of (c);dist(o i j,o i j+1) Indicating group membershipu i In the first placejA conditional point of interesto i jTo the firstj+1 conditional points of interesto i j+1The Euclidean distance of (c);dist(o i m ,d i ) Denotes the firstmA conditional point of interesto i m To group memberu i Target position ofd i The Euclidean distance of (c);o i 1、o i j 、o i j+1、o i m respectively representi1 st, second of group member passingjPerson to be examined andj+1, the firstmA conditional point of interest;mrepresenting the number of conditional points of interest.
10. The method of claim 8, wherein the total travel distance is obtained by the formula:
wherein, the first and the second end of the pipe are connected with each other,TDthe total distance of travel is represented as,nindicating the number of group members,idenotes the firstiCandidate path distances for the group members.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210710687.2A CN114780875B (en) | 2022-06-22 | 2022-06-22 | Dynamic group travel planning query method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210710687.2A CN114780875B (en) | 2022-06-22 | 2022-06-22 | Dynamic group travel planning query method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114780875A true CN114780875A (en) | 2022-07-22 |
| CN114780875B CN114780875B (en) | 2022-09-06 |
Family
ID=82422402
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210710687.2A Active CN114780875B (en) | 2022-06-22 | 2022-06-22 | Dynamic group travel planning query method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114780875B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030036848A1 (en) * | 2001-08-16 | 2003-02-20 | Sheha Michael A. | Point of interest spatial rating search method and system |
| CN103279560A (en) * | 2013-06-13 | 2013-09-04 | 清华大学 | Continuous keyword query method based on security region |
| US20150163256A1 (en) * | 2012-08-30 | 2015-06-11 | Google Inc. | Group travel planning |
| CN106446242A (en) * | 2016-10-12 | 2017-02-22 | 太原理工大学 | Efficient multi-keyword matchingoptimal pathquery method |
| CN107145526A (en) * | 2017-04-14 | 2017-09-08 | 浙江大学 | Geographical social activity keyword Reverse nearest neighbor inquiry processing method under a kind of road network |
| US10331753B1 (en) * | 2018-04-04 | 2019-06-25 | The Florida International University Board Of Trustees | Efficient progressive continuous k-nearest neighbor query algorithm for moving objects with a tree-like index |
| CN109977309A (en) * | 2019-03-21 | 2019-07-05 | 杭州电子科技大学 | Combination point of interest querying method based on multiple key and user preference |
-
2022
- 2022-06-22 CN CN202210710687.2A patent/CN114780875B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030036848A1 (en) * | 2001-08-16 | 2003-02-20 | Sheha Michael A. | Point of interest spatial rating search method and system |
| US20150163256A1 (en) * | 2012-08-30 | 2015-06-11 | Google Inc. | Group travel planning |
| CN103279560A (en) * | 2013-06-13 | 2013-09-04 | 清华大学 | Continuous keyword query method based on security region |
| CN106446242A (en) * | 2016-10-12 | 2017-02-22 | 太原理工大学 | Efficient multi-keyword matchingoptimal pathquery method |
| CN107145526A (en) * | 2017-04-14 | 2017-09-08 | 浙江大学 | Geographical social activity keyword Reverse nearest neighbor inquiry processing method under a kind of road network |
| US10331753B1 (en) * | 2018-04-04 | 2019-06-25 | The Florida International University Board Of Trustees | Efficient progressive continuous k-nearest neighbor query algorithm for moving objects with a tree-like index |
| CN109977309A (en) * | 2019-03-21 | 2019-07-05 | 杭州电子科技大学 | Combination point of interest querying method based on multiple key and user preference |
Non-Patent Citations (3)
| Title |
|---|
| ANIKA TABASSUM ET AL: "Dynamic Group Trip Planning Queries in Spatial Databases", 《PROCEEDINGS OF THE 29TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT》 * |
| TANZIMA HASHEM ET AL: "Efficient Computation of Trips with Friends and Families", 《PROCEEDINGS OF THE 24TH ACM INTERNATIONAL ON CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT》 * |
| ZIJUN CHEN ET AL: "Time-aware spatial keyword cover query", 《DATA & KNOWLEDGE ENGINEERING》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114780875B (en) | 2022-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yin et al. | LCARS: A spatial item recommender system | |
| Zheng et al. | Approximate keyword search in semantic trajectory database | |
| US10289717B2 (en) | Semantic search apparatus and method using mobile terminal | |
| CN102750375B (en) | Service and tag recommendation method based on random walk | |
| CN107145545B (en) | Top-k area user text data recommendation method in social network based on position | |
| CN104834679B (en) | A kind of expression of action trail, querying method and device | |
| CN109947904A (en) | A kind of preference space S kyline inquiry processing method based on Spark environment | |
| CN114218400A (en) | Semantic-based data lake query system and method | |
| CN108932347A (en) | A kind of spatial key querying method based on society's perception under distributed environment | |
| CN107145526B (en) | Reverse-nearest neighbor query processing method for geographic social keywords under road network | |
| CN109189959A (en) | A kind of method and device constructing image data base | |
| Mahmood et al. | FAST: frequency-aware indexing for spatio-textual data streams | |
| CN109840284A (en) | Family's affiliation knowledge mapping construction method and system | |
| CN107153687B (en) | Indexing method for social network text data | |
| KR20170086353A (en) | Method for providing interactive information service and apparatus therefor | |
| CN104615734A (en) | Community management service big data processing system and processing method thereof | |
| CN110580252B (en) | Space object indexing and query method under multi-objective optimization | |
| CN109190052A (en) | A kind of space index method based on society's perception under distributed environment | |
| Fu et al. | Mining frequent route patterns based on personal trajectory abstraction | |
| Ge et al. | MPG: not so random exploration of a city | |
| CN114780875B (en) | Dynamic group travel planning query method | |
| Cheng et al. | Quickly locating POIs in large datasets from descriptions based on improved address matching and compact qualitative representations | |
| CN112883272B (en) | Method for determining recommended object | |
| John et al. | Dynamic sorting and average skyline method for query processing in spatial-temporal data | |
| Xu et al. | A new self-adaptive hybrid Markov topic model POI recommendation in social networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |