CN102012908A - Method for inquiring visible neighbours of moving objects in environment with barriers - Google Patents
Method for inquiring visible neighbours of moving objects in environment with barriers Download PDFInfo
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Abstract
The invention discloses a method for inquiring visible neighbours of moving objects in the environment with barriers. The neighbour inquiry technology of the moving objects in a spatial database is utilized and the existing branch and bound algorithm is utilized to inquire the neighbours which are not blocked in the environment with barriers. In the spatial data, R-tree index is utilized for the barriers, TPR-tree index is utilized for the moving objects, the branch and bound algorithm is utilized to traverse the indexes, and meanwhile inquiry is realized by combining calculation of the blocking time of the moving objects. The method has the following beneficial effects: utilizing the existing indexing technology and the moving object neighbour inquiry technology in the spatial database and researches and achievements in the environment with barriers, providing the method of inquiring the moving objects which are not blocked in certain period in the environment with barriers, ensuring that the users can select the optimum inquiry time interval according to the application requirements and providing best performances.
Description
Technical field
The present invention relates to index and inquiring technology in the spatial database, particularly relate to the querying method of visual mobile neighbour in a kind of obstacle environment.
Background technology
Spatial database is the Database Systems of storage and management spatial data.In order to visit massive spatial data quickly and efficiently, experts and scholars have proposed a large amount of space index methods, and R tree index, R* tree index, K-D-B tree index, Hilbert curve index are wherein arranged.On this basis, various inquiries that differ from one another and solution thereof have been proposed more, as neighbour's inquiry, k nearest neighbor inquiry, neighbour's inquiry continuously, oppositely neighbour's inquiry, neighbours' inquiry farthest, skyline inquiry.
Along with the high speed development of mobile communication technology and GPS service, in the human lives, mobile data is with unpredicted speed increment.So many application impels people to seek more efficient methods to handle mobile object, improve the search efficiency of mobile object.Present mobile object is mainly by based on the TPR of linear function tree and based on the B of space filling curve
xSetting this two classes index and mutation thereof handles.
In many inquiries of mobile object, neighbour's inquiry is particularly important.It is the basis of other various neighbour's inquiries.The simplest, most popular at present processing mode is with the TPR tree mobile object to be carried out index, adopts branch and bound method to seek nearest-neighbors.
At real world, there are many barriers inevitably, such as buildings, highland, forest, blocked people's sight line.Because the existence of these barriers, the nearest-neighbors that originally found can be blocked on very big probability, the neighbour who finds like this there is no much meanings in many application, this provides a kind of new method to go to seek that be not blocked, visual nearest-neighbors with regard to requiring.
The neighbour of present mobile object in the environment of clear inquires about existing ripe solution, and the neighbour inquiry of stationary objects in obstacle environment is arranged simultaneously also has many solutions.Considering does not at the same time have a kind of suitable method to solve visual neighbour's inquiry under the situation of a large amount of shelters of existing in the movability of object and the environment.
Summary of the invention
The object of the present invention is to provide the querying method of visual mobile neighbour in a kind of obstacle environment.
The step that the present invention solves the technical scheme that its technical matters adopts is as follows:
Step 1) adopts R tree index to static barrier object, and mobile object is adopted TPR tree index;
Step 2) safeguards a Priority Queues and a results list of depositing the employing tolerance of TPR tree index node, deposited the current candidate target that satisfies visual neighbour's condition in the tabulation;
Step 3) is put into Priority Queues, the index node of cyclic access formation vacuum metrics minimum, metric calculation method and step 2 with the root node of TPR tree) identical;
Step 4) utilizes R tree index to judge the visuality of TPR tree index node;
Step 5) joins each following in visual index node child node in the Priority Queues, ignores not visible index node;
There is not index node in step 6) in Priority Queues, the object in the results list is exactly the neighbour of mobile object in obstacle environment who obtains.
Described step 2) tolerance in refers in a period of time index node in the Priority Queues with the minor increment between the query point; The calculating of this tolerance, consider in two kinds of situation:
1) when index node be under the situation of mobile object, calculate with the minor increment in a period of time between the query point; Because mobile object representation is the linear function about the time, according to mathematical principle, the distance table between the two is shown the quadratic function about the time, so should tolerance then be this quadratic function minimum value during this period of time;
2) when index node is middle index node on the TPR tree, because it is a quadrilateral that moves, take all factors into consideration each bar limit, obtaining this index node is a piecewise function with the distance of query point, and be based on the quadratic function of time at each, so this tolerance is this piecewise function minimum value during this period of time.
The visual step of calculating comprises in the described step 4):
1) obtains the orientation and the velocity information of current index node;
2), in R tree index, seek corresponding effective shutter body according to the during this period of time inswept scope of this index node;
3), calculate the visual effect of this TPR tree index node according to these effective shutter bodies;
4) the effective shutter body here is meant with respect to query point, can influence the shutter body of the visual effect of index node and mobile object.
Described step 5) is for being judged to not visible index node, and all the index child nodes under its index are not visible certainly, therefore need not to visit again down; And for being judged to visual index node, illustrate to have visual situation in the index child node under it; In this case, need to consider in two kinds of situation:
A) this node is middle index node: divided for three steps handled for the index child node under it under this situation:
A) relatively this index child node is followed between the query point apart from the distance of following with the candidate target of this time period in the results list between the query point; Obtain the former less time period of distance,, illustrate that then this index child node during this period of time can not be a neighbour, also need not following step b) if can not get;
B) ask for the common factor apart from the time period that visual time period that step 4) obtains and previous step obtain in rapid, in this occured simultaneously, this index child node just might be the visual neighbour of query point;
C) calculating this index child node under this occurs simultaneously, is added this index child node in the Priority Queues to as its tolerance with the minor increment of query point again;
B) this index node is mobile object: this situation divided for three steps for the processing of this node:
A) relatively this node is followed between the query point apart from the distance of following with the candidate target of this time period in the results list between the query point; Obtain the former less time period of distance,, illustrate that then this index node during this period of time can not be a neighbour, also need not following step b) if can not get.
B) ask for the common factor apart from the time period that visual time period that step 4) obtains and previous step obtain in rapid, in this occured simultaneously, this index node just might be the visual neighbour of query point.
C) during time period of obtaining in back occured simultaneously, this index node was current visual nearest-neighbors, so upgrade in the results list candidate neighbour during this period of time.
Described A), B) a) in relatively index node be divided into three steps with candidate target that should the time period in the results list with the distance between the query point with distance between the query point:
1), determines each split time and corresponding candidate target thereof according to the results list;
2) according to described method under second kind of situation in the claim 2, the computation index node is with the distance of query point in each segmentation;
3) ask for index node with the time period of query point distance less than candidate target.
The candidate target of the current nearest-neighbors that obtains the c described B)) is updated to it in the results list; This renewal was divided into for four steps:
1), determines each split time and corresponding candidate target thereof according to the results list;
2) determine b in second step) time period of obtaining occurs simultaneously;
3) common factor of two time periods in step above the calculating;
4) be updated in time period of obtaining the results list under occuring simultaneously with new candidate target.
The beneficial effect that the present invention has is:
The present invention has made full use of existing index technology in the spatial database, mobile object neighbour inquiring technology and the research under the obstacle environment has been arranged and realizing achievement, provide and in the obstacle environment, inquired about the mobile object that is not blocked in certain period, the user can select the only inquiry period according to application demand, and performance offers the best.
Description of drawings
Fig. 1 is the invention process flow chart of steps.
Fig. 2 is mobile object is inquired about the neighbour who is not blocked in obstacle environment a principle of work synoptic diagram.
Embodiment
Now with specific embodiment technical scheme of the present invention is described further in conjunction with the accompanying drawings.
1, as shown in Figure 1, specific implementation process of the present invention and principle of work are as follows:
Step 1) is carried out index to spatial data, promptly static barrier is adopted R tree index, and mobile object is adopted TPR tree index;
Step 2) safeguards a Priority Queues and a results list of depositing the employing tolerance of TPR tree index node, deposited the current candidate target that satisfies visual neighbour's condition in the tabulation;
Step 3) travels through mobile object indexing, and the root node of TPR tree is put into Priority Queues, the node of cyclic access formation vacuum metrics minimum;
Step 4) utilizes R tree index to judge occlusion effect, the time that the computation index node is not blocked;
Step 5) is calculated the child node of visual index node with the distance between the query point, and it is joined in the Priority Queues, ignores not visible index node;
Step 6) is upgraded the results list, does not have index node in Priority Queues, and the object in the results list is exactly the neighbour of mobile object in obstacle environment who obtains.
The object that needs among the present invention to handle has static barrier and mobile object, shown in the index module of Fig. 2, according to characteristic separately, adopts R tree index and TPR tree index corresponding to barrier index among Fig. 2 and mobile object indexing in step 1) respectively.
Step 2) tolerance in refers in a period of time index node in the Priority Queues with the minor increment between the query point; This tolerance is calculated gained by the distance calculation comparer shown in Fig. 2, and its computation process is considered in two kinds of situation:
1) when index node be under the situation of mobile object, calculate with the minor increment in a period of time between the query point; Because mobile object representation is the linear function about the time, according to mathematical principle, the distance table between the two is shown the quadratic function about the time, so should tolerance then be this quadratic function minimum value during this period of time;
2) when index node is middle index node on the TPR tree, because it is a quadrilateral that moves, take all factors into consideration each bar limit, obtaining this index node is a piecewise function with the distance of query point, and be based on the quadratic function of time at each, so this tolerance is this piecewise function minimum value during this period of time.
Step 3) is put into Priority Queues with the root node of TPR tree, travels through mobile object indexing, the node of cyclic access formation vacuum metrics minimum, wherein tolerance and step 2) described in tolerance identical.
Next judge occlusion effect, the time that the computation index node is not blocked, calculate acquisition by the counter that blocks shown in Figure 2, the step that concrete visuality is calculated comprises:
1) obtains the orientation and the velocity information of current index node;
2), in R tree index, seek corresponding effective shutter body according to the during this period of time inswept scope of this index node;
3), calculate the visual effect of this TPR tree index node according to these effective shutter bodies;
4) the effective shutter body here is meant with respect to query point, can influence the shutter body of the visual effect of index node and mobile object.
For being judged to not visible index node, all the index child nodes under its index are not visible certainly, therefore need not to visit again down; And for being judged to visual index node, illustrate to have visual situation in the index child node under it; In this case, need to consider in two kinds of situation:
1) this node is middle index node: divided for three steps handled for the index child node under it under this situation:
A) relatively this index child node is followed between the query point apart from the distance of following with the candidate target of this time period in the results list between the query point; Obtain the former less time period of distance,, illustrate that then this index child node during this period of time can not be a neighbour, also need not following step b) if can not get;
B) ask for the common factor apart from the time period that visual time period that step 4) obtains and previous step obtain in rapid, in this occured simultaneously, this index child node just might be the visual neighbour of query point;
C) calculating this index child node under this occurs simultaneously, is added this index child node in the Priority Queues to as its tolerance with the minor increment of query point again;
2) this index node is mobile object: this situation divided for three steps for the processing of this node:
A) relatively this node is followed between the query point apart from the distance of following with the candidate target of this time period in the results list between the query point; Obtain the former less time period of distance,, illustrate that then this index node during this period of time can not be a neighbour, also need not following step b) if can not get.
B) ask for the common factor apart from the time period that visual time period that step 4) obtains and previous step obtain in rapid, in this occured simultaneously, this index node just might be the visual neighbour of query point.
C) during time period of obtaining in back occured simultaneously, this index node was current visual nearest-neighbors, so upgrade in the results list candidate neighbour during this period of time.
In the distance calculation comparer of Fig. 2 relatively index node with distance between the query point should the time period in the results list candidate target specifically be divided into for three steps with the distance between the query point:
1), determines each split time and corresponding candidate target thereof according to the results list;
2), in the distance calculation comparer, calculate index node with the distance of query point in each segmentation according to described method under second kind of situation in the claim 2;
3) ask for index node with the time period of query point distance less than candidate target.
For the candidate target of the current nearest-neighbors that obtains, it is updated in the results list, to finish by the query processor of Fig. 2, update method specifically was divided into for four steps:
1), determines each split time and corresponding candidate target thereof according to the results list;
2) pass through apart from comparer, the time period that calculates occurs simultaneously;
3) common factor of the time period that two steps obtained above the calculating;
4) be updated in time period of obtaining the results list under occuring simultaneously with new candidate target.
Claims (6)
1. visual mobile neighbour's querying method in the obstacle environment is characterized in that the step of this method is as follows:
Step 1) adopts R tree index to static barrier object, and mobile object is adopted TPR tree index;
Step 2) safeguards a Priority Queues and a results list of depositing the employing tolerance of TPR tree index node, deposited the current candidate target that satisfies visual neighbour's condition in the tabulation;
Step 3) is put into Priority Queues, the index node of cyclic access formation vacuum metrics minimum, metric calculation method and step 2 with the root node of TPR tree) identical;
Step 4) utilizes R tree index to judge the visuality of TPR tree index node;
Step 5) joins each following in visual index node child node in the Priority Queues, ignores not visible index node;
There is not index node in step 6) in Priority Queues, the object in the results list is exactly the neighbour of mobile object in obstacle environment who obtains.
2. visual mobile neighbour's querying method in a kind of obstacle environment according to claim 1 is characterized in that: the tolerance described step 2) refers in a period of time index node in the Priority Queues with the minor increment between the query point; The calculating of this tolerance, consider in two kinds of situation:
1) when index node be under the situation of mobile object, calculate with the minor increment in a period of time between the query point; Because mobile object representation is the linear function about the time, according to mathematical principle, the distance table between the two is shown the quadratic function about the time, so should tolerance then be this quadratic function minimum value during this period of time;
2) when index node is middle index node on the TPR tree, because it is a quadrilateral that moves, take all factors into consideration each bar limit, obtaining this index node is a piecewise function with the distance of query point, and be based on the quadratic function of time at each, so this tolerance is this piecewise function minimum value during this period of time.
3. visual mobile neighbour's querying method in a kind of obstacle environment according to claim 1 is characterized in that: the visual step of calculating comprises in the described step 4):
1) obtains the orientation and the velocity information of current index node;
2), in R tree index, seek corresponding effective shutter body according to the during this period of time inswept scope of this index node;
3), calculate the visual effect of this TPR tree index node according to these effective shutter bodies;
4) the effective shutter body here is meant with respect to query point, can influence the shutter body of the visual effect of index node and mobile object.
4. visual mobile neighbour's querying method in a kind of obstacle environment according to claim 1, it is characterized in that: described step 5) is for being judged to not visible index node, all index child nodes under its index are not visible certainly, therefore need not to visit again down; And for being judged to visual index node, illustrate to have visual situation in the index child node under it; In this case, need to consider in two kinds of situation:
1) this node is middle index node: divided for three steps handled for the index child node under it under this situation:
A) relatively this index child node is followed between the query point apart from the distance of following with the candidate target of this time period in the results list between the query point; Obtain the former less time period of distance,, illustrate that then this index child node during this period of time can not be a neighbour, also need not following step b) if can not get;
B) ask for the common factor apart from the time period that visual time period that step 4) obtains and previous step obtain in rapid, in this occured simultaneously, this index child node just might be the visual neighbour of query point;
C) calculating this index child node under this occurs simultaneously, is added this index child node in the Priority Queues to as its tolerance with the minor increment of query point again;
2) this index node is mobile object: this situation divided for three steps for the processing of this node:
A) relatively this node is followed between the query point apart from the distance of following with the candidate target of this time period in the results list between the query point; Obtain the former less time period of distance,, illustrate that then this index node during this period of time can not be a neighbour, also need not following step b) if can not get.
B) ask for the common factor apart from the time period that visual time period that step 4) obtains and previous step obtain in rapid, in this occured simultaneously, this index node just might be the visual neighbour of query point.
C) during time period of obtaining in back occured simultaneously, this index node was current visual nearest-neighbors, so upgrade in the results list candidate neighbour during this period of time.
5. visual mobile neighbour's querying method in a kind of obstacle environment according to claim 4 is characterized in that: described two step 1), 2) a) in relatively index node be divided into three steps with candidate target that should the time period in the results list with the distance between the query point with distance between the query point:
1), determines each split time and corresponding candidate target thereof according to the results list;
2) according to described method under second kind of situation in the claim 2, the computation index node is with the distance of query point in each segmentation;
3) ask for index node with the time period of query point distance less than candidate target.
6. visual mobile neighbour's querying method in a kind of obstacle environment according to claim 4 is characterized in that: the candidate target of the current nearest-neighbors that obtains described c in second step) is updated to it in the results list; This renewal was divided into for four steps:
1), determines each split time and corresponding candidate target thereof according to the results list;
2) determine b in second step) time period of obtaining occurs simultaneously;
3) common factor of two time periods in step above the calculating;
4) be updated in time period of obtaining the results list under occuring simultaneously with new candidate target.
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| CN102646118A (en) * | 2012-02-20 | 2012-08-22 | 浪潮(北京)电子信息产业有限公司 | Data indexing method and device |
| CN103106280A (en) * | 2013-02-22 | 2013-05-15 | 浙江大学 | Uncertain space-time trajectory data range query method under road network environment |
| CN103365984A (en) * | 2013-07-04 | 2013-10-23 | 上海交通大学 | Landmark method and system for acquiring single reverse furthest neighbors on road network |
| CN106970983A (en) * | 2017-03-29 | 2017-07-21 | 东北大学 | Anti- farthest neighbours' verification method in a kind of outsourcing spatial database |
| CN110691990A (en) * | 2017-06-02 | 2020-01-14 | 华为技术有限公司 | Obstacle detection method and equipment |
| CN112989228A (en) * | 2021-04-25 | 2021-06-18 | 湖南视觉伟业智能科技有限公司 | Distributed space-time query method and system |
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| CN106970983A (en) * | 2017-03-29 | 2017-07-21 | 东北大学 | Anti- farthest neighbours' verification method in a kind of outsourcing spatial database |
| CN106970983B (en) * | 2017-03-29 | 2020-02-21 | 东北大学 | Verification method for anti-farthest neighbors in outsourced space database |
| CN110691990A (en) * | 2017-06-02 | 2020-01-14 | 华为技术有限公司 | Obstacle detection method and equipment |
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