Dynamic target trajectory indexing method based on multi-scale transportation network

Abstract

The invention provides a dynamic target trajectory indexing method based on a multi-level scale transportation network, which comprises the following steps: constructing an index tree structure of a multi-level scale transportation network; constructing a dynamic target trajectory index tree structure in a transportation network; linking the index tree structure of the multi-level scale transportation network and the dynamic target trajectory index tree structure in the transportation network through pointers; updating the index tree structure of the multi-level scale transportation network and the dynamic target trajectory index tree structure; and performing a joint query of the spatial dimension, the time dimension and the scale dimension of the multi-level scale dynamic target range to obtain a query result of the multi-level scale dynamic target range. The method can effectively and quickly index the dynamic target trajectory under the multi-level scale transportation network, and supports trajectory information query, range query and time slice query of the multi-level scale dynamic target, thus filling the blank of the dynamic target indexing structure under the multi-level scale transportation network.

Description

Technical Field

[0001] The invention relates to a dynamic target trajectory indexing method based on a multi-scale transportation network, and relates to a spatiotemporal indexing technology in a geographic information system. Background Art

[0002] In recent years, the management of dynamic targets has gradually become a hot topic of research. More and more location-related applications need to process spatial objects whose positions change over time, such as taxis, airplanes, armored vehicles, wild animals, etc. The main purpose of these application services is to effectively store and query dynamic targets in continuous motion, which requires a good index structure. The index is an important data structure in the database and directly affects the efficiency of the query. Dynamic targets have the attributes of dual changes in time and space, complex data structures, and massive spatiotemporal data, so the index structure of spatiotemporal data is particularly important.

[0003] In location-based service application systems, traffic network data is huge. In order to effectively and quickly analyze, process and express traffic network data, a multi-scale traffic network system is usually required. However, through the analysis of existing related research work, it is known that there is still a lack of effective access methods for dynamic target indexes in multi-scale traffic networks.

[0004] The above problems are issues that should be considered and solved in the dynamic target indexing process in multi-scale transportation networks. Summary of the invention

[0005] The purpose of the present invention is to provide a dynamic target trajectory indexing method based on a multi-scale transportation network to solve the problem existing in the prior art of how to effectively and quickly index dynamic target trajectories in a multi-scale transportation network.

[0006] The technical solution of the present invention is:

[0007] A dynamic target trajectory indexing method based on a multi-scale transportation network includes the following steps:

[0008] S1. Construct an index tree structure of a multi-scale transportation network. The index tree structure of a multi-scale transportation network integrates transportation networks of multiple scales into an R-tree.

[0009] S2. construct a dynamic target trajectory index tree structure in the traffic network, the dynamic target trajectory index tree structure in the traffic network records the running trajectory of the dynamic target on each road in the multi-scale traffic network index, and uses a 1-dimensional R-tree to store the dynamic target running trajectory of each road;

[0010] S3, linking the index tree structure of the multi-scale transportation network obtained in step S1 with the dynamic target index tree structure in the transportation network obtained in step S2 through pointers, taking the running trajectory of the dynamic target at the maximum scale as a reference, mapping the movement trajectory of the dynamic target to the small-scale transportation network respectively, and constructing a dynamic target index tree based on the multi-scale transportation network;

[0011] S4, updating the index tree structure of the multi-level scale transportation network and the dynamic target trajectory index tree structure in the dynamic target index tree based on the multi-level scale transportation network;

[0012] S5. Perform a joint query of the spatial dimension, the temporal dimension, and the scale dimension of the multi-scale dynamic target range to obtain a query result of the multi-scale dynamic target range.

[0013] Furthermore, in step S1, in the index tree structure of the multi-level scale traffic network, the node structure of the tree is N=(sign, MBR, data, scale, pchild, pmo), wherein sign identifies whether it is an object node or an intermediate node, an object node is a leaf node or a branch node and is used to store road object data, and an intermediate node is a branch node; MBR is the minimum bounding rectangle of the road object under the node subtree that has undergone comprehensive operations including selection and simplification operations; when the node is an object node, data points to a spatial object, and when the node is an intermediate node, the value is 0; if the node is an object node, it points to a road section object; scale indicates a scale value, and if the node is an object node, it indicates a scale value, and when the road section object has the same expression mode in multiple scale features, multiple scale values ​​are stored; if the node is an intermediate node, the scale value is 0, and the intermediate node does not store a road object; p child is a pointer to the subtree; p mo The dynamic target index tree corresponding to the road section is pointed to, and each object node has a corresponding dynamic target index tree; the depth S of the index tree structure of the multi-level scale transportation network = (s1, s2, ... sn) represents the scale sequence of the index tree structure of the multi-level scale transportation network.

[0014] Furthermore, in step S2, each leaf node of the dynamic target trajectory index tree structure in the traffic network represents the operating status of a dynamic target on a certain road section, including the following data items: the operating time interval on the road section, the dynamic target ID, the operating section ID and the section scale. The leaf node structure is expressed as: (MOID, SegID, Scale, Tbegin, Tend), wherein MOID represents the dynamic target ID, SegID represents the operating section ID, Scale represents the section scale, Tbegin represents the starting time point of the operating time interval, and Tend represents the ending time point of the operating time interval; the branch node structure is expressed as (p, Tbegin, Tend), wherein p is a pointer to a child node, and Tbegin and Tend are time intervals.

[0015] Furthermore, in step S3, the movement trajectory of the dynamic target is mapped to the small-scale traffic network based on the movement trajectory of the dynamic target at the maximum scale, specifically,

[0016] S31. If the road object exists in the large-scale traffic network, and after the selection operation, there is no corresponding road object in the small-scale traffic network, then the running trajectory of the dynamic target is not recorded in the small-scale traffic network;

[0017] S32. If the road object has a simpler expression from a large-scale traffic network to a small-scale traffic network after simplification, the large-scale traffic network and the small-scale traffic network correspond to different dynamic target 1-dimensional R-tree indexes respectively, and the running trajectory of the dynamic target in the large-scale traffic network is projected to the running trajectory of the simplified road object in the corresponding small-scale traffic network.

[0018] Furthermore, in step S4, the index tree structure of the multi-scale traffic network and the dynamic target trajectory index tree structure are updated, specifically,

[0019] S41, when a new road object is added to the index tree structure of the multi-scale transportation network or the road position is changed, the road object is inserted into the index tree structure of the multi-scale transportation network;

[0020] S42: When a new dynamic target is added or the dynamic target moves from one road section to another road section, the running track of the dynamic target is inserted into the dynamic target track index tree structure in the traffic network.

[0021] Furthermore, in step S41, the road object is inserted into the index tree structure of the multi-scale transportation network, specifically,

[0022] S411, searching for the insertion leaf node position of the road object, i.e., the road section; searching for the insertion leaf node according to the minimum coverage area optimization principle, i.e., the index node with the smallest area increment of the directory rectangle after surrounding the newly added road object; if the area increments are the same, the index node with the smallest area of ​​the directory rectangle is searched recursively from the root node until the insertion leaf node position of the road section is found;

[0023] S412, inserting the minimum scale road at the insertion leaf node position of the road segment obtained in step S411;

[0024] S413, based on the minimum scale section inserted in the previous step S412, insert the index information of other scales of the road in order from small scale to large scale; if the road section has no other comprehensive operation in different scales, that is, the road section has the same expression form in different scales, then only add the new scale value to the inserted scale section; if the road section has comprehensive operation in different scales, and the road section has different expression forms in different scales, then insert the scale section;

[0025] S414, adjusting the index tree structure of the multi-scale traffic network; if the leaf node is full, performing an overflow operation; if the leaf node is not full, completing the road object insertion operation.

[0026] Further, in step S42, the running trajectory of the dynamic target is inserted into the dynamic target trajectory index tree structure in the traffic network, specifically,

[0027] S421, through [L begin ,L end ] location parameter searches for the leaf node set of the road segment in the multi-scale traffic network index. This leaf node set contains the same road segment at different scales. begin ,L end ] is the trajectory of the dynamic target on the maximum scale road;

[0028] S422, according to the multi-scale road section found in the previous step S422, respectively calculating the running track of the dynamic target at each scale;

[0029] S423: Insert the running tracks of the dynamic targets at various scales into the dynamic target track index tree structure in the traffic network.

[0030] Furthermore, in step S5, a query window w = (x1, y1, x2, y2, t1, t2, s) is used to jointly query the spatial dimension, temporal dimension and scale dimension of the multi-level scale dynamic target range, wherein (x1, y1, x2, y2) are location parameters indicating the spatial range of the query, (t1, t2) are time parameters indicating the time interval of the query, and (s) is a scale parameter indicating the scale level of the query.

[0031] Furthermore, in step S5, a joint query of the spatial dimension, the temporal dimension and the scale dimension of the multi-scale dynamic target range is performed to obtain a query result of the multi-scale dynamic target range, specifically,

[0032] S51, using the spatial position parameter to obtain the roads included in the query window;

[0033] S52, according to the scale parameter, obtaining a road of a corresponding scale, that is, a leaf node of a multi-level scale traffic network index;

[0034] S52. In the dynamic target index tree corresponding to the road leaf node obtained in the previous step S52, a search operation is performed in combination with a time parameter to obtain a range query result.

[0035] The beneficial effects of the present invention are as follows: the dynamic target trajectory indexing method based on a multi-scale transportation network can effectively and quickly index the dynamic target trajectory under the multi-scale transportation network, and supports trajectory information query, range query, and time slice query of multi-scale dynamic targets, filling the gap in the dynamic target indexing structure under the multi-scale transportation network. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of a dynamic target trajectory indexing method based on a multi-scale transportation network according to an embodiment of the present invention.

[0037] Figure 2 It is a schematic diagram illustrating an example of a multi-scale transportation network in an embodiment.

[0038] Figure 3 Schematic diagram for explaining a multi-level scale traffic network index tree in an embodiment.

[0039] Figure 4 It is a schematic diagram for explaining the position projection of dynamic objects in a traffic network of different scales in the embodiment.

[0040] Figure 5 Schematic diagram of a dynamic target index tree based on a multi-scale transportation network in an embodiment. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0042] Example

[0043] A dynamic target trajectory indexing method based on multi-scale transportation network, such as Figure 1 , including the following steps,

[0044] S1. Construct an index tree structure of a multi-scale traffic network. The index tree structure of a multi-scale traffic network integrates traffic networks of multiple scales into an R-tree. In step S1, the index structure of the multi-scale traffic network is an improved R-tree. Based on the basic structure of the R-tree, multiple scales are integrated into an R-tree, and the R-tree is improved according to the characteristics of the multi-level scale, so as to support the multi-level scale representation of the traffic network and reduce the redundant data storage of roads at multiple scales. The leaf nodes or branch nodes of the multi-level scale traffic network tree can store road objects. Nodes of different depths store different scales. The larger the scale, the greater the depth, and the smaller the scale, the smaller the depth.

[0045] In the multi-level scale traffic network index structure, data objects can be stored in leaf nodes and branch nodes. Leaf nodes and branch nodes are used to store road object data, and the nodes in the multi-level scale traffic network tree are divided into object nodes and intermediate nodes. Object nodes are index items containing road segment objects, which can be leaf nodes or branch nodes; intermediate nodes are index items that do not contain road segment information, and they must be branch nodes. The problem that data objects in the traditional R-tree are only stored in leaf nodes is solved. Compared with the R-tree of the traditional spatial database in which branch nodes must be nodes that do not contain spatial object data, the multi-level scale traffic network tree in the embodiment improves the storage method of spatial object data.

[0046] In the multi-scale traffic network index structure, the node structure of the tree is represented by N = (sign, MBR, data, scale, p child ,p mo ). sign identifies whether it is an object node or an intermediate node. If the value is 1, it is an object node. If the value is 0, it is an intermediate node. MBR is the minimum enclosing rectangle of the subtree or data object. It should be noted that MBR is the minimum enclosing rectangle of the road object under the node subtree that has undergone comprehensive operations. When the node is an object node, data points to the spatial object. When the node is an intermediate node, the value is 0. If the node is an object node, it points to the road segment object. scale indicates the scale value. If the node is an object node, it indicates the scale value. When the road segment object has the same expression method in multiple scale features, multiple scale values ​​are stored. If the node is an intermediate node, the scale value is 0. The intermediate node does not store the road object. Scale is meaningless for the intermediate node.child is a pointer to the subtree. p mo Points to the dynamic target index tree corresponding to the road segment, and each object node has a corresponding dynamic target index tree.

[0047] Figure 2 is an example illustration diagram of a multi-scale traffic network in the embodiment. s1, s2, s3, and s4 are respectively the 4 scales of the traffic network, s1 < s2 < s3 < s4, s1 has the smallest scale, and s4 has the largest scale. From Figure 2 it can be seen that the road segments in s4 gradually degenerate into the road segments in s1 scale through comprehensive operations such as selection and simplification. Figure 3 is a multi-scale traffic network index tree and also Figure 2 the corresponding index structure. It can be seen that both nodes r1 and r1' are branch nodes and both store road segment objects. r 10 ”, r 11 ”, r2”, and r3” are all leaf nodes, and data objects can appear at both leaf nodes and branch nodes. Generally, as the number of layers of the index tree increases, the scale gradually increases. s1 is at the top layer, s2 and s3 are in the lower layers, and s4 is at the bottom layer. r1' is the child node of r1, and r 10 ”, r 11 ”, r2”, and r3” are the child nodes of r1'. The geometric features of road segment r1' do not change under the two scales of s2 and s3, so the information of the two scales is stored in the same node.

[0048] S2. Construct the dynamic target trajectory index tree structure in the traffic network. The dynamic target trajectory index tree structure in the traffic network records the running trajectories of dynamic targets on each road in the multi-scale traffic network index, and uses a 1D R-tree to store the running trajectories of dynamic targets on each road; each road segment with dynamic target running corresponds to a 1D R-tree, which records all dynamic targets passing through the road segment and their running times.

[0049] In step S2, each leaf node of the dynamic target index structure represents the running state of a dynamic target on a certain road segment, including the following data items: the running time interval on the road segment, the dynamic target ID, the running road segment ID, and the road segment scale. The leaf node structure is represented as (MOID, SegID, Scale, Tbegin, Tend), where MOID represents the dynamic target ID, SegID represents the running road segment ID, Scale represents the road segment scale, Tbegin represents the start time point of the running time interval, and Tend represents the end time point of the running time interval; the branch node structure is represented as (p, T begin , T end ), where p is a pointer to the child node, T begin , T endFor the time interval.

[0050] S3. Link the index tree structure of the multi-scale traffic network obtained in step S1 and the dynamic target index tree structure in the traffic network obtained in step S2 through pointers. After a variety of comprehensive operations, the same road has different expressions at multiple scales, so the movement trajectory of the dynamic target needs to correspond to the road expressions of different scales respectively. Since the larger the scale, the more detailed the traffic network, the more accurate the movement trajectory of the dynamic target; the smaller the scale, the simpler the traffic network, and the rougher the movement trajectory of the dynamic target; therefore, based on the movement trajectory of the dynamic target at the largest scale, the movement trajectory of the dynamic target is mapped to the small-scale traffic network respectively, and a dynamic target index tree based on the multi-scale traffic network is constructed. The specific method is as follows:

[0051] S31. If the road object exists in the large-scale traffic network, and after the selection operation, there is no corresponding road object in the small-scale traffic network, then the running trajectory of the dynamic target is not recorded in the small-scale traffic network;

[0052] S32. If the road object has a simpler expression from a large-scale traffic network to a small-scale traffic network after simplification, the large-scale traffic network and the small-scale traffic network correspond to different dynamic target 1-dimensional R-tree indexes respectively, and the running trajectory of the dynamic target in the large-scale traffic network is projected to the running trajectory of the simplified road object in the corresponding small-scale traffic network.

[0053] like Figure 4 As shown, Figure 4 The small and medium scale road points A', B', C', D', E', and F' are the orthographic projection positions of the points A, B, C, D, E, and F on the large scale road, respectively. That is, at a certain moment, when the dynamic target is at positions A, B, etc. in the large scale medium traffic network, the positions of the dynamic target in the small scale traffic network are A', B', etc. Figure 4 The projection value calculation formula of the middle section ABCDDEF to the section A'B'C'D'E'F' is as follows: Let l be the length of the section ABCDDEF, and A be the starting point of the coordinates. It represents the distance value of a point on the section. L B Indicates the length of the road from A to B, L D represents the length of the section from A to C to D; let x be the coordinate of the section A'B'C'D'E'F', A' is the starting point of the coordinate, it also represents the distance value of a point on the section, X C＇ represents the distance from A' to C'. Then, the value of x can be calculated from l according to the following formula:

[0054]

[0055] Figure 5 It is a schematic diagram illustrating the structure of the dynamic target index tree constructed based on the multi-level scale transportation network. Figure 5 In the figure, the upper part is the multi-scale traffic network index structure, and the lower part is the dynamic target index tree structure in the traffic network. Figure 5 The dynamic target index tree based on the multi-scale traffic network can index the dynamic targets moving on any road section at any scale and their movement time intervals.

[0056] S4. Update the index tree structure of the multi-level scale traffic network and the dynamic target trajectory index tree structure in the dynamic target index tree based on the multi-level scale traffic network; in step S4, there are two different objects that need to be inserted into the tree structure. One is that the road object, i.e., the road section, needs to be inserted into the multi-level scale traffic network index; the road object insertion is used to construct the multi-level scale traffic network index. When a new road object is added to the index tree or the road position changes, a road object insertion algorithm is required. The other is that the dynamic target needs to be inserted into the dynamic target index. When a new dynamic target is added or the dynamic target moves from one road section to another, a dynamic target insertion algorithm is required.

[0057] S41, when a new road object is added to the index tree structure of the multi-scale transportation network or the road position is changed, the road object is inserted into the index tree structure of the multi-scale transportation network;

[0058] S411, searching for the insertion leaf node position of the road section; searching for the insertion leaf node according to the minimum coverage area optimization principle, that is, searching for the index node with the smallest area increment of the directory rectangle after surrounding the newly added road object; if the area increments are the same, searching for the index node with the smallest area of ​​the directory rectangle, starting from the root node, recursively searching for items until the insertion leaf node position of the road section is found;

[0059] S412, inserting the minimum scale road at the insertion leaf node position of the road segment obtained in step S411;

[0060] S413, based on the minimum scale section inserted in the previous step S412, insert the index information of other scales of the road in sequence (from small scale to large scale); if the section has no other comprehensive operations in different scales, that is, the expression forms of the section in different scales are the same, then only the new scale value is recorded in the inserted scale section; if the section has comprehensive operations such as simplification in different scales, and the expression forms of the section in different scales are different, then insert the section of this scale;

[0061] S414, adjusting the index tree structure of the multi-scale traffic network; if the leaf node is full, performing an overflow operation; if the leaf node is not full, completing the road object insertion operation.

[0062] The embodiment method is different from the traditional R-tree in that the index object is single-scale. The traffic network index of the embodiment is multi-scale, so it is necessary to insert road objects of different scales into the index tree. The specific algorithm for inserting road objects is as follows:

[0063] The road object, i.e., road segment insertion algorithm, is described as follows:

[0064] Algorithm Road_Insert(N,R=(r0,r1,…,r s ))

[0065] InputN: the root node of the tree, R: the set of multi-scale road objects to be inserted, r0 is the minimum scale road object, r1,…,r s Road objects of different scales that gradually increase

[0066] OutputInsert the index tree of the new road object

[0067] Begin

[0068] N l =FindLeafNode(N,r0) / / Based on "minimum coverage

[0069] Area optimization principle to find the insertion node location N l

[0070] Insert r0 intoN1

[0071] For (road objects of other scales) / / insert into leaf node sets in sequence

[0072] Insert r i into r i-1

[0073] If N1 Overfill Then Split N1

[0074] End

[0075] S42, when a new dynamic target is added or the dynamic target moves from one road section to another, the dynamic target is inserted into the dynamic target trajectory index tree structure in the traffic network. In step S42, the dynamic target insertion is to insert the running trajectory of the dynamic target on the road section into the multi-level scale traffic network. When the dynamic target enters another road section from one road section, the insertion algorithm is triggered.

[0076] S421, the starting and ending positions of the dynamic target moving on the road section [L begin ,L end] as the position parameter, and search for the leaf node set of the road segment in the multi-scale traffic network index. This leaf node set contains the same road segment at different scales. The search process is similar to the traditional R-tree algorithm. begin ,L end ] is actually the trajectory of the dynamic target on the maximum scale road;

[0077] S422, according to the multi-scale road sections found in the previous step S421, respectively calculating the running track of the dynamic target at each scale;

[0078] S423: Insert the running tracks of the dynamic targets at various scales into the dynamic target index tree.

[0079] The pseudo code of the dynamic target insertion algorithm is as follows:

[0080] Algorithm MovingObj_Insert(MO id , [L begin ,L end ],[T begin ,T end ],N)

[0081] InputMO id : The identifier of the dynamic target, [L begin ,L end ]: The starting and ending positions of the dynamic target moving on the road segment, [T begin ,T end ]: The time period when the dynamic target runs on the road segment, which represents the time of entering the road segment and the time of leaving the road segment, N: The root node of the tree

[0082] OutputInsert the index tree of the new running trajectory of the dynamic target

[0083] Begin

[0084] N1=FindLeafNodeSet(N,[L begin ,L end ]) / / In the index tree, find the corresponding section of the dynamic target movement

[0085] For(s i =MinScale TO MaxScale) / / Process each scale in turn

[0086] [L sibegin ,L siend ]=TransformLocation([L begin ,L end ],s i) / / Get the position of the dynamic target under the corresponding scale

[0087] InsertMO id [L sibegin ,L siend ][L sibegin ,L siend intoN i .pMovingObjectIndex / / Insert the dynamic target trajectory into the dynamic target index tree of the corresponding scale

[0088] End

[0089] S5. Perform a joint query of the spatial dimension, time dimension and scale dimension of the multi-scale dynamic target range to obtain the query result of the multi-scale dynamic target range. In step S5, the dynamic target query refers to the query data object is a dynamic target whose position changes continuously. Since the dynamic target includes both time and space attributes, the dynamic target query types are very rich, such as range query, continuous query, etc. Range query is to query the dynamic target in a certain area at a certain time / time period. The index tree supports dynamic target range query under multi-scale transportation network. The multi-scale dynamic target range query includes the spatial dimension, time dimension and scale dimension, so it is essentially a joint query of the spatial dimension, time dimension and scale dimension.

[0090] The range query under the multi-scale transportation network also needs to indicate the scale of the query, that is, the range query window w = (x1, y1, x2, y2, t1, t2, s) of the dynamic target of the multi-scale transportation network, where (x1, y1, x2, y2) is the location parameter, indicating the spatial range of the query, (t1, t2) is the time parameter, indicating the time interval of the query, and (s) is the scale parameter, indicating the scale level of the query. The range query process under the multi-scale transportation network requires the judgment and selection of the scale of the transportation network.

[0091] In step S5, a joint query of the spatial dimension, the temporal dimension and the scale dimension of the multi-level scale dynamic target range is performed to obtain a query result of the multi-level scale dynamic target range, specifically,

[0092] S51, using the spatial position parameter to obtain the roads included in the query window;

[0093] S52, according to the scale parameter, obtaining a road of a corresponding scale, that is, a leaf node of a multi-level scale traffic network index;

[0094] S53. In the dynamic target index tree corresponding to the road leaf node obtained in the previous step S52, a search operation is performed in combination with the time parameter to obtain a range query result.

[0095] The pseudo code for multi-scale dynamic target range query is as follows:

[0096] Algorithm RangeQuery((x0,y0,x1,x2),Scales,N)

[0097] Input(x0,y0,x1,x2): query range; Scales: query scale; N: root node of the tree

[0098] Output moSet query result dynamic target set

[0099] Begin

[0100] / / In the index tree, find the smallest range rectangle that intersects the query range. Search is the search algorithm of the R tree, and NodeSet is the node set.

[0101] NodeSet=Search(N,x0,y0,x1,x2)

[0102] For(s=MinScale TO MaxScale) / / Process each scale in turn

[0103] ScaleNodeSet=SearchScales(NodeSet,s i ) / / Get the road segment object node of the required scale

[0104] For(i=1TO ScaleNodeSet.Counts) / / Process the required scales in sequence

[0105] SearchMOs(ScaleNodeSet.Node,moSet) / / Get the result from the dynamic target index corresponding to the road segment

[0106] Return moSet

[0107] End

[0108] This method of dynamic target trajectory indexing based on a multi-scale transportation network, the transportation network includes a multi-scale transportation network, and the dynamic target can run in transportation networks of different scales. A multi-scale transportation network index tree structure is established for the multi-scale transportation network, and a dynamic target trajectory index tree structure is established for the dynamic targets moving in the transportation networks of different scales. The multi-scale transportation network index structure stores the index information of multiple scale sections, and supports comprehensive operations such as selection, simplification, and merging. The dynamic target index structure stores the trajectory index information of the movement time interval and movement state of the dynamic target in each scale section. The transportation network index tree and the dynamic target index tree are linked by pointers. The index of the present invention supports trajectory information query, range query, and time slice query of dynamic targets.

[0109] This dynamic target trajectory indexing method based on multi-scale transportation networks can effectively and quickly index dynamic target trajectories under multi-scale transportation networks, and supports trajectory information query, range query, and time slice query of multi-scale dynamic targets, filling the gap in the dynamic target indexing structure under multi-scale transportation networks.

[0110] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic target trajectory indexing method based on a multi-scale transportation network, characterized by: The following steps are included: S1. Construct an index tree structure of a multi-scale transportation network. The index tree structure of a multi-scale transportation network integrates transportation networks of multiple scales into an R-tree. S2. construct a dynamic target trajectory index tree structure in the traffic network, the dynamic target trajectory index tree structure in the traffic network records the running trajectory of the dynamic target on each road in the multi-scale traffic network index, and uses a 1-dimensional R-tree to store the dynamic target running trajectory of each road; S3, linking the index tree structure of the multi-scale transportation network obtained in step S1 with the dynamic target index tree structure in the transportation network obtained in step S2 through pointers, taking the running trajectory of the dynamic target at the maximum scale as a reference, mapping the movement trajectory of the dynamic target to the small-scale transportation network respectively, and constructing a dynamic target index tree based on the multi-scale transportation network; S4, updating the index tree structure of the multi-level scale transportation network and the dynamic target trajectory index tree structure in the dynamic target index tree based on the multi-level scale transportation network; S5. Perform a joint query of the spatial dimension, the temporal dimension, and the scale dimension of the multi-scale dynamic target range to obtain a query result of the multi-scale dynamic target range.

2. The method for dynamic target trajectory indexing based on multi-scale transportation network according to claim 1, characterized in that: In step S1, in the index tree structure of the multi-scale transportation network, the node structure of the tree is N=(sign, MBR, data, scale, p child , p mo ), where sign identifies whether it is an object node or an intermediate node. An object node is a leaf node and is used to store road object data, and an intermediate node is a branch node; MBR It is the minimum bounding rectangle of the road objects under the node subtree that have undergone comprehensive operations including selection and simplification operations; when the node is an object node, data Points to the road segment object. When the node is an intermediate node, the value is 0. scale Indicates the scale value. If the node is an object node, it indicates the scale value. When the road segment object has the same expression method in multiple scale features, multiple scale values ​​are stored. If the node is an intermediate node, scale If the value is 0, the intermediate node does not store the road object; p child is a pointer to the subtree; p mo Pointing to the dynamic target index tree corresponding to the road section, each object node has a corresponding dynamic target index tree; the depth S = (s1, s2, …sn) of the index tree structure of the multi-level scale transportation network represents the scale sequence of the index tree structure of the multi-level scale transportation network.

3. The method for dynamic target trajectory indexing based on a multi-scale transportation network according to claim 1, characterized in that: In step S2, each leaf node of the dynamic target trajectory index tree structure in the traffic network represents the operating status of a dynamic target on a certain road section, including the following data items: the dynamic target ID, the operating section ID, the section scale, and the operating time interval on the section. The leaf node structure is expressed as: (MOID, SegID, Scale, Tbegin, Tend), where MOID represents the dynamic target ID, SegID represents the operating section ID, Scale represents the section scale, Tbegin represents the starting time point of the operating time interval, and Tend represents the ending time point of the operating time interval; the branch node structure is expressed as (p, Tbegin, Tend), where p is a pointer to a child node, and Tbegin and Tend are time intervals.

4. The method for dynamic target trajectory indexing based on a multi-scale transportation network according to any one of claims 1 to 3, characterized in that: In step S3, the movement trajectory of the dynamic target at the maximum scale is used as a reference, and the movement trajectory of the dynamic target is mapped to the small-scale traffic network respectively, specifically, S31. If the road object exists in the large-scale traffic network, and after the selection operation, there is no corresponding road object in the small-scale traffic network, then the running trajectory of the dynamic target is not recorded in the small-scale traffic network; S32. If the road object has a simpler expression from a large-scale traffic network to a small-scale traffic network after simplification, the large-scale traffic network and the small-scale traffic network correspond to different dynamic target 1-dimensional R-tree indexes respectively, and the running trajectory of the dynamic target in the large-scale traffic network is projected to the running trajectory of the simplified road object in the corresponding small-scale traffic network.

5. The method for dynamic target trajectory indexing based on a multi-scale transportation network as claimed in claim 4, characterized in that: In step S4, the index tree structure of the multi-scale traffic network and the dynamic target trajectory index tree structure are updated, specifically, S41, when a new road object is added to the index tree structure of the multi-scale transportation network or the road position is changed, the road object is inserted into the index tree structure of the multi-scale transportation network; S42: When a new dynamic target is added or the dynamic target moves from one road section to another road section, the running track of the dynamic target is inserted into the dynamic target track index tree structure in the traffic network.

6. The method for indexing dynamic target trajectories based on a multi-scale transportation network according to claim 5, characterized in that: In step S41, the road object is inserted into the index tree structure of the multi-scale transportation network, specifically, S411, searching for the insertion leaf node position of the road object, i.e., the road section; searching for the insertion leaf node according to the minimum coverage area optimization principle, i.e., searching for the index node with the smallest area increment of the directory rectangle after surrounding the newly added road object; If the area increments are the same, the index node with the smallest area of ​​the directory rectangle is searched recursively from the root node until the insertion leaf node position of the road segment is found; S412, inserting the minimum scale road at the insertion leaf node position of the road segment obtained in step S411; S413, based on the minimum scale section inserted in the previous step S412, insert the index information of other scales of the road in order from small scale to large scale; if the road section has no other comprehensive operation in different scales, that is, the road section has the same expression form in different scales, then only add the new scale value to the inserted scale section; if the road section has comprehensive operation in different scales, and the road section has different expression forms in different scales, then insert the scale section; S414, adjusting the index tree structure of the multi-scale transportation network; If the leaf node is full, the overflow operation is performed; if the leaf node is not full, the road object insertion operation is completed.

7. The method for indexing dynamic target trajectories based on a multi-scale transportation network according to claim 5, characterized in that: In step S42, the running trajectory of the dynamic target is inserted into the dynamic target trajectory index tree structure in the traffic network, specifically, S421, through [ L begin , L end ] The location parameter searches for the leaf node set of the road segment in the multi-scale traffic network index. This leaf node set contains the same road segment at different scales. L begin , L end ] is the trajectory of the dynamic target on the maximum scale road; S422, according to the multi-scale road section found in the previous step S422, respectively calculating the running track of the dynamic target at each scale; S423: Insert the running tracks of the dynamic targets at various scales into the dynamic target track index tree structure in the traffic network.

8. The dynamic target trajectory indexing method based on a multi-scale transportation network according to claim 4, characterized in that: In step S5, a query window w = ( x 1 , y 1 , x 2 , y 2 , t 1 , t 2 , s ),in,( x 1 , y 1 , x 2 , y 2 ) is a location parameter, indicating the spatial scope of the query, ( t 1 , t 2 ) is the time parameter, indicating the query time interval, and (s) is the scale parameter, indicating the query scale level.

9. The method for dynamic target trajectory indexing based on a multi-scale transportation network according to claim 8, characterized in that: In step S5, a joint query of the spatial dimension, the temporal dimension and the scale dimension of the multi-level scale dynamic target range is performed to obtain a query result of the multi-level scale dynamic target range, specifically, S51, using the spatial position parameter to obtain the roads included in the query window; S52, according to the scale parameter, obtaining a road of a corresponding scale, that is, a leaf node of a multi-level scale traffic network index; S52. In the dynamic target index tree corresponding to the road leaf node obtained in the previous step S52, a search operation is performed in combination with a time parameter to obtain a range query result.

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