Intersection entity extraction method and system based on large-scale vector road data set
By dynamically constructing an ordered set of intersection points in a large-scale vector road dataset and updating it using a binary search method, the problems of high computational complexity and high memory pressure in existing technologies are solved, achieving efficient and accurate intersection entity extraction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SICHUAN SURVEYING & MAPPING PROD QUALITY SUPERVISION & INSPECTION STATION OF THE MINIST OF NATURAL RESOURCES SICHUAN SURVEYING & MAPPING PROD QUALITY SUPERVISION & INSPECTION STATION
- Filing Date
- 2026-04-14
- Publication Date
- 2026-06-23
Smart Images
Figure CN122019826B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of surveying and mapping geographic information technology, and in particular to a method and system for extracting intersection entities based on a large-scale vector road dataset. Background Technology
[0002] Intersection entities, also known as road intersection entities, are important basic geographic entities. Together with road entities and road ancillary facilities entities, they constitute a complete set of traffic entities. They are the basic data source supporting GIS analysis algorithms such as road connectivity analysis and path analysis. The main method of entity production is to use digital line drawing (DLG) data to convert and produce basic geographic entities. Among them, the rapid extraction of intersection entities from large-scale DLG road datasets is the basic way to construct intersection entities.
[0003] Currently, the common approach for extracting intersection entities from large-scale DLG road datasets is to first extract the intersection points and then calculate the number of intersection point repetitions. While this method achieves high accuracy, it suffers from the following drawback: its algorithm complexity is O(n^2). 2 As the amount of data increases, the computation time increases dramatically, resulting in extremely low efficiency. In scenarios with large data volumes, the system memory requirements are extremely high, and memory shortages are likely to occur. It is difficult to complete the task of extracting intersection entities within an effective time, and it cannot meet the needs of large-scale data processing.
[0004] In summary, existing methods for extracting intersection entities still have significant drawbacks, including low efficiency, high computational complexity, and high memory pressure, and in particular, they are difficult to meet the needs of extracting large-scale datasets. Summary of the Invention
[0005] To address the shortcomings of the existing technologies, this invention provides a method and system for intersection entity extraction based on a large-scale vector road dataset. This solves the problems of low efficiency, high computational complexity, and high memory pressure in existing technologies for intersection entity extraction. This invention can meet the needs of rapid extraction from large-scale datasets. The technical solution provided by this invention includes:
[0006] The intersection entity extraction method based on large-scale vector road datasets includes the following steps:
[0007] S1. Traverse all road segment data in the road dataset, extract the starting point and ending point corresponding to each road segment data as insertion points, and update the ordered intersection point set according to preset rules based on the positional relationship between the insertion points and the coordinate points in the ordered intersection point set.
[0008] S2. Traverse all coordinate points in the ordered intersection point set, and output the coordinate points that are repeated more than 2 times as intersection entity points.
[0009] Preferably, the preset rules include:
[0010] S101. Based on the positional relationship between the insertion point and the coordinate points in the ordered intersection point set, determine whether the position of the insertion point is arranged after the first point in the ordered intersection point set. If so, proceed to step S102.
[0011] S102. Determine whether the position of the insertion point is arranged before the last point in the ordered intersection point set. If so, add the insertion point to the ordered intersection point set.
[0012] Preferably, the preset rules further include:
[0013] Based on the binary search method, the coordinate information of the insertion point is compared with the coordinates of the coordinates of the ordered intersection points by binary search, the search boundary is converged, and the positional relationship between the insertion point and the coordinates of the ordered intersection points is determined.
[0014] Preferably, the specific method for determining the positional relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set includes:
[0015] S111. Compare the difference between points P1 and P2 on the horizontal coordinate as the first difference. If the first difference is greater than the preset distance tolerance, then determine that P1 is located after P2; otherwise, proceed to step S112.
[0016] S112. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the first absolute value. If the first absolute value is less than the preset distance tolerance, then proceed to step S113; otherwise, determine that P1 is located before P2.
[0017] S113. Compare the difference between points P1 and P2 on the vertical coordinate as the second difference. If the second difference is greater than the preset distance tolerance, then it is determined that P1 is located after P2; otherwise, proceed to step S114.
[0018] S114. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the second absolute value. If the second absolute value is less than the preset distance tolerance, then P1 and P2 are determined to be duplicate points; otherwise, P1 is determined to be located before P2.
[0019] Preferably, in step S101, if not, the insertion point is arranged before the first point in the ordered intersection point set;
[0020] In step S102, if not, the insertion point is arranged after the first point in the ordered intersection point set.
[0021] This invention also provides an intersection entity extraction system based on a large-scale vector road dataset, the system comprising:
[0022] Intersection point insertion and update module: used to traverse all road segment data in the road dataset, extract the start and end points corresponding to each road segment data as insertion points, and update the ordered intersection point set according to preset rules based on the positional relationship between the insertion points and the coordinate points in the ordered intersection point set;
[0023] Intersection entity point acquisition module: connected to the intersection point insertion and update module, used to traverse all coordinate points in the ordered intersection point set and output the coordinate points with a repetition count greater than 2 as intersection entity points.
[0024] Preferably, the preset rules in the intersection point insertion and update module include:
[0025] S101. Based on the positional relationship between the insertion point and the coordinate points in the ordered intersection point set, determine whether the position of the insertion point is arranged after the first point in the ordered intersection point set. If so, proceed to step S102.
[0026] S102. Determine whether the position of the insertion point is arranged before the last point in the ordered intersection point set. If so, add the insertion point to the ordered intersection point set.
[0027] Preferably, the method for adding the insertion point to the ordered intersection point set in the intersection point insertion and update module includes:
[0028] Based on the binary search method, the coordinate information of the insertion point is compared with the coordinates of the coordinates of the ordered intersection points by binary search, the search boundary is converged, and the positional relationship between the insertion point and the coordinates of the ordered intersection points is determined.
[0029] Preferably, the specific method for determining the positional relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set includes:
[0030] S111. Compare the difference between points P1 and P2 on the horizontal coordinate as the first difference. If the first difference is greater than the preset distance tolerance, then determine that P1 is located after P2; otherwise, proceed to step S112.
[0031] S112. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the first absolute value. If the first absolute value is less than the preset distance tolerance, then proceed to step S113; otherwise, determine that P1 is located before P2.
[0032] S113. Compare the difference between points P1 and P2 on the vertical coordinate as the second difference. If the second difference is greater than the preset distance tolerance, then it is determined that P1 is located after P2; otherwise, proceed to step S114.
[0033] S114. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the second absolute value. If the second absolute value is less than the preset distance tolerance, then P1 and P2 are determined to be duplicate points; otherwise, P1 is determined to be located before P2.
[0034] Preferably, in step S101, if not, the insertion point is arranged before the first point in the ordered intersection point set;
[0035] In step S102, if not, the insertion point is arranged after the first point in the ordered intersection point set.
[0036] Beneficial effects
[0037] This invention dynamically constructs an ordered set of potential intersection entity points during road segment reading and dynamically updates the repetition count of potential intersection points. While ensuring detection accuracy, it reduces the algorithm's time complexity from O(n log n). 2 The time complexity is reduced to O(nlogn), which greatly improves the efficiency of intersection entity extraction and meets the needs of large-scale intersection entity extraction. Attached Figure Description
[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 This is a schematic diagram of the intersection entity extraction method based on a large-scale vector road dataset provided in a preferred embodiment of the present invention;
[0040] Figure 2 This is a schematic diagram of the intersection entity extraction system based on a large-scale vector road dataset provided in a preferred embodiment of the present invention. Detailed Implementation
[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0042] Example 1
[0043] like Figure 1As shown, this invention provides a method for intersection entity extraction based on a large-scale vector road dataset, the specific steps of which include:
[0044] S1. Traverse all road segment data in the road dataset, extract the start and end points corresponding to each road segment data as insertion points, and update the ordered intersection point set according to preset rules based on the positional relationship between the insertion points and the coordinate points in the ordered intersection point set.
[0045] The road dataset is a collection storing road segment data. The road segment data includes structured information such as road segment number, coordinates, and start and end points. Each road segment is the smallest unit constituting a road network, corresponding to a continuous road between two intersections. The ordered intersection point set is a set of intersection points formed after deduplication and sorting, possessing a unified order and topological association. The preset rule is a sorting rule designed based on the positional relationship between the insertion point and the coordinates of the points in the ordered intersection point set. In some preferred embodiments, this invention extracts the endpoints of each road segment from the road dataset and arranges all the endpoints of all road segments into the ordered intersection point set in an ordered manner according to the preset rule, where some road segment points may be duplicated.
[0046] S2. Traverse all coordinate points in the ordered intersection point set, and output the coordinate points that are repeated more than 2 times as intersection entity points.
[0047] The repetition count refers to the number of times a given intersection point is associated with different road segments in an ordered set of intersection points. In some preferred embodiments, the present invention counts the repetition count for each coordinate point in the ordered set of intersection points, and filters out coordinate points with a repetition count greater than 2 as intersection entity points for output.
[0048] Compared to existing technologies, this embodiment reduces the algorithm complexity of intersection extraction from O(n^2) to O(n^2). 2 The processing time is reduced to O(nlogn), which is more efficient and consumes less memory when dealing with large-scale DLG road data. At the same time, by dynamically constructing an ordered set of potential intersection points and dynamically updating the number of duplicates of potential intersection points, the entity points of intersections can be accurately identified, improving the accuracy and reliability of road network intersection extraction.
[0049] Example 2
[0050] Based on Embodiment 1, this embodiment provides a better preset rule for updating the ordered intersection point set.
[0051] When updating an ordered set of intersection points, existing conventional techniques typically involve comparing the coordinates of the inserted point with all points in the set sequentially to determine its position within the ordered set before performing the insertion operation. After insertion, the indices of subsequent points also need to be adjusted. This approach requires traversing and shifting the entire point set, which is computationally time-consuming and inefficient when dealing with large datasets, and also incurs unnecessary memory overhead.
[0052] To reduce computational complexity and save memory resources, in some preferred embodiments, the method of the preset rule of this application includes: S101. Based on the positional relationship between the insertion point and the coordinate points in the ordered intersection point set, determine whether the position of the insertion point is arranged after the first point in the ordered intersection point set. If yes, then execute step S102; if no, then arrange the insertion point before the first point in the ordered intersection point set and stop the insertion.
[0053] S102. Determine whether the position of the insertion point is arranged before the last point of the ordered intersection point set. If yes, add the insertion point to the ordered intersection point set; if no, arrange the insertion point after the first point of the ordered intersection point set and stop the insertion.
[0054] This application first determines whether the insertion point is located after the first point of the ordered intersection point set based on the coordinate information, and then determines whether it is located before the last point. If the insertion point meets both of the above conditions, it can be added to the ordered intersection point set by conventional technical means in this field.
[0055] This embodiment can quickly filter out a large number of points that do not need to be inserted, reducing the number of traversal and insertion operations. While ensuring the order of the point set, it significantly improves update efficiency and reduces computation and memory consumption.
[0056] Example 3
[0057] Based on Embodiment 2, this embodiment provides another preferred preset rule for updating the ordered intersection point set.
[0058] To address the high computational complexity of existing technologies that typically involve sequentially comparing the coordinates of the insertion point with all points in the point set, in some preferred embodiments, the method of the present invention further includes: based on a binary search method, comparing the coordinate information of the insertion point with the coordinates of the points in the ordered intersection point set by halving, converging the search boundary, and finally determining the positional relationship between the insertion point and the coordinates of the points in the ordered intersection point set; the positional relationship also includes the insertion point and the coordinates of the points in the ordered intersection point set being duplicate points. For example, in some preferred embodiments, the present invention assumes that there are currently L points in the ordered intersection point set RP, with the intermediate index value I = S + (ES) / 2; where the initial boundary S = 0 and the ending boundary E = L - 1;
[0059] S121. Compare the size of the insertion point P and point RP[I]. If P equals RP[I], increment the repetition count of point RP[I] by 1. Compare the insertion point P with the midpoint of the ordered intersection point set according to the coordinate information. If P equals RP[I], it means that the insertion point already exists in the point set and does not need to be inserted again. Just increment the repetition count of RP[I] by 1 (indicating that the number of road segments associated with the intersection point increases), and this update process ends. If the two points are not equal, proceed to step S122 to continue narrowing the search range.
[0060] S122. If P is less than RP[I], then S is updated to the smaller value between S and (E-1), and step S123 is executed; if P is greater than RP[I], then E is updated to the larger value between E and (I+1), and step S123 is executed. If P is less than RP[I], it means that P should be to the left of RP[I] (such as the first half), so the ending boundary E is updated to the smaller value between S and E-1 to avoid E going out of bounds and to shrink the right boundary; if P is greater than RP[I], it means that P should be to the right of RP[I] (such as the second half), so the starting boundary S is updated to the larger value between E and I+1 to avoid S going out of bounds and to expand the left boundary; after the boundary adjustment is completed, step S123 is executed to continue the loop search.
[0061] S123. Determine if S equals E. If yes, proceed to step S214; otherwise, update the value of I by calculating I = S + (ES) / 2 and repeat step S121. Check if the adjusted starting boundary S equals the ending boundary E. If S ≠ E, it means there are still untraversed intervals. Recalculate the intermediate index I = S + (ES) / 2 and repeat step S121 to continue matching. If S = E, it means the binary search has locked the unique position (RP[S]) and the range cannot be narrowed further. Proceed to step S124.
[0062] S124. If P equals RP[S], increment the repetition count of RP[I] by 1; if P is greater than RP[S], add point P to position S+1; if P is less than RP[S], add point P to position S-1. At this point, S=E, and only P needs to be compared with RP[S] (the uniquely locked point) to complete the final operation. If P=RP[S], it means P already exists, increment the repetition count of RP[S] by 1, and the process ends; if P is greater than RP[S], it means P should be placed after RP[S], insert P into position S+1 of RP to maintain the order of the point set; if P is less than RP[S], it means P should be placed before RP[S], insert P into position S-1 of RP to maintain the order of the point set.
[0063] In this embodiment, each step focuses only on the midpoint of the current interval, without traversing all points. This significantly improves the efficiency of point set updates for large-scale vector road datasets compared to Embodiment 2.
[0064] Example 4
[0065] Based on Example 1, this example is used to determine the positional relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set.
[0066] Existing methods for determining the positional relationship between two points include directly calculating the Euclidean distance between them and comparing it to a preset tolerance threshold. If the distance is less than the threshold, the points are considered duplicates; otherwise, the positional order is determined solely by the difference in a single dimension (such as the x-coordinate) or a random dimension. Some methods calculate the differences in both the x and y coordinates simultaneously, but without a hierarchical judgment logic. Instead, they weight the differences in both coordinates equally and then comprehensively determine the positional relationship without a clear priority distinction. These methods suffer from problems such as high computational cost, low efficiency, susceptibility to sorting errors, misjudgment or omission of duplicate points, and poor stability.
[0067] To address the above problems, in some preferred embodiments, the method for determining the positional relationship between two points according to the present invention includes:
[0068] S111. Compare the difference between points P1 and P2 on the horizontal coordinate as the first difference. If the first difference is greater than the preset distance tolerance, then determine that P1 is located after P2; otherwise, proceed to step S112.
[0069] S112. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the first absolute value. If the first absolute value is less than the preset distance tolerance, then proceed to step S113; otherwise, determine that P1 is located before P2.
[0070] S113. Compare the difference between points P1 and P2 on the vertical coordinate as the second difference. If the second difference is greater than the preset distance tolerance, then it is determined that P1 is located after P2; otherwise, proceed to step S114.
[0071] S114. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the second absolute value. If the second absolute value is less than the preset distance tolerance, then P1 and P2 are determined to be duplicate points; otherwise, P1 is determined to be located before P2.
[0072] This embodiment adopts a hierarchical tolerance comparison method, first comparing the horizontal coordinate and then the vertical coordinate, to sequentially determine the positional relationship between the insertion point and the set of ordered intersection points. This avoids the complex calculation of Euclidean distance, simplifies the calculation process, and reduces the amount of computation. By clearly defining the judgment priority of the horizontal and vertical coordinates, this embodiment can reduce the sorting disorder caused by multi-dimensional comprehensive weighted judgment. At the same time, the hierarchical tolerance verification can more accurately distinguish between duplicate and non-duplicate points, reducing the probability of misjudgment and omission. While ensuring the accuracy of position sorting, it also improves the update efficiency and stability of the set of ordered intersection points.
[0073] Example 5
[0074] like Figure 2 As shown, this invention provides an intersection entity extraction system based on a large-scale vector road dataset, the system comprising:
[0075] Intersection point insertion and update module: used to traverse all road segment data in the road dataset, extract the start and end points corresponding to each road segment data as insertion points, and update the ordered intersection point set according to preset rules based on the positional relationship between the insertion points and the coordinate points in the ordered intersection point set;
[0076] Intersection entity point acquisition module: connected to the intersection point insertion and update module, used to traverse all coordinate points in the ordered intersection point set and output the coordinate points with a repetition count greater than 2 as intersection entity points.
[0077] Preferably, the preset rules in the intersection point insertion and update module include:
[0078] S101. Based on the positional relationship between the insertion point and the coordinate points in the ordered intersection point set, determine whether the position of the insertion point is arranged after the first point in the ordered intersection point set. If so, proceed to step S102.
[0079] S102. Determine whether the position of the insertion point is arranged before the last point in the ordered intersection point set. If so, add the insertion point to the ordered intersection point set.
[0080] Preferably, the method for adding the insertion point to the ordered intersection point set in the intersection point insertion and update module includes:
[0081] Based on the binary search method, the coordinate information of the insertion point is compared with the coordinates of the coordinates of the ordered intersection points by binary search, the search boundary is converged, and the positional relationship between the insertion point and the coordinates of the ordered intersection points is determined.
[0082] Preferably, the specific method for determining the positional relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set includes:
[0083] S111. Compare the difference between points P1 and P2 on the horizontal coordinate as the first difference. If the first difference is greater than the preset distance tolerance, then determine that P1 is located after P2; otherwise, proceed to step S112.
[0084] S112. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the first absolute value. If the first absolute value is less than the preset distance tolerance, then proceed to step S113; otherwise, determine that P1 is located before P2.
[0085] S113. Compare the difference between points P1 and P2 on the vertical coordinate as the second difference. If the second difference is greater than the preset distance tolerance, then it is determined that P1 is located after P2; otherwise, proceed to step S114.
[0086] S114. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the second absolute value. If the second absolute value is less than the preset distance tolerance, then P1 and P2 are determined to be duplicate points; otherwise, P1 is determined to be located before P2.
[0087] Preferably, in step S101, if not, the insertion point is arranged before the first point in the ordered intersection point set;
[0088] In step S102, if not, the insertion point is arranged after the first point in the ordered intersection point set.
[0089] Experimental Example 1
[0090] To further verify the beneficial effects of the technical solution provided in this disclosure, the following experimental example is provided. This experimental example, through performance comparison with a control example, demonstrates that the technical solution of this invention has higher computational efficiency while ensuring extraction accuracy for intersection entities in large-scale vector road datasets.
[0091] 1. Experimental Objective
[0092] This experiment aims to compare the performance of the embodiment of the present invention (which extracts intersection entity points by inserting the start and end points of a road segment into an ordered intersection point set based on their positional relationships) with the following comparative example in a continuous surface error detection task in a geographic area:
[0093] Comparative example: Using the same road dataset as the embodiment of the present invention, the detection method is to first extract the intersection points and then calculate the number of intersection point repetitions.
[0094] 2. Experimental Setup
[0095] Study area: Taking a vector road dataset of a certain city as an example, intersection entity extraction was performed. There are a total of 512,818 road segments, including 256,836 intersection entities.
[0096] 3. Experimental Procedure
[0097] Execution of the experimental examples of this invention:
[0098] Following the method described in the aforementioned preferred embodiment, a vector road dataset of a certain city is used as an example to form the road dataset of this invention, and intersection entity extraction is performed: First, all road segment data in the road dataset are traversed, and the starting point and ending point corresponding to each road segment are extracted as insertion points. Based on the positional relationship between the insertion point and the coordinate points in the ordered intersection point set, the ordered intersection point set is updated according to a preset rule. The preset rule is implemented as follows: First, it is determined whether the position of the insertion point is before the first point or after the last point in the ordered intersection point set. If neither is the case, a binary search method is used to compare the coordinate information of the insertion point and the coordinate points in the ordered intersection point set by binary search, converging the search boundary, and determining the positional relationship between the insertion point and the coordinate points in the ordered intersection point set. Finally, all coordinate points in the ordered intersection point set are traversed, and coordinate points with a repetition count greater than 2 are output as intersection entity points.
[0099] Execution of the example:
[0100] Using a vector road dataset from a specific city as an example for intersection entity extraction, the following steps are taken: First, the vector road dataset is standardized to unify the coordinate reference system and data structure of road segments, ensuring no redundant or erroneous road segment data. Second, intersection point extraction is performed by traversing all road segments in the dataset and calculating the coordinates of the intersection points of any two road segments one by one using a segment intersection algorithm, generating an initial intersection point set (containing actual road intersections, line segment endpoints, and non-intersection redundant intersections). Then, the intersection repetition count is calculated by matching the coordinates of each intersection point in the initial intersection point set and counting the number of times each intersection point appears in the set, thus distinguishing between non-intersection intersections that appear only once and candidate intersection intersections that appear multiple times. Finally, a repetition threshold is set (e.g., a repetition threshold of 2), and intersections with a repetition count reaching the threshold are identified as intersection entities, completing the extraction of all intersection entities.
[0101] 4. Experimental Results and Analysis
[0102] The experimental results are shown in Table 1. The number of intersection entity points extracted using the method of the present invention was 256,836, with an accuracy rate of 100% and a detection time of 67 seconds. The number of intersection entity points extracted using the control example method was 256,836, with an accuracy rate of 100% and a detection time of 3,572 seconds.
[0103] Table 1 Comparison of Experimental Results
[0104]
[0105] As can be seen from Table 1, under the same experimental conditions, both the method of the present invention and the method of the control example can extract the actual number of intersection entities with 100% accuracy. However, the method of the present invention takes only 67 seconds, which is much shorter than the 3572 seconds of the control example. This shows that the method of the present invention has a faster extraction efficiency while ensuring the extraction accuracy.
[0106] 5. Experimental Conclusions
[0107] The method of this invention reduces the time taken from 3572 seconds in the control example to 67 seconds while ensuring 100% accuracy in intersection entity extraction. Under the same data scale and extraction accuracy requirements, the method of this invention achieves an efficiency improvement of about 53 times, and has significant advantages in time performance and comprehensive processing capabilities. It provides an efficient and reliable technical solution for intersection entity extraction of large-scale vector road data.
[0108] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.
Claims
1. A method for intersection entity extraction based on a large-scale vector road dataset, characterized in that, Includes the following steps: S1. Traverse all road segment data in the road dataset, extract the starting point and ending point corresponding to each road segment data as insertion points, and update the ordered intersection point set according to preset rules based on the positional relationship between the insertion points and the coordinate points in the ordered intersection point set. S2. Traverse all coordinate points in the ordered intersection point set and output the coordinate points with a repetition count greater than 2 as intersection entity points; The specific method for determining the positional relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set includes: S111. Compare the difference between points P1 and P2 on the horizontal coordinate as the first difference. If the first difference is greater than the preset distance tolerance, then determine that P1 is located after P2; otherwise, proceed to step S112. S112. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the first absolute value. If the first absolute value is less than the preset distance tolerance, then proceed to step S113. Otherwise, determine that P1 is located before P2; S113. Compare the difference between points P1 and P2 on the vertical coordinate as the second difference. If the second difference is greater than the preset distance tolerance, then it is determined that P1 is located after P2; otherwise, proceed to step S114. S114. Compare the absolute value of the difference between points P1 and P2 on the vertical coordinate as the second absolute value. If the second absolute value is less than the preset distance tolerance, then P1 and P2 are determined to be duplicate points; otherwise, P1 is determined to be located before P2.
2. The intersection entity extraction method based on a large-scale vector road dataset as described in claim 1, characterized in that, The preset rules include: S101. Based on the positional relationship between the insertion point and the coordinate points in the ordered intersection point set, determine whether the position of the insertion point is arranged after the first point in the ordered intersection point set. If so, proceed to step S102. S102. Determine whether the position of the insertion point is arranged before the last point in the ordered intersection point set. If so, add the insertion point to the ordered intersection point set.
3. The intersection entity extraction method based on a large-scale vector road dataset as described in claim 2, characterized in that, The method for adding the insertion point to the ordered intersection point set includes: Based on the binary search method, the coordinate information of the insertion point is compared with the coordinates of the coordinates of the ordered intersection points by binary search, the search boundary is converged, and the positional relationship between the insertion point and the coordinates of the ordered intersection points is determined.
4. The intersection entity extraction method based on a large-scale vector road dataset as described in claim 2, characterized in that, In step S101, if not, the insertion point is arranged before the first point in the ordered intersection point set; in step S102, if not, the insertion point is arranged after the first point in the ordered intersection point set.
5. An intersection entity extraction system based on a large-scale vector road dataset, characterized in that, The system includes: Intersection point insertion and update module: used to traverse all road segment data in the road dataset, extract the start and end points corresponding to each road segment data as insertion points, and update the ordered intersection point set according to preset rules based on the positional relationship between the insertion points and the coordinate points in the ordered intersection point set; Intersection entity point acquisition module: connected to the intersection point insertion and update module, used to traverse all coordinate points in the ordered intersection point set and output the coordinate points with a repetition count greater than 2 as intersection entity points; The specific method for determining the positional relationship between the insertion point P1 and the coordinate point P2 in the ordered intersection point set includes: S111. Compare the difference between points P1 and P2 on the horizontal coordinate as the first difference. If the first difference is greater than the preset distance tolerance, then determine that P1 is located after P2; otherwise, proceed to step S112. S112. Compare the absolute value of the difference between points P1 and P2 on the horizontal coordinate as the first absolute value. If the first absolute value is less than the preset distance tolerance, then proceed to step S113; otherwise, determine that P1 is located before P2. S113. Compare the difference between points P1 and P2 on the vertical coordinate as the second difference. If the second difference is greater than the preset distance tolerance, then it is determined that P1 is located after P2; otherwise, proceed to step S114. S114. Compare the absolute value of the difference between points P1 and P2 on the vertical coordinate as the second absolute value. If the second absolute value is less than the preset distance tolerance, then P1 and P2 are determined to be duplicate points; otherwise, P1 is determined to be located before P2.
6. The intersection entity extraction system based on a large-scale vector road dataset as described in claim 5, characterized in that, The preset rules in the intersection point insertion and update module include: S101. Based on the positional relationship between the insertion point and the coordinate points in the ordered intersection point set, determine whether the position of the insertion point is arranged after the first point in the ordered intersection point set. If so, proceed to step S102. S102. Determine whether the position of the insertion point is arranged before the last point in the ordered intersection point set. If so, add the insertion point to the ordered intersection point set.
7. The intersection entity extraction system based on a large-scale vector road dataset as described in claim 6, characterized in that, The method for adding the insertion point to the ordered intersection point set in the intersection point insertion and update module includes: Based on the binary search method, the coordinate information of the insertion point is compared with the coordinates of the coordinates of the ordered intersection points by binary search, the search boundary is converged, and the positional relationship between the insertion point and the coordinates of the ordered intersection points is determined.
8. The intersection entity extraction system based on a large-scale vector road dataset as described in claim 6, characterized in that, In step S101, if not, the insertion point is arranged before the first point in the ordered intersection point set; in step S102, if not, the insertion point is arranged after the first point in the ordered intersection point set.