Road surface width adaptive calculation method based on data elements in intelligent road network

Abstract

The invention belongs to the technical field of geographic information, Internet application, software research and development technologies and services, and discloses a road surface width adaptive calculation method based on data elements in an intelligent road network. The method comprises the following specific steps: S1, data preprocessing; s2, processing road network elements; s3, road surface element processing; s4, calculating and adding intersection points; s5, expanding point elements and generating line elements; s6, screening angles; and S7, carrying out attribute hooking. The method has remarkable advantages in pavement width calculation: by utilizing a data processing technology, the automation level is improved, manual intervention is avoided, the calculation efficiency is improved, and the cost is saved for a traffic department; the accuracy is high, data is accurately processed through data analysis, manual errors are avoided, and reliable decision planning basis is guaranteed; the method is high in adaptability, can adapt to various road conditions, can expand to obtain isolation belt data and deduct the isolation belt data to calculate more accurate road width, meets different road management planning requirements, and shows powerful development potential.

Description

Technical Field

[0001] The present invention belongs to the fields of geographic information, Internet application, software development technology and service technology, and specifically is a method for adaptively calculating road width based on data elements in an intelligent road network. Background Art

[0002] With the development of big data and cloud computing technologies, intelligent road network systems can collect and process massive amounts of traffic data, providing possibilities for real-time processing and intelligent decision-making. They can calculate road parameters such as road width based on real-time data, helping traffic management departments to improve road conditions.

[0003] Traditional methods for calculating road width are mainly based on empirical formulas and design specifications, which require the improvement of infrastructure. In addition, for road networks in counties, towns and other places with few people, manual comparison of images or field measurements are required, which is not only labor-intensive and time-consuming, but also inefficient and inaccurate. Vectorized road and road network elements can provide precise geometric information, and vector data can be automatically processed and analyzed by computer programs to calculate road width more quickly and accurately.

[0004] In view of the many drawbacks of traditional methods, we hope to develop an adaptive calculation method for road width based on data elements in the intelligent road network, and use computer programs to automatically process and analyze road network and road data, so as to quickly obtain the road width of each road. This can greatly improve work efficiency and bring more convenience to traffic management, so as to better grasp road information, provide a basis for formulating scientific and reasonable traffic flow control plans, improve road capacity and safety, and promote the further development of intelligent road network technology. Summary of the invention

[0005] The purpose of the present invention is to provide a method for adaptively calculating road width based on data elements in an intelligent road network to solve the problems raised in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: The specific steps of the road surface width adaptive calculation method based on data elements in the intelligent road network are as follows:

[0007] S1: Data preprocessing: preprocessing the acquired road surface elements and road network elements, involving removing redundant information, correcting erroneous data, and unifying the coordinate system to prepare data for subsequent processing;

[0008] S2: Road network element processing: traverse the road network element data and assign a new attribute LW_OBJECTID as a unique value ID;

[0009] S3: Road feature processing: For each center point, find the intersection point with the nearest line feature Line_Data, assign the LW_OBJECTID attribute to the intersection point, and add it to the point feature data to form a new point feature data Two_Center_Points;

[0010] S4: Calculate and add intersection points: For each center point, find the intersection point with the line feature Line_Data at the closest distance, assign the LW_OBJECTID attribute to the intersection point, and add it to the point feature data to form a new point feature data Two_Center_Points;

[0011] S5: Point feature expansion and line feature generation: Generate lines for the Two_Center_Points points in pairs and extend them 10 times in the reverse direction, find the intersection with Line_Data and add them to generate Three_Center_Points, calculate the length of the composite line to get Center_Points_Lines;

[0012] S6: Angle screening: traverse the line feature data Center_Points_Lines, query the geometry corresponding to the road network feature data according to the LW_OBJECTID attribute, and calculate the absolute value difference between the angle between the two and 90°;

[0013] S7: Attribute joining: Attribute joining is performed through the LW_OBJECTID attribute of the line feature data New_Center_Points_Lines and the road network feature data, so as to obtain the road width corresponding to the road network data.

[0014] Preferably, the data preprocessing in S1 refers to the comprehensive preprocessing operations that must be performed first when processing road surface elements and road network elements, which include carefully removing redundant information to avoid data redundancy and confusion; carefully correcting erroneous data to ensure data accuracy; and unifying the coordinate system to ensure that data from different sources are in the same coordinate system. These preprocessing tasks are the basis for subsequent processing and can provide high-quality data support for various subsequent data operations, such as attribute assignment, screening, intersection calculation, etc., making the entire processing flow smoother and more accurate.

[0015] Preferably, the specific steps of processing the road network elements in S2 are as follows:

[0016] Step 1: Attribute assignment and unique identification: First, traverse the road network element data and add a new attribute LW_OBJECTID to each element. This attribute will serve as a unique identification ID to facilitate subsequent identification and operation of different elements;

[0017] Step 2: Data screening and center point generation: Next, filter out the parts with a length greater than 5m from the road network feature data, calculate the coordinates of the center points of the filtered data, and assign the previously assigned LW_OBJECTID attributes to these center points, thereby generating the center point feature data Center_Points;

[0018] Step 3: Spatial query and unique value generation: Finally, use spatial query technology to find the point feature data that is not in the road surface feature and remove it; for the remaining point feature data, generate a unique value POINT_OBJECTID for them to ensure that each point feature has a unique identifier to facilitate subsequent precise operations and data management.

[0019] Preferably, the road feature processing in S3 refers to finding the intersection point of each center point with the line feature Line_Data at the closest distance, assigning the LW_OBJECTID attribute to the intersection point, and adding it to the point feature data to form a new point feature data Two_Center_Points.

[0020] Preferably, the intersection calculation and addition in S4 refers to finding the intersection point of each center point with the line element Line_Data at the closest distance, assigning the LW_OBJECTID attribute to the intersection point, and adding it to the point element data to form a new point element data Two_Center_Points.

[0021] Preferably, the specific steps of point element expansion and line element generation in S5 are as follows:

[0022] Step 1: Line feature generation and extension: For the new point feature data Two_Center_Points, combine the points in it in pairs to generate new line features. Then, extend these newly generated line features in the reverse direction according to certain rules, with the extension multiple being 10 times the length, in preparation for the subsequent intersection calculation;

[0023] Step 2: Find and add intersection points: Find the intersection point between the extended line feature and the existing line feature Line_Data. Once the intersection point is found, add it to the Two_Center_Points data to form a new point feature set, namely the Three_Center_Points data.

[0024] Step 3: Point synthesis and line feature generation: In the Three_Center_Points data, find 3 points with the same LW_OBJECTID attribute, connect these three points to form a line, calculate the length of this line, and finally generate new line feature data Center_Points_Lines.

[0025] Preferably, the specific steps of angle screening in S6 are as follows:

[0026] Step 1: Geometric data query: First, traverse the existing line feature data Center_Points_Lines. During the traversal process, query the corresponding geometric information in the road network feature data based on the LW_OBJECTID attribute of each line feature. This step is the basis for subsequent operations to ensure that we can associate and compare the line feature data with the road network feature data;

[0027] Step 2: Angle calculation: Next, for each set of geometric information of line elements and road network elements queried, calculate the angle between them, and then find the absolute value difference between the angle and 90°. This calculation result will serve as an important basis for screening, helping us to screen out line elements that meet specific angle conditions;

[0028] Step 3: Filter and generate new data: Finally, based on the absolute value difference calculated in the previous step, filter out the line feature data whose absolute value difference is no more than 5 degrees. Arrange these filtered line feature data together to form new line feature data New_Center_Points_Lines for subsequent operations or analysis.

[0029] Preferably, the attribute attachment in S7 refers to attaching the attribute of the road network element data to the LW_OBJECTID attribute of the line element data New_Center_Points_Lines, so as to obtain the road width corresponding to the road network data.

[0030] The beneficial effects of the present invention are as follows:

[0031] 1. The present invention shows significant advantages over the prior art through the adaptive calculation method of road width based on data elements in the intelligent road network. It makes full use of advanced data processing and analysis technology, greatly improves the level of automation, and avoids a large amount of manual intervention in traditional methods. Traditional calculation methods often require manual comparison of images or field measurements, which is not only labor-intensive but also inefficient. The present invention automatically processes and analyzes road network and road data through computer programs, quickly and accurately calculates road width, significantly improves calculation efficiency, saves time and labor costs for traffic management departments, and promotes the automation process of road width calculation.

[0032] 2. The present invention has higher accuracy in calculating the road width. The traditional calculation method relies on manual operation, is greatly affected by human factors, is prone to errors, and is difficult to ensure the accuracy of the results. The adaptive calculation method of the present invention relies on data processing and analysis technology to accurately process a large amount of data, conduct a detailed analysis of the road surface and road network information, effectively avoid errors caused by manual operation, and provide more reliable results for the calculation of road width, which helps traffic management departments obtain more accurate road information, thereby providing a more scientific and accurate basis for subsequent decision-making and planning, and ensuring the safety and reliability of road traffic.

[0033] 3. The present invention can automatically adapt to various types of road networks and road surface data by being able to adjust according to different road conditions and data situations, and can give full play to its advantages regardless of whether it is a busy road in a city or a sparsely populated road in a county, district, or township. On the other hand, it also has subsequent expansion functions, such as being able to obtain isolation belt data and deduct it from the calculation to obtain more accurate road width information. This flexible processing method provides traffic management departments with richer information, so that the method can be flexibly adjusted according to actual needs to meet different road management and planning requirements, showing strong adaptability and development potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of a method for adaptively calculating road width based on data elements in an intelligent road network of the present invention;

[0035] Figure 2 It is a diagram of the execution steps of the program of the present invention;

[0036] Figure 3 This is a diagram of the operating results of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figures 1 to 3 As shown, an embodiment of the present invention provides a method for adaptively calculating road width based on data elements in an intelligent road network. The specific steps of the method for adaptively calculating road width based on data elements in an intelligent road network are as follows:

[0039] S1: Data preprocessing: preprocessing the acquired road surface elements and road network elements, involving removing redundant information, correcting erroneous data, and unifying the coordinate system to prepare data for subsequent processing;

[0040] S2: Road network element processing: traverse the road network element data and assign a new attribute LW_OBJECTID as a unique value ID;

[0041] S3: Road feature processing: For each center point, find the intersection point with the nearest line feature Line_Data, assign the LW_OBJECTID attribute to the intersection point, and add it to the point feature data to form a new point feature data Two_Center_Points;

[0042] S4: Calculate and add intersection points: For each center point, find the intersection point with the line feature Line_Data at the closest distance, assign the LW_OBJECTID attribute to the intersection point, and add it to the point feature data to form a new point feature data Two_Center_Points;

[0043] S5: Point feature expansion and line feature generation: Generate lines for the Two_Center_Points points in pairs and extend them 10 times in the reverse direction, find the intersection with Line_Data and add them to generate Three_Center_Points, calculate the length of the composite line to get Center_Points_Lines;

[0044] S6: Angle screening: traverse the line feature data Center_Points_Lines, query the geometry corresponding to the road network feature data according to the LW_OBJECTID attribute, and calculate the absolute value difference between the angle between the two and 90°;

[0045] S7: Attribute joining: Attribute joining is performed through the LW_OBJECTID attribute of the line feature data New_Center_Points_Lines and the road network feature data, so as to obtain the road width corresponding to the road network data.

[0046] The first is data preprocessing, which removes redundancy, corrects errors, and unifies coordinate systems for road and road network elements, laying the foundation for the follow-up. Then, the road network elements are processed, and the unique attribute LW_OBJECTID is assigned to them. In the road surface element processing and intersection calculation and addition stage, the intersection of the center point and the line element is found and added to the new point element data Two_Center_Points; then the point element is expanded and the line element is generated, and Center_Points_Lines is generated by extending the line to find the intersection point and other operations; the subsequent angle screening calculates the angle difference of the line element, and finally the road width is obtained through attribute attachment, forming a complete road network data processing process; this process systematically processes road network data, from data preparation to the final acquisition of road width, each step is closely connected, and different data operations and calculations are used to provide an efficient and accurate road width calculation and data processing solution for the intelligent road network system, improving the analysis and application capabilities of road network data.

[0047] Among them, the data preprocessing in S1 refers to the comprehensive preprocessing operations that must be performed first when processing road surface elements and road network elements, which include carefully removing redundant information to avoid data redundancy and confusion; carefully correcting erroneous data to ensure data accuracy; and unifying the coordinate system to ensure that data from different sources are in the same coordinate system. These preprocessing tasks are the basis for subsequent processing and can provide high-quality data support for various subsequent data operations, such as attribute assignment, screening, intersection calculation, etc., making the entire processing flow smoother and more accurate.

[0048] The specific steps of processing the road network elements in S2 are as follows:

[0049] Step 1: Attribute assignment and unique identification: First, traverse the road network element data and add a new attribute LW_OBJECTID to each element. This attribute will serve as a unique identification ID to facilitate subsequent identification and operation of different elements;

[0050] Step 2: Data screening and center point generation: Next, filter out the parts with a length greater than 5m from the road network feature data, calculate the coordinates of the center points of the filtered data, and assign the previously assigned LW_OBJECTID attributes to these center points, thereby generating the center point feature data Center_Points;

[0051] Step 3: Spatial query and unique value generation: Finally, use spatial query technology to find the point feature data that is not in the road surface feature and remove it; for the remaining point feature data, generate a unique value POINT_OBJECTID for them to ensure that each point feature has a unique identifier to facilitate subsequent precise operations and data management.

[0052] Traverse the entire road network feature dataset, assign a new attribute LW_OBJECTID to each road network feature one by one, and ensure that this ID is a globally unique value to facilitate subsequent data management and reference. Next, filter out those road network features with a length greater than 5 meters according to actual needs. For each screened road network feature, calculate its center point coordinates, and associate this center point coordinates with the corresponding LW_OBJECTID attribute to generate a new center point feature dataset (Center_Points). Subsequently, the Center_Points dataset is further processed through spatial query technology to remove point feature data that are not within the scope of the road surface feature. This step aims to eliminate irrelevant or erroneous center points to ensure the accuracy and effectiveness of subsequent analysis. Finally, generate a new unique value attribute POINT_OBJECTID for the screened point feature data so that each point feature can be uniquely identified in subsequent processing;

[0053]

[0054]

[0055] The road feature processing in S3 refers to finding the intersection point of each center point with the line feature Line_Data at the closest distance, assigning the LW_OBJECTID attribute to the intersection point, and adding it to the point feature data to form a new point feature data Two_Center_Points.

[0056] Convert the road features into line feature data (Line_Data), traverse the generated line feature data set, and assign a LINE_OBJECTID unique value ID to each line feature to ensure that each line feature can be uniquely identified and referenced in the dataset, laying the foundation for subsequent data management and analysis;

[0057]

[0058]

[0059] The intersection calculation and addition in S4 refers to finding the intersection of each center point with the line element Line_Data at the closest distance, assigning the LW_OBJECTID attribute to the intersection, and adding it to the point element data to form a new point element data Two_Center_Points.

[0060] Specifically, the intersection points between each center point and the nearest line feature Line_Data will be searched, and then the LW_OBJECTID attribute will be assigned to the found intersection points so that they have corresponding identification information; finally, these processed intersection points are added to the point feature data to form new point feature data Two_Center_Points, which lays the foundation for subsequent data processing and analysis and helps to improve the information structure of the entire road network data.

[0061] The specific steps of point feature expansion and line feature generation in S5 are as follows:

[0062] Step 1: Line feature generation and extension: For the new point feature data Two_Center_Points, combine the points in it in pairs to generate new line features. Then, extend these newly generated line features in the reverse direction according to certain rules, with the extension multiple being 10 times the length, in preparation for the subsequent intersection calculation;

[0063] Step 2: Find and add intersection points: Find the intersection point between the extended line feature and the existing line feature Line_Data. Once the intersection point is found, add it to the Two_Center_Points data to form a new point feature set, namely the Three_Center_Points data.

[0064] Step 3: Point synthesis and line feature generation: In the Three_Center_Points data, find 3 points with the same LW_OBJECTID attribute, connect these three points to form a line, calculate the length of this line, and finally generate new line feature data Center_Points_Lines.

[0065] Every two points in the new point feature data (Two_Center_Points) will be used as the starting point and the end point to generate a series of new line features. These new line features will be extended in the opposite direction based on the original ones, and the extended length is 10 times the length of the original line. In order to more effectively capture the potential intersection points with the existing line features (Line_Data). By performing a spatial intersection analysis, we find the first intersection point of these extended lines with each line in Line_Data, and add these intersection points to the Two_Center_Points dataset, thereby generating an updated dataset Three_Center_Points containing more points. Next, we will find a total of 3 points with the same LW_OBJECTID attribute in the Three_Center_Points dataset. The reason why these points have the same LW_OBJECTID is that they are intersection points found from the same center point in step S2. These three points are combined into a new line and the length is calculated. These lines with length attributes are stored in a new line feature dataset Center_Points_Lines.

[0066]

[0067]

[0068]

[0069]

[0070] The specific steps of angle screening in S6 are as follows:

[0071] Step 1: Geometric data query: First, traverse the existing line feature data Center_Points_Lines. During the traversal process, query the corresponding geometric information in the road network feature data based on the LW_OBJECTID attribute of each line feature. This step is the basis for subsequent operations to ensure that we can associate and compare the line feature data with the road network feature data;

[0072] Step 2: Angle calculation: Next, for each set of geometric information of line elements and road network elements queried, calculate the angle between them, and then find the absolute value difference between the angle and 90°. This calculation result will serve as an important basis for screening, helping us to screen out line elements that meet specific angle conditions;

[0073] Step 3: Filter and generate new data: Finally, based on the absolute value difference calculated in the previous step, filter out the line feature data whose absolute value difference is no more than 5 degrees. Arrange these filtered line feature data together to form new line feature data New_Center_Points_Lines for subsequent operations or analysis.

[0074] Traversing each line in the line feature data (Center_Points_Lines), we query the corresponding geometric shape in the road network feature data based on its LW_OBJECTID attribute for subsequent angle calculations. Next, we use the method of spatial geometry calculation to find the angle between the two, and calculate the absolute difference between this angle and 90°. Filtering out Center_Points_Lines with an absolute value difference of no more than 5° can help us identify those line features that are nearly perpendicular to the road network features. Finally, we reorganize the filtered line feature data to form a new line feature dataset, named New_Center_Points_Lines. The following code is mainly about angle calculations;

[0075]

[0076]

[0077] The attribute attachment in S7 refers to attaching the attribute of the LW_OBJECTID attribute of the line element data New_Center_Points_Lines to the road network element data, so as to obtain the road width corresponding to the road network data.

[0078] Through the LW_OBJECTID attribute in the line feature data (New_Center_Points_Lines), we can perform attribute hooking operations with the road network feature data. After establishing this association, we can run the operation based on the road network feature data and the corresponding road width data in New_Center_Points_Lines. Figure 2 .

[0079] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0080] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for adaptively calculating road width based on data elements in an intelligent road network, characterized in that: The specific steps of the road width adaptive calculation method based on data elements in the intelligent road network are as follows: S1: Data preprocessing: preprocessing the acquired road surface elements and road network elements, involving removing redundant information, correcting erroneous data, and unifying the coordinate system to prepare data for subsequent processing; S2: Road network element processing: traverse the road network element data and assign a new attribute LW_OBJECTID as a unique value ID; S3: Road feature processing: For each center point, find the intersection point with the nearest line feature Line_Data, assign the LW_OBJECTID attribute to the intersection point, and add it to the point feature data to form a new point feature data Two_Center_Points; S4: Calculate and add intersection points: For each center point, find the intersection point with the line feature Line_Data at the closest distance, assign the LW_OBJECTID attribute to the intersection point, and add it to the point feature data to form a new point feature data Two_Center_Points; S5: Point feature expansion and line feature generation: Generate lines for the Two_Center_Points points in pairs and extend them 10 times in the reverse direction, find the intersection with Line_Data and add them to generate Three_Center_Points, calculate the length of the composite line to get Center_Points_Lines; S6: Angle screening: traverse the line feature data Center_Points_Lines, query the geometry corresponding to the road network feature data according to the LW_OBJECTID attribute, and calculate the absolute value difference between the angle between the two and 90°; S7: Attribute joining: Attribute joining is performed with the road network feature data through the LW_OBJECTID attribute of the line feature data New_Center_Points_Lines, so as to obtain the road width corresponding to the road network data.

2. The method for adaptively calculating road width based on data elements in an intelligent road network according to claim 1, characterized in that: The data preprocessing in S1 refers to the comprehensive preprocessing operation that must be performed first when processing road surface elements and road network elements, which includes carefully removing redundant information to avoid data redundancy and confusion; Carefully correct erroneous data to ensure data accuracy; And a unified coordinate system to ensure that data from different sources are in the same coordinate system. These preprocessing tasks are the basis for subsequent processing and can provide high-quality data support for various subsequent data operations, such as attribute assignment, screening, intersection calculation, etc., making the entire processing flow smoother and more accurate.

3. The method for adaptively calculating road width based on data elements in an intelligent road network according to claim 1, characterized in that: The specific steps of processing the road network elements in S2 are as follows: Step 1: Attribute assignment and unique identification: First, traverse the road network element data and add a new attribute LW_OBJECTID to each element. This attribute will serve as a unique identification ID to facilitate subsequent identification and operation of different elements; Step 2: Data screening and center point generation: Next, filter out the parts with a length greater than 5m from the road network feature data, calculate the coordinates of the center points of the filtered data, and assign the previously assigned LW_OBJECTID attributes to these center points, thereby generating the center point feature data Center_Points; Step 3: Spatial query and unique value generation: Finally, use spatial query technology to find the point feature data that is not in the road surface feature and remove it; for the remaining point feature data, generate a unique value POINT_OBJECTID for them to ensure that each point feature has a unique identifier to facilitate subsequent precise operations and data management.

4. The method for adaptively calculating road width based on data elements in an intelligent road network according to claim 1, characterized in that: The road feature processing in S3 refers to finding the intersection point of each center point with the line feature Line_Data with the shortest distance, assigning the LW_OBJECTID attribute to the intersection point, and adding it to the point feature data to form a new point feature data Two_Center_Points.

5. The method for adaptively calculating road width based on data elements in an intelligent road network according to claim 1, characterized in that: The intersection calculation and addition in S4 refers to finding the intersection point of each center point with the line element Line_Data at the nearest distance, assigning the LW_OBJECTID attribute to the intersection point, and adding it to the point element data to form a new point element data Two_Center_Points.

6. The method for adaptively calculating road width based on data elements in an intelligent road network according to claim 1, characterized in that: The specific steps of point feature expansion and line feature generation in S5 are as follows: Step 1: Line feature generation and extension: For the new point feature data Two_Center_Points, combine the points in it in pairs to generate new line features. Then, extend these newly generated line features in the reverse direction according to certain rules, with the extension multiple being 10 times the length, in preparation for the subsequent intersection calculation; Step 2: Find and add intersection points: Find the intersection point between the extended line feature and the existing line feature Line_Data. Once the intersection point is found, add it to the Two_Center_Points data to form a new point feature set, namely the Three_Center_Points data. Step 3: Point synthesis and line feature generation: In the Three_Center_Points data, find 3 points with the same LW_OBJECTID attribute, connect these three points to form a line, calculate the length of this line, and finally generate new line feature data Center_Points_Lines.

7. The method for adaptively calculating road width based on data elements in an intelligent road network according to claim 1, characterized in that: The specific steps of angle screening in S6 are as follows: Step 1: Geometric data query: First, traverse the existing line feature data Center_Points_Lines. During the traversal process, query the corresponding geometric information in the road network feature data based on the LW_OBJECTID attribute of each line feature. This step is the basis for subsequent operations to ensure that we can associate and compare the line feature data with the road network feature data; Step 2: Angle calculation: Next, for each set of geometric information of line elements and road network elements queried, calculate the angle between them, and then find the absolute value difference between the angle and 90°. This calculation result will serve as an important basis for screening, helping us to screen out line elements that meet specific angle conditions; Step 3: Filter and generate new data: Finally, based on the absolute value difference calculated in the previous step, filter out the line feature data whose absolute value difference is no more than 5 degrees. Arrange these filtered line feature data together to form new line feature data New_Center_Points_Lines for subsequent operations or analysis.

8. The method for adaptively calculating road width based on data elements in an intelligent road network according to claim 1, characterized in that: The attribute attachment in S7 refers to attaching the attribute of the LW_OBJECTID attribute of the line element data New_Center_Points_Lines to the road network element data, so as to obtain the road width corresponding to the road network data.

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