A railway line multi-coordinate data fusion method

Abstract

The present invention relates to the field of engineering measurement technology, and specifically discloses a method for fusion of multi-coordinate data of railway lines, the method comprising: obtaining the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map, screening the best common point according to the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map; obtaining the coordinate system of the best common point, determining the coordinate conversion parameters corresponding to the target coordinate system according to the coordinate system of the best common point; correcting the coordinate conversion parameters according to the coordinate value of the best common point, determining the coordinate conversion values ​​of all the points to be measured in the coordinate system to be converted in the current railway map according to the correction result; calculating the coordinate conversion values ​​of the points to be measured in each coordinate system to be converted, and converting the points to be measured in each coordinate system to be converted to a unified coordinate system. The coordinate conversion parameters are set and corrected based on the best common point, so as to realize the unified fusion conversion of coordinates between different coordinate systems on the railway line, and facilitate unified data analysis and processing.

Description

Technical Field

[0001] The present application relates to the technical field of engineering surveying, and more specifically, to a railway line multi-coordinate data fusion method. Background Art

[0002] Since the founding of the People's Republic of China, China has established the 1954 Beijing coordinate system and the 1980 Xi'an coordinate system in the 1950s and 1980s, respectively, and surveyed and produced topographic maps of various scales, playing an important role in the national economy, social development and scientific research. Limited by the technical conditions at that time, China's geodetic coordinate system was basically realized by traditional technical means. With the progress of society, national economic construction, national defense construction, social development, scientific research, etc. have put forward new requirements for the national geodetic coordinate system. It is urgent to adopt a coordinate system with the origin at the center of the earth's mass (hereinafter referred to as the geocentric coordinate system) as the national geodetic coordinate system. The use of the geocentric coordinate system is conducive to the use of modern space technology to maintain and quickly update the coordinate system, determine the three-dimensional coordinates of high-precision geodetic control points, and improve the efficiency of mapping work.

[0003] During the construction of railway lines, since each construction project is responsible for different areas and adopts different coordinate systems, the coordinate systems of the obtained coordinate data are diverse. During the coordinate conversion process, the coordinate errors of individual points will be large, making it difficult to integrate and manage data in a unified manner. Summary of the invention

[0004] The present invention provides a railway line multi-coordinate data fusion method to solve the problem in the prior art that railway line coordinate data is too complex and difficult to be fused and unified. It includes:

[0005] Obtain the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map, and select the best common point according to the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map;

[0006] Obtain the coordinate system of the best common point, and determine the coordinate conversion parameters corresponding to the target coordinate system according to the coordinate system of the best common point;

[0007] The coordinate conversion parameters are corrected according to the coordinate values ​​of the best common point, and the coordinate conversion values ​​of all the points to be measured in the coordinate system to be converted in the current railway map are determined according to the correction results;

[0008] Calculate the coordinate conversion value of the test point in each coordinate system to be converted, and convert the test point in each coordinate system to be converted into a unified coordinate system.

[0009] Furthermore, the best common points are selected according to the coordinate values ​​of the common points in the coordinate system to be converted in the current railway map, including:

[0010] Calculate the average coordinate value of each common point according to the coordinate value of each common point in the current railway map, and update the coordinate origin according to the average coordinate value of each common point;

[0011] Determine a first search range boundary according to the updated coordinate origin and based on a polygon fast convex hull algorithm, and determine a common point search range according to the first search range boundary;

[0012] The common point search conditions are set based on the k-means clustering algorithm, the common points that meet the conditions are screened out within the common point search range, and the number of common points that meet the conditions is determined;

[0013] Determine whether the number of common points reaches the preset number of common points, and if so, determine the coordinates of the final common points;

[0014] If not, the common point search condition is modified according to the number of common points, and the number of common points that meet the condition is re-determined according to the modified common point search condition;

[0015] The iteration is repeated until the number of common points reaches the preset number of common points, and the final common point is determined as the optimal common point.

[0016] Further, determining the first search range boundary according to the updated coordinate origin and based on a polygon fast convex hull algorithm includes:

[0017] Randomly select three common points, form the polygon enclosed by the three common points into an initial convex hull, and store the initial convex hull in an initial double-ended queue;

[0018] Select the next common point Bi, and determine whether Bi is in the convex hull formed by the head and tail of the double-ended queue based on the vector cross product;

[0019] If Bi is in the convex hull formed by the head and tail of the double-ended queue, add Bi to the head or tail of the double-ended queue;

[0020] If Bi is not in the convex hull formed by the head and tail of the double-ended queue, the head or tail of the double-ended queue is removed, and the point Bi is re-determined whether it is in the convex hull formed between the newly formed head or tail, until Bi is in the convex hull formed by the head or tail of the current double-ended queue;

[0021] Repeat the above steps until all public points are added to the double-ended queue;

[0022] All common points in the double-ended queue are connected in sequence to form a final convex hull, and the polygon formed by the final convex hull is set as the first search range boundary.

[0023] Further, determining the common point search range according to the first search range boundary includes:

[0024] Calculate the average distance from all common points to the coordinate origin according to the coordinate values ​​of each common point, and determine the range control parameter according to the average distance from all common points to the coordinate origin;

[0025] Obtain the polygon vertex coordinate values ​​of the first search range boundary, subtract the polygon vertex coordinate values ​​of the first search range boundary from the range control parameter, determine the second search range boundary according to the calculation result, and set the area enclosed by the first search range boundary and the second search range boundary as the common point search range.

[0026] Furthermore, the common point search conditions are set based on the k-means clustering algorithm, and the common points that meet the conditions are screened out within the common point search range, including:

[0027] Randomly select k initial cluster centers according to the coordinate values ​​of the common points within the common point search range, and calculate the distance from each common point to the initial cluster center;

[0028] Divide each common point into the initial cluster center with the shortest distance, and determine the cluster interval of each common point according to the division result;

[0029] According to the clustering interval of each common point, the average distance from each common point in the clustering interval to the cluster center is calculated, and the new cluster center is determined according to the average distance from each common point in the clustering interval to the cluster center;

[0030] Repeat the above steps until the cluster center no longer changes, and obtain k final cluster centers;

[0031] The search condition control parameter is set, and the distance between the common point search range and the final cluster center is set as the common point search condition, and the common points whose distance is less than the search condition control parameter are screened out.

[0032] Further, the common point search condition is modified according to the number of common points, including:

[0033] Set the common point quantity control parameter, and calculate the difference between the common point quantity and the common point quantity control parameter;

[0034] Determine whether the difference between the number of common points and the common point number control parameter is less than a first preset threshold value, and if the difference between the number of common points and the common point number control parameter is less than the first preset threshold value, correct the search condition control parameter according to the first correction value;

[0035] If the difference between the number of common points and the common point number control parameter is greater than or equal to the first preset threshold, determining whether the difference between the number of common points and the common point number control parameter is less than a second preset threshold;

[0036] If the difference between the number of common points and the common point number control parameter is less than a second preset threshold, the search condition control parameter is corrected according to the second correction value;

[0037] If the difference between the number of common points and the common point number control parameter is greater than or equal to the second preset threshold, the search condition control parameter is corrected according to the third correction value.

[0038] Further, the coordinate conversion parameters corresponding to the target coordinate system are determined according to the coordinate system of the best common point, including:

[0039] A seven-parameter coordinate transformation model is established according to the coordinate values ​​of the best common points in the current coordinate system and the target coordinate system. The model parameters are solved based on the least squares method, and the coordinate transformation parameters corresponding to the target coordinate system are determined according to the solution results.

[0040] Furthermore, the coordinate conversion parameters are corrected according to the coordinate value of the best common point, including:

[0041] Obtaining the coordinate values ​​of the best common point before and after the coordinate transformation, and calculating the coordinate difference of the best common point before and after the coordinate transformation according to the coordinate values ​​of the best common point before and after the coordinate transformation;

[0042] Obtaining an initial weight matrix in a seven-parameter coordinate transformation model, and correcting the initial weight matrix according to the coordinate difference of the best common point before and after the coordinate transformation;

[0043] The seven-parameter coordinate transformation model is updated according to the corrected initial weight matrix, and the final coordinate transformation parameters are determined according to the updated seven-parameter coordinate transformation model.

[0044] Further, the final coordinate transformation parameters are determined according to the updated seven-parameter coordinate transformation model, including:

[0045] Set the coordinate difference tolerance parameter, and calculate the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated;

[0046] If the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated is greater than a third preset threshold, the seven-parameter coordinate transformation model is updated according to the new coordinate difference;

[0047] If the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated is less than or equal to the third preset threshold, the updating is stopped and the final coordinate transformation parameters are output.

[0048] Furthermore, the initial weight matrix is ​​corrected according to the coordinate difference of the best common point before and after the coordinate transformation, including:

[0049] The weight matrix correction formula is set according to the coordinate difference of the best common point before and after the coordinate transformation, and the initial weight matrix is ​​corrected according to the weight matrix correction formula. The weight matrix correction formula is specifically as follows:

[0050] P * =Pexp(l α -l i )

[0051] Among them, P * is the modified weight matrix, P is the initial weight matrix, l α is the preset difference control parameter, l i is the coordinate difference of the best common point before and after the coordinate transformation, and exp is the natural exponential function.

[0052] The beneficial effects of the present invention are:

[0053] By applying the above technical scheme, the present invention can screen out common points with reasonable distribution and large coverage by screening the best common points, which greatly improves the solution accuracy of coordinate transformation parameters. At the same time, by correcting the coordinate transformation parameters, the weight of common points with large errors on the coordinate transformation model is reduced, which further improves the accuracy of the coordinate transformation parameters, realizes the unified fusion conversion of coordinates between different coordinate systems on the railway line, and facilitates unified data analysis and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 The figure shows an overall flow chart of a railway line multi-coordinate data fusion method proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] The present application embodiment provides a railway line multi-coordinate data fusion method, such as Figure 1 As shown, including:

[0058] S101, obtaining coordinate values ​​of each common point in the coordinate system to be converted in the current railway map, and selecting the best common point according to the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map;

[0059] In this embodiment, by screening the best common points, common points with uniform distribution and wide coverage are determined, thereby improving the accuracy of subsequent coordinate conversion parameters.

[0060] In some embodiments of the present application, the best common point is selected according to the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map, including: calculating the average coordinate value of each common point according to the coordinate values ​​of the common points in the current railway map, and updating the coordinate origin according to the average coordinate value of each common point; determining the first search range boundary according to the updated coordinate origin and based on the polygon fast convex hull algorithm, and determining the common point search range according to the first search range boundary; setting common point search conditions based on the k-means clustering algorithm, selecting common points that meet the conditions within the common point search range, and determining the number of common points that meet the conditions; judging whether the number of common points reaches the preset number of common points, and if so, determining the coordinates of the final common point; if not, revising the common point search conditions according to the number of common points, and re-determining the number of common points that meet the conditions according to the revised common point search conditions; repeating the iteration until the number of common points reaches the preset number of common points, and determining the final common point as the best common point.

[0061] In this embodiment, the coordinate origin is updated to unify the coordinate origin to the center position of all common points. By setting the common point search range and screening the common points that meet the conditions based on the k-means clustering algorithm, the common points in the best position can be quickly found.

[0062] In some embodiments of the present application, the first search range boundary is determined based on the updated coordinate origin and the polygon fast convex hull algorithm, including: randomly selecting three common points, forming an initial convex hull with the polygon surrounded by the three common points, and storing the initial convex hull in an initial double-ended queue; selecting the next common point B i , judge B based on vector cross product i Is it in the convex hull formed by the head and tail of the double-ended queue? If B i In the convex hull formed by the head and tail of the double-ended queue, B i Add to the head or tail of the double-ended queue; if B i If it is not in the convex hull formed by the head and tail of the double-ended queue, remove the head or tail of the double-ended queue and re-determine point B. i Is it in the convex hull formed between the newly formed column heads or column tails until B iLocated in the convex hull formed by the column head or column tail of the current bidirectional queue; repeat the above steps until all common points are added to the double-ended queue; connect all common points in the double-ended queue in sequence to form a final convex hull, and set the polygon formed by the final convex hull as the first search range boundary.

[0063] In this embodiment, a final convex hull including all common points is determined according to a polygon fast convex hull algorithm, and the polygon formed by the final convex hull is set as the first search range boundary to facilitate subsequent search for the best common point within the maximum coverage range.

[0064] In some embodiments of the present application, a common point search range is determined based on a first search range boundary, including: calculating an average distance from all common points to a coordinate origin based on coordinate values ​​of each common point, and determining a range control parameter based on the average distance from all common points to the coordinate origin; obtaining polygon vertex coordinate values ​​of the first search range boundary, subtracting the polygon vertex coordinate values ​​of the first search range boundary from the range control parameter, determining a second search range boundary based on the calculation result, and setting an area enclosed by the first search range boundary and the second search range boundary as the common point search range.

[0065] In this embodiment, the second search range boundary is determined by the average distance from all common points to the coordinate origin, and the search range is controlled to the location where all common points are concentrated.

[0066] In some embodiments of the present application, a common point search condition is set based on a k-means clustering algorithm, and common points that meet the condition are screened out within the common point search range, including: randomly selecting k initial cluster centers according to the coordinate values ​​of the common points within the common point search range, and calculating the distance from each common point to the initial cluster center; dividing each common point into the initial cluster center with the shortest distance, and determining the cluster interval of each common point according to the division result; calculating the average distance from each common point to the cluster center within the cluster interval according to the cluster interval of each common point, and determining a new cluster center according to the average distance from each common point to the cluster center within the cluster interval; iterating the above steps repeatedly until the cluster center no longer changes, and obtaining k final cluster centers; setting a search condition control parameter, setting the distance from the final cluster center within the common point search range as the common point search condition, and screening out common points whose distance is less than the search condition control parameter.

[0067] In this embodiment, the number of cluster centers k is set according to the accuracy requirement. The higher the accuracy requirement, the larger the k value and the higher the calculation complexity. The cluster centers are selected by the k-means clustering algorithm so that each cluster center is distributed at the best position of each common point.

[0068] In some embodiments of the present application, the common point search condition is corrected according to the number of common points, including: setting a common point number control parameter, calculating the difference between the common point number and the common point number control parameter; judging whether the difference between the common point number and the common point number control parameter is less than a first preset threshold value, if the difference between the common point number and the common point number control parameter is less than the first preset threshold value, correcting the search condition control parameter according to the first correction value; if the difference between the common point number and the common point number control parameter is greater than or equal to the first preset threshold value, judging whether the difference between the common point number and the common point number control parameter is less than a second preset threshold value; if the difference between the common point number and the common point number control parameter is less than the second preset threshold value, correcting the search condition control parameter according to the second correction value; if the difference between the common point number and the common point number control parameter is greater than or equal to the second preset threshold value, correcting the search condition control parameter according to the third correction value.

[0069] In this embodiment, the first preset threshold is set to 0, the second preset threshold is greater than 0, the range of the first correction value is [1, 1.2), the range of the second correction value is [0.5, 1), and the range of the third correction value is [0, 0.5). When the difference between the number of common points and the common point number control parameter is less than 0, the common point search range is proportionally expanded according to the first correction value, thereby increasing the common point search range. When the difference between the number of common points and the common point number control parameter is greater than 0, the common point search range is proportionally reduced according to the second or third correction value, thereby reducing the common point search range to optimize the search range.

[0070] S102, obtaining a coordinate system of the best common point, and determining coordinate conversion parameters corresponding to a target coordinate system according to the coordinate system of the best common point.

[0071] In some embodiments of the present application, the coordinate conversion parameters corresponding to the target coordinate system are determined based on the coordinate system of the best common point, including: establishing a seven-parameter coordinate conversion model based on the coordinate values ​​of the best common point in the current coordinate system and the target coordinate system, solving the model parameters based on the least squares method, and determining the coordinate conversion parameters corresponding to the target coordinate system based on the solution results.

[0072] S103, correcting the coordinate conversion parameters according to the coordinate value of the best common point, and determining the coordinate conversion values ​​of all the points to be measured in the coordinate system to be converted in the current railway map according to the correction result.

[0073] In this embodiment, by correcting the coordinate conversion parameters, the accuracy of the coordinate conversion value is further improved and the coordinate conversion error is reduced.

[0074] In some embodiments of the present application, the coordinate transformation parameters are corrected according to the coordinate value of the best common point, including: obtaining the coordinate value of the best common point before and after the coordinate transformation, and calculating the coordinate difference of the best common point before and after the coordinate transformation according to the coordinate value of the best common point before and after the coordinate transformation; obtaining the initial weight matrix in the seven-parameter coordinate transformation model, and correcting the initial weight matrix according to the coordinate difference of the best common point before and after the coordinate transformation; updating the seven-parameter coordinate transformation model according to the corrected initial weight matrix, and determining the final coordinate transformation parameters according to the updated seven-parameter coordinate transformation model.

[0075] In this embodiment, the coordinate transformation parameters are solved by the seven-parameter coordinate transformation model of the best common point, which can ensure the accuracy of the coordinate transformation data and further improve the data precision. At the same time, the initial weight matrix is ​​corrected according to the coordinate difference of the best common point before and after the coordinate transformation, which can effectively prevent the interference of gross errors on the coordinate transformation parameters.

[0076] In some embodiments of the present application, the final coordinate transformation parameters are determined according to the updated seven-parameter coordinate transformation model, including: setting the coordinate difference allowable parameters, calculating the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated; if the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated is greater than a third preset threshold, the seven-parameter coordinate transformation model is updated according to the new coordinate difference; if the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated is less than or equal to the third preset threshold, the updating is stopped and the final coordinate transformation parameters are output.

[0077] In this embodiment, the seven-parameter coordinate transformation model is iteratively updated based on the coordinate difference allowable parameters, so that the updated coordinate transformation parameters reach the optimal values.

[0078] In some embodiments of the present application, the initial weight matrix is ​​corrected according to the coordinate difference of the best common point before and after the coordinate transformation, including: setting a weight matrix correction formula according to the coordinate difference of the best common point before and after the coordinate transformation, and correcting the initial weight matrix according to the weight matrix correction formula, wherein the weight matrix correction formula is specifically,

[0079] P * =Pexp(l α -l i )

[0080] Among them, P * is the modified weight matrix, P is the initial weight matrix, l α is the preset difference control parameter, l i is the coordinate difference of the best common point before and after the coordinate transformation, and exp is the natural exponential function.

[0081] S104, calculating the coordinate conversion value of the point to be measured in each coordinate system to be converted, and converting the point to be measured in each coordinate system to be converted into a unified coordinate system.

[0082] In this embodiment, the above method is used to calculate the coordinate conversion values ​​of all the points to be measured in each coordinate system to be converted, and all the points to be measured are unified into the 2000 National Geodetic Coordinate System to achieve multi-coordinate data fusion of the railway line.

[0083] By applying the above technical scheme, the present invention obtains the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map, selects the best common point according to the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map; obtains the coordinate system of the best common point, and determines the coordinate conversion parameters corresponding to the target coordinate system according to the coordinate system of the best common point; corrects the coordinate conversion parameters according to the coordinate value of the best common point, and determines the coordinate conversion values ​​of all the points to be measured in the coordinate system to be converted in the current railway map according to the correction result; calculates the coordinate conversion values ​​of the points to be measured in each coordinate system to be converted, and converts the points to be measured in each coordinate system to be converted to a unified coordinate system. The coordinate conversion parameters are set and corrected based on the best common point, so as to realize the unified fusion conversion of coordinates between different coordinate systems on the railway line, which is convenient for unified data analysis and processing.

[0084] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various implementation scenarios of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A railway line multi-coordinate data fusion method, characterized in that: Methods include: Obtain the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map, and select the best common point according to the coordinate values ​​of each common point in the coordinate system to be converted in the current railway map; Obtain the coordinate system of the best common point, and determine the coordinate conversion parameters corresponding to the target coordinate system according to the coordinate system of the best common point; The coordinate conversion parameters are corrected according to the coordinate values ​​of the best common point, and the coordinate conversion values ​​of all the points to be measured in the coordinate system to be converted in the current railway map are determined according to the correction results; Calculate the coordinate conversion value of the measured point in each coordinate system to be converted, and convert the measured point in each coordinate system to be converted into a unified coordinate system; The best common points are selected according to the coordinate values ​​of the common points in the coordinate system to be converted in the current railway map, including: Calculate the average coordinate value of each common point according to the coordinate value of each common point in the current railway map, and update the coordinate origin according to the average coordinate value of each common point; Determine a first search range boundary according to the updated coordinate origin and based on a polygon fast convex hull algorithm, and determine a common point search range according to the first search range boundary; The common point search conditions are set based on the k-means clustering algorithm, the common points that meet the conditions are screened out within the common point search range, and the number of common points that meet the conditions is determined; Determine whether the number of common points reaches the preset number of common points, and if so, determine the coordinates of the final common points; If not, the common point search condition is modified according to the number of common points, and the number of common points that meet the condition is re-determined according to the modified common point search condition; The iteration is repeated until the number of common points reaches the preset number of common points, and the final common point is determined as the optimal common point.

2. The railway line multi-coordinate data fusion method according to claim 1, characterized in that: Determine the first search range boundary according to the updated coordinate origin and based on the polygon fast convex hull algorithm, including: Randomly select three common points, form the polygon enclosed by the three common points into an initial convex hull, and store the initial convex hull in an initial double-ended queue; Select the next common point B i , judge B based on vector cross product i Whether it is within the convex hull formed by the head and tail of the double-ended queue; If B i In the convex hull formed by the head and tail of the double-ended queue, B i Add to the head or tail of the double-ended queue; If B i If it is not in the convex hull formed by the head and tail of the double-ended queue, remove the head or tail of the double-ended queue and re-determine point B. i Is it in the convex hull formed between the newly formed column heads or column tails until B i Located in the convex hull formed by the head or tail of the current bidirectional queue; Repeat the above steps until all public points are added to the double-ended queue; All common points in the double-ended queue are connected in sequence to form a final convex hull, and the polygon formed by the final convex hull is set as the first search range boundary.

3. The railway line multi-coordinate data fusion method according to claim 2, characterized in that: Determining a common point search range according to a first search range boundary includes: Calculate the average distance from all common points to the coordinate origin according to the coordinate values ​​of each common point, and determine the range control parameter according to the average distance from all common points to the coordinate origin; Obtain the polygon vertex coordinate values ​​of the first search range boundary, subtract the polygon vertex coordinate values ​​of the first search range boundary from the range control parameter, determine the second search range boundary according to the calculation result, and set the area enclosed by the first search range boundary and the second search range boundary as the common point search range.

4. The railway line multi-coordinate data fusion method according to claim 3, characterized in that: The common point search conditions are set based on the k-means clustering algorithm, and the common points that meet the conditions are screened out within the common point search range, including: Randomly select k initial cluster centers according to the coordinate values ​​of the common points within the common point search range, and calculate the distance from each common point to the initial cluster center; Divide each common point into the initial cluster center with the shortest distance, and determine the cluster interval of each common point according to the division result; According to the clustering interval of each common point, the average distance from each common point in the clustering interval to the cluster center is calculated, and the new cluster center is determined according to the average distance from each common point in the clustering interval to the cluster center; Repeat the above steps until the cluster center no longer changes, and obtain k final cluster centers; The search condition control parameter is set, and the distance between the common point search range and the final cluster center is set as the common point search condition, and the common points whose distance is less than the search condition control parameter are screened out.

5. The railway line multi-coordinate data fusion method according to claim 4, characterized in that: Modify the common point search criteria based on the number of common points, including: Set the common point quantity control parameter, and calculate the difference between the common point quantity and the common point quantity control parameter; Determine whether the difference between the number of common points and the common point number control parameter is less than a first preset threshold value, and if the difference between the number of common points and the common point number control parameter is less than the first preset threshold value, correct the search condition control parameter according to the first correction value; If the difference between the number of common points and the common point number control parameter is greater than or equal to the first preset threshold, determining whether the difference between the number of common points and the common point number control parameter is less than a second preset threshold; If the difference between the number of common points and the common point number control parameter is less than a second preset threshold, the search condition control parameter is corrected according to the second correction value; If the difference between the number of common points and the common point number control parameter is greater than or equal to the second preset threshold, the search condition control parameter is corrected according to the third correction value.

6. The railway line multi-coordinate data fusion method according to claim 1, characterized in that: The coordinate transformation parameters corresponding to the target coordinate system are determined according to the coordinate system of the best common point, including: A seven-parameter coordinate transformation model is established according to the coordinate values ​​of the best common points in the current coordinate system and the target coordinate system. The model parameters are solved based on the least squares method, and the coordinate transformation parameters corresponding to the target coordinate system are determined according to the solution results.

7. The railway line multi-coordinate data fusion method according to claim 6, characterized in that: The coordinate transformation parameters are corrected according to the coordinate values ​​of the best common point, including: Obtaining the coordinate values ​​of the best common point before and after the coordinate transformation, and calculating the coordinate difference of the best common point before and after the coordinate transformation according to the coordinate values ​​of the best common point before and after the coordinate transformation; Obtaining an initial weight matrix in a seven-parameter coordinate transformation model, and correcting the initial weight matrix according to the coordinate difference of the best common point before and after the coordinate transformation; The seven-parameter coordinate transformation model is updated according to the corrected initial weight matrix, and the final coordinate transformation parameters are determined according to the updated seven-parameter coordinate transformation model.

8. The railway line multi-coordinate data fusion method according to claim 7, characterized in that: The final coordinate transformation parameters are determined according to the updated seven-parameter coordinate transformation model, including: Set the coordinate difference tolerance parameter, and calculate the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated; If the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated is greater than a third preset threshold, the seven-parameter coordinate transformation model is updated according to the new coordinate difference; If the coordinate difference of the best common point before and after the coordinate transformation after the seven-parameter coordinate transformation model is updated is less than or equal to the third preset threshold, the updating is stopped and the final coordinate transformation parameters are output.

9. The railway line multi-coordinate data fusion method according to claim 8, characterized in that: The initial weight matrix is ​​modified according to the coordinate difference of the best common point before and after the coordinate transformation, including: The weight matrix correction formula is set according to the coordinate difference of the best common point before and after the coordinate transformation, and the initial weight matrix is ​​corrected according to the weight matrix correction formula. The weight matrix correction formula is specifically as follows: P * =Pexp(l α -l i ) Among them, P * is the modified weight matrix, P is the initial weight matrix, l α is the preset difference control parameter, l i is the coordinate difference of the best common point before and after the coordinate transformation, and exp is the natural exponential function.

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