A method for processing congestion data of a map in sequence

Abstract

This invention relates to a method for sequential processing of disordered coordinate strings in map congestion data, comprising: S1, acquiring map congestion data, extracting valid coordinates, and constructing a list of valid coordinates; S2, extracting possible starting point coordinates from the valid coordinates and constructing a list of possible starting point coordinates; S3, selecting a possible starting point coordinate as the starting point, finding the nearest coordinate point to the starting point in the list of valid coordinates, and then using the nearest coordinate point as the starting point again, and searching for the nearest coordinate point again, until all valid coordinates have been calculated, obtaining a latitude and longitude sequence; S4, selecting the next possible starting point coordinate as the starting point, repeating S3, until all possible starting point coordinates have been traversed, and taking the latitude and longitude sequence with the shortest distance as the sorting result of the temporal coordinate string of the map congestion data. This invention can construct a sequential coordinate string from the starting point to the ending point or from the ending point to the starting point, and obtain the curve length of the congested road segment from the starting point to the ending point from the calculation result.

Description

Technical Field

[0001] This invention relates to the field of map congestion data processing technology, and in particular to a method for reordering disordered coordinate strings in map congestion data. Background Technology

[0002] Algorithms for sorting a set of disordered latitude and longitude points typically employ the minimum spanning tree algorithm. This requires constructing a minimum spanning tree for each point as the original point, traversing all points sequentially to determine the final minimum spanning tree. To perform this calculation, each iteration requires constructing a distance matrix between one point and all other points. However, for sequential processing of disordered coordinate strings in map congestion data, the aforementioned minimum spanning tree algorithm consumes significant computational resources and results in poor real-time computational performance. Therefore, this invention proposes a method and system for sequential processing of disordered coordinate strings in map congestion data. By improving the minimum spanning tree algorithm and process, it achieves high-efficiency computation in the processing. Summary of the Invention

[0003] The purpose of this invention is to provide a method for processing disordered coordinate strings in map congestion data into sequential ones. This method extracts latitude and longitude coordinate string point information under different congestion levels from the congestion data and removes duplicate latitude and longitude coordinate points to obtain disordered latitude and longitude coordinate strings. The disordered latitude and longitude coordinate strings are then arranged sequentially to construct sequential coordinate strings from the starting point to the ending point or from the ending point to the starting point.

[0004] To achieve the above objectives, the present invention provides the following solution:

[0005] A method for reordering out-of-order coordinate strings in map congestion data includes:

[0006] S1. Obtain map congestion data, extract valid coordinates from the map congestion data, and construct a list of valid coordinates;

[0007] S2. Extract possible starting point coordinates from the valid coordinates and construct a list of possible starting point coordinates;

[0008] S3. Select a possible starting point coordinate as the starting point, find the nearest coordinate point of the starting point in the list of valid coordinates, and then use the nearest coordinate point as the starting point again to find the nearest coordinate point again, until all valid coordinates in the list of valid coordinates have been calculated, and obtain the latitude and longitude sequence.

[0009] S4. Select the next possible starting point coordinate as the starting point, repeat S3, until all possible starting point coordinates in the possible starting point coordinate list have been traversed. Take the latitude and longitude sequence with the shortest distance as the sorting result of the time-series coordinate string of the map congestion data to complete the sequential processing.

[0010] Optionally, in step S1, extracting valid coordinates from the map congestion data and constructing a list of valid coordinates includes:

[0011] Extract the linkstate field from the map congestion data;

[0012] The linkstate field is parsed to obtain the coordinate values;

[0013] The coordinate values ​​are deduplicated, and the list of valid coordinates is formed based on the deduplicated coordinate values.

[0014] Optionally, in step S2, extracting possible starting point coordinates from the valid coordinates and constructing a list of possible starting point coordinates includes:

[0015] Based on the linkstate field corresponding to the deduplicated coordinate values ​​in the list of valid coordinates, obtain several key-value pairs;

[0016] Traverse the key-value pairs, obtain the values ​​in the key-value pairs and split them to extract several paths;

[0017] The first coordinate point of the segmented path is used as the possible starting point coordinates to construct the list of possible starting point coordinates.

[0018] Optionally, the list of possible starting point coordinates may also include possible starting point coordinates derived from the congestion event point coordinate field given in the map congestion data.

[0019] Optionally, in step S3, finding the nearest coordinate point to the starting point in the list of valid coordinates includes:

[0020] S3.1.1 Find the coordinate point corresponding to the starting point in the list of valid coordinates, and remove the coordinate point from the list of valid coordinates;

[0021] S3.1.2 Calculate the spatial distance between the coordinate point and all other coordinate points in the valid coordinate list;

[0022] S3.1.3 Select the coordinate point with the shortest distance as the nearest coordinate point of the given coordinate point.

[0023] Optionally, in step S3, after selecting the possible starting point coordinates, the latitude and longitude sequence is obtained based on the principle of shortest path distance, including:

[0024] S3.2.1. Based on the possible starting point coordinates, perform S3.1.1-S3.1.3 to obtain the nearest coordinate point of the possible starting point coordinates;

[0025] S3.2.2. Using the nearest coordinate point calculated as the new starting point, perform S3.1.1-S3.1.3 to obtain the next nearest coordinate point;

[0026] S3.2.3 Repeat steps S3.2.1-S3.2.2 until the nearest coordinate point of each coordinate point in the valid coordinate list is calculated to obtain the latitude and longitude sequence.

[0027] Optionally, in step S3, after traversing all possible starting point coordinates in the list of possible starting point coordinates, the final starting point and its corresponding latitude and longitude sequence are determined from all obtained latitude and longitude sequence sequences based on the principle of minimizing the total distance, including:

[0028] S3.3.1 Select any possible starting point coordinate from the list of possible starting point coordinates as the starting point, and perform S3.2.1-S3.2.3 to calculate the latitude and longitude sequence corresponding to each possible starting point coordinate, and calculate the total distance corresponding to the latitude and longitude sequence.

[0029] S3.3.2 Sort all total distances and find the latitude and longitude sequence with the shortest total distance and its corresponding starting point.

[0030] Optionally, in the calculation process of S3.2.1-S3.2.3, a stopping rule based on the shortest path distance is introduced, including:

[0031] S3.4.1 Set the current shortest path distance parameter. During the calculation process of S3.2.1-S3.2.3, after obtaining each nearest coordinate point, calculate the cumulative path distance. When the cumulative path distance is greater than the current shortest path distance parameter, the current calculation stops and a latitude and longitude sequence and the corresponding path distance are obtained.

[0032] S3.4.2. The path distance corresponding to the latitude and longitude sequence obtained after each calculation stops is introduced into S3.4.1 as the current shortest path distance parameter for the next calculation.

[0033] Optionally, in S3.4.1, when performing the first calculation, the current shortest path distance parameter is set to infinity.

[0034] The beneficial effects of this invention are as follows:

[0035] This invention extracts latitude and longitude coordinates from congestion data at different congestion levels, removes duplicate coordinates, and generates disordered latitude and longitude coordinate strings. These disordered coordinate strings are then sequentially arranged to construct ordered coordinate strings from the start point to the end point or vice versa, completing the sequential processing. This invention can obtain the curve length of the congested road segment from the start point to the end point from the calculation results, and uses the cumulative point spacing as a metric to determine whether to continue calculation, thereby improving overall calculation efficiency. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.

[0037] Figure 1 This is a flowchart of a method for reordering map congestion data coordinate strings according to an embodiment of the present invention.

[0038] Figure 2 This is a flowchart of a method for extracting possible starting point coordinates according to an embodiment of the present invention;

[0039] Figure 3 This is a flowchart of a method for sequentially sorting a disordered coordinate string after determining the starting point, according to an embodiment of the present invention.

[0040] Figure 4 This is a flowchart illustrating a method for sequentially sorting a disordered coordinate string from an unknown starting point, as described in an 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] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] This embodiment provides a method for reordering out-of-order coordinate strings in map congestion data, including:

[0044] S1. Obtain map congestion data, extract valid coordinates from the map congestion data, and construct a list of valid coordinates;

[0045] S2. Extract possible starting point coordinates from the valid coordinates and construct a list of possible starting point coordinates;

[0046] S3. Select a possible starting point coordinate as the starting point, find the nearest coordinate point of the starting point in the list of valid coordinates, and then use the nearest coordinate point as the starting point again to find the nearest coordinate point again, until all valid coordinates in the list of valid coordinates have been calculated, and obtain the latitude and longitude sequence.

[0047] S4. Select the next possible starting point coordinate as the starting point, repeat S3, until all possible starting point coordinates in the possible starting point coordinate list have been traversed. Take the latitude and longitude sequence with the shortest distance as the sorting result of the time-series coordinate string of the map congestion data to complete the sequential processing.

[0048] Specifically, this embodiment extracts latitude and longitude coordinate string point information under different congestion levels from congestion data, and removes duplicate latitude and longitude coordinate points to obtain a disordered latitude and longitude coordinate string. The disordered latitude and longitude coordinate strings are then arranged sequentially to construct a sequential coordinate string from the start point to the end point or vice versa, completing the sequential processing. The curve length of the congested road segment from the start point to the end point can be obtained from the calculation results. During the calculation process, the cumulative point spacing is used as a metric to determine whether to continue the calculation, thereby improving the overall calculation efficiency.

[0049] Further, in step S1, extracting valid coordinates from the map congestion data and constructing a list of valid coordinates includes:

[0050] Extract the linkstate field from the map congestion data;

[0051] The linkstate field is parsed to obtain the coordinate values;

[0052] The coordinate values ​​are deduplicated, and the list of valid coordinates is formed based on the deduplicated coordinate values.

[0053] Further, in step S2, extracting possible starting point coordinates from the valid coordinates and constructing a list of possible starting point coordinates includes:

[0054] Based on the linkstate field corresponding to the deduplicated coordinate values ​​in the list of valid coordinates, obtain several key-value pairs;

[0055] Traverse the key-value pairs, obtain the values ​​in the key-value pairs and split them to extract several paths;

[0056] The first coordinate point of the segmented path is used as the possible starting point coordinates to construct the list of possible starting point coordinates.

[0057] Furthermore, the list of possible starting point coordinates also includes possible starting point coordinates derived from the congestion event point coordinate field given in the map congestion data.

[0058] Further, in step S3, finding the nearest coordinate point to the starting point in the list of valid coordinates includes:

[0059] S3.1.1 Find the coordinate point corresponding to the starting point in the list of valid coordinates, and remove the coordinate point from the list of valid coordinates;

[0060] S3.1.2 Calculate the spatial distance between the coordinate point and all other coordinate points in the valid coordinate list;

[0061] S3.1.3 Select the coordinate point with the shortest distance as the nearest coordinate point of the given coordinate point.

[0062] Further, in S3, after selecting the possible starting point coordinates, the latitude and longitude sequence is obtained based on the principle of shortest path distance, including:

[0063] S3.2.1. Based on the possible starting point coordinates, perform S3.1.1-S3.1.3 to obtain the nearest coordinate point of the possible starting point coordinates;

[0064] S3.2.2. Using the nearest coordinate point calculated as the new starting point, perform S3.1.1-S3.1.3 to obtain the next nearest coordinate point;

[0065] S3.2.3 Repeat steps S3.2.1-S3.2.2 until the nearest coordinate point of each coordinate point in the valid coordinate list is calculated to obtain the latitude and longitude sequence.

[0066] Further, in S3, after traversing all possible starting point coordinates in the list of possible starting point coordinates, the final starting point and its corresponding latitude and longitude sequence are determined from all obtained latitude and longitude sequence sequences based on the principle of minimizing the total distance, including:

[0067] S3.3.1 Select any possible starting point coordinate from the list of possible starting point coordinates as the starting point, and perform S3.2.1-S3.2.3 to calculate the latitude and longitude sequence corresponding to each possible starting point coordinate, and calculate the total distance corresponding to the latitude and longitude sequence.

[0068] S3.3.2 Sort all total distances and find the latitude and longitude sequence with the shortest total distance and its corresponding starting point.

[0069] Furthermore, in the calculation process of S3.2.1-S3.2.3, a stopping rule based on the shortest path distance is introduced, including:

[0070] S3.4.1 Set the current shortest path distance parameter. During the calculation process of S3.2.1-S3.2.3, after obtaining each nearest coordinate point, calculate the cumulative path distance. When the cumulative path distance is greater than the current shortest path distance parameter, the current calculation stops and a latitude and longitude sequence and the corresponding path distance are obtained.

[0071] S3.4.2. The path distance corresponding to the latitude and longitude sequence obtained after each calculation stops is introduced into S3.4.1 as the current shortest path distance parameter for the next calculation.

[0072] Furthermore, in S3.4.1, during the first calculation, the current shortest path distance parameter is set to infinity.

[0073] The following is combined with Figures 1-4 This embodiment provides a detailed description of a method for reordering out-of-order coordinate strings in map congestion data, which includes the following steps:

[0074] Step 1: Obtain map congestion data, extract valid coordinates from the map congestion data, and construct a list of valid coordinates;

[0075] Step 2: Extract possible starting point coordinates from the valid coordinates and construct a list of possible starting point coordinates;

[0076] Step 3: Select a possible starting point coordinate as the starting point, find the nearest coordinate point of the starting point in the list of valid coordinates, and then use the nearest coordinate point as the starting point again to find the nearest coordinate point again, until all valid coordinates in the list of valid coordinates have been calculated, and obtain the latitude and longitude sequence.

[0077] Step 4: Select the next possible starting point coordinates as the starting point, repeat S3, until all possible starting point coordinates in the possible starting point coordinate list have been traversed. Take the latitude and longitude sequence with the shortest distance as the sorting result of the time-series coordinate string of the map congestion data, and complete the sequential processing.

[0078] Further, in step 1, valid coordinates are extracted from the map congestion data, and a list of valid coordinates is constructed, specifically including:

[0079] Step 1.1: Extract fields containing linkstate information from the map congestion data. Linkstate information includes congested road segments and congestion levels.

[0080] The extracted linkstate data is shown in the example data from Baidu below:

[0081] linkstate="{'4':'116.776492,27.873715,116.776476,27.874258;116.776485,27.873455,116.77 6492,27.873715','3':'116.776476,27.874258,116.776476,27.874293;116.776476,27.874293,116 .776473,27.874752116.773969,27.863493','2':'116.776437,27.876609,116.776473,27.874752;116.776388,27.879654,116.776401,27.878965;116.776388,27.879828,116.776388,27.879654'}".

[0082] Step 1.2: Parse the linkstate field and extract the coordinate values.

[0083] The string defined by linkstate is parsed into a dictionary, and the values ​​of all key-value pairs are extracted and compiled into a list.

[0084] Step 1.3: Remove duplicate coordinate values ​​and create a list of valid coordinates.

[0085] The list of values, which contains all the latitude and longitude coordinates of congestion, is deduplicated to obtain a list without duplicate data, thereby reducing the number of elements in the list and reducing the computational burden. As shown below:

[0086] ['116.776388,27.879654','116.776476,27.874293','116.776476,27.874258','116.776437,27.876609','116.776485,27.873455','116.776492,27.873715','116.776388,27.879828','116.776473,27.874752'].

[0087] Furthermore, such as Figure 2 As shown, step 2, extracting possible starting point coordinates from the valid coordinates and constructing a list of possible starting point coordinates, specifically includes:

[0088] Step 2.1: Although the location field (coordinates of congested points) provided by map providers such as Baidu and Gaode in the congestion data may not necessarily be the starting coordinates of the latitude and longitude coordinate string, it is stored as a possible starting coordinate in an empty list key_start_point_list.

[0089] Step 2.2: Extract each key-value pair from the dictionary linkstate;

[0090] Step 2.3: Iterate through each key-value pair and extract the value from each key-value pair individually;

[0091] Step 2.4: For each value, use ';' as a separator, which means extracting multiple paths of the same congestion level separately.

[0092] Step 2.5: Extract the first coordinate point of each path segment and store it in the list key_start_point_list.

[0093] Furthermore, in step 3, finding the nearest coordinate point to the starting point in the list of valid coordinates specifically includes:

[0094] Step 3.1.1: Select point A from the list of valid coordinates as the origin;

[0095] Step 3.1.2: Remove pointA from the list of valid coordinates to obtain a new list of valid coordinates;

[0096] Step 3.1.3: Traverse all coordinate points in the new list of valid coordinates and calculate the spatial distance between each point and pointA;

[0097] Step 3.1.4: Find the point B with the smallest distance and set point B as the nearest coordinate point to point A.

[0098] Furthermore, in step 3, after selecting the possible starting point coordinates, a latitude and longitude sequence is obtained based on the principle of shortest path distance, including:

[0099] like Figure 3 As shown, sorting the time-series coordinate strings from a known starting point and a disordered coordinate string involves the following steps:

[0100] Step 3.2.1: Remove the known starting coordinates coord_A from the elements of the valid coordinate list.

[0101] Step 3.2.2: Based on coord_A, calculate the nearest next adjacent point coord_B from the elements of the valid coordinate list, according to the technical solutions in steps 3.1.1-3.1.4 above, and calculate the nearest distance value.

[0102] Step 3.2.3: Based on the calculated coord_B, remove it from the list of valid coordinates, and further calculate the next adjacent point coord_C, and sum the distances of the three points coord_A / B / C.

[0103] Step 3.2.4: According to the method described in Step 3.2.3, further calculate until all elements in the valid coordinate list have been traversed and calculated, and finally obtain the list of coordinate points arranged in order of all elements in the valid coordinate list starting from coord_A, as well as the total distance sum.

[0104] Furthermore, in steps 3 and 4, after traversing all possible starting point coordinates in the list of possible starting point coordinates, the final starting point and its corresponding latitude and longitude sequence are determined from all obtained latitude and longitude sequence sequences, based on the principle of minimizing the total distance. This includes:

[0105] like Figure 4 As shown, sorting time-series coordinate strings for unknown starting points and disordered coordinate strings specifically includes the following steps:

[0106] Step 3.3.1: Select any possible starting point coordinate (coord_X) from the list of possible starting point coordinates and calculate its corresponding latitude and longitude list sequence and the corresponding total distance (total_distance) through steps 3.2.1-3.2.3 above.

[0107] Step 3.3.2: Extract the next element from the list of possible starting points, and use the same steps 3.2.1-3.2.3 above to calculate the corresponding latitude and longitude list sequence and the total distance. Then, use this method to traverse all possible starting point coordinates.

[0108] Step 3.3.3: Find the smallest value among the total_distance values ​​calculated each time;

[0109] Step 3.3.4: When performing step 3.3.2 above, in order to improve the calculation speed and reduce unnecessary calculations, the total_distance obtained from the previous calculation is introduced into the calculation process of steps 3.2.1-3.2.3 above. When it is found that the accumulated distance in the latitude and longitude point calculation process is greater than the current minimum total_distance, the calculation is interrupted. In the first calculation, the minimum total_distance is set to infinity.

[0110] Step 3.3.5: Select the latitude and longitude sequence with the smallest total_distance to obtain the final time-series coordinate string sorting.

[0111] The method for reordering out-of-order coordinate strings in map congestion data proposed in this embodiment has the following technical advantages:

[0112] (1) Extract necessary points for minimum spanning tree calculation: First, the data is analyzed to extract the coordinates of possible starting points, thereby avoiding a large amount of repeated calculations and eliminating the need to construct a minimum spanning tree for all points.

[0113] (2) Remove points that do not need to be calculated repeatedly in a timely manner. Each time the minimum spanning tree is calculated, remove the points that have already been calculated in a timely manner to avoid a lot of useless calculations. For example, if there are four points [a,c,b,d] in the list, the first time point a is selected for calculation, and point b is found to be the point with the smallest weight (distance). Therefore, the next point of point a is point b. Then, it is not necessary to calculate the distance between point b and points a,c,d. Remove the two points [a,b] from the list and only calculate the distance between point b and points [c,d] to reduce the amount of calculation and improve the calculation efficiency.

[0114] (3) A comparison process with the current shortest minimum spanning tree is introduced in each minimum spanning tree calculation process. For example, in the minimum spanning tree calculation process starting from point b, if it is found that the accumulated distance is greater than the total distance of the minimum spanning tree calculated from point a in the previous calculation, it can be proven that the tree generated by point b is definitely larger than the tree generated by point a. Therefore, it can be deduced that point b is definitely not the final starting point, and the minimum spanning tree calculation for point b can be stopped immediately. Through this method, the amount of calculation is further reduced.

[0115] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for reordering disordered coordinate strings in map congestion data, characterized in that, include: S1. Obtain map congestion data, extract valid coordinates from the map congestion data, and construct a list of valid coordinates; S2. Extract possible starting point coordinates from the valid coordinates and construct a list of possible starting point coordinates; S3. Select a possible starting point coordinate as the starting point, find the nearest coordinate point of the starting point in the list of valid coordinates, and then use the nearest coordinate point as the starting point again to find the nearest coordinate point again, until all valid coordinates in the list of valid coordinates have been calculated, and obtain the latitude and longitude sequence. S4. Select the next possible starting point coordinate as the starting point, repeat S3, until all possible starting point coordinates in the possible starting point coordinate list have been traversed. Take the latitude and longitude sequence with the shortest distance as the sorting result of the time-series coordinate string of the map congestion data to complete the sequential processing. In step S1, extracting valid coordinates from the map congestion data and constructing a list of valid coordinates includes: Extract the linkstate field from the map congestion data; the linkstate field contains congested road segments and congestion levels. The linkstate field is parsed to obtain the coordinate values; The coordinate values ​​are deduplicated, and the list of valid coordinates is formed based on the deduplicated coordinate values. In step S2, extracting possible starting point coordinates from the valid coordinates and constructing a list of possible starting point coordinates includes: Based on the linkstate field corresponding to the deduplicated coordinate values ​​in the list of valid coordinates, obtain several key-value pairs; Traverse the key-value pairs, obtain the values ​​in the key-value pairs and split them to extract several paths; The first coordinate point of the segmented path is used as the possible starting point coordinates to construct the list of possible starting point coordinates; The list of possible starting point coordinates also includes possible starting point coordinates that will be obtained from the congestion event point coordinate field given in the map congestion data. In step S3, finding the nearest coordinate point to the starting point in the list of valid coordinates includes: S3.1.1 Find the coordinate point corresponding to the starting point in the list of valid coordinates, and remove the coordinate point from the list of valid coordinates; S3.1.2 Calculate the spatial distance between the coordinate point and all other coordinate points in the valid coordinate list; S3.1.3 Select the coordinate point with the shortest distance as the nearest coordinate point of the given coordinate point; In step S3, after selecting the possible starting point coordinates, the latitude and longitude sequence is obtained based on the principle of shortest path distance, including: S3.2.

1. Based on the possible starting point coordinates, perform S3.1.1-S3.1.3 to obtain the nearest coordinate point of the possible starting point coordinates; S3.2.

2. Using the nearest coordinate point calculated as the new starting point, perform S3.1.1-S3.1.3 to obtain the next nearest coordinate point; S3.2.3 Repeat steps S3.2.1-S3.2.2 until the nearest coordinate point of each coordinate point in the valid coordinate list is calculated, and the latitude and longitude sequence is obtained; In step S3, after traversing all possible starting point coordinates in the list of possible starting point coordinates, the final starting point and its corresponding latitude and longitude sequence are determined from all obtained latitude and longitude sequence sequences, based on the principle of minimizing the total distance. This includes: S3.3.1 Select any possible starting point coordinate from the list of possible starting point coordinates as the starting point, and perform S3.2.1-S3.2.3 to calculate the latitude and longitude sequence corresponding to each possible starting point coordinate, and calculate the total distance corresponding to the latitude and longitude sequence. S3.3.2 Sort all total distances and find the latitude and longitude sequence with the shortest total distance and its corresponding starting point; In the calculation process of S3.2.1-S3.2.3, a stopping rule based on the shortest path distance is introduced, including: S3.4.1 Set the current shortest path distance parameter. During the calculation process of S3.2.1-S3.2.3, after obtaining each nearest coordinate point, calculate the cumulative path distance. When the cumulative path distance is greater than the current shortest path distance parameter, the current calculation stops and a latitude and longitude sequence and the corresponding path distance are obtained. S3.4.

2. The path distance corresponding to the latitude and longitude sequence obtained after each calculation stops is introduced into S3.4.1 as the current shortest path distance parameter for the next calculation. In S3.4.1, during the first calculation, the current shortest path distance parameter is set to infinity.

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