Fusion Method Based on Internet Map Congestion Data

Abstract

The present invention discloses a fusion method based on congestion data of Internet maps, including: obtaining congestion data from different Internet map companies; performing data cleaning on the obtained congestion data for parameters to obtain the cleaned data; sequentially inputting one piece of the congestion data and the corresponding one piece of the cleaned data into a fusion algorithm for judgment until a repeated congested section is obtained; and fusing the repeated congested sections to obtain a fusion result. The present invention can fuse congestion data of multiple navigation maps, eliminate false information, and identify and output a standardized congestion road condition result in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of data fusion, and particularly relates to a fusion method based on congestion data of Internet maps. Background Art

[0002] Currently, traffic management departments need to use the congestion data of Internet maps to carry out work such as traffic condition perception and abnormal event discovery. Internet maps obtain information such as congestion locations, times, speeds, and distances based on the floating car data of their navigation users. The mainstream Internet maps include multiple maps such as Baidu, Amap, and Tencent Maps, each of which has its own different floating car users, and thus the congestion information is also different. The congestion information output by a single navigation map is prone to errors compared with the actual traffic conditions. Most of the existing technologies are methods for congestion perception and prediction, which analyze the road surface traffic conditions based on floating car data, GPS trajectory data, video surveillance data, sensor data, etc. to obtain traffic congestion conditions. The determination of congestion often only relies on one data source and does not perform fusion verification on multiple data sources. The data sources are independent of each other, resulting in a large error in congestion recognition. At the same time, different data sources are identified separately, leading to repeated warnings of congestion events and waste of congestion monitoring and disposal resources. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a fusion method based on congestion data of Internet maps, which can fuse the congestion data of multiple navigation maps, eliminate false information, and real-time identify and output the standardized congestion road condition results.

[0004] To achieve the above object, the present invention provides a fusion method based on congestion data of Internet maps, including: obtaining congestion data according to different Internet map companies;

[0005] Performing data cleaning on the obtained congestion data for parameters to obtain the cleaned data;

[0006] Sequentially inputting one piece of the congestion data and the corresponding one piece of the cleaned data into a fusion algorithm for judgment until a repeated congestion section is obtained;

[0007] Fusing the repeated congestion sections to obtain a fusion result.

[0008] Optionally, obtaining the congestion data includes:

[0009] Obtaining through relevant service interfaces of Internet map companies or by data scraping of maps.

[0010] Optionally, the parameters include but are not limited to unified city codes, province codes, coordinate system types, congestion levels, and congestion levels representing different intervals of congestion sections.

[0011] Optionally, one piece of the congestion data and the corresponding piece of the cleaned data are sequentially input into a fusion algorithm to determine whether they are in the same congested section until a repeated congested section is obtained, including:

[0012] At the beginning, the data source B is empty, and the first piece of data extracted from the data source A is directly stored in the data source B; where the data source A is a data source that aggregates the congestion information of each Internet map, and the data source B is a fusion table.

[0013] Continue to extract one piece from the data source A and match it one by one with the data in the data source B to determine whether they are in the same congested section.

[0014] If they are in the same congested section, further determine whether to replace and update the existing data in the data source B or not to store it in the data source B.

[0015] If they are not in the same congested section, directly store it in the data source B.

[0016] Optionally, fusing the repeated congested sections includes:

[0017] Perform string splicing on the repeated congested sections to obtain a fusion result.

[0018] Optionally, the data input into the fusion algorithm further includes any two pieces of congestion data, and further determine whether any two pieces of congestion data are in the same congested section.

[0019] Optionally, determining whether any two pieces of congestion data are in the same congested section includes:

[0020] Extract one piece of data from the data source that aggregates the congestion information of each Internet map, extract one piece of data from the fusion table, and sequentially determine the data extracted from the congestion information data source with all the data in the fusion table. Store the data extracted from the congestion information data source that does not meet the judgment conditions in the fusion table, and continue to extract one piece of data from the congestion information data source to determine with all the data in the fusion table. Denote one piece of data extracted from the congestion information data source as congested line A, and denote one piece of data extracted from the fusion table as congested line B.

[0021] Respectively judge the city dimensions where the congested line A and the congested line B are located.

[0022] Perform an azimuth-related judgment on the congested line A and the congested line B in the same city.

[0023] Perform a starting point distance judgment on the azimuth-related congested line A and congested line B.

[0024] Construct a longitude and latitude sequence arranged in the order of starting and ending points for the congested route A and the congested route B whose distances from the starting point are not greater than the preset distance, and further construct a curve segment function corresponding to the sequence;

[0025] Judge whether the congested route A and the congested route B intersect according to the constructed corresponding curve segment function;

[0026] Calculate the corresponding curve segment lengths for the intersecting congested route A and congested route B;

[0027] If the curve segment length of the congested route B is greater than that of the congested route A, update each field of the data in the fusion table based on the newly input data, based on the event ID, and splice the event IDs of the two data sources;

[0028] If the curve segment length of the congested route B is not greater than that of the congested route A, splice the event IDs.

[0029] Optionally, judging whether the congested route A and the congested route B intersect according to the constructed corresponding curve segment function includes:

[0030] In space, if the congested route A and the congested route B are located on the same road section, then line segment intersection occurs;

[0031] If the congested route A and the congested route B do not intersect, it is determined that the two congested routes are only in the same spatial area but do not belong to the same road section.

[0032] The technical effect of the present invention: The present invention discloses a method for fusing congested data based on Internet map, which can fuse congested data of multiple navigation maps, eliminate false information, identify and output standardized congested road condition results in real time, and provide highly accurate and reliable road condition information for the traffic management department of the public security organ, solving the problems of needing to connect different navigation maps and manually judge, and difficult to accurately identify congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0034] Figure 1 It is a flow chart of the method for fusing congested data based on Internet map in an embodiment of the present invention;

[0035] Figure 2 It is a flow chart for judging whether two congested data in the same data source are on the same road section in an embodiment of the present invention;

[0036] Figure 3Schematic diagram of the process for determining whether any two pieces of congestion data in the embodiments of the present invention are on the same road section and performing fusion and update. Detailed implementation manners

[0037] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0038] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0039] As Figure 1 shown, the fusion method based on congestion data of Internet maps provided in this embodiment includes:

[0040] Obtain congestion data according to different Internet map companies;

[0041] Perform data cleaning on the obtained congestion data for parameters to obtain the cleaned data;

[0042] Input a piece of congestion data and the corresponding piece of cleaned data into the fusion algorithm in sequence for judgment until a repeated congestion road section is obtained;

[0043] Fuse the repeated congestion road sections to obtain a fusion result.

[0044] Furthermore, obtaining congestion data includes:

[0045] Obtain according to the relevant service interfaces of Internet map companies or perform data scraping on the map.

[0046] Optionally, the parameters include but are not limited to unified city codes, province codes, coordinate system types, congestion levels, and congestion levels representing different intervals of the congestion road section.

[0047] Furthermore, as Figure 2 shown, inputting a piece of congestion data and the corresponding piece of cleaned data into the fusion algorithm in sequence for judgment as to whether it is the same congestion road section until a repeated congestion road section is obtained includes:

[0048] At the beginning, if the data source B is empty, the first piece of data extracted from the data source A is directly stored in the data source B; wherein the data source A is a data source that aggregates the congestion information of each Internet map, and the data source B is a fusion table;

[0049] Continue to extract one piece from the data source A and match it one by one with the data in the data source B to determine whether it is the same congestion road section;

[0050] If it is the same congested section, further determine whether to replace and update the existing data in data source B, or not store it in data source B anymore;

[0051] If it is not the same congested section, directly store it in the said data source B.

[0052] Furthermore, the fusion of repeated congested sections includes:

[0053] Perform string concatenation on the repeated congested sections to obtain the fusion result.

[0054] Specifically, for the fusion and storage of data, specifically, for the calculated two congested data belonging to the same section, perform string concatenation according to the event ID. For example, if the event ID of event A is BD_YD01 and the event ID of event B is GD_02, then the event ID after string concatenation can be concatenated as BD_YD01&GD_02. Its function is that when obtaining congested data next time, it can be determined whether it is the same event as the previously calculated fusion data according to its event ID, so as to update the congested information of the event in a timely manner.

[0055] Furthermore, the data input into the fusion algorithm also includes any two congested data, and further determine whether any two congested data are the same congested section.

[0056] Furthermore, as Figure 3 shown, determining whether any two congested data are the same congested section includes:

[0057] Extract one piece of data from the congested information data sources that converge the Internet maps, extract one piece of data from the fusion table, and sequentially judge the data extracted from the congested information data sources with all the data in the fusion table. Store the data extracted from the congested information data sources that do not meet the judgment conditions in the fusion table, and continue to extract one piece of data from the congested information data sources to judge with all the data in the fusion table. Denote one piece of data extracted from the congested information data sources as congested route A, and denote one piece of data extracted from the fusion table as congested route B;

[0058] Respectively judge the city dimensions where congested route A and congested route B are located;

[0059] Perform azimuth-related judgment on congested route A and congested route B in the same city;

[0060] Perform starting point distance judgment on azimuth-related congested route A and congested route B;

[0061] Construct a longitude and latitude sequence arranged in the order of starting and ending points for congested route A and congested route B with a starting point distance not greater than the preset distance, and further construct a curve segment function corresponding to the sequence;

[0062] Judge whether the congested line A and the congested line B intersect according to the corresponding curve segment function constructed;

[0063] Calculate the corresponding curve segment lengths for the intersecting congested line A and congested line B;

[0064] If the curve segment length of the congested line B is greater than that of the congested line A, update each field of the data in the fusion table based on the newly input data, based on the event ID, and splice the event IDs of the two data sources;

[0065] If the curve segment length of the congested line B is not greater than that of the congested line A, splice the event IDs.

[0066] Specifically, for city judgment, it mainly relies on the citycode field of the congestion data. By judging whether it is the same value, it is determined whether it belongs to the same city. If it does not belong to the same city, then the two pieces of data definitely do not belong to the same congested section, and thus can be directly stored in the fused data table. If it belongs to the same city, then subsequent calculations are further carried out. For azimuth correlation judgment, specifically, for different Internet map companies, the congestion data all contains an azimuth field, but the expression methods are different. For example, Baidu expresses it as east to west, while Gaode may express it as southeast to northwest. Therefore, it is necessary to construct the following related data calculation table 1.

[0067] Table 1

[0068]

[0069] According to the above corresponding table of azimuth and related azimuths, calculate whether their azimuths are consistent. If the two pieces of data are not related, it can be judged that they do not belong to the same section, and the related data can be directly stored in the fusion table. If the two pieces of data are azimuth-related, further calculations are required.

[0070] Starting point distance judgment is used to judge whether two congestion data are close in space. The reason for not using information such as road names for judgment here is that for different Internet map companies on the same road section, the expression methods of road names are inconsistent. Therefore, it is necessary to directly judge whether they are in the same spatial area through the spatial distance of the congestion starting points. If the starting point spacing is greater than 500 meters, it is considered that the two pieces of data are not in the same area in space, and they are directly stored in the fusion table. If the starting point spacing is less than 500 meters, the congestion information expressed by the two pieces of data is in the same spatial area, and there is a possibility of belonging to the same road section, and further fusion calculations are required.

[0071] Construct a longitude and latitude sequence that can be arranged in order of starting and ending points. The specific implementation logic is as follows. Currently, the congestion data provided by Internet map companies contains two fields, location and link_state. The location field is the starting point coordinate, and link_state is the congestion level represented by the longitude and latitude strings in different intervals in the congestion data. The sample data is as follows (example data, due to too many longitude and latitude in the sample size, only a small part of the data is retained as an example):

[0072] #point1:

[0073] #location_point = '116.776473,27.874752'

[0074] #linkstate = "{"

[0075] '4': '116.776492,27.873715,116.776476,27.874258;116.776485,27.873455,116.776492,27.873715;

[0076] '3': '116.776476,27.874258,116.776476,27.874293;116.776476,27.874293,116.776473,27.874752116.773969,27.863493,

[0077] '2':

[0078] '116.776437,27.876609,116.776473,27.874752;116.776388,27.879654,116.776401,27.878965;116.776388,27.879828,116.776388,27.879654;

[0079] }"

[0080] In the above example, the core data in the link_state field is in dictionary format, which contains three levels of congestion intervals including "4", "3", and "2". Each interval is composed of a longitude and latitude coordinate string. In the coordinate string, ";" represents other non-connected intervals with the same congestion level. Therefore, although the link_state field can project the congested road sections on the map, its data is a non-continuous coordinate string, lacking a continuous longitude and latitude coordinate string from the starting point to the ending point. For this reason, a continuous longitude and latitude trajectory needs to be calculated for further fusion calculation in the lower part.

[0081] Constructing a curve segment function that can represent the sequence lies in constructing a curve segment function that fits the coordinate string based on the calculated longitude and latitude coordinate strings of the start and end points in order. The function mentioned here can be a specific fitting curve equation, or can be directly represented by a data object calculated based on longitude and latitude and capable of representing the curve segment.

[0082] Judging whether congested route A and congested route B intersect according to the constructed corresponding curve segment function includes: spatially, if congested route A and congested route B are located on the same road section, then line segment intersection occurs; if congested route A and congested route B do not intersect, it is determined that the two congested routes are only in the same spatial area but do not belong to the same road section.

[0083] Calculating the lengths of the respective curve segments constructed based on the two data sources. In order to further fuse the congested data on the same road section, it is necessary to calculate the curve segment lengths for the following calculations.

[0084] Judging the data items to be updated based on the curve segment lengths. By comparing the lengths of the two curve segments, the shortest congested data is taken to represent the congested data obtained from the Internet map company. A certain road section is congested and reaches at least a certain number of kilometers. Taking the shortest congested data here aims to improve the overall usability of the congested data. After determining the shortest curve segment, the different field information corresponding to the curve segment is updated into the fusion table, and the event IDs are concatenated as strings.

[0085] The present invention discloses a method for fusing Internet map congested data, which can fuse congested data of multiple navigation maps, eliminate false information, identify and output standardized congested road conditions results in real time, and provide road condition information with strong accuracy and high reliability for the traffic management department of the public security organ, solving the problems of needing to dock different navigation maps and manually judge, and difficult to accurately identify congestion.

[0086] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A fusion method based on Internet map congestion data, characterized in that Including: Obtaining congestion data according to different Internet map companies; Performing data cleaning on the obtained congestion data for parameters to obtain the cleaned data; Sequentially inputting one piece of the congestion data and the corresponding piece of the cleaned data into a fusion algorithm for judgment until a repeated congested section is obtained; Fusing the repeated congested sections to obtain a fusion result; The data input into the fusion algorithm further includes any two pieces of congestion data. Further judge whether any two pieces of congestion data are the same congested section. Extract one piece of data from the congestion information data sources that converge the congestion information of each Internet map, extract one piece of data from the fusion table, and sequentially judge the data extracted from the congestion information data sources with all the data in the fusion table. The data extracted from the congestion information data sources that do not meet the judgment conditions is stored in the fusion table, and continue to extract one piece of data from the congestion information data sources to judge with all the data in the fusion table. Denote one piece of data extracted from the congestion information data sources as congested route A, and denote one piece of data extracted from the fusion table as congested route B; Respectively judge the city dimensions where the congested route A and the congested route B are located; Perform an orientation-related judgment on the congested route A and the congested route B in the same city; Perform a starting point distance judgment on the congested route A and the congested route B that are orientation-related; Construct a longitude and latitude sequence arranged in the order of the starting and ending points for the congested route A and the congested route B with a starting point distance not greater than a preset distance, and further construct a curve segment function corresponding to the sequence; Judge whether the congested route A and the congested route B intersect according to the constructed corresponding curve segment function; Calculate the corresponding curve segment lengths for the intersecting congested route A and congested route B; If the curve segment length of the congested route B is greater than that of the congested route A, update each field of the data in the fusion table based on the newly input data, based on the event ID, and splice the event IDs of the two data sources as a string; If the curve segment length of the congested route B is not greater than that of the congested route A, splice the event IDs as a string.

2. The fusion method based on Internet map congestion data according to claim 1, characterized in that Obtaining congestion data includes: Obtaining through relevant service interfaces of Internet map companies or by data scraping of maps.

3. The fusion method based on Internet map congestion data according to claim 1, characterized in that The parameters include but are not limited to unified city codes, province codes, coordinate system types, congestion levels, and congestion levels representing different intervals of congested sections.

4. The fusion method based on Internet map congestion data according to claim 1, characterized in that Sequentially inputting one piece of the congestion data and the corresponding piece of the cleaned data into a fusion algorithm to judge whether it is the same congested section until a repeated congested section is obtained includes: At the beginning, the data source B is empty, then the first piece of data extracted from the data source A is directly stored in the data source B; where the data source A is the congestion information data source that converges the congestion information of each Internet map, and the data source B is the fusion table; Continue to extract one from the data source A and match it one by one with the data from the data source B to determine whether it is the same congested section; If it is the same congested section, further determine whether to replace and update the existing data in the data source B or not store it in the data source B anymore; If it is not the same congested section, directly store it in the data source B.

5. The fusion method based on Internet map congestion data according to claim 1, wherein: The fusion of the repeated congested sections includes: Perform string splicing on the repeated congested sections to obtain a fusion result.

6. The fusion method based on Internet map congestion data according to claim 1, wherein: Judging whether the congested line A and the congested line B intersect according to the constructed corresponding curve segment function includes: Spatially, if the congested line A and the congested line B are located in the same section, a line segment intersection occurs; If the congested line A and the congested line B do not intersect, it is determined that the two congested lines are only in the same spatial area but do not belong to the same section.

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