Road generation method, device, equipment and storage medium

Abstract

The present disclosure provides a road generation method, device, equipment and storage medium, relates to the field of data processing, in particular to the field of electronic map, cloud computing and big data technology. The specific implementation scheme is: obtaining position data to be processed; performing clustering analysis on the position data by using a density-based clustering algorithm to obtain data clusters; obtaining longitude and latitude coordinates of boundary points in the data clusters to obtain a boundary point set; connecting the longitude and latitude coordinates in the boundary point set in sequence by using a boundary point discovery algorithm, and drawing a road graph according to the connected boundary points. Through the above scheme, the development and operation cost is saved, and the flexibility and applicability of road generation are ensured.

Description

Technical Field

[0001] This disclosure relates to electronic maps, cloud computing, and big data technologies in the field of data processing, and in particular to a road generation method, apparatus, device, and storage medium. Background Technology

[0002] Electronic maps, also known as digital maps, are maps stored and viewed digitally using computer technology. Electronic maps serve as a carrier of information, providing data and information to users.

[0003] Today, electronic maps have become an essential means for people to understand life. As a result, people have an increasing need for the precision and accuracy of electronic maps. Usually, electronic maps are drawn by manually obtaining regional information.

[0004] However, manually obtaining regional information through third-party tools leads to higher costs for map road drawing. Summary of the Invention

[0005] This disclosure provides a method, apparatus, device, and storage medium for generating roads for map drawing.

[0006] According to a first aspect of this disclosure, a road generation method is provided, comprising:

[0007] Acquire location data to be processed, which includes the latitude and longitude coordinates of multiple vehicles;

[0008] The location data is clustered using a density-based clustering algorithm to obtain data clusters, which include multiple latitude and longitude coordinates that satisfy the parameter conditions set by the clustering algorithm.

[0009] Obtain the latitude and longitude coordinates of the boundary points in the data cluster to obtain the set of boundary points;

[0010] A boundary point discovery algorithm is used to connect the latitude and longitude coordinates in the boundary point set sequentially, and a road map is drawn based on the connected boundary points. The boundary point algorithm is an algorithm that filters boundary points based on a set range radius and angle threshold.

[0011] According to a second aspect of this disclosure, a road generation apparatus is provided, comprising:

[0012] The first acquisition unit is used to acquire location data to be processed, the location data including the latitude and longitude coordinates of multiple vehicles;

[0013] The analysis unit is used to perform cluster analysis on the location data using a density-based clustering algorithm to obtain data clusters, wherein the data clusters include multiple latitude and longitude coordinates that satisfy the parameter conditions set by the clustering algorithm.

[0014] The second acquisition unit is used to acquire the latitude and longitude coordinates of the boundary points in the data cluster to obtain a set of boundary points.

[0015] The connection unit is used to connect the latitude and longitude coordinates in the set of boundary points sequentially using a boundary point discovery algorithm, and draw a road map based on the connected boundary points. The boundary point algorithm is an algorithm that filters boundary points based on a set range radius and angle threshold.

[0016] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0020] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect.

[0021] According to a fifth aspect of this disclosure, a computer program product is provided, the computer program product comprising: a computer program stored in a readable storage medium, wherein at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the method described in the first aspect.

[0022] The technology disclosed herein solves the problem of high cost in map road drawing. It uses a density-based clustering algorithm to perform cluster analysis on the acquired location data to obtain data clusters, and then employs a boundary point discovery algorithm to connect the latitude and longitude coordinates of the boundary points in the data clusters to achieve road drawing. By using the algorithm to analyze the location data, it eliminates the dependence on data processing tools, thereby reducing drawing costs.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0024] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0025] Figure 1 This is a scene diagram that can implement the road generation method of the embodiments of this disclosure;

[0026] Figure 2 A schematic flowchart illustrating a road generation method provided in the first embodiment of this disclosure;

[0027] Figure 3 This is a system architecture diagram that enables the acquisition of location data to be processed;

[0028] Figure 4 A schematic flowchart illustrating a road generation method provided in the second embodiment of this disclosure;

[0029] Figure 5 This is a structural diagram of the target boundary points and target line segments;

[0030] Figure 6 This is the first schematic diagram of the target boundary point screening process;

[0031] Figure 7 This is the second schematic diagram of the target boundary point screening process;

[0032] Figure 8 This is the third schematic diagram of the target boundary point screening process;

[0033] Figure 9 This is the fourth schematic diagram of the target boundary point screening process;

[0034] Figure 10 A schematic flowchart illustrating a road generation method provided in the third embodiment of this disclosure;

[0035] Figure 11 This is a schematic diagram of the public data cluster;

[0036] Figure 12 This is a schematic diagram of the publicly disclosed set of boundary points;

[0037] Figure 13 A schematic flowchart illustrating a road generation method provided in the fourth embodiment of this disclosure;

[0038] Figure 14 A schematic flowchart illustrating a road generation method provided in the fifth embodiment of this disclosure;

[0039] Figure 15 This is a block diagram of an electronic device used to implement the road generation method of the embodiments of this disclosure. Detailed Implementation

[0040] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0041] In related technologies, electronic maps, also known as digital maps, are maps stored and viewed digitally using computer technology. Electronic maps serve as information carriers, providing data to users. Currently, electronic maps have become an essential means for people to understand their lives; therefore, the demand for their accuracy and precision is increasing. The generation of electronic maps typically involves manually acquiring regional information and then drawing them. However, manually acquiring regional information through third-party tools leads to high costs in map drawing and can also affect map accuracy, thus having certain limitations.

[0042] To address these existing problems, the technical conception process of this disclosure is as follows: During their research on electronic map road drawing, the inventors discovered that manual drawing of electronic maps is easily influenced by human subjectivity. Drawing electronic maps using remote sensing imagery and geographic information systems requires significant development and maintenance costs. Furthermore, remote sensing imagery is limited not only by resolution but also by remote areas, resulting in insufficient road accuracy. To save costs and improve the accuracy of electronic maps, it is possible to combine other location data and eliminate the need for drawing tools. Therefore, the inventors considered whether it is possible to analyze location information reported by multiple vehicles and combine it with clustering algorithms and boundary point discovery algorithms to draw electronic maps.

[0043] Based on the above technical concept, this disclosure provides a road generation method applied in a backend server. It filters latitude and longitude coordinates from the vehicle's own location information, then performs cluster analysis on these coordinates using a density-based clustering algorithm to obtain data clusters. Finally, a boundary point discovery algorithm connects the boundary points within these data clusters to obtain a road map. This achieves low-cost map creation. By utilizing the vehicle's own location information, it reduces the development and maintenance costs associated with acquiring location information using third-party tools. The algorithmic analysis of location information avoids the low-resolution map accuracy issues caused by third-party tools, while also enhancing applicability.

[0044] Figure 1 This is a scene diagram that can implement the road generation method of the embodiments of this disclosure. For example... Figure 1As shown, this application scenario includes at least: an application platform 100, a server 101, a display 102, multiple vehicles 103, and an on-board device 104 configured in each vehicle 103. Only one vehicle is used for illustration in the figure; in actual applications, a large amount of location data reported by vehicles is required. When map drawing is needed, the application platform 100 controls the vehicles 103 to report location information. This location information may include Global Positioning System (GPS) data, BeiDou Navigation Satellite System (BDS) data, or Galileo satellite navigation system (GSNS) data, etc., and this solution does not impose any restrictions on this. In this solution, it should be understood that the location information can be reported by the positioning module of the vehicle 103 itself, or by the on-board device 104, or may include both the location information reported by the positioning module of the vehicle 103 and the location information reported by the on-board device 104. After receiving the location information reported by vehicle 103, server 101 preprocesses the location information to obtain location data to be processed, then clusters the location data using a density-based clustering algorithm to obtain data clusters, filters out the set of boundary points, and finally connects each point in the set of boundary points using a boundary point discovery algorithm to obtain a road map, which is then displayed on monitor 102.

[0045] Optional, Figure 1 A vehicle and an onboard device configured in that vehicle are shown. This disclosure does not limit the number of vehicles or onboard devices, which can be determined according to the actual scenario, and will not be elaborated here.

[0046] Understandable, Figure 1 The illustrated scenario diagram is merely an example. In practical applications, this scenario diagram may also include other devices, such as storage devices, and can be adjusted according to actual needs. This disclosure does not limit these devices. Furthermore, the embodiments of this disclosure do not limit the actual form of the various devices included in the application scenario, nor do they limit the interaction methods between devices. In the specific application of the solution, these can be set according to actual needs.

[0047] It should be noted that the vehicle-mounted device 104 can be any electronic device that can be installed in the vehicle 103 and is equipped with a positioning system. The application platform 100 can be any electronic platform that has map-making requirements.

[0048] This disclosure provides a road generation method, apparatus, device, and storage medium, which are applied to electronic maps, cloud computing, and big data technologies in the field of data processing, in order to reduce mapping costs.

[0049] The following detailed description uses specific embodiments to illustrate the technical solutions of this disclosure and how these solutions solve the aforementioned technical problems. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this disclosure will now be described with reference to the accompanying drawings.

[0050] The specific implementation scheme of the road generation method provided in this disclosure is described below.

[0051] Figure 2 This is a flowchart illustrating a road generation method provided in the first embodiment of this disclosure, with reference to... Figure 2 The road generation method shown can be configured as a road generation device, which can be located in an electronic device, such as a mobile phone, cloud server, server cluster, computer, laptop, etc. This disclosure does not impose excessive limitations on the specific type of electronic device. The road generation method may include the following steps:

[0052] S201: Obtain the location data to be processed.

[0053] Optionally, the location data to be processed may refer to the latitude and longitude coordinates of multiple vehicles. Figure 3 This is a system architecture diagram that enables the acquisition of location data to be processed, such as... Figure 3 As shown, obtaining the location data to be processed can include: obtaining location information through the vehicle's own positioning system module, obtaining location information through the vehicle's onboard equipment, or obtaining location information through both the vehicle's own positioning system module and the vehicle's onboard equipment. (Reference) Figure 3 The location information reported by the vehicle's own positioning system module and on-board equipment can be obtained through a data platform. Since the obtained location information includes multiple vehicles, the location information of each vehicle is aggregated to obtain a location information set. Abnormal data in the location information set is then preprocessed to obtain the location data to be processed. The location information of each vehicle includes the vehicle's latitude and longitude coordinates, and may also include the vehicle's location attributes and operating status.

[0054] Optionally, the vehicle's own positioning system module can be a GPS positioning system, a Galileo positioning system, or a Beidou positioning system, etc. This disclosure does not specifically limit the specific type of the vehicle's own positioning system module.

[0055] It should be noted that the vehicle may be equipped with one or more in-vehicle devices. These devices may include in-vehicle Bluetooth devices, in-vehicle mobile phones, in-vehicle watches, and other electronic devices. Furthermore, these in-vehicle devices are also equipped with a positioning system. There are no specific restrictions on the number or type of in-vehicle devices or the specific type of positioning system they are equipped with.

[0056] Optionally, both the vehicle's self-positioning system module and the on-board equipment include at least one receiver and one microprocessor. The receiver is used to receive satellite signals, and the microprocessor is used to calculate the latitude and longitude coordinates of the location based on the satellite signals.

[0057] S202: Density-based clustering algorithm is used to perform cluster analysis on location data to obtain data clusters.

[0058] In this step, after obtaining the location data to be processed, in order to reduce costs and simplify the processing, a density-based clustering algorithm is used to perform cluster analysis on the location data to obtain data clusters. The data clusters are obtained by setting the parameters of the algorithm and performing cluster analysis based on the set parameters. The meaning of a data cluster is the set of all points corresponding to latitude and longitude coordinates within a certain distance. Therefore, the data clusters include multiple latitude and longitude coordinates that meet the parameter conditions set by the clustering algorithm.

[0059] Specifically, the location data is converted into the input data format of the density-based clustering algorithm. Based on the parameters set by the clustering algorithm, the location data is clustered into data clusters containing boundary points, core points, and noise points through the clustering analysis calculation of the density-based clustering algorithm.

[0060] It should be noted that the density-based clustering algorithm can be the DBSCAN algorithm. The parameters set for the clustering algorithm include: neighborhood radius and minimum number of points. The neighborhood radius and minimum number of points are used to classify the location data, specifically into boundary points, core points, and noise points. They can also be used to determine the relationships between different types of points, specifically including density-directed, density-reachable, density-connected, and non-density-connected relationships.

[0061] S203: Obtain the latitude and longitude coordinates of the boundary points in the data cluster to obtain the set of boundary points.

[0062] In this step, after obtaining data clusters by clustering the location data using a density-based clustering algorithm, and considering that roads have boundaries, determining the road outline through boundary points is more accurate. Therefore, the latitude and longitude coordinates of the boundary points in the data clusters are obtained and combined into a boundary point set.

[0063] S204: The boundary point discovery algorithm is used to connect the latitude and longitude coordinates in the boundary point set in sequence, and the road map is drawn based on the connected boundary points.

[0064] In this step, after obtaining the set of boundary points, a boundary point discovery algorithm is used to filter the boundary points in the set and connect them sequentially in order to draw the road quickly and easily. Finally, the road map is drawn based on the connected boundary points. The boundary point algorithm is one that filters boundary points based on a set radius and angle threshold.

[0065] Specifically, in order to make the drawn map roads more accurate and smooth, the boundary points in the boundary point set are filtered in turn according to the range radius and angle threshold, and the filtered boundary points are connected in turn to obtain the road map.

[0066] It should be noted that the range radius and angle threshold can be set according to the actual needs of the scenario, and this disclosed embodiment does not limit them.

[0067] The road generation method provided in this embodiment employs a density-based clustering algorithm to perform cluster analysis on the acquired location data to be processed. Then, it filters out a set of boundary points from the clustered data clusters. Next, a boundary point discovery algorithm is used to filter and connect the boundary points in the set. Finally, a road map is drawn based on the connected boundary points. By using vehicle location data for road drawing and employing a simple and convenient algorithm for data analysis, this method saves development and maintenance costs compared to manually using regional information acquisition and drawing tools. It also avoids the problem of low road map accuracy caused by the low resolution of regional information acquisition tools, while enhancing applicability.

[0068] To help readers gain a deeper understanding of the implementation principles of this disclosure, the following will be discussed in conjunction with... Figures 4-12 right Figure 2 The illustrated embodiments are further refined.

[0069] Figure 4 This is a flowchart illustrating a road generation method according to a second embodiment of the present disclosure, with reference to... Figure 4 The road generation method shown differs from the previous embodiment in that it further refines the method of using a boundary point discovery algorithm to sequentially connect the latitude and longitude coordinates in the boundary point set. The corresponding refinement scheme may include the following steps:

[0070] S401: Select any boundary point from the set of boundary points as the starting point.

[0071] In this step, the boundary points in the boundary point set are filtered and connected, and any point can be selected as the starting point.

[0072] S402: Based on the latitude and longitude coordinates of each boundary point in the boundary point set, obtain the target boundary point that is closest to the starting point from the boundary point set, and connect the target boundary point with the starting point to obtain the target line segment.

[0073] In this step, to make the drawn road map more accurate and precise, the distance between each boundary point and the starting point is calculated based on the latitude and longitude coordinates of each boundary point in the boundary point set. This yields the target boundary point that is closest to the starting point, and the target boundary point is then connected to the starting point to obtain the target line segment.

[0074] For example, Figure 5 This is a structural diagram of the target boundary points and target line segments, for reference. Figure 5 Taking the starting point A with coordinates (1, 1, 1) as an example, the coordinates of the boundary point B in the boundary point set are (5, 1, 1) and the coordinates of point C are (1, 3, 1). Using the point-to-point distance formula, the distances from point B and point C to point A are calculated respectively. The distance from point B to point A is 4, and the distance from point C to point A is 2. Therefore, the target boundary point is C, and the line segment AC is the target line segment.

[0075] It should be noted that the above examples are only for illustrating the target boundary points and target line segments. This disclosure does not limit the coordinates of the starting point, the coordinates and number of boundary points in the boundary point set, and can be determined according to the actual situation.

[0076] S403: From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, find the boundary point whose angle with the target line segment is the smallest within the angle threshold range and use it as the new target boundary point. Connect the target boundary point with the new boundary point to obtain a new target line segment. Repeat this step until all boundary points in the set of boundary points are connected.

[0077] In this step, after determining the target boundary points and target line segments, in order to make the road map drawing more flexible and to ensure the smoothness of the road map drawing, new target boundary points are filtered by a preset angle threshold. Then, from the boundary point set, within a circle with the target boundary point as the center and the radius as the range, the boundary point with the smallest angle formed with the target line segment within the angle threshold range is obtained as the new target boundary point. Then, the target boundary point is connected with the new boundary point to obtain the new target line segment. This step is repeated until all boundary points in the boundary point set are connected.

[0078] Optionally, within the circular area, the boundary point with the smallest angle formed with the target line segment within the angle threshold range is obtained as the new target boundary point. The boundary point with the smallest clockwise angle formed with the target line segment within the angle threshold range can be used as the new target boundary point, and the clockwise angle is used for judgment when repeatedly determining the new target boundary point.

[0079] Alternatively, the boundary point with the smallest counterclockwise angle with the target line segment within the angle threshold range can be used as the new target boundary point, and the counterclockwise angle can be used to determine the new target boundary point repeatedly.

[0080] The road generation method provided in this embodiment selects any point in the boundary point set as the starting point, and selects the point in the boundary point set that is closest to the starting point as the target boundary point. The line segment between the target boundary point and the starting point is taken as the target line segment. Then, a circle is drawn with the target boundary point as the center and the radius as the range radius. The boundary point within the circle that forms the smallest angle with the target line segment within the angle threshold range is taken as the new target boundary point. The target boundary point is connected to the new boundary point to obtain a new target line segment. This step is repeated until all boundary points in the boundary point set are connected. Setting the range radius increases the flexibility of this solution, the preset angle threshold ensures the smoothness of the road, and the simple algorithm makes the drawing of the road map easy to implement.

[0081] Furthermore, based on any of the above embodiments, in step S403, the boundary point within a circular area centered on the target boundary point and with a radius equal to the range radius is selected from the set of boundary points. The boundary point with the smallest angle formed with the target line segment within the angle threshold range is then selected as the new target boundary point. This can be implemented in the following ways:

[0082] The first method involves selecting the boundary point from the set of boundary points that has the smallest clockwise angle with the target line segment within the radius of a circle centered on the target boundary point.

[0083] For example, taking an angle threshold range of [90°, 120°], a target boundary point of C, and a range radius of 3 as an example, Figure 6 This is the first schematic diagram of the target boundary point filtering process, see reference. Figure 6 Within the radius of the circle, select boundary points D, E, and F. Connect these three points to the target boundary point C. The resulting line segments DC, EC, and FC form angles with the target line segment of 100°, 109°, and 130°, respectively. According to the angle threshold range, point F does not meet the requirements and needs to be filtered out. The boundary point with the smallest angle with the target line segment within the angle threshold range is point D. Therefore, point D is taken as the new target boundary point, and line segment DC is taken as the new target line segment.

[0084] The second method involves selecting the boundary point from the set of boundary points that has the smallest counterclockwise angle with the target line segment within the radius of a circle centered on the target boundary point.

[0085] For example, taking an angle threshold range of [90°, 120°], a target boundary point of C, and a range radius of 3 as an example, Figure 7 This is the second schematic diagram of the target boundary point filtering process, for reference. Figure 7 Within the circular radius of the target line segment, point D, point E, and point F are selected as boundary points. The counterclockwise angles formed by these three points and the target line segment are 100°, 165°, and 265°, respectively. According to the angle threshold, only point D satisfies the condition that the counterclockwise angle formed by these three points and the target line segment is within the angle threshold range. Therefore, point D is taken as the new target boundary point, and line segment DC is taken as the new target line segment.

[0086] It should be noted that the above two methods are only examples of new target boundary points and new target line segments. This disclosure does not limit the coordinates of the starting point, the coordinates and number of boundary points in the boundary point set, the range radius, the number of boundary points within the range radius, and the angle between the boundary points and the target line segment. These can be determined according to the actual situation.

[0087] The road generation method provided in this embodiment filters boundary points within a circular radius, and selects the boundary point within the circular range whose clockwise or counterclockwise angle with the target line segment is the smallest within an angle threshold range as the new target boundary point. This method improves the flexibility and accuracy of road map drawing by determining the new target boundary point.

[0088] Furthermore, based on any of the above embodiments, before obtaining the set of boundary points and sequentially connecting the latitude and longitude coordinates in the set of boundary points using the boundary point discovery algorithm, it is also necessary to configure the range radius and angle threshold in response to the user's algorithm parameter configuration operation.

[0089] Before using the boundary point discovery algorithm to filter and connect boundary points in the boundary point set, users need to configure the parameters of the boundary point discovery algorithm according to their actual needs. In response to the user's algorithm parameter configuration operation, the range radius and angle threshold are configured.

[0090] For example, a user can send algorithm parameter configuration instructions to a server via a terminal device, and the server configures the range radius and angle threshold according to the algorithm parameter configuration instructions. The algorithm parameter configuration instructions include range radius configuration instructions and angle threshold configuration instructions.

[0091] It should be noted that the user's terminal device can be any operable smart device, such as a smartphone or laptop. The server that can execute algorithm parameter configuration instructions can also be other electronic devices that can execute instructions, such as a laptop. In addition to the aforementioned range radius configuration instructions and angle threshold configuration instructions, the algorithm parameter configuration instructions can also include other parameter configuration instructions, which can be any parameters set by the user.

[0092] It should be noted that the above example is only one example of configuring algorithm parameters, and the embodiments of this disclosure do not specifically limit the type and number of the above-mentioned physical devices and parameters.

[0093] The road generation method provided in this embodiment pre-configures the range radius and angle threshold to provide a parameter basis for the boundary point discovery algorithm. Furthermore, the range radius and angle threshold can be adjusted according to the actual scenario, enhancing flexibility. By setting the range radius and angle threshold, the accuracy and smoothness of road map drawing are improved.

[0094] Furthermore, based on any of the above embodiments, if the boundary point discovery algorithm is further configured with a path length threshold between boundary points, then step S403, which involves obtaining the boundary point with the smallest angle formed by the target line segment within a circular area centered on the target boundary point and with a radius equal to the range radius, as the new target boundary point, may further include:

[0095] From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, select the boundary point whose distance from the target boundary point is less than the path length threshold and whose angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point.

[0096] In this step, if the boundary point discovery algorithm is also configured with a path length threshold between boundary points, in order to make the drawn road map more accurate, the boundary point with the smallest distance from the target boundary point and the smallest angle formed with the target line segment within the circular range centered on the target boundary point and with the radius of the range is selected as the new target boundary point.

[0097] Optionally, within the circular area, the boundary point that forms the smallest angle with the target line segment within the angle threshold range and whose distance from the target boundary point is less than the path length threshold is selected as the new target boundary point. The boundary point that forms the smallest clockwise angle with the target line segment within the angle threshold range can be selected as the new target boundary point, and the clockwise angle is used for judgment when repeatedly determining the new target boundary point.

[0098] Optionally, within the circular area, the boundary point that forms the smallest angle with the target line segment within the angle threshold range and whose distance from the target boundary point is less than the path length threshold is selected as the new target boundary point. Alternatively, the boundary point that forms the smallest counterclockwise angle with the target line segment within the angle threshold range can be selected as the new target boundary point, and the counterclockwise angle is used for judgment when repeatedly determining the new target boundary point.

[0099] It should be noted that the determination of whether the angle formed with the target line segment is the smallest within the angle threshold range and the distance from the target boundary point is less than the path length threshold can be made by first determining the angle and then determining the distance; or by first determining the distance and then determining the angle. This embodiment does not impose a specific limitation on the order of determining the distance and the angle.

[0100] The road generation method provided in this embodiment determines new target boundary points based on angle thresholds and path length thresholds, making the selection of boundary points more accurate, and thus making the drawn road map more accurate.

[0101] Furthermore, based on the above embodiments, from the set of boundary points, within a circular area centered on the target boundary point and with the radius of the range as the radius, the boundary point whose distance from the target boundary point is less than the path length threshold and whose angle with the target line segment is the smallest within the angle threshold range is obtained as the new target boundary point. This can be specifically implemented in the following ways:

[0102] The first method involves selecting a new target boundary point from a set of boundary points within a circle centered on the target boundary point and with the radius of the circle as the radius. The boundary point whose distance from the target boundary point is less than the path length threshold and whose clockwise angle with the target line segment is the smallest within the angle threshold range is selected as the new target boundary point.

[0103] For example, taking an angle threshold range of [90°, 180°], a target boundary point of C, a range radius of 5, and a path length threshold of 3 as an example... Figure 8 This is the third schematic diagram of the target boundary point filtering process, for reference. Figure 8Within the radius of the circle, boundary points D, E, F, and G can be selected from the set of boundary points. Connecting these four points to the target boundary point C, the clockwise angles formed by the four points and the target line segment are 110°, 135°, 160°, and 200°, respectively. Based on the angle threshold range, the four points are further filtered, and boundary points D, E, and F are selected as being within the angle threshold range. Next, the distances from points D, E, and F to point C are calculated, and the distances from point D to point C are 2.8, 2.5, and 4, respectively. Based on the path length threshold, points D, E, and F are further filtered, and points D and E are selected as those with path lengths less than the threshold. Furthermore, the point with the smallest angle formed by points D and E with the target line segment is point D. Therefore, point D is selected as the new target boundary point, and line segment DC is selected as the new target line segment.

[0104] The second method involves selecting a new target boundary point from a set of boundary points within a circle centered on the target boundary point and with the radius of the circle as the radius. The boundary point whose distance from the target boundary point is less than the path length threshold and whose counterclockwise angle with the target line segment is the smallest within the angle threshold range is selected as the new target boundary point.

[0105] For example, taking an angle threshold range of [90°, 180°], a target boundary point of C, a range radius of 5, and a path length threshold of 3 as an example... Figure 9 This is the fourth diagram of the target boundary point filtering process, for reference. Figure 9 Within the radius of the circle, boundary points D, E, F, and G can be selected from the set of boundary points. Connecting these four points to the target boundary point C, the counterclockwise angles formed by the four points and the target line segment are 110°, 135°, 160°, and 200°, respectively. Based on the angle threshold range, the four points are further filtered, and boundary points D, E, and F are selected as being within the angle threshold range. Next, the distances from points D, E, and F to point C are calculated, and the distances from point D to point C are 3.8, from point E to point C are 2, and from point F to point C are 4. Based on the path length threshold, points D, E, and F are further filtered, and points E and F are selected as those with a path length less than the threshold. Furthermore, the point with the smallest angle formed by points E and F with the target line segment is point E. Therefore, point E is selected as the new target boundary point, and line segment EC is selected as the new target line segment.

[0106] It should be noted that the above two methods are only examples of new target boundary points and new target line segments. This disclosure does not limit the coordinates of the starting point, the coordinates and number of boundary points in the boundary point set, the range radius, the number of boundary points within the range radius, the angle between the boundary point and the target line segment, or the path length threshold. These can be determined according to the actual situation.

[0107] The road generation method provided in this embodiment filters boundary points within a circular radius. The boundary point within this circular radius that is less than a path length threshold and has the smallest clockwise or counterclockwise angle with the target line segment within an angle threshold range is selected as the new target boundary point. This method improves the flexibility and accuracy of road map drawing by determining new target boundary points.

[0108] Optionally, based on the above embodiments, before sequentially connecting the latitude and longitude coordinates in the boundary point set using the boundary discovery algorithm, in response to the user's algorithm parameter configuration operation, the range radius and angle threshold can be configured, and the path length threshold can also be configured.

[0109] The road generation method provided in this embodiment improves the accuracy of road drawing by configuring a path length threshold in response to the user's algorithm parameter configuration operation.

[0110] Figure 10 This is a flowchart illustrating a road generation method provided in the third embodiment of this disclosure, with reference to... Figure 10 The road generation method shown differs from the previous embodiments in that this embodiment uses a density-based clustering algorithm to perform cluster analysis on the location data, resulting in further refinement of the data clusters. The corresponding refinement scheme may include the following steps:

[0111] S1001: Input the latitude and longitude coordinates from the location data into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, to perform cluster analysis and obtain data clusters including core points, boundary points, and noise points.

[0112] In this step, in order to process the acquired location data quickly and easily and thus draw the road map, the latitude and longitude coordinates in the location data are input into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, for cluster analysis, thereby obtaining data clusters including core points, boundary points and noise points.

[0113] For example, taking a radius of 3 around each point and a minimum number of points around each point of 5 as an example, cluster analysis of the location data to be processed first classifies the location data into core points, boundary points, and noise points. If the number of location data points within a circular area of ​​radius 3 centered on any point is greater than or equal to the minimum number of points around each point of 5, then that point is classified as a core point. Location data points within the circular area of ​​radius 3 around the core point are classified as boundary points. Location data points that are neither core points nor boundary points are classified as noise points. Data objects with a density reachable from the core point form data clusters. Each data cluster contains a set of core points, a set of boundary points, and a set of noise points. Figure 11 This is a schematic diagram of the public data cluster, for reference. Figure 11 , Figure 11 The data clusters (Core samples) obtained after cluster analysis using the DBSCAN algorithm are shown. Figure 12 This is a schematic diagram of the publicly disclosed set of boundary points. (For reference) Figure 12 , Figure 12 The set of boundary points in the data cluster is shown.

[0114] It should be noted that the above example is only an illustration of the DBSCAN algorithm for cluster analysis. This embodiment does not limit the minimum number of points around each point or the radius around each point in the location data to be processed, and can be determined according to the actual situation.

[0115] Optionally, the following steps are required before performing the cluster analysis described above:

[0116] S1002: Based on the input data format corresponding to the DBSCAN algorithm, perform format conversion processing on the location data to obtain the processed location data.

[0117] In this step, in order for the DBSCAN algorithm to analyze the location data to be processed, it is also necessary to first convert the location data according to the input data format corresponding to the DBSCAN algorithm, so as to obtain the processed location data.

[0118] Optionally, the location data to be processed can be loaded into an array, list, or matrix to obtain the processed location data.

[0119] Furthermore, based on the above steps, the latitude and longitude coordinates from the location data are input into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, for cluster analysis. This yields data clusters including core points, boundary points, and noise points, specifically used for:

[0120] The latitude and longitude coordinates from the processed location data are input into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, for cluster analysis.

[0121] The road generation method provided in this embodiment converts the format of the location data to be processed and inputs the processed location data into the DBSCAN algorithm for cluster analysis to obtain data clusters. Processing the location data using the DBSCAN algorithm reduces development and maintenance costs and enables automated maintenance and repair. Simultaneously, it also performs noise processing on the location data, eliminating the need for separate noise processing and saving manpower and resources.

[0122] Figure 13 This is a flowchart illustrating a road generation method provided in the fourth embodiment of this disclosure, with reference to... Figure 13 The road generation method shown differs from the previous embodiment in that this embodiment further refines the acquired location data to be processed. The corresponding refinement scheme may include the following steps:

[0123] S1301: Receive vehicle location information reported by multiple vehicles and / or on-board devices to obtain a set of vehicle locations.

[0124] In this step, in order to save development and maintenance costs, the location information reported by vehicles is directly used as the basic data to realize the drawing of the road map, and then the location information of multiple vehicles and on-board devices is received to obtain the vehicle location set.

[0125] It should be noted that the location set can be the location information reported by the positioning system modules of multiple vehicles themselves, the location information reported by the on-board equipment of multiple vehicles, or the location information reported by the positioning system modules of multiple vehicles and the location information reported by the on-board equipment.

[0126] It is worth noting that this embodiment does not specifically limit the number of vehicles reporting location information or the type of location information included in the location set.

[0127] S1302: Clean the abnormal data in the vehicle location set to obtain the location data to be processed.

[0128] In this step, after receiving the set of vehicle locations, in order to ensure the accuracy and precision of the road map drawing, it is necessary to clean the abnormal data in the vehicle location set to obtain the location data to be processed.

[0129] Specifically, for cleaning and processing abnormal data, Excel, Python, and Java programs can be used to remove abnormal data. Abnormal data refers to data in the location set with incomplete latitude and longitude coordinates, such as data that is far from other latitude and longitude coordinates or has no latitude or longitude coordinates.

[0130] The road generation method provided in this embodiment obtains the location set reported by vehicles and cleans the abnormal data in the location set to obtain the location data to be processed. By using the vehicle's own data as the basic data for drawing road maps, compared with the existing method of obtaining data using third-party tools, it saves development and maintenance costs and makes the solution more applicable. Moreover, the cleaning of abnormal data makes the drawn road maps more accurate.

[0131] Figure 14 This is a flowchart illustrating a road generation method according to the fifth embodiment of this disclosure, with reference to... Figure 14 The road generation device 1400 shown may include the following units:

[0132] The first acquisition unit 1401 is used to acquire location data to be processed, which includes the latitude and longitude coordinates of multiple vehicles.

[0133] Analysis unit 1402 is used to perform cluster analysis on location data using a density-based clustering algorithm to obtain data clusters, which include multiple latitude and longitude coordinates that meet the parameter conditions set by the clustering algorithm.

[0134] The second acquisition unit 1403 is used to acquire the latitude and longitude coordinates of the boundary points in the data cluster to obtain the set of boundary points.

[0135] The connection unit 1404 is used to connect the latitude and longitude coordinates in the boundary point set sequentially using a boundary point discovery algorithm, and draw a road map based on the connected boundary points. The boundary point algorithm is an algorithm that filters boundary points based on a set range radius and angle threshold.

[0136] In one possible design, the connection unit 1404 includes:

[0137] The selection module is used to select any boundary point from the set of boundary points as the starting point;

[0138] The first acquisition module is used to obtain the target boundary point that is closest to the starting point from the boundary point set based on the latitude and longitude coordinates of each boundary point in the boundary point set, and connect the target boundary point with the starting point to obtain the target line segment;

[0139] The second acquisition module is used to acquire the boundary point with the smallest angle formed with the target line segment within a circle centered on the target boundary point and with the radius of the range as the radius from the boundary point set. The new target boundary point is then obtained by connecting the target boundary point with the new boundary point. This step is repeated until all boundary points in the boundary point set are connected.

[0140] Optionally, the second acquisition module includes:

[0141] The first acquisition submodule is used to acquire, from the set of boundary points, the boundary point whose clockwise angle with the target line segment is the smallest within the range of the circle centered on the target boundary point and with the radius of the range as the radius, and use it as the new target boundary point;

[0142] or,

[0143] The second acquisition submodule is used to acquire, from the set of boundary points, the boundary point with the smallest counterclockwise angle with the target line segment within the circular range centered on the target boundary point and with the range radius as the radius, as the new target boundary point.

[0144] In one possible design, the road generation device also includes:

[0145] The first configuration unit 1405 is used to configure the range radius and angle threshold in response to the user's algorithm parameter configuration operation.

[0146] In one possible design, if the boundary point discovery algorithm also includes a path length threshold between boundary points, then the second acquisition module further includes:

[0147] The third acquisition submodule is used to acquire, from the set of boundary points, the boundary point whose distance from the target boundary point is less than the path length threshold and whose angle with the target line segment is the smallest within the range of the circle centered on the target boundary point and with the radius of the range as the radius, as the new target boundary point.

[0148] In one possible design, the third acquisition submodule is specifically used for:

[0149] From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, select the boundary point whose distance from the target boundary point is less than the path length threshold and whose clockwise angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point; or, from the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, select the boundary point whose distance from the target boundary point is less than the path length threshold and whose counterclockwise angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point.

[0150] In one possible design, the road generation device also includes:

[0151] The second configuration unit 1406 is used to configure the range radius, angle threshold and path length threshold in response to the user's algorithm parameter configuration operation.

[0152] In one possible design, the analysis unit includes:

[0153] The clustering analysis module is used to perform clustering analysis on the latitude and longitude coordinates in the location data, using the DBSCAN algorithm which is pre-configured with the radius around each point and the minimum number of points around each point, to obtain data clusters including core points, boundary points, and noise points.

[0154] In one possible design, the analysis unit also includes:

[0155] The format conversion module is used to convert the location data according to the input data format corresponding to the DBSCAN algorithm, so as to obtain the processed location data.

[0156] Correspondingly, the cluster analysis module is specifically used for:

[0157] The clustering analysis submodule is used to perform clustering analysis by inputting the latitude and longitude coordinates from the processed location data into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point.

[0158] In one possible design, the first acquisition unit includes:

[0159] The receiving module is used to receive vehicle location information reported by multiple vehicles and / or on-board devices, and obtain a set of vehicle locations, wherein the location information of each vehicle includes the vehicle's latitude and longitude coordinates.

[0160] The cleaning module is used to clean up abnormal data in the vehicle location set to obtain the location data to be processed.

[0161] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0162] According to embodiments of this disclosure, this disclosure also provides a computer program product comprising: a computer program stored in a readable storage medium, at least one processor of an electronic device being able to read the computer program from the readable storage medium, and the at least one processor executing the computer program causing the electronic device to perform the scheme provided in any of the above embodiments.

[0163] Figure 15A schematic block diagram of an example electronic device 1500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0164] like Figure 15 As shown, device 1500 includes a computing unit 1501, which can perform various appropriate actions and processes according to a computer program stored in read-only memory (ROM) 1502 or a computer program loaded from storage unit 1508 into random access memory (RAM) 1503. The RAM 1503 may also store various programs and data required for the operation of device 1500. The computing unit 1501, ROM 1502, and RAM 1503 are interconnected via bus 1504. Input / output (I / O) interface 1505 is also connected to bus 1504.

[0165] Multiple components in device 1500 are connected to I / O interface 1505, including: input unit 1506, such as keyboard, mouse, etc.; output unit 1507, such as various types of monitors, speakers, etc.; storage unit 1508, such as disk, optical disk, etc.; and communication unit 1509, such as network card, modem, wireless transceiver, etc. Communication unit 1509 allows device 1500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0166] The computing unit 1501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1501 performs the various methods and processes described above, such as the road generation method. For example, in some embodiments, the road generation method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1508. In some embodiments, part or all of the computer program may be loaded and / or installed on device 1500 via ROM 1502 and / or communication unit 1509. When the computer program is loaded into RAM 1503 and executed by the computing unit 1501, one or more steps of the road generation method described above may be performed. Alternatively, in other embodiments, the computing unit 1501 may be configured to perform the road generation method by any other suitable means (e.g., by means of firmware).

[0167] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0168] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0169] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0170] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0171] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0172] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0173] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0174] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A road generation method, comprising: Acquire location data to be processed, which includes the latitude and longitude coordinates of multiple vehicles; The location data is clustered using a density-based clustering algorithm to obtain data clusters, which include multiple latitude and longitude coordinates that satisfy the parameter conditions set by the clustering algorithm. Obtain the latitude and longitude coordinates of the boundary points in the data cluster to obtain the set of boundary points; A boundary point discovery algorithm is used to connect the latitude and longitude coordinates in the boundary point set in sequence, and a road map is drawn based on the connected boundary points. The boundary point discovery algorithm is an algorithm that filters boundary points based on a set range radius and angle threshold. The step of using a boundary point discovery algorithm to sequentially connect the latitude and longitude coordinates in the boundary point set includes: Select any boundary point from the set of boundary points as the starting point; Based on the latitude and longitude coordinates of each boundary point in the boundary point set, the target boundary point closest to the starting point is obtained from the boundary point set, and the target boundary point is connected to the starting point to obtain the target line segment; Within a circle centered on the target boundary point and with the radius of the range, select the boundary point whose angle with the target line segment is the smallest within the angle threshold range from the set of boundary points. Then, connect the target boundary point with the new target boundary point to obtain a new target line segment. Repeat this step until all boundary points in the set of boundary points are connected.

2. The method according to claim 1, wherein obtaining the boundary point with the smallest angle formed with the target line segment within the circular area centered on the target boundary point and with the radius of the range from the set of boundary points as the new target boundary point comprises: From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, the boundary point whose clockwise angle with the target line segment is the smallest within the angle threshold range is selected as the new target boundary point; or, From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, the boundary point whose counterclockwise angle with the target line segment is the smallest within the angle threshold range is selected as the new target boundary point.

3. The method according to any one of claims 1 to 2, further comprising: In response to the user's algorithm parameter configuration operation, the range radius and the angle threshold are configured.

4. The method according to claim 1, wherein if the boundary point discovery algorithm further includes a path length threshold between boundary points, then the step of obtaining the boundary point with the smallest angle formed by it and the target line segment within a circle centered on the target boundary point and with the radius of the range as the radius from the boundary point set as the new target boundary point includes: From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, select the boundary point whose distance from the target boundary point is less than the path length threshold and whose angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point.

5. The method according to claim 4, wherein from the set of boundary points, within a circular area centered on the target boundary point and with the radius of the range as the radius, a new target boundary point is selected, wherein the boundary point whose distance from the target boundary point is less than the path length threshold and whose angle with the target line segment is smallest within the angle threshold range is obtained as the new target boundary point, comprising: From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, obtain the boundary point whose distance from the target boundary point is less than the path length threshold and whose clockwise angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point; or, From the set of boundary points, within a circular area centered on the target boundary point and with the radius of the range as the radius, select the boundary point whose distance from the target boundary point is less than the path length threshold and whose counterclockwise angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point.

6. The method according to claim 4 or 5, further comprising: In response to the user's algorithm parameter configuration operation, the range radius, the angle threshold, and the path length threshold are configured.

7. The method according to any one of claims 1 to 2, wherein, The location data is clustered using a density-based clustering algorithm to obtain data clusters, including: The latitude and longitude coordinates in the location data are input into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, for cluster analysis to obtain the data cluster including core points, boundary points, and noise points.

8. The method according to claim 7, further comprising: Based on the input data format corresponding to the DBSCAN algorithm, the location data is converted to obtain the processed location data. Accordingly, the step of inputting the latitude and longitude coordinates from the location data into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, for cluster analysis includes: The latitude and longitude coordinates from the processed location data are input into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, for cluster analysis.

9. The method according to any one of claims 1 to 2, wherein, The acquisition of the location data to be processed includes: Receive vehicle location information reported by multiple vehicles and / or on-board devices to obtain a set of vehicle locations, wherein the location information of each vehicle includes the latitude and longitude coordinates of the vehicle; The abnormal data in the vehicle location set is cleaned to obtain the location data to be processed.

10. A road generation device, comprising: The first acquisition unit is used to acquire location data to be processed, the location data including the latitude and longitude coordinates of multiple vehicles; The analysis unit is used to perform cluster analysis on the location data using a density-based clustering algorithm to obtain data clusters, wherein the data clusters include multiple latitude and longitude coordinates that satisfy the parameter conditions set by the clustering algorithm. The second acquisition unit is used to acquire the latitude and longitude coordinates of the boundary points in the data cluster to obtain a set of boundary points. The connection unit is used to connect the latitude and longitude coordinates in the set of boundary points sequentially using a boundary point discovery algorithm, and draw a road map based on the connected boundary points. The boundary point discovery algorithm is an algorithm that filters boundary points based on a set range radius and angle threshold. The connection unit includes: The selection module is used to select any one boundary point from the set of boundary points as the starting point; The first acquisition module is used to acquire the target boundary point that is closest to the starting point from the boundary point set based on the latitude and longitude coordinates of each boundary point in the boundary point set, and connect the target boundary point with the starting point to obtain the target line segment; The second acquisition module is used to acquire, from the set of boundary points, the boundary point with the smallest angle formed with the target line segment within the circular range centered on the target boundary point and with the range radius as the radius, the boundary point with the smallest angle within the angle threshold range as the new target boundary point, and connect the target boundary point with the new target boundary point to obtain a new target line segment. This step is repeated until all boundary points in the set of boundary points are connected.

11. The apparatus according to claim 10, wherein the second acquisition module comprises: The first acquisition submodule is used to acquire, from the set of boundary points, the boundary point whose clockwise angle with the target line segment is the smallest within the range of the circle centered on the target boundary point and the radius of the range as the radius, as the new target boundary point; or, The second acquisition submodule is used to acquire, from the set of boundary points, the boundary point whose counterclockwise angle with the target line segment is the smallest within the range of the circle centered on the target boundary point and with the range radius as the radius, as the new target boundary point.

12. The apparatus according to any one of claims 10 to 11, wherein, The device further includes: The first configuration unit is used to configure the range radius and the angle threshold in response to the user's algorithm parameter configuration operation.

13. The apparatus according to claim 10, wherein, If the boundary point discovery algorithm is further configured with a path length threshold between boundary points, then the second acquisition module includes: The third acquisition submodule is used to acquire, from the set of boundary points, the boundary point whose distance from the target boundary point is less than the path length threshold and whose angle with the target line segment is the smallest within the range of the range radius, as the new target boundary point.

14. The apparatus according to claim 13, wherein, The third acquisition submodule is specifically used for: From the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, select the boundary point whose distance from the target boundary point is less than the path length threshold and whose clockwise angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point; or, from the set of boundary points, within a circle centered on the target boundary point and with the radius of the range as the radius, select the boundary point whose distance from the target boundary point is less than the path length threshold and whose counterclockwise angle with the target line segment is the smallest within the angle threshold range, and use it as the new target boundary point.

15. The apparatus according to claim 13 or 14, wherein, The device further includes: The second configuration unit is used to configure the range radius, the angle threshold, and the path length threshold in response to the user's algorithm parameter configuration operation.

16. The apparatus according to any one of claims 10 to 11, wherein, The analysis unit includes: The clustering analysis module is used to input the latitude and longitude coordinates in the location data into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, to perform clustering analysis and obtain the data clusters including core points, boundary points and noise points.

17. The apparatus according to claim 16, wherein, The analysis unit further includes: The format conversion module is used to perform format conversion processing on the location data according to the input data format corresponding to the DBSCAN algorithm, so as to obtain the processed location data. Accordingly, the clustering analysis module is specifically used for: The clustering analysis submodule is used to input the latitude and longitude coordinates in the processed location data into the DBSCAN algorithm, which is pre-configured with the radius around each point and the minimum number of points around each point, for clustering analysis.

18. The apparatus according to any one of claims 10 to 11, wherein, The first acquisition unit includes: The receiving module is used to receive vehicle location information reported by multiple vehicles and / or on-board devices to obtain a set of vehicle locations, wherein the location information of each vehicle includes the latitude and longitude coordinates of the vehicle. The cleaning module is used to clean the abnormal data in the vehicle location set to obtain the location data to be processed.

19. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

20. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-9.

21. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-9.

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