Method and apparatus for processing guiding points

Abstract

The present disclosure provides a method and apparatus for processing guiding points, which relate to data processing and image processing, mainly relate to map technology and navigation technology, and can be applied to autonomous driving and intelligent transportation. The implementation solution is as follows: Obtain navigation trajectory points with a target point of interest as the navigation destination, perform filtering processing on the navigation trajectory points to obtain candidate guiding points, and the filtering processing includes: road network filtering processing and / or node relationship information filtering processing of the target point of interest, where the node relationship information is used to represent the topological relationship between the various components of the target point of interest. According to the obtained panoramic image, determine the guiding point of the target point of interest from the candidate guiding points, and the panoramic image is a panoramic view of the road network to which the navigation trajectory points belong. This avoids the disadvantages of low accuracy and efficiency caused by determining the guiding point of the target point of interest in an artificial manner, improves the efficiency and accuracy of determining the guiding point of the target point of interest, facilitates users' travel, and improves users' travel experience.

Description

Technical Field

[0001] The present disclosure relates to data processing and image processing, mainly to map technology and navigation technology, and can be applied to autonomous driving and intelligent transportation. In particular, it relates to a method and apparatus for processing guiding points. Background Art

[0002] A guiding point belongs to a navigation track point and is a navigation guiding end point based on a Point of Interest (POI), aiming to provide the best arrival position for a user's trip.

[0003] In related technologies, the determination of guiding points is usually achieved manually. For example, staff determine guiding points based on panoramic images.

[0004] However, this method requires staff to verify the data of each navigation track point for exclusion and confirmation one by one, resulting in a technical problem of low efficiency. Summary of the Invention

[0005] The present disclosure provides a method and apparatus for processing guiding points to improve efficiency.

[0006] According to a first aspect of the present disclosure, there is provided a method for processing guiding points, including:

[0007] Obtaining navigation track points with a target point of interest as a navigation destination;

[0008] Performing filtering processing on the navigation track points to obtain candidate guiding points, where the filtering processing includes: road network filtering processing, and / or, filtering processing of node relationship information of the target point of interest, and the node relationship information is used to represent the topological relationship between the components of the target point of interest;

[0009] Determining a guiding point of the target point of interest from the candidate guiding points according to the obtained panoramic image, where the panoramic image is a panoramic view of the road network to which the navigation track points belong.

[0010] According to a second aspect of the present disclosure, there is provided an apparatus for processing guiding points, including:

[0011] A first obtaining unit for obtaining navigation track points with a target point of interest as a navigation destination;

[0012] A filtering unit for performing filtering processing on the navigation track points to obtain candidate guiding points, where the filtering processing includes: road network filtering processing, and / or, filtering processing of node relationship information of the target point of interest, and the node relationship information is used to represent the topological relationship between the components of the target point of interest;

[0013] A determination unit, configured to determine a guiding point of the target interest point from the candidate guiding points according to the acquired panoramic image, where the panoramic image is a panoramic view of the road network to which the navigation track point belongs.

[0014] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0018] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause the computer to execute the method described in the first aspect.

[0019] According to a fifth aspect of the present disclosure, there is provided a computer program product, where the computer program product includes: a computer program, the computer program is stored in a readable storage medium, and 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 so that the electronic device executes the method described in the first aspect.

[0020] According to the technical solution of the present disclosure that filters navigation track points by combining road network and / or node relationship information to obtain candidate guiding points, and determines the guiding point of the target interest point from the candidate guiding points based on the panoramic image, it avoids the disadvantages of low accuracy and efficiency caused by determining the guiding point of the target interest point manually, improves the efficiency and accuracy of determining the guiding point of the target interest point, facilitates the user's travel, and improves the user's travel experience.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. Description of the Drawings

[0022] The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. Among them:

[0023] Figure 1 is a schematic diagram of candidate points according to an embodiment of the present disclosure;

[0024] Figure 2Schematic diagram of the correspondence between candidate points and panoramic images according to an embodiment of the present disclosure;

[0025] Figure 3 Schematic diagram of a panoramic image according to an embodiment of the present disclosure;

[0026] Figure 4 Schematic diagram according to the first embodiment of the present disclosure;

[0027] Figure 5 Schematic diagram according to the second embodiment of the present disclosure;

[0028] Figure 6 Schematic diagram according to the third embodiment of the present disclosure;

[0029] Figure 7 Schematic diagram according to the fourth embodiment of the present disclosure;

[0030] Figure 8 Schematic diagram according to the fifth embodiment of the present disclosure;

[0031] Figure 9 Block diagram of an electronic device for implementing the method for processing guiding points according to an embodiment of the present disclosure. Detailed implementation manners

[0032] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0033] A point of interest is a term in a geographic information system, referring to a geographic object that can be abstracted as a point. In a geographic information system, a point of interest can be a house, a store, a mailbox, a bus stop, etc.

[0034] A road network refers to a road system that is interconnected and intertwined in a reticular distribution by various roads within a certain area.

[0035] A panoramic image refers to a 360-degree panoramic view.

[0036] A guiding point belongs to navigation track points and refers to the navigation guiding end point based on a point of interest. The main objective is to establish the association relationship between the point of interest and the road network to provide the best arrival position for user travel. That is, a guiding point can be understood as providing the best position for a user to reach a point of interest.

[0037] In some embodiments, after obtaining the point of interest for which the guiding point needs to be determined, a staff member can determine the panoramic image near the point of interest from the panoramic image, and the staff member can determine the guiding point from the panoramic image near the point of interest.

[0038] In other embodiments, after obtaining the point of interest for which the guiding point needs to be determined, a staff member can also determine candidate points near the point of interest, and then retrieve the panoramic image near the candidate points, so that the staff member can determine the guiding point from the retrieved panoramic image.

[0039] Exemplarily, after determining the point of interest, such as Figure 1 the "XX Park" shown in Figure 1 the candidate points are those enclosed by the rectangular frame shown in

[0040] The staff member can retrieve the panoramic image corresponding to each candidate point one by one through a click operation to output the corresponding panoramic image.

[0041] For example, when the candidate point selected by the staff member is the "Candidate Point A" shown in Figure 2 the staff member can retrieve the panoramic image corresponding to the "Candidate Point A" to output the panoramic image shown in Figure 2

[0042] However, the above methods are mainly implemented manually, and the determination of the guiding point is easily affected by human subjective factors, resulting in low accuracy and reliability. Moreover, when determining the guiding point by combining the panoramic image, it is necessary to exclude and confirm each data in each panoramic image one by one, resulting in low efficiency.

[0043] And if the output panoramic image is the image shown in Figure 3 it is very difficult for the staff member to determine whether the gate selected in Figure 3 is in a closed state or an open state, thus resulting in an inability to accurately determine the guiding point.

[0044] In order to avoid at least one of the above technical problems, the inventors of the present disclosure have obtained the inventive concept of the present disclosure through creative labor: combining the topological relationship between the road network and / or the constituent elements of the target point of interest to filter the navigation trajectory points, etc. to obtain candidate guiding points, so as to determine the guiding point of the target point of interest from the candidate guiding points by combining the panoramic image.

[0045] Based on the above inventive concept, the present disclosure provides a method and device for processing guiding points, which relate to data processing and image processing, mainly relate to map technology and navigation technology, and can be applied to autonomous driving and intelligent transportation to improve the efficiency and accuracy of determining guiding points.

[0046] ​Figure 4 is a schematic diagram according to the first embodiment of the present disclosure. As Figure 4 shown, the method for processing guiding points of the present disclosure includes:

[0047] S401: Obtain navigation track points with a target point of interest as the navigation destination.

[0048] Exemplarily, the execution subject of this embodiment may be a guiding processing device (hereinafter simply referred to as the processing device). The processing device may be a server (such as a cloud server, or a local server, or a server cluster, etc.), or a computer, or a terminal device, or a processor, or a chip, etc. This embodiment does not make any limitations.

[0049] The target point of interest refers to the point of interest for which the guiding point is to be determined, that is, through the method of this embodiment, the guiding point of the target point of interest can be determined.

[0050] The navigation track point refers to the point of the driving position obtained by the vehicle based on the navigation route.

[0051] This embodiment does not limit the acquisition method of the navigation track points. For example, the navigation track points can be obtained by obtaining the navigation log to obtain the navigation track points from the navigation log.

[0052] S402: Perform filtering processing on the navigation track points to obtain candidate guiding points.

[0053] Among them, the filtering processing includes: road network filtering processing, and / or node relationship information filtering processing of the target point of interest. The node relationship information is used to represent the topological relationship between the components of the target point of interest.

[0054] Exemplarily, the node relationship can be a parent-child point topological graph.

[0055] For example, if the target point of interest is "XX Park", the components of "XX Park" may include the south gate, north gate, west gate, and east gate of "XX Park". Correspondingly, "XX Park" can be the parent node in the parent-child node topological graph, and the south gate, north gate, west gate, and east gate can be the child nodes in the parent-child node topological graph respectively.

[0056] The road network filtering processing can be understood as filtering the navigation track points based on the road network. The node relationship information filtering processing can be understood as filtering the navigation track points based on the node relationship information.

[0057] In some embodiments, a single dimension can be used to perform filtering processing on the navigation track points. For example, filtering the navigation track points based on the road network, or filtering the navigation track points based on the node relationship information.

[0058] In other embodiments, multi-dimensions can be adopted to filter navigation track points, such as filtering navigation track points based on road network and node relationship information. And the sequence of the filtering processes for the two dimensions is not limited.

[0059] For example, the navigation track points can be filtered based on the road network first, and on this basis, the navigation track points can be filtered in combination with the node relationship information.

[0060] For another example, the navigation track points can be filtered based on the node relationship information first, and on this basis, the navigation track points can be filtered in combination with the road network.

[0061] For yet another example, the navigation track points can be filtered based on the road network and the node relationship information respectively to obtain the corresponding filtering results, and candidate guiding points can be determined for the corresponding filtering results respectively.

[0062] It should be noted that in this embodiment, by filtering the navigation track points based on the road network and / or the node relationship information, the range of determining the guiding points of the target interest points can be greatly reduced, so as to improve the efficiency of determining the guiding points of the target interest points.

[0063] S403: Determine the guiding point of the target interest point from the candidate guiding points according to the obtained panoramic image.

[0064] Wherein, the panoramic image is a panoramic view of the road network to which the navigation track point belongs.

[0065] The following examples can be used to obtain the panoramic image:

[0066] In one example, the processing device can be connected to the image acquisition device and receive the panoramic image sent by the image acquisition device.

[0067] In another example, the processing device can provide a tool for loading images, and the user can transmit the panoramic image to the processing device through the tool for loading images.

[0068] Wherein, the tool for loading images can be an interface for connecting to an external device, such as an interface for connecting to other storage devices, and the panoramic image transmitted by the external device can be obtained through this interface; the tool for loading images can also be a display device. For example, the processing device can input an interface for the function of loading images on the display device, and the user can import the panoramic image into the processing device through this interface.

[0069] As can be seen from the above analysis, the embodiments of the present disclosure provide a method for processing guiding points, including: obtaining navigation track points with a target point of interest as the navigation destination, and filtering the navigation track points to obtain candidate guiding points, where the filtering process includes: road network filtering process and / or node relationship information filtering process of the target point of interest, and the node relationship information is used to represent the topological relationship between the components of the target point of interest; determining the guiding point of the target point of interest from the candidate guiding points according to the obtained panoramic image, where the panoramic image is a panoramic view of the road network to which the navigation track points belong. In this embodiment, by combining the road network and / or node relationship information to filter the navigation track points to obtain candidate guiding points, and then determining the guiding point of the target point of interest from the candidate guiding points based on the panoramic image, the disadvantages of low accuracy and efficiency caused by determining the guiding point of the target point of interest in an artificial manner are avoided, and the efficiency and accuracy of determining the guiding point of the target point of interest are improved, so as to facilitate the user's travel and improve the user's travel experience.

[0070] Figure 5 It is a schematic diagram according to the second embodiment of the present disclosure. As Figure 5 shown, the method for processing guiding points of the present disclosure includes:

[0071] S501: Obtain the position of the target point of interest and the navigation data with the target point of interest as the navigation end point.

[0072] It should be understood that, in order to avoid cumbersome statements, for the technical features that are the same as those of the above embodiments in this embodiment, this embodiment will not be described in detail.

[0073] The position information of the target point of interest may be the coordinates of the target point of interest.

[0074] The navigation track data refers to the data obtained by the vehicle driving based on the navigation route, including navigation track points, and the navigation track points have positions, such as the coordinates of the navigation track points, that is, the coordinates of the point where the vehicle travels to.

[0075] S502: Obtain the navigation track points from the navigation data whose distance from the position of the target point of interest is less than a preset second distance threshold.

[0076] Among them, the second distance threshold can be determined based on requirements, historical records, and tests, etc., and this embodiment does not make a limitation.

[0077] Exemplarily, the navigation data includes N navigation track points, and each navigation track point has a position, such as each navigation track point has coordinates (for the sake of distinction, called the first coordinates), and the target point of interest also has coordinates (similarly, for the sake of distinction, called the second coordinates).

[0078] Correspondingly, the distance between each first coordinate and the second coordinate can be calculated to obtain N distances. From the obtained N distances, the distances less than the second distance threshold are determined, and the navigation track points corresponding to the distances less than the second distance threshold are determined as the obtained navigation track points.

[0079] In this embodiment, by obtaining navigation track points from navigation data in combination with the second distance threshold, it is possible to avoid the drawback of low efficiency in determining the guiding points of target points of interest caused by the large amount of navigation data, thereby improving the efficiency of determining the guiding points of target points of interest.

[0080] S503: Filter the navigation track points according to the road network to obtain the filtered guiding points.

[0081] Since the road network is a road system composed of various roads that are interconnected and intertwined in a network distribution, when filtering the navigation track points in combination with the road network, it is possible to eliminate the navigation track points that do not conform to the composition of the road system in the navigation track points, so as to reduce the resource consumption of subsequent processing.

[0082] In some embodiments, S503 may include the following steps:

[0083] The first step: Generate a predicted navigation track for the vehicle to travel on the road network.

[0084] The second step: Filter the navigation track points according to the predicted navigation track.

[0085] Exemplarily, a navigation track (i.e., the predicted navigation track) of a vehicle traveling on the road network can be predicted based on the road network, and the deviation information of the navigation track points deviating from the predicted navigation track can be determined, so as to filter the navigation track points according to the deviation information.

[0086] For example, the vertical distance between the navigation track points and the predicted navigation track can be calculated (the deviation information includes the vertical distance). The greater the vertical distance, the more the navigation track points deviate from the predicted navigation track. The navigation track points that deviate from the predicted navigation track greatly are eliminated from the navigation track points. For example, the navigation track points with a vertical distance greater than a preset threshold (similarly, the preset threshold can be determined based on requirements, historical records, and experiments, etc., and this embodiment does not make a limitation) are eliminated.

[0087] S504: Filter the filtered guiding points according to the node relationship information to obtain candidate guiding points.

[0088] Since the node information can characterize the topological relationship between the constituent elements of the target point of interest, based on the node relationship information, filtering the filtered guiding points again can eliminate the navigation track points that do not conform to the real scene and environment of the target point of interest, so that the candidate guiding points fit well with the target point of interest, thereby improving the accuracy and reliability of determining the target point of interest based on the candidate guiding points in the subsequent process.

[0089] And in this embodiment, the road network filtering process and the node relationship information filtering process are executed in sequence. The road network filtering process is a relatively large-scale granularity filtering process, and the node relationship information is a relatively fine-grained filtering process. It is equivalent to first performing a relatively rough filtering process and then a relatively fine filtering process, so that the filtering process has strong hierarchy and progression, thereby improving the effectiveness and reliability of the filtering process.

[0090] In some embodiments, S504 may include the following steps:

[0091] The first step: Calculate the distance between the filtered guiding point and each node in the node relationship information.

[0092] Exemplarily, if there are four nodes in the node relationship information, calculate the distance between the filtered guiding point and each of the four nodes to obtain four distances.

[0093] The second step: Obtain the minimum distance from the distances, and filter the filtered guiding point according to the minimum distance to obtain candidate guiding points.

[0094] Correspondingly, obtain the minimum distance from the four distances to filter the filtered guiding point based on the minimum distance.

[0095] In this embodiment, by filtering the filtered guiding point in combination with the minimum distance, the candidate guiding point can be highly associated with the target point of interest, achieving the technical effect of improving the accuracy and reliability of the guiding point of the target point of interest.

[0096] In some embodiments, filtering the filtered guiding point according to the minimum distance to obtain candidate guiding points includes: obtaining the filtered guiding points with the minimum distance less than the preset first distance threshold, and determining the obtained filtered guiding nodes as candidate guiding points.

[0097] Similarly, the first distance threshold can be determined based on requirements, historical records, and experiments, etc., and this embodiment does not make limitations.

[0098] Or, this embodiment can be understood as: if the minimum distance is greater than the preset first distance threshold, the filtered guiding nodes are removed.

[0099] Exemplarily, if the node corresponding to the minimum distance is node A, that is, the distance between the filtered guiding node and node A is the minimum distance. If this minimum distance is greater than the first distance threshold, it indicates that the distance between the filtered guiding node and node A is relatively far, and the distance from the other three nodes except node A among the four nodes is even farther. Therefore, it is relatively unreasonable to use the filtered guiding node as the guiding point of the target point of interest. So, the filtered guiding node is removed to improve the rationality and reliability of the guiding point of the finally determined target point of interest.

[0100] In some embodiments, the node relationship information is a topology graph, the target point of interest is the parent node in the topology graph, each constituent element is a child node in the topology graph, and each child node has a weight coefficient, and the weight coefficient of each child node is determined based on the combination elements corresponding to the child node according to the historical driving trajectory.

[0101] Exemplarily, in combination with the above analysis, "XX Park" is the parent node in the topology graph, and the south gate, north gate, west gate, and east gate of "XX Park" are child nodes respectively. For the weight coefficient of the "south gate" child node, it can be determined based on the historical driving trajectory of the "south gate".

[0102] In some embodiments, the number of vehicles corresponding to the historical driving trajectory can be determined, and the number of vehicles is proportional to the weight coefficient.

[0103] For example, if it is determined that the number of vehicles corresponding to the historical driving trajectory of the "south gate" is Y, and the number of vehicles corresponding to the historical driving trajectory of the "north gate" is Z, and Z > Y, then the weight coefficient of the "north gate" child node > the weight coefficient of the "south gate" child node.

[0104] Correspondingly, the filtered guiding points are filtered according to the node relationship information to obtain candidate guiding points, including: filtering the filtered guiding points according to the weight coefficients corresponding to each child node to obtain candidate guiding points.

[0105] Exemplarily, the highest weight coefficient is determined from the weight coefficients, and the filtered guiding points are filtered according to the highest weight coefficient to obtain candidate guiding points.

[0106] For example, if the weight coefficient of the "north gate" child node is the highest among the weight coefficients of each child node, then the guiding points that do not belong to the "north gate" child node among the filtered guiding points are removed.

[0107] Similarly, it is also possible to determine whether the filtered guiding point belongs to the guiding point of the "north gate" child node by calculating the distance.

[0108] For example, if the distance between a filtered guiding point and the "North Gate" sub-node is within a preset distance range (which can also be determined based on requirements, historical records, experiments, etc., and this embodiment does not make any limitations), it indicates that the filtered guiding point belongs to the guiding points of the "North Gate" sub-node; otherwise, it does not belong.

[0109] In this embodiment, by determining the weight coefficient based on the historical driving trajectory, the weight coefficient can represent the driving preference of the user. Thus, when determining the candidate guiding points in combination with the weight coefficient, the candidate guiding points can meet the driving preference and requirements of the user, improving the reliability and effectiveness of the candidate guiding points.

[0110] In some embodiments, constructing the node relationship information may include the following steps:

[0111] The first step: Obtain the respective constituent elements of the target point of interest and obtain the navigation end points corresponding to each of the constituent elements.

[0112] The second step: Construct the node relationship information according to each navigation end point and the target point of interest.

[0113] Exemplarily, in combination with the above analysis, the target point of interest is "XX Park", and the respective constituent elements of "XX Park" include the South Gate, North Gate, West Gate, and East Gate of "XX Park". Determine the navigation end points corresponding to the South Gate, North Gate, West Gate, and East Gate of "XX Park" respectively, and construct the node relationship in combination with the determined navigation end points.

[0114] The navigation end point represents the end point of the vehicle's travel, that is, the point corresponding to the parking location. By constructing the node relationship information in combination with each navigation end point, the node relationship information can represent that the vehicle travels to the parking locations corresponding to different constituent elements, so that the guiding points of the target point of interest determined based on the node relationship information have strong reliability and accuracy.

[0115] S505: Extract the suspected guiding points from the candidate guiding points according to the navigation end points in the navigation trajectory points.

[0116] Exemplarily, the navigation trajectory points can be divided into a navigation start point, navigation intermediate points, and navigation end points. Among them, the navigation start point can be understood as the point corresponding to the navigation initial location; the navigation intermediate points can be understood as the points between the navigation start point and the navigation end point; the navigation end point can be understood as the point corresponding to the navigation destination.

[0117] For example, in this embodiment, the navigation destination is the target point of interest, and the target point of interest is "XX Park", then the navigation end point can be a point near "XX Park", such as the points corresponding to each gate of "XX Park", or the point corresponding to the parking garage of "XX Park", etc., and will not be listed one by one here.

[0118] A suspected guiding point can be understood as a point that may be a guiding point for a target point of interest. In this embodiment, by combining the navigation destination, a suspected guiding point is determined from the candidate guiding points, which can further narrow down the range of the guiding points for the target point of interest, so as to provide the best arrival position for the user to travel.

[0119] In some embodiments, S505 may include the following steps:

[0120] The first step: Perform clustering processing on the navigation destination to obtain the clustered destination.

[0121] In this embodiment, the way of clustering processing is not limited, such as density clustering, grid clustering, and median clustering, etc., which will not be listed one by one here.

[0122] The second step: Extract the suspected guiding points from the candidate guiding points according to the clustered destination.

[0123] Exemplarily, if the candidate guiding point is the clustered destination, then this candidate guiding point can be determined as the suspected guiding point.

[0124] In this embodiment, the clustered destination has strong representativeness for each trajectory destination, and can reflect the more concentrated destinations of vehicle driving, that is, it can meet the travel needs of many users to a greater extent, so that the suspected guiding points determined based on the clustered destination have strong reliability and effectiveness.

[0125] S506: Obtain a panoramic image within a preset range of the suspected guiding point.

[0126] Similarly, the preset range can be determined based on requirements, historical records, and experiments, etc., and this embodiment does not make a limit.

[0127] Exemplarily, after determining the suspected guiding point, a panoramic image within the preset range (such as within 500 meters) of the suspected guiding point can be automatically loaded.

[0128] S507: Determine the guiding point of the target point of interest from the candidate guiding points according to the panoramic image within the preset range of the suspected guiding point.

[0129] In this embodiment, by combining the panoramic image within the preset range of the suspected guiding point to determine the guiding point of the target point of interest is equivalent to a verification process for the candidate guiding points, so as to improve the accuracy and reliability of the guiding point of the target point of interest.

[0130] In some embodiments, S507 may include the following steps:

[0131] The first step: Output the panoramic image within the preset range of the suspected guiding point.

[0132] Second step: In response to the user's selection operation on the panoramic image, determine the suspected guiding point corresponding to the selection operation as the guiding point of the target interest point.

[0133] Among them, the selection operation is used to represent a box selection operation or a click operation on the suspected guiding point in the panoramic image.

[0134] Exemplarily, after determining the suspected guiding point, the panoramic image within the preset range of the suspected guiding point can be automatically loaded and output. The user can perform selection operations such as box selection or clicking on the output panoramic image, such as clicking on the suspected guiding point in the panoramic image through an external device (such as a mouse) to indicate that the suspected guiding point is selected. The processing device determines the suspected guiding point as the guiding point of the target interest point according to the selection operation.

[0135] In this embodiment, by automatically loading and outputting the panoramic image within the preset range of the suspected guiding point, it is possible to avoid the disadvantages such as low efficiency caused by the staff selecting candidate points and determining the panoramic image near the candidate points from the full amount of panoramic images, and improve the efficiency and accuracy of determining the guiding point of the target interest point.

[0136] Figure 6 It is a schematic diagram according to the third embodiment of the present disclosure, as Figure 6 shown, the guiding point processing device 600 of the present disclosure includes:

[0137] The first acquisition unit 601 is used to acquire navigation trajectory points with the target interest point as the navigation destination.

[0138] The filtering unit 602 is used to perform filtering processing on the navigation trajectory points to obtain candidate guiding points, where the filtering processing includes: road network filtering processing, and / or, node relationship information filtering processing of the target interest point, and the node relationship information is used to represent the topological relationship between the components of the target interest point.

[0139] The determination unit 603 is used to determine the guiding point of the target interest point from the candidate guiding points according to the acquired panoramic image.

[0140] Figure 7 It is a schematic diagram according to the fourth embodiment of the present disclosure, as Figure 7 shown, the guiding point processing device 700 of the present disclosure includes:

[0141] The first acquisition unit 701 is used to acquire navigation trajectory points with the target interest point as the navigation destination.

[0142] Combined Figure 7 it can be seen that in some embodiments, the first acquisition unit 701 includes:

[0143] The second acquisition subunit 7011 is configured to acquire the location of the target point of interest and the navigation data with the target point of interest as the navigation destination.

[0144] The third acquisition subunit 7012 is configured to acquire, from the navigation data, navigation track points whose distances from the location of the target point of interest are less than a preset second distance threshold.

[0145] The third acquisition unit 702 is configured to acquire the respective constituent elements of the target point of interest and acquire the navigation destinations corresponding to the respective constituent elements.

[0146] The construction unit 703 is configured to construct node relationship information based on the respective navigation destinations and the target point of interest.

[0147] The filtering unit 704 is configured to perform filtering processing on the navigation track points to obtain candidate guiding points, where the filtering processing includes: road network filtering processing and / or node relationship information filtering processing of the target point of interest, and the node relationship information is used to represent the topological relationship between the respective constituent elements of the target point of interest.

[0148] Combined Figure 7 It can be seen that in some embodiments, the filtering unit 704 includes:

[0149] The first filtering subunit 7041 is configured to perform filtering processing on the navigation track points according to the road network to obtain filtered guiding points.

[0150] The second filtering subunit 7042 is configured to perform filtering processing on the filtered guiding points according to the node relationship information to obtain candidate guiding points.

[0151] In some embodiments, the second filtering subunit 7042 includes:

[0152] A calculation module is configured to calculate the distance between each node in the node relationship information and the filtered guiding points.

[0153] An acquisition module is configured to acquire the minimum distance from the respective distances.

[0154] A filtering module is configured to perform filtering processing on the filtered guiding points according to the minimum distance to obtain candidate guiding points.

[0155] In some embodiments, the number of filtered guiding nodes is multiple; the filtering module includes:

[0156] An acquisition sub-module is configured to acquire the filtered guiding points whose minimum distance is less than a preset first distance threshold.

[0157] A determination sub-module is configured to determine the acquired filtered guiding nodes as candidate guiding points.

[0158] In some embodiments, the node relationship information is a topological graph, the target point of interest is the parent node in the topological graph, each constituent element is a child node in the topological graph, each child node has a weight coefficient, and the weight coefficient of each child node is determined according to the historical driving trajectory corresponding to the constituent element of the child node.

[0159] The second filtering subunit 7042 is configured to perform a filtering process on the filtered guiding points according to the weight coefficients corresponding to the respective child nodes, so as to obtain candidate guiding points.

[0160] The extraction unit 705 is configured to extract suspected guiding points from the candidate guiding points according to the navigation end point, wherein the navigation trajectory points include the navigation end point.

[0161] Combine Figure 7 It can be seen that in some embodiments, the extraction unit 705 includes:

[0162] The clustering subunit 7051 is configured to perform a clustering process on the navigation end point to obtain the clustered end point.

[0163] The extraction subunit 7052 is configured to extract suspected guiding points from the candidate guiding points according to the clustered end point.

[0164] The second acquisition unit 706 is configured to acquire a panoramic image within a preset range of the candidate guiding points.

[0165] The determination unit 707 is configured to determine the guiding point of the target point of interest from the candidate guiding points according to the acquired panoramic image.

[0166] Combine Figure 7 It can be seen that in some embodiments, the determination unit 707 includes:

[0167] The first acquisition subunit 7071 is configured to acquire a panoramic image within a preset range of the candidate guiding points.

[0168] The first determination subunit 7072 is configured to determine the guiding point of the target point of interest from the candidate guiding points according to the panoramic image within a preset range of the candidate guiding points.

[0169] Combine Figure 7 It can be seen that in some embodiments, the determination unit 707 includes:

[0170] The output subunit 7073 is configured to output the panoramic image.

[0171] The second determination subunit 7074 is configured to, in response to a selection operation of the user on the panoramic image, determine the candidate guiding point corresponding to the selection operation as the guiding point of the target point of interest.

[0172] Figure 8is a schematic diagram according to the fifth embodiment of the present disclosure, as Figure 8 shown, the electronic device 800 in the present disclosure may include: a processor 801 and a memory 802.

[0173] The memory 802 is used to store programs; the memory 802 may include volatile memory (English: volatile memory), such as random access memory (English: random-access memory, abbreviation: RAM), such as static random access memory (English: static random-access memory, abbreviation: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviation: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory 802 is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc. The above computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 802. And the above computer programs, computer instructions, data, etc. may be called by the processor 801.

[0174] The above computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 802. And the above computer programs, computer data, etc. may be called by the processor 801.

[0175] The processor 801 is used to execute the computer programs stored in the memory 802 to implement each step in the methods involved in the above embodiments.

[0176] Specifically, reference may be made to the relevant descriptions in the foregoing method embodiments.

[0177] The processor 801 and the memory 802 may be independent structures or integrated structures integrated together. When the processor 801 and the memory 802 are independent structures, the memory 802 and the processor 801 may be coupled and connected through a bus 803.

[0178] The electronic device in this embodiment may execute the technical solutions in the above methods, and the specific implementation processes and technical principles are the same, and will not be described herein again.

[0179] In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information and other processes all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0180] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium, and a computer program product.

[0181] According to an embodiment of the present disclosure, the present disclosure further provides a computer program product, which includes: a computer program stored in a readable storage medium, and at least one processor of the electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to enable the electronic device to execute the solution provided in any of the above embodiments.

[0182] Figure 9 FIG. shows a schematic block diagram of an exemplary electronic device 900 that can be used to implement the embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital assistant, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0183] As Figure 9 shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0184] A plurality of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0185] The computing unit 901 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated 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 901 executes the various methods and processes described above, such as the processing method of the guiding point. For example, in some embodiments, the processing method of the guiding point can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the processing method of the guiding point described above can be executed. Alternatively, in other embodiments, the computing unit 901 can be configured to execute the processing method of the guiding point in any other suitable manner (e.g., by means of firmware).

[0186] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-a-chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0187] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0188] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection 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 include, 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 a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0189] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, speech input, or tactile input).

[0190] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0191] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system or a server combined with a blockchain.

[0192] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.

[0193] The above specific embodiments do not constitute a limitation on the protection scope 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 principle of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for processing guiding points, comprising: Obtain navigation track points with the target point of interest as the navigation destination; Filter the navigation track points according to the road network to obtain filtered guiding points; Filter the filtered guiding points according to the node relationship information to obtain candidate guiding points; wherein, the filtering process includes: road network filtering process, and / or, node relationship information filtering process of the target point of interest, and the node relationship information is used to characterize the topological relationship between the constituent elements of the target point of interest; Obtain panoramic images within a preset range of the candidate guiding points; Output panoramic images within a preset range of the candidate guiding points; In response to a selection operation by the user on the panoramic image, determine the candidate guiding point corresponding to the selection operation as the guiding point of the target point of interest; wherein, the panoramic image is a panoramic view of the road network to which the navigation track points belong; Wherein, the node relationship information is a topological graph, the target point of interest is the parent node in the topological graph, each constituent element is a child node in the topological graph, each child node has a weight coefficient, and the weight coefficient of each child node is determined according to the historical driving track corresponding to the constituent element of the child node; the number of vehicles corresponding to the historical driving track is proportional to the weight coefficient; Filter the filtered guiding points according to the node relationship information to obtain the candidate guiding points, including: filtering the filtered guiding points according to the weight coefficients corresponding to each child node to obtain the candidate guiding points.

2. The method according to claim 1, wherein Filter the filtered guiding points according to the node relationship information to obtain the candidate guiding points, including: Calculate the distance between the filtered guiding points and each node in the node relationship information; Obtain the minimum distance from the distances, and filter the filtered guiding points according to the minimum distance to obtain the candidate guiding points.

3. The method according to claim 2, wherein The number of the filtered guiding nodes is multiple; filtering the filtered guiding points according to the minimum distance to obtain the candidate guiding points, including: Obtain the filtered guiding points with the minimum distance less than a preset first distance threshold, and determine the obtained filtered guiding nodes as the candidate guiding points.

4. The method according to any one of claims 1 - 3, wherein the navigation track points include a navigation end point; after filtering the navigation track points to obtain candidate guiding points, the method further comprises: Extract suspected guiding points from the candidate guiding points according to the navigation end point, and obtain panoramic images within a preset range of the candidate guiding points.

5. The method according to claim 4, wherein The extracting suspected guiding points from the candidate guiding points according to the navigation end point includes: Perform clustering processing on the navigation end point to obtain the clustered end point; Extract the suspected guiding points from the candidate guiding points according to the clustered end point.

6. The method according to any one of claims 1 - 5, before filtering the navigation track points to obtain candidate guiding points, the method further comprises: Obtain the constituent elements of the target point of interest, and obtain the navigation end points corresponding to each constituent element; Construct the node relationship information according to each navigation end point and the target point of interest.

7. The method according to any one of claims 1 - 6, wherein Obtain navigation track points with the target point of interest as the navigation destination, including: Obtain the position of the target point of interest and navigation data with the target point of interest as the navigation end point; From the navigation data, obtain navigation track points whose distance from the position of the target point of interest is less than a preset second distance threshold.

8. A device for processing guiding points, comprising: A first acquisition unit, configured to acquire navigation track points with a target point of interest as a navigation destination; A filtering unit, configured to perform filtering processing on the navigation track points to obtain candidate guiding points, where the filtering processing includes: road network filtering processing, and / or node relationship information filtering processing of the target point of interest, and the node relationship information is used to characterize the topological relationship between the constituent elements of the target point of interest; A determination unit, configured to acquire a panoramic image within a preset range of the candidate guiding points; output the panoramic image within the preset range of the candidate guiding points; and in response to a selection operation of the user on the panoramic image, determine the candidate guiding point corresponding to the selection operation as the guiding point of the target point of interest, where the panoramic image is a panoramic view of the road network to which the navigation track points belong; The filtering unit includes: A first filtering subunit, configured to perform filtering processing on the navigation track points according to the road network to obtain filtered guiding points; A second filtering subunit, configured to perform filtering processing on the filtered guiding points according to the node relationship information to obtain the candidate guiding points; The node relationship information is a topological graph, the target point of interest is the parent node in the topological graph, each constituent element is a child node in the topological graph, each child node has a weight coefficient, and the weight coefficient of each child node is determined according to the historical driving track corresponding to the constituent element of the child node; the number of vehicles corresponding to the historical driving track is proportional to the weight coefficient; The second filtering subunit is configured to perform filtering processing on the filtered guiding points according to the weight coefficients corresponding to the respective child nodes to obtain the candidate guiding points.

9. The apparatus according to claim 8, wherein, The second filtering subunit includes: A calculation module, configured to calculate the distance between the filtered guiding points and each node in the node relationship information; An acquisition module, configured to acquire the minimum distance from the distances; A filtering module, configured to perform filtering processing on the filtered guiding points according to the minimum distance to obtain the candidate guiding points.

10. The apparatus according to claim 9, wherein, The number of the filtered guiding nodes is multiple; the filtering module includes: An acquisition sub-module, configured to acquire the filtered guiding points with the minimum distance less than a preset first distance threshold; A determination sub-module, configured to determine the acquired filtered guiding nodes as the candidate guiding points.

11. The apparatus according to any one of claims 8-10, wherein the navigation track points include a navigation end point; the apparatus further comprises: An extraction unit, configured to extract suspected guiding points from the candidate guiding points according to the navigation end point; A second acquisition unit, configured to acquire a panoramic image within a preset range of the candidate guiding points.

12. The apparatus according to claim 11, wherein, The extraction unit includes: A clustering sub-unit, configured to perform clustering processing on the navigation end point to obtain a clustered end point; An extraction sub-unit, configured to extract the suspected guiding points from the candidate guiding points according to the clustered end point.

13. The apparatus according to any one of claims 8-12, the apparatus further comprises: A third acquisition unit, configured to acquire the constituent elements of the target point of interest and acquire navigation end points corresponding to the respective constituent elements; A construction unit, configured to construct the node relationship information according to the respective navigation end points and the target point of interest.

14. The apparatus according to any one of claims 8-13, wherein, The first acquisition unit includes: A second acquisition subunit, configured to acquire the location of the target point of interest and navigation data with the target point of interest as the navigation destination; A third acquisition subunit, configured to acquire, from the navigation data, navigation trajectory points whose distance from the location of the target point of interest is less than a preset second distance threshold.

15. An electronic device, comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method according to any one of claims 1-7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

17. A computer program product, comprising a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1-7.

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