Map data clustering method, device, equipment and storage medium

Abstract

The present application relates to a map data clustering method, apparatus, device, and storage medium, and relates to the field of intelligent transportation technology. The method includes: clustering the map data in a second map data set in sequence based on the map data of different known subcategories in a first map data set, obtaining a plurality of first clustering results corresponding one-to-one to the different known subcategories, until the first target map data does not exist in the first map data set, or the second target map data does not exist in the second map data set. The first map data set includes map data of multiple known subcategories in a preset category, the second map data set includes map data of unknown subcategories in a preset category, the first target map data is map data that has not been clustered, and the second target map data is map data that is not included in any of the first clustering results. In this way, the category of the unknown subcategory can be accurately identified, and the accuracy of the clustering results can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, in particular to the field of high-precision map production technology, and specifically to a map data clustering method, device, equipment and storage medium. Background Art

[0002] A technical approach to creating high-precision maps involves collecting map data through vehicle controllers, clustering the data, and then fitting the resulting clusters into road features. However, when collecting map data using vehicle controllers, subcategories of map data may not be recognized due to factors such as vehicle obstruction or blurred ground. In such cases, simply excluding unrecognized subcategories of map data can result in missing data, affecting clustering accuracy and, consequently, the accuracy of subsequent map production.

[0003] Therefore, how to cluster map data including unknown subcategories is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] This application provides a map data clustering method, apparatus, device, and storage medium to at least address the technical problem in related art of lacking the ability to cluster map data containing unknown subcategories. The technical solution of this application is as follows:

[0005] According to a first aspect of the present application, a map data clustering method is provided, comprising: obtaining a first map data set and a second map data set; the first map data set including map data of multiple known subcategories within a preset category; and the second map data set including map data of unknown subcategories within the preset category; sequentially clustering the map data in the second map data set based on the map data of different known subcategories in the first map data set to obtain a plurality of first clustering results corresponding one-to-one to the different known subcategories, until first target map data no longer exists in the first map data set or the second target map data no longer exists in the second map data set; the first target map data being map data that has not been clustered, and the second target map data being map data that is not included in any of the first clustering results; if the first target map data does not exist in the first map data set, clustering the second target map data if the second target map data exists in the second map data set; and if the second target map data does not exist in the second map data set, clustering the first target map data if the first target map data exists in the first map data set.

[0006] Based on the above technical means, this application can cluster map data from multiple known subcategories of a preset category with map data from unknown subcategories of the preset category, thereby obtaining clustering results corresponding to different known subcategories. This allows the unknown subcategories to be clustered with different known subcategories, enabling accurate identification of the category of the map data from the unknown subcategories. At the same time, this avoids directly excluding map data from unknown subcategories, which would result in reduced clustering result accuracy, effectively improving the accuracy of subsequent map production based on the clustering results.

[0007] In one possible implementation, the method further includes: after obtaining the first clustering result corresponding to the first subcategory, determining third target map data from the first clustering result corresponding to the first subcategory; the first subcategory is any one of different known subcategories, and the third target map data is map data belonging to the second map data set; and deleting the third target map data from the second map data set to update the second map data set.

[0008] According to the above technical means, the present application removes the third target map data from the second map data set, and can perform clustering processing on the updated second map data set and the map data of known subcategories obtained without clustering processing. In this way, when clustering different known subcategories, it is possible to be compatible with the map data of unknown subcategories that are not clustered with known subcategories, thereby effectively improving the recognition accuracy of unknown subcategories and further improving the accuracy of clustering results.

[0009] In one possible implementation, clustering is performed on map data in a second map data set based on map data of a first subcategory to obtain a first clustering result corresponding to the first subcategory, including: constructing a first data list; the first data list includes map data of the first subcategory and map data in the second map data set; sorting the map data in the first data list based on area parameters of the map data included in the first data list to obtain a second data list; and clustering is performed on the second data list to obtain a first clustering result corresponding to the first subcategory.

[0010] According to the above technical means, before clustering the map data of the first subcategory and the map data in the second map data set, the present application sorts the map data based on the area parameter, which can effectively improve the efficiency of subsequent clustering processing.

[0011] In a possible implementation, in the case where the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, clustering processing is performed on the first target map data, including: in the case where the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, clustering processing is performed on the first target map data according to a second subcategory to obtain a second clustering result corresponding to the second subcategory; the second subcategory is any known subcategory of the first target map data.

[0012] Based on the above technical means, when the second target map data does not exist in the second map data set, and the first target map data exists in the first map data set, the first map data is clustered according to the known subcategories included in the first target map data. In this way, map data of the same known subcategory can be clustered, ensuring the accuracy of the clustering results, and further ensuring the accuracy of subsequent map production.

[0013] According to a second aspect of the present application, a map data clustering device is provided, comprising an acquisition unit and a processing unit; the acquisition unit is configured to acquire a first map data set and a second map data set; the first map data set includes map data of a plurality of known subcategories in a preset category; the second map data set includes map data of unknown subcategories in the preset category; the processing unit is configured to, after the acquisition unit acquires the first map data set and the second map data set, sequentially cluster the map data in the second map data set based on the map data of different known subcategories in the first map data set, to obtain a plurality of first clustering results corresponding one-to-one to the different known subcategories, until the first map data set is obtained. The first target map data does not exist in the first map data set, or the second target map data does not exist in the second map data set; the first target map data is map data that has not been clustered, and the second target map data is map data that is not located in any first clustering result; the processing unit is further configured to, if the first target map data does not exist in the first map data set, perform clustering processing on the second target map data if the second target map data exists in the second map data set; the processing unit is further configured to, if the second target map data does not exist in the second map data set, perform clustering processing on the first target map data if the first target map data exists in the first map data set.

[0014] In one possible embodiment, the apparatus further includes a determining unit; the determining unit is configured to, after obtaining the first clustering result corresponding to the first subcategory, determine third target map data from the first clustering result corresponding to the first subcategory; the first subcategory is any one of different known subcategories, and the third target map data is map data belonging to the second map data set; and the processing unit is further configured to delete the third target map data from the second map data set to update the second map data set.

[0015] In one possible implementation, the processing unit is specifically configured to: construct a first data list; the first data list includes map data of a first subcategory and map data in a second map data set; sort the map data in the first data list according to area parameters of the map data included in the first data list to obtain a second data list; and perform clustering processing on the second data list to obtain a first clustering result corresponding to the first subcategory.

[0016] In one possible implementation, the processing unit is specifically configured to: when the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, perform clustering processing on the first target map data according to a second subcategory to obtain a second clustering result corresponding to the second subcategory; the second subcategory being any known subcategory of the first target map data.

[0017] According to the third aspect provided by the present application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the above-mentioned first aspect and any possible implementation method thereof.

[0018] According to the fourth aspect provided by the present application, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by the processor of an electronic device, the electronic device is enabled to execute the method of the above-mentioned first aspect and any possible implementation method thereof.

[0019] According to the fifth aspect provided by the present application, a computer program product is provided, which includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation method thereof.

[0020] Therefore, the above technical features of this application have the following beneficial effects:

[0021] (1) Unknown subcategories can be clustered together with different known subcategories, enabling accurate identification of the categories of map data of unknown subcategories. At the same time, this avoids directly eliminating map data of unknown subcategories, which would result in a decrease in clustering result accuracy. This can effectively improve the accuracy of subsequent map production based on clustering results.

[0022] (2) It can be compatible with unknown subcategory map data that are not clustered with known subcategories when clustering different known subcategories, thereby effectively improving the recognition accuracy of unknown subcategories and further improving the accuracy of clustering results.

[0023] (3) Before clustering the map data of the first subcategory and the map data in the second map data set, sorting the map data based on the area parameter can effectively improve the efficiency of subsequent clustering processing.

[0024] (4) Map data of the same known subcategory can be clustered to ensure the accuracy of the clustering results, and further ensure the accuracy of subsequent map production.

[0025] It should be noted that the technical effects brought about by any implementation method in the second to fifth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0026] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0028] Figure 1 is a flowchart of a map data clustering method according to an exemplary embodiment;

[0029] Figure 2 is a flowchart of another map data clustering method according to an exemplary embodiment;

[0030] Figure 3 is a flowchart of another map data clustering method according to an exemplary embodiment;

[0031] Figure 4 is a flowchart of another map data clustering method according to an exemplary embodiment;

[0032] Figure 5 is a block diagram of a map data clustering device according to an exemplary embodiment;

[0033] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0034] In order to enable ordinary people in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0035] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0036] For ease of understanding, the map data clustering method provided in this application is specifically introduced below with reference to the accompanying drawings.

[0037] Figure 1 This is a flow chart of a map data clustering method according to an exemplary embodiment. The map data clustering method can be applied to an electronic device, which can be a server or a server group. Figure 1 As shown, the map data clustering method includes the following steps:

[0038] S101: The electronic device obtains a first map data set and a second map data set.

[0039] The first map data set includes map data of a plurality of known subcategories in a preset category, and the second map data set includes map data of an unknown subcategory in the preset category.

[0040] As a possible implementation, the vehicle controller collects map data of a preset category and sends the map data of the preset category to the electronic device. The electronic device receives the map data of the preset category and divides the map data into a first map data set and a second map data set.

[0041] For example, a vehicle controller collects map data of a preset category {1, 1, 1, 2, 2, 2, 3, 3, 1000, 1000} and sends the map data of the preset category to an electronic device. The electronic device receives the map data of the preset category and divides the map data into a first map data set {1, 1, 1, 2, 2, 2, 3, 3} and a second map data set {1000, 1000}. 1, 2, and 3 represent map data of known subcategories, while 1000 represents map data of unknown subcategories.

[0042] For example, the preset category may be an arrow or a sign. In the case where the preset category is an arrow, the known subcategories may be a left turn arrow, a right turn arrow, or a straight ahead arrow.

[0043] As another possible implementation, the vehicle controller collects map data of a preset category and transmits the map data of the preset category to the electronic device. The electronic device receives the map data of the preset category and identifies the map data of the preset category based on the preset category and multiple known subcategories, thereby obtaining map data containing the identification. The electronic device then divides the map data into a first map data set and a second map data set based on the identification of the map data.

[0044] For example, taking the identifier of the preset category as 1, the vehicle controller collects map data of the preset category {1, 1, 1, 2, 2, 2, 3, 3, 1000, 1000} and sends the map data of the preset category to the electronic device. The electronic device receives the map data of the preset category and identifies the map data of the preset category based on the preset category 1 and multiple known subcategories 1, 2, 3, 1000, to obtain map data including the identifiers {(1-1, 1), (1-1, 1), (1-1, 1), (1-2, 2), (1-2, 2), (1-2, 2), (1-3, 3), (1-3, 3), (1-1000, 1000), (1-1000, 1000)}. The electronic device then divides the map data into a first map data set {1, 1, 1, 2, 2, 2, 3, 3} and a second map data set {1000, 1000} based on the map data identifiers 1-1, 1-2, 1-3, and 1-1000. 1, 2, and 3 are map data of known subcategories, and 1000 is map data of unknown subcategories.

[0045] S102. The electronic device sequentially clusters the map data in the second map data set based on the map data of different known subcategories in the first map data set, obtaining a plurality of first clustering results corresponding one-to-one to the different known subcategories, until the first target map data no longer exists in the first map data set or the second target map data no longer exists in the second map data set.

[0046] The first target map data is map data that has not been clustered, and the second target map data is map data that is not included in any first clustering result.

[0047] As a possible implementation manner, the electronic device determines a first subcategory from different known subcategories included in the first map data set, and clusters the map data of the first subcategory with the map data in the second map data set to obtain a first clustering result corresponding to the first subcategory.

[0048] S103: The electronic device determines whether the first target map data exists in the first map data set, and whether the second target map data exists in the second map data set.

[0049] As a possible implementation, after obtaining the first clustering result corresponding to the first subcategory, the electronic device updates the second map data set based on the first clustering result corresponding to the first subcategory. Furthermore, the electronic device determines whether the first target map data exists in the first map data set and whether the second target map data exists in the second map data set.

[0050] S104: When the first target map data set contains the first target map data and the second target map data set contains the second target map data, the electronic device repeats the above step S102.

[0051] As a possible implementation, when the first target map data exists in the first map data set and the second target map data exists in the second map data set, the electronic device repeats step S102 to obtain multiple first clustering results corresponding to different known subcategory pairs.

[0052] S105 : When the first target map data does not exist in the first map data set, the electronic device performs clustering processing on the second target map data if the second target map data exists in the second map data set.

[0053] As a possible implementation, if the first target map data does not exist in the first map data set, the electronic device determines whether the second target map data exists in the second map data set. Thereafter, if the second target map data exists in the second map data set, the electronic device performs clustering processing on the second target map data.

[0054] S106 : When the second target map data set does not contain the second target map data, the electronic device performs clustering processing on the first target map data if the first target map data exists in the first map data set.

[0055] As a possible implementation, if the second target map data does not exist in the second map data set, the electronic device determines whether the first target map data exists in the first map data set. Furthermore, if the first target map data exists in the first map data set, the electronic device performs clustering processing on the first target data based on known subcategories.

[0056] For the specific implementation of this step, please refer to the subsequent description of the embodiments of this application and will not be repeated here.

[0057] It is understood that this application can cluster map data from multiple known subcategories of a preset category with map data from unknown subcategories of the preset category, thereby obtaining clustering results corresponding to different known subcategories. This allows the unknown subcategories to be clustered with different known subcategories, enabling accurate classification of the map data from the unknown subcategories. This also avoids directly excluding map data from unknown subcategories, which would result in reduced clustering result accuracy, effectively improving the accuracy of subsequent map production based on the clustering results.

[0058] In some embodiments, in order to obtain the updated second map data set, such as Figure 2 As shown, the map data clustering method provided in the embodiment of the present application also includes:

[0059] S201: After obtaining a first clustering result corresponding to a first subcategory, the electronic device determines third target map data from the first clustering result corresponding to the first subcategory.

[0060] The first subcategory is any one of different known subcategories, and the third target map data is map data belonging to the second map data set.

[0061] As a possible implementation manner, the electronic device determines the map data belonging to the second map data set in the first clustering result corresponding to the first subcategory as the third target map data.

[0062] S202: The electronic device deletes the third target map data from the second map data set to update the second map data set.

[0063] It is understood that by deleting the third target map data from the second map data set, the updated second map data set can be clustered with the map data of known subcategories obtained without clustering. In this way, when clustering different known subcategories, map data of unknown subcategories that are not clustered with known subcategories can be compatible, thereby effectively improving the recognition accuracy of unknown subcategories and further improving the accuracy of clustering results.

[0064] In some embodiments, in order to obtain the first clustering result corresponding to the first subcategory, such as Figure 3 As shown, based on the map data of the first subcategory, the map data in the second map data set are clustered to obtain a first clustering result corresponding to the first subcategory, including:

[0065] S301: The electronic device constructs a first data list.

[0066] The first data list includes map data of the first subcategory and map data in the second map data set.

[0067] As a possible implementation, the electronic device determines any one known subcategory from multiple known subcategories as the first subcategory based on the first map data set, and constructs a first data list based on the map data of the first subcategory and the map data in the second map data set.

[0068] For example, taking the first map data set acquired by the electronic device as {1, 1, 1, 2, 2, 2, 3, 3} and the second map data set as {1000, 1000} as an example, the electronic device determines any one known subcategory from multiple known subcategories 1, 2, 3 as the first subcategory (taking the first subcategory as 1 as an example), and constructs a first data list {1, 1, 1, 1000, 1000} based on the map data {1, 1, 1} of the first subcategory and the second map data set {1000, 1000}.

[0069] S302: The electronic device sorts the map data in the first data list according to the area parameters of the map data included in the first data list to obtain a second data list.

[0070] As a possible implementation manner, the electronic device sorts the map data in the first data list in descending order according to the area parameters of the map data included in the first data list to obtain the second data list.

[0071] For example, taking the first data list as {1, 1, 1, 1000, 1000} and the area parameters corresponding to the map data included in the first data list as {10, 9, 5, 6, 3} respectively, the electronic device sorts the map data {1, 1, 1, 1000, 1000} in the first data list in descending order according to the area parameters {10, 9, 5, 6, 3} corresponding to the map data included in the first data list, and the resulting second data list is {1, 1, 1000, 1, 1000}.

[0072] As another possible implementation manner, the electronic device sorts the map data in the first data list in ascending order according to the area parameters of the map data included in the first data list to obtain the second data list.

[0073] For example, taking the first data list as {1, 1, 1, 1000, 1000} and the area parameters corresponding to the map data included in the first data list as {10, 9, 5, 6, 3} respectively, the electronic device sorts the map data {1, 1, 1, 1000, 1000} in the first data list in ascending order according to the area parameters {10, 9, 5, 6, 3} corresponding to the map data included in the first data list, and the resulting second data list is {1000, 1, 1000, 1, 1}.

[0074] S303: The electronic device performs clustering processing on the second data list to obtain a first clustering result corresponding to the first subcategory.

[0075] As a possible implementation manner, the electronic device performs clustering processing on the second data list based on a preset clustering algorithm to obtain a first clustering result corresponding to the first subcategory.

[0076] Exemplarily, the preset clustering algorithm may be a density-based spatial clustering of applications with noise (DBSCAN).

[0077] It is understandable that, before clustering the map data of the first subcategory and the map data in the second map data set, the present application sorts the map data based on the area parameter, which can effectively improve the efficiency of subsequent clustering processing.

[0078] In some embodiments, in order to obtain the second clustering result corresponding to the second subcategory, such as Figure 4 As shown, the above S106 can be implemented as follows:

[0079] S401: When the second target map data set does not contain the second target map data, the electronic device clusters the first target map data according to the second subcategory to obtain a second clustering result corresponding to the second subcategory if the first target map data exists in the first map data set.

[0080] The second subcategory is any known subcategory of the first target map data.

[0081] As a possible implementation, if the second target map data does not exist in the second map data set, the electronic device determines whether the first target map data exists in the first map data set. Thereafter, if the first target map data exists in the first map data set, the electronic device determines the second subcategory from known subcategories included in the first target map data.

[0082] Furthermore, if the first target map data includes only one known subcategory, the electronic device determines that the known subcategory included in the first target map data is the second subcategory. If the first target map data includes multiple known subcategories, the electronic device determines that any one of the multiple known subcategories included in the first target map data is the second subcategory.

[0083] Afterwards, the electronic device performs clustering processing on the map data of the second subcategory to obtain a second clustering result corresponding to the second subcategory.

[0084] It is understood that, in this application, if the second target map data does not exist in the second map data set, and the first target map data exists in the first map data set, the first map data is clustered according to the known subcategories included in the first target map data. In this way, map data of the same known subcategory can be clustered, ensuring the accuracy of the clustering results, and further ensuring the accuracy of subsequent map production.

[0085] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, the map data clustering device or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] The embodiments of the present application can, according to the above method, exemplarily divide the functional modules of the map data clustering device or electronic device. For example, the map data clustering device or electronic device can include functional modules corresponding to the functional divisions, or two or more functions can be integrated into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0087] Figure 5 FIG. 1 is a block diagram of a map data clustering device according to an exemplary embodiment. Figure 5 The map data clustering device 500 includes an acquisition unit 501 and a processing unit 502.

[0088] The acquisition unit 501 is configured to acquire a first map data set and a second map data set. The first map data set includes map data of multiple known subcategories in a preset category. The second map data set includes map data of unknown subcategories in the preset category.

[0089] The processing unit 502 is configured to, after the acquisition unit 501 acquires the first and second map data sets, sequentially cluster the map data in the second map data set based on the map data of different known subcategories in the first map data set, to obtain a plurality of first clustering results corresponding to the different known subcategories, until the first target map data no longer exists in the first map data set or the second target map data no longer exists in the second map data set. The first target map data is map data that has not been clustered, and the second target map data is map data that is not included in any of the first clustering results.

[0090] The processing unit 502 is further configured to, when the first target map data does not exist in the first map data set, perform clustering processing on the second target map data if the second target map data exists in the second map data set.

[0091] The processing unit 502 is further configured to, when the second target map data does not exist in the second map data set, perform clustering processing on the first target map data if the first target map data exists in the first map data set.

[0092] Optionally, in order to obtain an updated second map data set, such as Figure 5 As shown, the map data clustering device provided in this embodiment of the present application further includes a determination unit 503.

[0093] The determining unit 503 is configured to determine third target map data from the first clustering results corresponding to the first subcategory after obtaining the first clustering results corresponding to the first subcategory. The first subcategory is any one of different known subcategories, and the third target map data is map data belonging to the second map data set.

[0094] The processing unit 502 is further configured to delete the third target map data from the second map data set to update the second map data set.

[0095] Optionally, in order to obtain the first clustering result corresponding to the first subcategory, such as Figure 5 As shown, the processing unit 502 is specifically configured to:

[0096] A first data list is constructed, wherein the first data list includes map data of the first subcategory and map data in the second map data set.

[0097] The map data in the first data list are sorted according to the area parameters of the map data included in the first data list to obtain a second data list.

[0098] Clustering is performed on the second data list to obtain a first clustering result corresponding to the first subcategory.

[0099] Optionally, in order to obtain the second clustering result corresponding to the second subcategory, such as Figure 5 As shown, the processing unit 502 is specifically configured to:

[0100] In the case where the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, clustering processing is performed on the first target map data according to the second subcategory to obtain a second clustering result corresponding to the second subcategory. The second subcategory is any known subcategory of the first target map data.

[0101] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0102] Figure 6 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 6 As shown, the electronic device 600 includes but is not limited to: a processor 601 and a memory 602 .

[0103] The memory 602 is used to store executable instructions of the processor 601. It is understandable that the processor 601 is configured to execute instructions to implement the map data clustering method in the above embodiment.

[0104] It should be noted that those skilled in the art can understand that Figure 6 The electronic device structure shown in the figure does not limit the electronic device, and the electronic device may include Figure 6 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0105] The processor 601 is the control center of the electronic device. It connects the various parts of the entire electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 602 and calling data stored in the memory 602, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 601 may include one or more processing units. Optionally, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly handles wireless communications. It is understood that the modem processor may not be integrated into the processor 601.

[0106] The memory 602 can be used to store software programs and various data. The memory 602 can mainly include a program storage area and a data storage area. The program storage area can store an operating system and application programs required by at least one functional module (such as the acquisition unit 501, the processing unit 502, and the determination unit 503). In addition, the memory 602 can include high-speed random access memory and non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0107] In an exemplary embodiment, a computer-readable storage medium including instructions is further provided, such as a memory 602 including instructions. The above instructions can be executed by the processor 601 of the electronic device 600 to implement the map data clustering method in the above embodiment.

[0108] In actual implementation, Figure 5 The functions of the acquisition unit 501, the processing unit 502 and the determination unit 503 can all be represented by Figure 6 The processor 601 in the embodiment calls the computer program stored in the memory 602. The specific execution process can be referred to the description of the map data clustering method in the above embodiment, which will not be repeated here.

[0109] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0110] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, which can be executed by a processor of an electronic device to implement the map data clustering method in the above embodiment.

[0111] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned map data clustering method embodiment are implemented, and the same technical effect as the above-mentioned map data clustering method can be achieved. To avoid repetition, they will not be described here.

[0112] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0114] Units described as separate components may or may not be physically separate, and components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0115] In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit 502, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in the form of hardware or software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0117] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A map data clustering method, characterized in that: include: Acquire a first map data set and a second map data set; The first map data set includes map data of a plurality of known subcategories in a preset category; The second map data set includes map data of an unknown subcategory in the preset category; performing clustering processing on the map data in the second map data set sequentially based on the map data of different known subcategories in the first map data set, to obtain a plurality of first clustering results corresponding one-to-one to the different known subcategories, until the first target map data no longer exists in the first map data set or the second target map data no longer exists in the second map data set; The first target map data is map data that has not been clustered, and the second target map data is map data that is not included in any first clustering result; In a case where the first target map data does not exist in the first map data set, if the second target map data exists in the second map data set, clustering the second target map data; In a case where the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, clustering processing is performed on the first target map data.

2. The method according to claim 1, characterized in that The method further comprises: After obtaining a first clustering result corresponding to a first subcategory, determining third target map data from the first clustering result corresponding to the first subcategory; the first subcategory is any one of the different known subcategories, and the third target map data is map data belonging to the second map data set; The third target map data is deleted from the second map data set to update the second map data set.

3. The method according to claim 2, characterized in that Clustering the map data in the second map data set based on the map data of the first subcategory to obtain a first clustering result corresponding to the first subcategory includes: Constructing a first data list; the first data list includes map data of the first subcategory and map data in the second map data set; sorting the map data in the first data list according to the area parameters of the map data included in the first data list to obtain a second data list; Clustering is performed on the second data list to obtain a first clustering result corresponding to the first subcategory.

4. The method according to any one of claims 1 to 3, characterized in that In the case that the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, clustering the first target map data includes: In a case where the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, clustering processing is performed on the first target map data according to a second subcategory to obtain a second clustering result corresponding to the second subcategory; the second subcategory is any known subcategory of the first target map data.

5. A map data clustering device, characterized in that: including an acquisition unit and a processing unit; The acquisition unit is configured to acquire a first map data set and a second map data set; the first map data set includes map data of a plurality of known subcategories within a preset category; and the second map data set includes map data of unknown subcategories within the preset category; the processing unit is configured to, after the acquisition unit acquires the first map data set and the second map data set, sequentially cluster the map data in the second map data set based on map data of different known subcategories in the first map data set, to obtain a plurality of first clustering results corresponding one-to-one to the different known subcategories, until the first target map data no longer exists in the first map data set or the second target map data no longer exists in the second map data set; The first target map data is map data that has not been clustered, and the second target map data is map data that is not included in any first clustering result; The processing unit is further configured to, when the first target map data does not exist in the first map data set, perform clustering processing on the second target map data if the second target map data exists in the second map data set; The processing unit is further configured to, when the second target map data does not exist in the second map data set, perform clustering processing on the first target map data if the first target map data exists in the first map data set.

6. The device according to claim 5, characterized in that The apparatus further includes a determining unit; the determining unit is configured to, after obtaining the first clustering result corresponding to the first subcategory, determine third target map data from the first clustering result corresponding to the first subcategory; the first subcategory being any one of the different known subcategories, and the third target map data being map data belonging to the second map data set; The processing unit is further configured to delete the third target map data from the second map data set to update the second map data set.

7. The device according to claim 6, characterized in that The processing unit is specifically configured to: Constructing a first data list; the first data list includes map data of the first subcategory and map data in the second map data set; sorting the map data in the first data list according to the area parameters of the map data included in the first data list to obtain a second data list; Clustering is performed on the second data list to obtain a first clustering result corresponding to the first subcategory.

8. The device according to any one of claims 5 to 7, characterized in that The processing unit is specifically configured to: In a case where the second target map data does not exist in the second map data set, if the first target map data exists in the first map data set, clustering processing is performed on the first target map data according to a second subcategory to obtain a second clustering result corresponding to the second subcategory; the second subcategory is any known subcategory of the first target map data.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that When the computer-executable instructions stored in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can perform the method according to any one of claims 1 to 4.

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