CN117290741B - Vehicle clustering method, device, computer equipment and storage medium - Google Patents
Vehicle clustering method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a vehicle clustering method, a device, a computer device and a storage medium, comprising: determining a target vehicle in vehicles in a target area, and adding the identification of the target vehicle and the corresponding reachable distance of the target vehicle to a reachable distance list; updating a target vehicle queue, determining a first vehicle with an reachable distance meeting a preset distance condition in the updated target vehicle queue as a new target vehicle, and adding the identification of the target vehicle and the reachable distance corresponding to the target vehicle into an reachable distance list; generating an reachable distance two-dimensional graph according to the identifications of the vehicles in the reachable distance list and the reachable distances corresponding to the vehicles; and determining candidate vehicles meeting the target clustering density condition in the reachable distance two-dimensional graph, and carrying out clustering processing according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph to obtain a vehicle clustering result corresponding to the target area. The clustering efficiency can be improved by adopting the method.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a vehicle clustering method, device, computer equipment, and storage medium.
Background
With the development of internet technology, shared vehicles begin to operate in more and more urban areas so as to facilitate people to travel. For cities operating shared electric bicycles, vehicles are often required to be clustered to realize management tasks of the shared vehicles, such as executing tasks of vehicle moving, power changing and the like.
In the conventional technology, a K-Means clustering algorithm is generally adopted to cluster shared vehicles in a certain area, so that a vehicle clustering result corresponding to the area is obtained.
However, based on the method, the clustering granularity of each clustering process is single, and the clustering process needs to be repeatedly executed for a plurality of times under the condition of facing different clustering granularities, so that the clustering efficiency is lower.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a vehicle clustering method, apparatus, computer device, and storage medium that can improve clustering efficiency.
In a first aspect, the present application provides a vehicle clustering method, including:
determining a target vehicle in vehicles in a target area, and adding the identification of the target vehicle and the corresponding reachable distance of the target vehicle to a reachable distance list;
under the condition that a target vehicle is a core vehicle, updating a target vehicle queue according to the target vehicle and the reachable distance between vehicles in a neighborhood vehicle set corresponding to the target vehicle, determining a first vehicle with the reachable distance meeting a preset distance condition in the updated target vehicle queue, and executing the steps of adding the identification of the target vehicle and the reachable distance corresponding to the target vehicle to a reachable distance list as a new target vehicle until a preset stopping condition is reached;
Generating an reachable distance two-dimensional graph according to the identifications of the vehicles in the reachable distance list and the reachable distances corresponding to the vehicles;
and determining candidate vehicles meeting the target clustering density condition in the reachable distance two-dimensional graph, and carrying out clustering processing according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph to obtain a vehicle clustering result corresponding to the target region.
In one embodiment, the method further comprises:
determining the distance between the target vehicle and other vehicles in the target area according to the vehicle position information of the target vehicle and the vehicle position information of the other vehicles in the target area;
determining the number of vehicles in a target neighborhood corresponding to the target vehicle according to the distance between the target vehicle and the other vehicles;
if the number of vehicles is greater than or equal to a preset number threshold, determining that the target vehicle is a core vehicle; and if the number of the vehicles is smaller than a preset number threshold, determining that the target vehicle is a non-core vehicle.
In one embodiment, the vehicles in the target vehicle queue are arranged in order of small to large reach distances; the updating the target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle comprises the following steps:
Acquiring a current target vehicle queue, a neighborhood vehicle set corresponding to the target vehicle and an reachable distance between the target vehicle and vehicles in the neighborhood vehicle set corresponding to the target vehicle;
determining other vehicles except for the vehicles in the neighborhood vehicle set in the current target vehicle queue;
and sorting the other vehicles and the vehicles in the neighborhood vehicle set according to the order of the reachable distances from small to large based on the reachable distances corresponding to the other vehicles and the reachable distances between the target vehicle and the vehicles in the neighborhood vehicle set, so as to obtain an updated target vehicle queue.
In one embodiment, in the updated target vehicle queue, the determining, as a new target vehicle, the first vehicle whose reachable distance meets the preset distance condition includes:
and determining the first vehicle with the smallest reachable distance in the updated target vehicle queue as a new target vehicle.
In one embodiment, the target cluster density condition includes an reachable distance threshold, and the determining the candidate vehicles meeting the target cluster density condition in the reachable distance two-dimensional map includes:
And determining vehicles with the reachable distance smaller than or equal to the reachable distance threshold in the reachable distance two-dimensional graph as candidate vehicles meeting the target cluster density condition.
In one embodiment, the clustering process is performed according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph, so as to obtain a vehicle clustering result corresponding to the target area, including:
and dividing the candidate vehicles with continuous arrangement sequences into a cluster according to the arrangement sequence of the candidate vehicles in the reachable distance two-dimensional graph, and obtaining a vehicle cluster result corresponding to the target area.
In one embodiment, the method further comprises:
responding to a selection instruction of a cluster density level, and generating a target cluster density condition corresponding to the target cluster density level according to the target cluster density level corresponding to the selection instruction.
In a second aspect, the present application further provides a vehicle clustering apparatus, the apparatus including:
the first determining module is used for determining a target vehicle in vehicles in a target area and adding the identification of the target vehicle and the reachable distance corresponding to the target vehicle to a reachable distance list;
The updating module is used for updating a target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle under the condition that the target vehicle is a core vehicle, determining a first vehicle with the reachable distance meeting a preset distance condition in the updated target vehicle queue, and executing the identification of the target vehicle and the reachable distance corresponding to the target vehicle to be added to a reachable distance list as a new target vehicle until a preset stopping condition is reached;
the first generation module is used for generating an reachable distance two-dimensional graph according to the identifications of the vehicles in the reachable distance list and the reachable distances corresponding to the vehicles;
and the clustering module is used for determining candidate vehicles meeting the target clustering density condition in the reachable distance two-dimensional graph, and carrying out clustering processing according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph to obtain a vehicle clustering result corresponding to the target region.
In one embodiment, the apparatus further comprises:
a second determining module, configured to determine a distance between the target vehicle and other vehicles in the target area according to vehicle position information of the target vehicle and vehicle position information of the other vehicles in the target area;
A third determining module, configured to determine, according to a distance between the target vehicle and the other vehicle, a number of vehicles in a target neighborhood corresponding to the target vehicle;
a fourth determining module, configured to determine that the target vehicle is a core vehicle if the number of vehicles is greater than or equal to a preset number threshold; and if the number of the vehicles is smaller than a preset number threshold, determining that the target vehicle is a non-core vehicle.
In one embodiment, the vehicles in the target vehicle queue are arranged in order of small to large reach distances; the updating module is used for:
acquiring a current target vehicle queue, a neighborhood vehicle set corresponding to the target vehicle and an reachable distance between the target vehicle and vehicles in the neighborhood vehicle set corresponding to the target vehicle;
determining other vehicles except for the vehicles in the neighborhood vehicle set in the current target vehicle queue;
and sorting the other vehicles and the vehicles in the neighborhood vehicle set according to the order of the reachable distances from small to large based on the reachable distances corresponding to the other vehicles and the reachable distances between the target vehicle and the vehicles in the neighborhood vehicle set, so as to obtain an updated target vehicle queue.
In one embodiment, the updating module is configured to:
and determining the first vehicle with the smallest reachable distance in the updated target vehicle queue as a new target vehicle.
In one embodiment, the target cluster density condition includes an achievable distance threshold, and the clustering module is configured to:
and determining vehicles with the reachable distance smaller than or equal to the reachable distance threshold in the reachable distance two-dimensional graph as candidate vehicles meeting the target cluster density condition.
In one embodiment, the clustering module is configured to:
and dividing the candidate vehicles with continuous arrangement sequences into a cluster according to the arrangement sequence of the candidate vehicles in the reachable distance two-dimensional graph, and obtaining a vehicle cluster result corresponding to the target area.
In one embodiment, the apparatus further comprises:
the second generation module is used for responding to a selection instruction of the cluster density level and generating a target cluster density condition corresponding to the target cluster density level according to the target cluster density level corresponding to the selection instruction.
In a third aspect, the present application also provides a computer device comprising a memory storing a computer program and a processor implementing the above method steps when executing the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the above-mentioned method steps.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-mentioned method steps.
The method, the device, the computer equipment, the storage medium and the computer program product for clustering the vehicles are characterized in that the target vehicles are determined in the vehicles in the target area, the identification of the target vehicles and the reachable distances corresponding to the target vehicles are added to a reachable distance list, under the condition that the target vehicles are core vehicles, the target vehicle queue is updated according to the reachable distances between the target vehicles and the vehicles in the neighborhood vehicle set corresponding to the target vehicles, the first vehicles with the reachable distances meeting the preset distance condition are determined in the updated target vehicle queue, the identification of the target vehicles and the reachable distances corresponding to the target vehicles are added to the reachable distance list as new target vehicles, the reachable distance two-dimensional map is generated according to the identification of each vehicle in the reachable distance list and the reachable distances corresponding to each vehicle, the candidate vehicles meeting the target cluster density condition are determined in the reachable distance two-dimensional map, and the clustering processing is carried out according to the distribution of the candidate vehicles in the reachable distance two-dimensional map, so that the clustering result of the vehicles corresponding to the target area is obtained. According to the method and the device, the reachable distance two-dimensional graph capable of reflecting the position association relation among vehicles in the target area can be generated, different target cluster density conditions can be set based on the reachable distance two-dimensional graph to realize clusters with different cluster granularity, and the whole clustering process is not required to be repeatedly executed, so that the clustering efficiency is effectively improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a flow diagram of a method of clustering vehicles in one embodiment;
FIG. 2 is a schematic diagram of core distance and reach distance in one embodiment;
FIG. 3 is a schematic diagram of a two-dimensional map of the achievable distance in one embodiment;
FIG. 4 is a flow diagram of an example of a vehicle clustering method in one embodiment;
FIG. 5 is a block diagram of a vehicle cluster unit in one embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a vehicle clustering method is provided, where the method is applied to a terminal to illustrate, it is understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
and 102, determining a target vehicle in the vehicles in the target area, and adding the identification of the target vehicle and the corresponding reachable distance of the target vehicle to the reachable distance list.
The target area may be a preconfigured area, or the target area may be an area determined in response to an area selection instruction. The target area may be a city or a pre-divided arbitrary area.
In the embodiment of the application, the terminal can acquire the vehicle information of all the vehicles located in the target area. The vehicle information may include an identification of the vehicle, and location information of the vehicle. For example, the position information of the vehicle may be a latitude and longitude coordinate point. The terminal may also have created therein a list of reachable distances and an empty queue (i.e., an initial target vehicle queue). The reachable distance list may be used to store two-tuple data, that is, (i.e., the identification of the vehicle, the reachable distance), the identification of the vehicle may be the vehicle number, and the initial reachable distance corresponding to each vehicle is positive infinity (e.g., 100). When the terminal processes for the first time, a vehicle can be randomly determined as a target vehicle in the vehicles in the target area, and then the initialization reachable distance corresponding to the target vehicle can be stored into the reachable distance list.
Step 104, under the condition that the target vehicle is a core vehicle, updating the target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle, determining a first vehicle with the reachable distance meeting the preset distance condition in the updated target vehicle queue, and executing to add the identification of the target vehicle and the reachable distance corresponding to the target vehicle to the reachable distance list as a new target vehicle until the preset stop condition is reached.
In this embodiment of the present application, the terminal may determine whether the target vehicle is a core vehicle, and if the target vehicle is a non-core vehicle, may mark the target vehicle as a boundary vehicle, and does not execute subsequent processing. If the terminal judges that the target vehicle is a core vehicle, the terminal can acquire a neighborhood vehicle set corresponding to the target vehicle and Euclidean distance between the target vehicle and each vehicle in the neighborhood vehicle set, further order the vehicles in the vehicle set according to the sequence of the Euclidean distance between the target vehicle and each vehicle in the neighborhood vehicle set from small to large, and in the ordered sequence, determine the Euclidean distance when the number of the vehicles is equal to a preset number threshold value, and take the Euclidean distance as the core distance of the target vehicle. The terminal may then determine an achievable distance from each vehicle in the set of neighborhood vehicles to the target vehicle based on the core distance of the target vehicle. In one example, the core vehicle may be denoted as C, each vehicle in the neighborhood vehicle set is denoted as S, and the reachable distance between C and S is max (core distance of C, euclidean distance of C and S). Referring to fig. 2, a schematic diagram of a core distance and an reachable distance provided by an embodiment of the present application is shown, where R represents a neighborhood radius, v is a core vehicle, R is a core distance of the core vehicle v, p and q are two vehicles in a neighborhood vehicle set, the reachable distance corresponding to the vehicle p is R, and the reachable distance corresponding to the vehicle q is an euclidean distance between the vehicle q and the vehicle v.
After determining the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle, the terminal can update the current target vehicle queue based on the calculated reachable distance of each vehicle to obtain an updated target vehicle queue, and the specific updating process will be described in detail later. The vehicles contained in the target vehicle consist may be ranked from small to large in terms of reachable distance. The terminal may determine, in the updated target vehicle queue, a first vehicle whose reachable distance satisfies a preset distance condition, and return to execute adding, as a new target vehicle, the identifier of the target vehicle and the reachable distance corresponding to the target vehicle to the reachable distance list. It can be understood that the reachable distance corresponding to the target vehicle at this time is the reachable distance obtained by this calculation, and is not the initial reachable distance. In this way, the terminal repeatedly executes the above-described processing until a preset stop condition is reached, such as the target vehicle queue being empty.
And 106, generating an reachable distance two-dimensional map according to the identifications of the vehicles in the reachable distance list and the reachable distances corresponding to the vehicles.
In this embodiment of the present application, the terminal may construct the reachable distance two-dimensional map according to the arrangement order of each vehicle in the reachable distance list (i.e., the adding order of the identifiers of each vehicle), with the identifier of the vehicle as the x-axis and the reachable distance as the y-axis. As is clear from the above processing procedure, the vehicles are sequentially selected according to the reachable distances, and therefore, the reachable distance two-dimensional map can reflect the positional association relationship between the vehicles in the target area. Referring to fig. 3, a schematic diagram of a two-dimensional map of reachable distances according to an embodiment of the present application is provided.
And step 108, determining candidate vehicles meeting the target clustering density condition in the reachable distance two-dimensional graph, and carrying out clustering processing according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph to obtain a vehicle clustering result corresponding to the target area.
In the embodiment of the application, after the terminal generates the reachable distance two-dimensional graph, candidate vehicles meeting the target clustering density condition can be screened from the reachable distance two-dimensional graph, and the candidate vehicles meeting the preset proximity condition in the distribution of the reachable distance two-dimensional graph are divided into one cluster according to the distribution condition of the candidate vehicles in the reachable distance two-dimensional graph, so that a plurality of clusters are obtained, and the clustering of the vehicles in the target area is realized. The target cluster density conditions may be multiple, and may be specifically set by a technician according to actual cluster density requirements.
In the embodiment of the application, the terminal can generate the reachable distance two-dimensional graph capable of reflecting the position incidence relation among vehicles in the target area, the reachable distance two-dimensional graph can also reflect the hierarchical structure of the clustering clusters, the clustering of different clustering granularities can be realized through different target clustering density conditions based on the reachable distance two-dimensional graph, the whole clustering process is not required to be repeatedly executed, and the clustering efficiency is improved while the clustering flexibility and the clustering granularity are improved.
Optionally, the specific process of determining whether the target vehicle is a core vehicle is: determining the distance between the target vehicle and other vehicles according to the vehicle position information of the target vehicle and the vehicle position information of other vehicles in the target area; determining the number of vehicles in a target neighborhood corresponding to the target vehicle according to the distance between the target vehicle and other vehicles; if the number of vehicles is greater than or equal to a preset number threshold, determining the target vehicle as a core vehicle; and if the number of the vehicles is smaller than the preset number threshold, determining that the target vehicle is a non-core vehicle.
In this embodiment of the present application, the terminal may store a preset neighborhood radius and a preset number threshold. The terminal may calculate euclidean distances between the target vehicle and other vehicles according to the vehicle position information of each vehicle in the vehicle information, and then the terminal may count the number of vehicles whose euclidean distances are smaller than the neighborhood radius, and compare the number of vehicles with a preset number threshold. If the number of vehicles is greater than or equal to a preset number threshold, determining the target vehicle as a core vehicle; and if the number of the vehicles is smaller than the preset number threshold, determining that the target vehicle is a non-core vehicle.
Based on the processing procedure, the terminal can identify core vehicles in the target area, and the neighborhood vehicles of the core vehicles are distributed to reach a certain density, so that the terminal updates the reachable distance list based on the vehicle with the smallest reachable distance to the core vehicle, the position association degree of the adjacent vehicles in the reachable distance two-dimensional graph is larger, and the accuracy of vehicle clustering is further improved.
Optionally, the vehicles in the target vehicle queue are arranged in order of small to large reachable distances; correspondingly, updating the target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle, comprising: acquiring a current target vehicle queue, a neighborhood vehicle set corresponding to the target vehicle and an reachable distance between the target vehicle and vehicles in the neighborhood vehicle set corresponding to the target vehicle; in the current target vehicle queue, determining other vehicles except for the vehicles in the neighborhood vehicle set; and sorting the other vehicles and the vehicles in the neighborhood vehicle set according to the order of the reachable distances from small to large based on the reachable distances corresponding to the other vehicles and the reachable distances between the target vehicle and the vehicles in the neighborhood vehicle set, so as to obtain an updated target vehicle queue.
In this embodiment of the present invention, an empty target vehicle queue may be stored in advance in the terminal, and in a case where the terminal determines a first core vehicle, the terminal may rank each vehicle in a neighborhood vehicle set corresponding to the core vehicle in order of a short reach distance, and store a ranking result to the target vehicle reachable queue. For the target vehicle determined by the terminal to be the nth core vehicle (N is greater than or equal to 2), the terminal may acquire the current target vehicle queue, the set of neighboring vehicles corresponding to the target vehicle, and the reachable distance between the target vehicle and the vehicles in the set of neighboring vehicles corresponding to the target vehicle. Because the current target vehicle queue and the neighborhood vehicle set may have the same vehicles, the terminal may determine other vehicles (may be referred to as a first vehicle set) except for the vehicles in the neighborhood vehicle set in the current target vehicle queue, and further comprehensively sort the vehicles in the first vehicle set and the neighborhood vehicle set according to the order of the short reachable distances from the short reachable distances based on the reachable distances corresponding to the vehicles in the first vehicle set and the reachable distances corresponding to the vehicles in the neighborhood vehicle set of the target vehicle, so as to obtain the updated target vehicle queue.
Based on the processing procedure, the terminal can update the target vehicle queue according to the latest reachable distance, so that the vehicle with the smallest reachable distance with the current core vehicle is searched and added into the reachable distance list, thereby improving the position association degree of the adjacent vehicles in the reachable distance two-dimensional graph and further improving the accuracy of vehicle clustering.
In the above-mentioned processing, the neighborhood vehicle set may include the processed target vehicles, so that the terminal may remove the processed target vehicles from the neighborhood vehicle set, and only perform the above-mentioned processing on other vehicles in the neighborhood vehicle set, thereby reducing the processing amount of the terminal and improving the vehicle clustering efficiency.
Optionally, in the updated target vehicle queue, determining the first vehicle whose reachable distance meets the preset distance condition as a new target vehicle includes: and determining the first vehicle with the smallest reachable distance in the updated target vehicle queue as a new target vehicle.
In this embodiment of the present invention, the vehicles in the target vehicle queue may be ranked in order of small to large reachable distances, and at this time, the terminal may use the vehicles in the updated target vehicle queue, which are ranked at the head of the queue, as new target vehicles. Alternatively, the vehicles in the target vehicle queue may be arranged in order of the reaching distance from the big end to the small end, and the terminal may use the vehicles arranged at the end of the queue as new target vehicles.
Optionally, the terminal may determine, in the target vehicle fleet, a plurality of first vehicles with the smallest reachable distances. The terminal may perform parallel processing on multiple target vehicles, or the terminal may also randomly select, or select, a first vehicle with the smallest vehicle number from multiple first vehicles as a new target vehicle, which is not limited to a specific implementation manner in the present application.
Based on the processing procedure, the terminal can search the vehicle with the smallest reachable distance with the current core vehicle and add the vehicle to the reachable distance list, so that the position association degree of the adjacent vehicles in the reachable distance two-dimensional graph is improved, and the accuracy of vehicle clustering is further improved.
Optionally, the target cluster density condition includes an achievable distance threshold, and determining the candidate vehicles meeting the target cluster density condition in the achievable distance two-dimensional graph includes: and determining vehicles with the reachable distance smaller than or equal to the reachable distance threshold value in the reachable distance two-dimensional graph as candidate vehicles meeting the target cluster density condition.
In this embodiment of the present application, the terminal may compare the reachable distance corresponding to the identifier of each vehicle in the reachable distance two-dimensional map with the reachable distance threshold, determine that the reachable distance is smaller than or equal to the reachable distance threshold, and use the vehicle as the candidate vehicle satisfying the target cluster density condition, and referring to fig. 3, the reachable distance threshold T is 38, and the vehicles bike5, bike10, bike4, bike6, bike9, bike7, and bike8 are candidate vehicles.
Optionally, clustering is performed according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph, so as to obtain a vehicle clustering result corresponding to the target area, which comprises the following steps: and dividing the candidate vehicles with continuous arrangement sequences into a cluster according to the arrangement sequence of each candidate vehicle in the reachable distance two-dimensional graph, and obtaining a vehicle clustering result corresponding to the target area.
In the embodiment of the application, the terminal can determine the arrangement sequence of each candidate vehicle in the reachable distance two-dimensional graph according to the direction of the x-axis in the reachable distance two-dimensional graph, and then divide the candidate vehicles with continuous arrangement sequence into one cluster to obtain the vehicle clustering result corresponding to the target area. Referring to fig. 3, the reachable distance threshold T is 38, the neighborhood radius R is 50, and the vehicles bike5, bike10, bike4 and bike6 are candidate vehicles with continuous arrangement sequences and are divided into a cluster; vehicles rake 9, rake 7 and rake 8 are candidate vehicles which are continuous in arrangement order, and are divided into another cluster, and vehicles rake 1, rake 3, rake 2 and rake 11 are boundary points (i.e., noise points).
Based on the processing procedure, the reachable distance two-dimensional graph can be generated, and the reachable distance two-dimensional graph can reflect the hierarchical structure of the cluster clusters, so that the real-time clustering of vehicles with different density levels is realized. The clustering results with different granularities can be obtained by adjusting the parameter reachable distance threshold and the preset number threshold.
Optionally, the method further comprises: responding to a selection instruction of the cluster density level, and generating a target cluster density condition corresponding to the target cluster density level according to the target cluster density level corresponding to the selection instruction.
In the embodiment of the application, the user can input the selection instruction of the cluster density level to the terminal through the input device. The terminal responds to the selection instruction of the cluster density level, can determine the target cluster density level corresponding to the selection instruction, and can further generate a target cluster density condition according to the target cluster density level. In one example, the terminal may determine an reachable distance threshold corresponding to the target cluster density level according to the target cluster density level selected by the user, and then generate the target cluster density condition according to the reachable distance threshold. Or the terminal can also display a plurality of reachable distance thresholds corresponding to the target cluster density level, and then generate the target cluster density condition according to the reachable distance thresholds selected by the user.
Based on the processing, a user can set a target cluster density level according to actual cluster granularity requirements, so that vehicle cluster processing with different cluster granularities is realized based on the reachable distance two-dimensional graph.
In one embodiment, as shown in fig. 4, an example of a vehicle clustering method is provided, comprising the steps of:
in step 401, a vehicle is randomly determined among vehicles in a target area as a target vehicle.
Step 402, adding the identification of the target vehicle and the corresponding reachable distance of the target vehicle to the reachable distance list.
Wherein the reachable distance of the target vehicle is initialized to positive infinity.
Step 403, identify whether the target vehicle is a core vehicle.
If the target vehicle is a core vehicle, step 404 is performed. If the target vehicle is a non-core vehicle, the target vehicle is deleted in the target vehicle queue and step 407 is performed.
Step 404, calculating a core distance of the target vehicle.
Step 405, calculating the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle according to the core distance of the target vehicle.
Step 406, updating the target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set.
The vehicles in the target vehicle queue are ranked from small to large according to the reachable distance, and the target vehicle queue does not contain the identified vehicles.
In step 407, if the target vehicle queue is not empty, the first vehicle having the smallest reachable distance is determined as a new target vehicle in the updated target vehicle queue, and the flow returns to step 402.
In step 408, when the target vehicle queue is empty, a two-dimensional map of the reachable distances is generated according to the identifiers of the vehicles in the reachable distance list and the reachable distances corresponding to the vehicles.
And 409, determining vehicles with the reachable distance smaller than or equal to the reachable distance threshold in the reachable distance two-dimensional graph as candidate vehicles meeting the target clustering density condition, and dividing the candidate vehicles with continuous arrangement sequences into a cluster according to the arrangement sequence of each candidate vehicle in the reachable distance two-dimensional graph to obtain a vehicle clustering result corresponding to the target area.
In the embodiment of the application, the terminal can generate the reachable distance two-dimensional graph capable of reflecting the position incidence relation among vehicles in the target area, the reachable distance two-dimensional graph can also reflect the hierarchical structure of the clustering clusters, the clustering of different clustering granularities can be realized through different target clustering density conditions based on the reachable distance two-dimensional graph, the whole clustering process is not required to be repeatedly executed, and the clustering efficiency is improved while the clustering flexibility and the clustering granularity are improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a vehicle clustering device for realizing the vehicle clustering method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiment of one or more vehicle clustering devices provided below may be referred to the limitation of the vehicle clustering method hereinabove, and will not be repeated here.
In one exemplary embodiment, as shown in fig. 5, there is provided a vehicle clustering apparatus including: a first determination module 510, an update module 520, a first generation module 530, and a clustering module 540, wherein:
a first determining module 510, configured to determine a target vehicle in vehicles in a target area, and add an identifier of the target vehicle and a reachable distance corresponding to the target vehicle to a reachable distance list;
an updating module 520, configured to update a target vehicle queue according to an reachable distance between the target vehicle and a vehicle in a neighboring vehicle set corresponding to the target vehicle when the target vehicle is a core vehicle, determine, in the updated target vehicle queue, a first vehicle whose reachable distance meets a preset distance condition, and execute, as a new target vehicle, the adding, to a reachable distance list, an identifier of the target vehicle and the reachable distance corresponding to the target vehicle until a preset stop condition is reached;
a first generating module 530, configured to generate an reachable distance two-dimensional map according to the identifier of each vehicle in the reachable distance list and the reachable distance corresponding to each vehicle;
and the clustering module 540 is configured to determine a candidate vehicle satisfying a target cluster density condition in the reachable distance two-dimensional graph, and perform clustering processing according to the distribution of the candidate vehicle in the reachable distance two-dimensional graph, so as to obtain a vehicle cluster result corresponding to the target region.
In one embodiment, the apparatus further comprises:
a second determining module, configured to determine a distance between the target vehicle and other vehicles in the target area according to vehicle position information of the target vehicle and vehicle position information of the other vehicles in the target area;
a third determining module, configured to determine, according to a distance between the target vehicle and the other vehicle, a number of vehicles in a target neighborhood corresponding to the target vehicle;
a fourth determining module, configured to determine that the target vehicle is a core vehicle if the number of vehicles is greater than or equal to a preset number threshold; and if the number of the vehicles is smaller than a preset number threshold, determining that the target vehicle is a non-core vehicle.
In one embodiment, the vehicles in the target vehicle queue are arranged in order of small to large reach distances; the update module 520 is configured to:
acquiring a current target vehicle queue, a neighborhood vehicle set corresponding to the target vehicle and an reachable distance between the target vehicle and vehicles in the neighborhood vehicle set corresponding to the target vehicle;
determining other vehicles except for the vehicles in the neighborhood vehicle set in the current target vehicle queue;
And sorting the other vehicles and the vehicles in the neighborhood vehicle set according to the order of the reachable distances from small to large based on the reachable distances corresponding to the other vehicles and the reachable distances between the target vehicle and the vehicles in the neighborhood vehicle set, so as to obtain an updated target vehicle queue.
In one embodiment, the updating module 520 is configured to:
and determining the first vehicle with the smallest reachable distance in the updated target vehicle queue as a new target vehicle.
In one embodiment, the target cluster density condition includes an achievable distance threshold, the clustering module 540 is configured to:
and determining vehicles with the reachable distance smaller than or equal to the reachable distance threshold in the reachable distance two-dimensional graph as candidate vehicles meeting the target cluster density condition.
In one embodiment, the clustering module 540 is configured to:
and dividing the candidate vehicles with continuous arrangement sequences into a cluster according to the arrangement sequence of the candidate vehicles in the reachable distance two-dimensional graph, and obtaining a vehicle cluster result corresponding to the target area.
In one embodiment, the apparatus further comprises:
The second generation module is used for responding to a selection instruction of the cluster density level and generating a target cluster density condition corresponding to the target cluster density level according to the target cluster density level corresponding to the selection instruction.
The respective modules in the above-described vehicle clustering apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In an exemplary embodiment, a computer device, which may be a terminal, is provided, and an internal structure diagram thereof may be as shown in fig. 6. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a vehicle clustering method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the above-described vehicle clustering method steps when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the above-described vehicle clustering method steps.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, implements the above-described vehicle clustering method steps.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can take many forms, such as static Random access memory (Static Random Access Memory, SRAM) or Dynamic Random access memory (Dynamic Random AccessMemory, DRAM), among others. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of clustering vehicles, the method comprising:
determining a target vehicle in vehicles in a target area, and adding the identification of the target vehicle and the corresponding reachable distance of the target vehicle to a reachable distance list;
determining the distance between the target vehicle and other vehicles in the target area according to the vehicle position information of the target vehicle and the vehicle position information of the other vehicles in the target area; determining the number of vehicles in a target neighborhood corresponding to the target vehicle according to the distance between the target vehicle and the other vehicles; if the number of vehicles is greater than or equal to a preset number threshold, determining that the target vehicle is a core vehicle;
Under the condition that a target vehicle is a core vehicle, determining the reachable distance between each vehicle in a neighborhood vehicle set and the target vehicle according to the core distance of the target vehicle, updating a target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle, determining a first vehicle with the reachable distance meeting a preset distance condition in the updated target vehicle queue, and executing the steps of adding the identification of the target vehicle and the reachable distance corresponding to the target vehicle to a reachable distance list as a new target vehicle until a preset stop condition is reached;
generating an reachable distance two-dimensional graph according to the identifications of the vehicles in the reachable distance list and the reachable distances corresponding to the vehicles;
and determining candidate vehicles meeting the target clustering density condition in the reachable distance two-dimensional graph, and carrying out clustering processing according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph to obtain a vehicle clustering result corresponding to the target region.
2. The method according to claim 1, wherein the method further comprises:
and if the number of the vehicles is smaller than the preset number threshold, determining that the target vehicle is an uncore vehicle.
3. The method of claim 1, wherein vehicles in the target vehicle consist are ordered in order of small to large reach; the updating the target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle comprises the following steps:
acquiring a current target vehicle queue, a neighborhood vehicle set corresponding to the target vehicle and an reachable distance between the target vehicle and vehicles in the neighborhood vehicle set corresponding to the target vehicle;
determining other vehicles except for the vehicles in the neighborhood vehicle set in the current target vehicle queue;
and sorting the other vehicles and the vehicles in the neighborhood vehicle set according to the order of the reachable distances from small to large based on the reachable distances corresponding to the other vehicles and the reachable distances between the target vehicle and the vehicles in the neighborhood vehicle set, so as to obtain an updated target vehicle queue.
4. The method of claim 1, wherein the determining, in the updated target vehicle queue, the first vehicle whose reachable distance satisfies the preset distance condition as the new target vehicle comprises:
And determining the first vehicle with the smallest reachable distance in the updated target vehicle queue as a new target vehicle.
5. The method of claim 1, wherein the target cluster density condition comprises an reachable distance threshold, the determining a candidate vehicle in the reachable distance two-dimensional map that satisfies the target cluster density condition comprising:
and determining vehicles with the reachable distance smaller than or equal to the reachable distance threshold in the reachable distance two-dimensional graph as candidate vehicles meeting the target cluster density condition.
6. The method of claim 1, wherein the clustering according to the distribution of the candidate vehicles in the reachable distance two-dimensional map to obtain a vehicle clustering result corresponding to the target area comprises:
and dividing the candidate vehicles with continuous arrangement sequences into a cluster according to the arrangement sequence of the candidate vehicles in the reachable distance two-dimensional graph, and obtaining a vehicle cluster result corresponding to the target area.
7. The method according to claim 1, wherein the method further comprises:
responding to a selection instruction of a cluster density level, and generating a target cluster density condition corresponding to the target cluster density level according to the target cluster density level corresponding to the selection instruction.
8. A vehicle clustering apparatus, characterized in that the apparatus comprises:
the first determining module is used for determining a target vehicle in vehicles in a target area and adding the identification of the target vehicle and the reachable distance corresponding to the target vehicle to a reachable distance list;
a second determining module, configured to determine a distance between the target vehicle and other vehicles in the target area according to vehicle position information of the target vehicle and vehicle position information of the other vehicles in the target area;
a third determining module, configured to determine, according to a distance between the target vehicle and the other vehicle, a number of vehicles in a target neighborhood corresponding to the target vehicle;
a fourth determining module, configured to determine that the target vehicle is a core vehicle if the number of vehicles is greater than or equal to a preset number threshold;
the updating module is used for determining the reachable distance between each vehicle in the neighborhood vehicle set and the target vehicle according to the core distance of the target vehicle when the target vehicle is the core vehicle, updating a target vehicle queue according to the reachable distance between the target vehicle and the vehicles in the neighborhood vehicle set corresponding to the target vehicle, determining a first vehicle with the reachable distance meeting a preset distance condition in the updated target vehicle queue, and executing the steps of adding the identification of the target vehicle and the reachable distance corresponding to the target vehicle to a reachable distance list as a new target vehicle until a preset stopping condition is reached;
The first generation module is used for generating an reachable distance two-dimensional graph according to the identifications of the vehicles in the reachable distance list and the reachable distances corresponding to the vehicles;
and the clustering module is used for determining candidate vehicles meeting the target clustering density condition in the reachable distance two-dimensional graph, and carrying out clustering processing according to the distribution of the candidate vehicles in the reachable distance two-dimensional graph to obtain a vehicle clustering result corresponding to the target region.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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