CN116383689A - Parking area identification method and device, vehicle and storage medium - Google Patents
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Abstract
The application relates to the technical field of automatic driving, in particular to a parking area identification method, a device, a vehicle and a storage medium, wherein the method comprises the following steps: receiving current parking spot data of a vehicle, acquiring historical parking spot data of the vehicle, and generating a parking spot data set according to the current parking spot data and the historical parking spot data; and carrying out cluster analysis on the parking point data set based on a preset clustering algorithm to obtain a clustering result, and sending the clustering result to the vehicle so as to carry out parking personalized service based on personalized service content corresponding to the common parking area when the vehicle recognizes that the vehicle is in the common parking area based on the clustering result. Therefore, the problems that the conventional parking area of the user cannot be identified and personalized service can be provided in the related technology and the algorithm is complex are solved, the conventional parking area of the user can be identified, the parking intention of a vehicle owner is known, the related personalized service content of parking is customized, and the cabin intelligence level is improved.
Description
Technical Field
The present disclosure relates to the field of automatic driving technologies, and in particular, to a parking area identification method, device, vehicle, and storage medium.
Background
With the increasing emphasis on intellectualization of automobile products, intelligent cabins have become key nodes for automobile intelligent revolution, and the demands of users for cabin comfort and intelligence are continuously improved. Focusing on the intelligent cabin, the method and the device expect to improve the cabin driving experience of the user.
In the related art, a method for identifying the resident points of a vehicle is provided, and the actual resident points of the vehicle are identified in an artificial intelligent analysis mode through judging and identifying a series of non-specific-meaning resident points, so that a plurality of resident points with specific meanings of the vehicle in the running process can be obtained.
However, the related art cannot identify a user's general parking area and provide personalized services, and the algorithm is complicated.
Disclosure of Invention
The application provides a parking area identification method, a device, a vehicle and a storage medium, which are used for solving the problems that the related technology cannot identify the common parking area of a user and provide personalized service and the algorithm is complex, identifying the common parking area of the user, knowing the parking intention of a vehicle owner, customizing the related personalized service content of parking and improving the cabin intelligence level.
An embodiment of a first aspect of the present application provides a parking area identifying method, including the following steps: receiving current parking spot data of a vehicle; acquiring historical parking spot data of the vehicle, and generating a parking spot data set according to the current parking spot data and the historical parking spot data; and carrying out cluster analysis on the parking spot data set based on a preset cluster algorithm to obtain a cluster result, and sending the cluster result to the vehicle so as to carry out individual parking service based on individual service content corresponding to the common parking area when the vehicle recognizes that the vehicle is in the common parking area based on the cluster result.
According to the technical means, the method and the system can solve the problems that the related technology cannot identify the common parking area of the user and provide personalized service and the algorithm is complex, can identify the common parking area of the user, know the parking intention of a vehicle owner, customize the related personalized service content of parking and improve the cabin intelligence level.
Optionally, in some embodiments, the performing cluster analysis on the parking spot dataset based on a preset clustering algorithm to obtain a clustering result includes: selecting any non-access data object point from the parking point data set, and judging whether the non-access data object point meets the preset core point condition or not based on the preset neighborhood radius and the preset neighborhood data object number threshold; if the non-access data object point meets the preset core point condition, acquiring a target data object point meeting a preset density condition based on the non-access data object point, generating a first neighborhood set according to the non-access data object point and the target data object point, and forming a new cluster according to the first neighborhood set; judging whether the first neighborhood set meets the preset unclassified point condition, selecting any unclassified point from the first neighborhood set when the first neighborhood set meets the preset unclassified point condition, and judging whether any unclassified point meets the preset access condition; if any unclassified point meets the preset access condition, taking the any unclassified point as a boundary point, adding the any unclassified point into the new cluster, and re-judging whether the unclassified point exists in the first neighborhood set or not until the unclassified point does not exist in the first neighborhood set; if any unclassified point does not meet the preset access condition, a second neighborhood set is obtained according to calculation of the any unclassified point, whether the any unclassified point meets the preset core point condition is judged, when the any unclassified point meets the preset core point condition, the any unclassified point is added into the new cluster, the first neighborhood set and the second neighborhood set are used as new first neighborhood sets, and whether the unclassified point exists in the first neighborhood set is judged again.
According to the technical means, the parking spot data set can be subjected to cluster analysis, so that the common parking area of the user is identified.
Optionally, in some embodiments, after determining whether the first neighborhood set meets the preset unclassified point condition, the method further includes: if the first neighborhood set does not meet the preset unclassified point condition, judging whether the parking point data set meets the preset unclassified point condition, and when the parking point data set meets the preset unclassified point condition, selecting any unaccessed data object point from the parking point data set again; otherwise, outputting the clustering result.
According to the technical means, the method and the device can traverse the unclassified points and output more accurate clustering results.
Optionally, in some embodiments, after determining whether the any unclassified point meets the preset core point condition, the method further includes: if any unclassified point does not meet the preset core point condition, judging whether the any unclassified point meets the preset unclassified point condition; and if any unclassified point does not meet the preset unclassified point condition, taking the any unclassified point as the boundary point, adding the boundary point into the new cluster, and judging whether the unclassified point exists in the first neighborhood set again.
According to the technical means, the method and the device can solve the problems that the related technology cannot identify the common parking area of the user and provide personalized service and the algorithm is complex, and can identify the common parking area of the user.
Optionally, in some embodiments, after determining whether the data object point meets the preset core point condition, the method further includes: and if the data object point does not meet the preset core point condition, marking the data object point as a noise point.
According to the technical means, whether the current parking spot is a common parking area of the user can be judged, and when the preset core spot condition is not met, the current parking spot is marked as a noise spot, namely the current parking spot is not the common parking area of the user.
An embodiment of a second aspect of the present application provides a parking area identifying apparatus, including: the system comprises a receiving module, an acquisition module and a storage module, wherein the receiving module is used for receiving current parking spot data of a vehicle, the acquisition module is used for acquiring historical parking spot data of the vehicle, and generating a parking spot data set according to the current parking spot data and the historical parking spot data; the identification module is used for carrying out cluster analysis on the parking spot data set based on a preset cluster algorithm to obtain a cluster result, and sending the cluster result to the vehicle so as to carry out individual parking service based on individual service content corresponding to the common parking area when the vehicle identifies that the vehicle is in the common parking area based on the cluster result.
Optionally, in some embodiments, the identification module further includes: the selecting unit is used for selecting any non-accessed data object point from the parking point data set, and judging whether the non-accessed data object point meets the preset core point condition or not based on the preset neighborhood radius and the preset neighborhood data object number threshold; the acquisition unit is used for acquiring target data object points meeting preset density conditions based on the non-access data object points when the non-access data object points meet the preset core point conditions, generating a first neighborhood set according to the non-access data object points and the target data object points, and forming a new cluster according to the first neighborhood set; the judging unit is used for judging whether the first neighborhood set meets the preset unclassified point condition, selecting any unclassified point from the first neighborhood set when the first neighborhood set meets the preset unclassified point condition, and judging whether any unclassified point meets the preset access condition; the first classification unit is used for taking any unclassified point as a boundary point when the any unclassified point meets the preset access condition, adding the any unclassified point into the new cluster, and re-judging whether the unclassified point exists in the first neighborhood set or not until the unclassified point does not exist in the first neighborhood set; the second classification unit is configured to calculate a second neighborhood set according to any unclassified point when the any unclassified point does not meet the preset access condition, determine whether the any unclassified point meets the preset core point condition, add the any unclassified point into the new cluster when the any unclassified point meets the preset core point condition, and take the first neighborhood set and the second neighborhood set as new first neighborhood sets, and re-determine whether the unclassified point exists in the first neighborhood set.
Optionally, in some embodiments, after determining whether the first neighborhood set meets the preset unclassified point condition, the determining unit is further configured to: if the first neighborhood set does not meet the preset unclassified point condition, judging whether the parking point data set meets the preset unclassified point condition, and when the parking point data set meets the preset unclassified point condition, selecting any unaccessed data object point from the parking point data set again; otherwise, outputting the clustering result.
Optionally, in some embodiments, after determining whether the any unclassified point meets the preset core point condition, the second classification unit is further configured to: when any unclassified point does not meet the preset core point condition, judging whether the any unclassified point meets the preset unclassified point condition; and when any unclassified point does not meet the preset unclassified point condition, taking the any unclassified point as the boundary point, adding the boundary point into the new cluster, and judging whether the unclassified point exists in the first neighborhood set again.
Optionally, in some embodiments, after determining whether the data object point meets the preset core point condition, the second classification unit is further configured to: and marking the data object point as a noise point when the data object point does not meet the preset core point condition.
An embodiment of a third aspect of the present application provides a vehicle, including: the parking area identification device comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the parking area identification method according to the embodiment.
An embodiment of the fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program that is executed by a processor for implementing the parking area identifying method as described in the above embodiment.
The method comprises the steps of receiving current parking spot data of a vehicle, obtaining historical parking spot data of the vehicle, generating a parking spot data set according to the current parking spot data and the historical parking spot data, carrying out cluster analysis on the parking spot data set based on a preset clustering algorithm to obtain a clustering result, and sending the clustering result to the vehicle, so that when the vehicle is identified to be in a common parking area based on the clustering result, parking personalized service is carried out based on personalized service content corresponding to the common parking area. Therefore, the problems that the conventional parking area of the user cannot be identified and personalized service can be provided in the related technology and the algorithm is complex are solved, the conventional parking area of the user can be identified, the parking intention of a vehicle owner is known, the related personalized service content of parking is customized, and the cabin intelligence level is improved.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a parking area identification method according to an embodiment of the present application;
FIG. 2 is a flowchart of a clustering algorithm for DBSCAN (Density-Based Spatial Clustering of Applications with Noise, density space based clustering algorithm) to perform cluster analysis on a parking spot dataset according to one embodiment of the present application;
FIG. 3 is a schematic diagram of a parking area identification method according to one embodiment of the present application;
FIG. 4 is a flow chart of a parking area identification method provided in accordance with one embodiment of the present application;
fig. 5 is a schematic block diagram of a parking area identifying apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Reference numerals illustrate: 10-parking area recognition device, 100-receiving module, 200-acquisition module, 300-recognition module.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes a parking area recognition method, device, vehicle, and storage medium of the embodiments of the present application with reference to the accompanying drawings. Aiming at the problems that the related technology mentioned in the background art can not identify the common parking area of a user and provide personalized service and has complex algorithm, the application provides a parking area identification method. Therefore, the problems that the conventional parking area of the user cannot be identified and personalized service can be provided in the related technology and the algorithm is complex are solved, the conventional parking area of the user can be identified, the parking intention of a vehicle owner is known, the related personalized service content of parking is customized, and the cabin intelligence level is improved.
Specifically, fig. 1 is a schematic flow chart of a parking area identifying method according to an embodiment of the present application.
As shown in fig. 1, the parking area recognition method includes the steps of:
in step S101, current parking spot data of a vehicle is received.
Specifically, when the vehicle shift position is changed from other shift positions to the P range, and the vehicle is turned off, the current position at this time may be determined as a parking spot. In general, the vehicle machine is not turned off immediately after the vehicle is flameout, and at the moment, the vehicle machine can upload current vehicle position data to the cloud end through the buried point, and the cloud end receives current parking point data of the vehicle.
In step S102, historical parking spot data of the vehicle is acquired, and a parking spot data set is generated from the current parking spot data and the historical parking spot data.
Specifically, the cloud operation database adds the current parking spot data to the database, reads all historical parking spot data (including the current parking spot) of the vehicle from the database, and finally forms a user parking spot data set.
In step S103, a cluster analysis is performed on the parking spot dataset based on a preset cluster algorithm, a cluster result is obtained, and the cluster result is sent to the vehicle, so that when the vehicle recognizes that the vehicle is in a common parking area based on the cluster result, a parking personalized service is performed based on personalized service content corresponding to the common parking area.
The preset clustering algorithm may be a DBSCAN algorithm.
Those skilled in the art will appreciate that the clustering algorithm is of a wide variety, and by comparing several clustering algorithms, the application of DBSCAN algorithm to the embodiments of the present application is most suitable. For example, when the conventional K-Means clustering algorithm is used for clustering, the required number K of clusters needs to be determined in advance, noise points cannot be identified, and the clustering requirement of the scheme is difficult to realize due to the several defects of the K-Means algorithm. While the DBSCAN algorithm just avoids these several drawbacks, it matches the requirements of the embodiments of the present application.
It should be noted that DBSCAN is a density-based clustering algorithm that defines clusters as the largest set of points that are densely connected, can divide areas with a sufficiently high density into clusters, and can find clusters of arbitrary shape in a noisy spatial database.
DBSCAN describes the compactness of a sample set based on a set of neighborhoods, and parameters (e, minPts) are used to describe the sample distribution compactness of the neighborhoods, wherein e describes a neighborhood distance threshold for a certain sample, and MinPts describes a threshold for the number of samples in the neighborhood with a distance e for a certain sample.
When the DBSCAN clustering algorithm is used for clustering the parking point data set, the parameter e describes a neighborhood distance threshold, the neighborhood distance threshold is similar to the radius of a circle, the value of the neighborhood distance threshold influences the size of an identified parking area, for example, the product defines that the parking area is a 100 m-100 m area, e can be adjusted according to an actual clustering result, and finally the optimal parameter e value is obtained through parameter e adjustment, so that the area size of each cluster (the parking area commonly used by a user) of the cluster is suitable, and the actual logic and product requirements are met.
The parameter MinPts describes a threshold value for the number of samples in a neighborhood of distance e, which corresponds to defining a number of density reachable points in a region of radius e centered on the core object. In the embodiment of the application, the parameter MinPts may be used to limit the minimum number of parks near a certain parking spot when forming a cluster. If it is considered that parking is performed 10 times or more around a certain parking point, it is considered that a common parking area is calculated, the MinPts parameter may be set to 10, and at this time, only 10 or more density reachable points having a certain core point may form a cluster, and less than 10 clusters may not form a cluster.
In practical situations, i.e. in a distance e around the parking spot, the number of other parking spots is 10 or more, so that the parking spot can be identified as a common parking area. Finally, regarding a certain cluster generated by clustering, the area surrounded by all edge points of the cluster can be considered as a parking area commonly used by a user; each cluster corresponds to an identified parking area commonly used by the user. The noise point is considered as a temporary parking point and is not in the parking area commonly used by users.
Optionally, in some embodiments, performing cluster analysis on the parking spot dataset based on a preset clustering algorithm to obtain a clustering result includes: selecting any non-access data object point from the parking point data set, and judging whether the non-access data object point meets the preset core point condition or not based on the preset neighborhood radius and the preset neighborhood data object number threshold; if the non-accessed data object point meets the preset core point condition, acquiring a target data object point meeting the preset density condition based on the non-accessed data object point, generating a first neighborhood set according to the non-accessed data object point and the target data object point, and forming a new cluster according to the first neighborhood set; judging whether the first neighborhood set meets the preset unclassified point condition, selecting any unclassified point from the first neighborhood set when the first neighborhood set meets the preset unclassified point condition, and judging whether any unclassified point meets the preset access condition; if any unclassified point meets the preset access condition, taking any unclassified point as a boundary point, adding any unclassified point into a new cluster, and re-judging whether unclassified points exist in the first neighborhood set or not until the unclassified points do not exist in the first neighborhood set; if any unclassified point does not meet the preset access condition, a second neighborhood set is obtained according to calculation of any unclassified point, whether any unclassified point meets the preset core point condition is judged, when any unclassified point meets the preset core point condition, any unclassified point is added into a new cluster, the first neighborhood set and the second neighborhood set are used as new first neighborhood sets, and whether unclassified points exist in the first neighborhood set is judged again.
The preset domain radius and the preset threshold value of the number of the data objects in the neighborhood may be preset by a user, may be obtained through limited experiments, may be obtained through limited computer simulation, and are not particularly limited herein, for example, the preset domain radius may be 10m, and the preset threshold value of the number of the data objects in the neighborhood may be 10. The preset core point condition may be at a core position of the parking point data set, the preset density condition may be greater than a certain set value, the set value is not specifically limited herein, the preset unclassified point condition may be that an unclassified point exists, and the preset access condition may be that the access has been performed.
It should be noted that, when the historical data samples of all parking positions are large enough, the cloud uses a clustering algorithm to perform data processing on the parking space position data set, and finally if the parking space position data set is clustered into one or more clusters, the area formed by the clusters can be considered as a common parking area of the user.
Specifically, as shown in fig. 2, the embodiment of the present application is based on a preset clustering algorithm, namely, a parking spot data set D, a neighborhood radius e, and a neighborhood data object number threshold MinPts.
And randomly selecting an unaccessed data object point q from the data set D, if the selected data object point q is a core point, finding out all data object points which can be reached from q density, generating a neighborhood set N, forming a new cluster C, judging whether an unclassified point exists in the N, selecting an unclassified point q ' in the N, judging whether the q ' is accessed, if the q ' is accessed and is not classified, taking the point q ' as a boundary point, adding the point q ' as the boundary point into the cluster C, and judging whether the unclassified point exists in the D again until the unclassified point does not exist in the first neighborhood set.
If q ' is not accessed, calculating a q ' neighborhood set N ', judging whether q ' is a core point, if yes, adding q ' into a cluster C, and judging whether unclassified points exist in the first neighborhood set again.
Optionally, in some embodiments, after determining whether the first neighborhood set meets the preset unclassified point condition, the method further includes: if the first neighborhood set does not meet the preset unclassified point condition, judging whether the parking point data set meets the preset unclassified point condition, and when the parking point data set meets the preset unclassified point condition, selecting any unaccessed data object point from the parking point data set again; otherwise, outputting the clustering result.
Specifically, as shown in fig. 2, if there are unclassified points in the first neighborhood set N, determining whether there are unaccessed points in the parking point data set D, and when there are unaccessed points in the parking point data set D, selecting any unaccessed data object point q from the parking point data set again, otherwise, indicating that all points are processed, outputting a clustering result, and performing density communication on clusters C1, C2 and C3 … Cn, wherein the algorithm is finished, i.e., the parking area commonly used by the user.
Optionally, in some embodiments, after determining whether any unclassified point satisfies the preset core point condition, the method further includes: if any unclassified point does not meet the preset core point condition, judging whether any unclassified point meets the preset unclassified point condition; if any unclassified point does not meet the preset unclassified point condition, taking any unclassified point as a boundary point, adding the boundary point into a new cluster, and judging whether unclassified points exist in the first neighborhood set again.
Specifically, as shown in fig. 2, if any unclassified point q 'is not a core point, determining whether any unclassified point q' is classified; if any unclassified point q' is not classified, taking any unclassified point as a boundary point, adding the boundary point into a new cluster C, and judging whether unclassified points exist in the first neighborhood set again.
Optionally, in some embodiments, after determining whether the data object point meets the preset core point condition, further includes: and if the data object point does not meet the preset core point condition, marking the data object point as a noise point.
Specifically, as shown in fig. 2, if the selected data object point q is not a core point, it is marked as a noise point.
In the actual execution process, carrying out cluster analysis on the parking point data set based on a preset cluster algorithm to obtain a cluster result, sending the cluster result to the vehicle, and customizing the relevant personalized service content of parking when the vehicle is identified to be in a common parking area based on the cluster result, for example, playing light music to relieve driving fatigue after the vehicle reaches the vicinity of the common parking area; reminding the car owner of arriving at the common parking area, paying attention to observing the parking environment and the like.
For further understanding of the parking area recognition method according to the embodiments of the present application, a detailed description will be given below with reference to specific embodiments.
As shown in fig. 3, the embodiment of the present application may implement a parking area identifying method by using a system module as shown in fig. 3, where the system module is mainly divided into: the system comprises a personal service module and a database for user vehicle terminals, cloud terminals and user parking. The user car machine end includes: the system comprises a parking judging module, a buried point uploading module and a common parking area data processing module; the cloud includes: the system comprises a buried point center, a DBSCAN cluster analysis module and a data processing module.
As shown in fig. 4, fig. 4 is a flowchart illustrating a parking area recognition method according to an embodiment of the present application, which may be performed using the system module shown in fig. 3.
S11: and when the user parks and the automobile gear is changed from other gears to the P gear and the automobile is flameout, the current position of the automobile is considered to be a parking spot.
S12: in general, the vehicle machine is not turned off immediately after the vehicle is flameout, and the vehicle machine can upload the current parking spot (vehicle position data) to the cloud end at the time of embedding the spot. Because DBSCAN measures distance between data according to density clustering, euclidean distance is commonly used, the vehicle position data can be two-dimensional point data with longitude and latitude information or three-dimensional data with altitude information,
S13: and the embedded point center of the cloud receives the current parking point data uploaded by the vehicle machine side.
S14: and (3) cloud operation of the database, adding the current parking point data into the database, and reading all historical parking point data (including the current parking point) of the vehicle from the database to finally form a user parking point data set.
S15: and the cloud performs cluster analysis on the user parking spot data set by using a DBSCAN clustering algorithm to generate a clustering result.
S16: and the cloud returns a clustering result to the vehicle terminal. For the clustering result, a certain cluster generated by clustering can be considered that the area surrounded by all edge points of the cluster is a parking area commonly used by a user; each cluster corresponds to an identified parking area commonly used by the user. The noise point is considered as a temporary parking point and is not in the parking area commonly used by users.
S17: the vehicle machine end processes the clustering result, such as visualization to a common parking area for display; and outputting a common parking area of the user to the user parking individuation module, and calling the service of the common parking area.
S18: the user parking individuation module better knows the parking intention of the vehicle owner according to the data of the common parking area of the user, so as to customize the relevant individuation service content of parking, for example, after the user arrives near the common parking area, playing light music to relieve driving fatigue; reminding the car owner of arriving at the common parking area, paying attention to observing the parking environment and the like.
According to the parking area identification method provided by the embodiment of the application, the current parking point data of the vehicle is received, the historical parking point data of the vehicle is obtained, the parking point data set is generated according to the current parking point data and the historical parking point data, the clustering analysis is carried out on the parking point data set based on the preset clustering algorithm, the clustering result is obtained, and the clustering result is sent to the vehicle, so that when the vehicle is identified to be in the common parking area based on the clustering result, the parking personalized service is carried out based on personalized service content corresponding to the common parking area. Therefore, the problems that the conventional parking area of the user cannot be identified and personalized service can be provided in the related technology and the algorithm is complex are solved, the conventional parking area of the user can be identified, the parking intention of a vehicle owner is known, the related personalized service content of parking is customized, and the cabin intelligence level is improved.
Next, a parking area recognition apparatus according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 5 is a block schematic diagram of a parking area recognition apparatus according to an embodiment of the present application.
As shown in fig. 5, the parking area identifying apparatus 10 includes: a receiving module 100, an acquiring module 200 and an identifying module 300.
The receiving module 100 is configured to receive current parking spot data of a vehicle, and the obtaining module 200 is configured to obtain historical parking spot data of the vehicle, and generate a parking spot data set according to the current parking spot data and the historical parking spot data; the identifying module 300 is configured to perform cluster analysis on the parking spot dataset based on a preset clustering algorithm, obtain a clustering result, and send the clustering result to the vehicle, so as to perform parking personalized service based on personalized service content corresponding to the common parking area when the vehicle identifies that the vehicle is in the common parking area based on the clustering result.
Optionally, in some embodiments, the identification module 300 further comprises: the selecting unit is used for selecting any non-accessed data object point from the parking point data set, and judging whether the non-accessed data object point meets the preset core point condition or not based on the preset neighborhood radius and the preset neighborhood data object number threshold; the acquisition unit is used for acquiring target data object points meeting preset density conditions based on the non-access data object points when the non-access data object points meet preset core point conditions, generating a first neighborhood set according to the non-access data object points and the target data object points, and forming a new cluster according to the first neighborhood set; the judging unit is used for judging whether the first neighborhood set meets the preset unclassified point condition, selecting any unclassified point from the first neighborhood set when the first neighborhood set meets the preset unclassified point condition, and judging whether any unclassified point meets the preset access condition; the first classification unit is used for taking any unclassified point as a boundary point when any unclassified point meets a preset access condition, adding any unclassified point into a new cluster, and re-judging whether the unclassified point exists in the first neighborhood set or not until the unclassified point does not exist in the first neighborhood set; the second classification unit is used for calculating a second neighborhood set according to any unclassified point when any unclassified point does not meet a preset access condition, judging whether any unclassified point meets a preset core point condition, adding any unclassified point into a new cluster when any unclassified point meets the preset core point condition, taking the first neighborhood set and the second neighborhood set as new first neighborhood sets, and re-judging whether the unclassified point exists in the first neighborhood set.
Optionally, in some embodiments, after determining whether the first neighborhood set meets the preset unclassified point condition, the determining unit is further configured to: if the first neighborhood set does not meet the preset unclassified point condition, judging whether the parking point data set meets the preset unclassified point condition, and when the parking point data set meets the preset unclassified point condition, selecting any unaccessed data object point from the parking point data set again; otherwise, outputting the clustering result.
Optionally, in some embodiments, after determining whether any unclassified point satisfies the preset core point condition, the second classification unit is further configured to: when any unclassified point does not meet the preset core point condition, judging whether any unclassified point meets the preset unclassified point condition; when any unclassified point does not meet the preset unclassified point condition, taking any unclassified point as a boundary point, adding the boundary point into a new cluster, and judging whether unclassified points exist in the first neighborhood set again.
Optionally, in some embodiments, after determining whether the data object point meets the preset core point condition, the second classification unit is further configured to: and marking the data object point as a noise point when the data object point does not meet the preset core point condition.
It should be noted that the foregoing explanation of the embodiment of the parking area identifying method is also applicable to the parking area identifying apparatus of this embodiment, and will not be repeated here.
According to the parking area identification device provided by the embodiment of the application, the current parking point data of the vehicle is received, the historical parking point data of the vehicle is obtained, the parking point data set is generated according to the current parking point data and the historical parking point data, the clustering analysis is carried out on the parking point data set based on the preset clustering algorithm, the clustering result is obtained, and the clustering result is sent to the vehicle, so that when the vehicle is identified to be in the common parking area based on the clustering result, the parking personalized service is carried out based on the personalized service content corresponding to the common parking area. Therefore, the problems that the conventional parking area of the user cannot be identified and personalized service can be provided in the related technology and the algorithm is complex are solved, the conventional parking area of the user can be identified, the parking intention of a vehicle owner is known, the related personalized service content of parking is customized, and the cabin intelligence level is improved.
Fig. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
A memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.
The processor 602 implements the parking area identifying method provided in the above-described embodiment when executing a program.
Further, the vehicle further includes:
a communication interface 603 for communication between the memory 601 and the processor 602.
A memory 601 for storing a computer program executable on the processor 602.
The memory 601 may include a high-speed RAM (Random Access Memory ) memory, and may also include a nonvolatile memory, such as at least one disk memory.
If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component, external device interconnect) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may perform communication with each other through internal interfaces.
The processor 602 may be a CPU (Central Processing Unit ) or ASIC (Application Specific Integrated Circuit, application specific integrated circuit) or one or more integrated circuits configured to implement embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the parking area identifying method as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable gate arrays, field programmable gate arrays, and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A parking area identification method, characterized by comprising the steps of:
current parking spot data of the vehicle is received,
acquiring historical parking spot data of the vehicle, and generating a parking spot data set according to the current parking spot data and the historical parking spot data; and
and carrying out cluster analysis on the parking spot data set based on a preset cluster algorithm to obtain a cluster result, and sending the cluster result to the vehicle so as to carry out individual parking service based on individual service content corresponding to the common parking area when the vehicle recognizes that the vehicle is in the common parking area based on the cluster result.
2. The method according to claim 1, wherein the performing cluster analysis on the parking spot dataset based on a preset clustering algorithm to obtain a cluster result includes:
selecting any non-access data object point from the parking point data set, and judging whether the non-access data object point meets the preset core point condition or not based on the preset neighborhood radius and the preset neighborhood data object number threshold;
if the non-access data object point meets the preset core point condition, acquiring a target data object point meeting a preset density condition based on the non-access data object point, generating a first neighborhood set according to the non-access data object point and the target data object point, and forming a new cluster according to the first neighborhood set;
judging whether the first neighborhood set meets the preset unclassified point condition, selecting any unclassified point from the first neighborhood set when the first neighborhood set meets the preset unclassified point condition, and judging whether any unclassified point meets the preset access condition;
if any unclassified point meets the preset access condition, taking the any unclassified point as a boundary point, adding the any unclassified point into the new cluster, and re-judging whether the unclassified point exists in the first neighborhood set or not until the unclassified point does not exist in the first neighborhood set;
If any unclassified point does not meet the preset access condition, a second neighborhood set is obtained according to calculation of the any unclassified point, whether the any unclassified point meets the preset core point condition is judged, when the any unclassified point meets the preset core point condition, the any unclassified point is added into the new cluster, the first neighborhood set and the second neighborhood set are used as new first neighborhood sets, and whether the unclassified point exists in the first neighborhood set is judged again.
3. The method of claim 2, further comprising, after determining whether the first neighborhood set satisfies the preset unclassified point condition:
if the first neighborhood set does not meet the preset unclassified point condition, judging whether the parking point data set meets the preset unclassified point condition, and when the parking point data set meets the preset unclassified point condition, selecting any unaccessed data object point from the parking point data set again; otherwise, outputting the clustering result.
4. The method according to claim 2, further comprising, after determining whether the any unclassified point satisfies the preset core point condition:
If any unclassified point does not meet the preset core point condition, judging whether the any unclassified point meets the preset unclassified point condition;
and if any unclassified point does not meet the preset unclassified point condition, taking the any unclassified point as the boundary point, adding the boundary point into the new cluster, and judging whether the unclassified point exists in the first neighborhood set again.
5. The method according to claim 2, further comprising, after determining whether the data object point satisfies the preset core point condition:
and if the data object point does not meet the preset core point condition, marking the data object point as a noise point.
6. A parking area identifying apparatus, comprising:
a receiving module for receiving current parking spot data of the vehicle,
the receiving module is used for acquiring historical parking spot data of the vehicle and generating a parking spot data set according to the current parking spot data and the historical parking spot data; and
the identification module is used for carrying out cluster analysis on the parking spot data set based on a preset cluster algorithm to obtain a cluster result, and sending the cluster result to the vehicle so as to carry out individual parking service based on individual service content corresponding to the common parking area when the vehicle identifies that the vehicle is in the common parking area based on the cluster result.
7. The apparatus of claim 6, wherein the identification module further comprises:
the selecting unit is used for selecting any non-accessed data object point from the parking point data set, and judging whether the non-accessed data object point meets the preset core point condition or not based on the preset neighborhood radius and the preset neighborhood data object number threshold;
the acquisition unit is used for acquiring target data object points meeting preset density conditions based on the non-access data object points when the non-access data object points meet the preset core point conditions, generating a first neighborhood set according to the non-access data object points and the target data object points, and forming a new cluster according to the first neighborhood set;
the judging unit is used for judging whether the first neighborhood set meets the preset unclassified point condition, selecting any unclassified point from the first neighborhood set when the first neighborhood set meets the preset unclassified point condition, and judging whether any unclassified point meets the preset access condition;
the first classification unit is used for taking any unclassified point as a boundary point when the any unclassified point meets the preset access condition, adding the any unclassified point into the new cluster, and re-judging whether the unclassified point exists in the first neighborhood set or not until the unclassified point does not exist in the first neighborhood set;
The second classification unit is configured to calculate a second neighborhood set according to any unclassified point when the any unclassified point does not meet the preset access condition, determine whether the any unclassified point meets the preset core point condition, add the any unclassified point into the new cluster when the any unclassified point meets the preset core point condition, and take the first neighborhood set and the second neighborhood set as new first neighborhood sets, and re-determine whether the unclassified point exists in the first neighborhood set.
8. The apparatus according to claim 7, wherein after determining whether the first neighborhood set satisfies the preset unclassified point condition, the determining unit is further configured to:
if the first neighborhood set does not meet the preset unclassified point condition, judging whether the parking point data set meets the preset unclassified point condition, and when the parking point data set meets the preset unclassified point condition, selecting any unaccessed data object point from the parking point data set again; otherwise, outputting the clustering result.
9. A vehicle, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the parking area identification method as claimed in any one of claims 1 to 5.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for realizing the parking area identification method as claimed in any one of claims 1 to 5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116798234A (en) * | 2023-08-28 | 2023-09-22 | 北京阿帕科蓝科技有限公司 | Method, device, computer equipment and storage medium for determining station parameter information |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116798234A (en) * | 2023-08-28 | 2023-09-22 | 北京阿帕科蓝科技有限公司 | Method, device, computer equipment and storage medium for determining station parameter information |
| CN116798234B (en) * | 2023-08-28 | 2024-01-26 | 北京阿帕科蓝科技有限公司 | Method, device, computer equipment and storage medium for determining station parameter information |
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