CN114842666B - Parking data processing method and device, electronic equipment and storage medium - Google Patents
Parking data processing method and device, electronic equipment and storage medium Download PDFInfo
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
Abstract
The application provides a method and a device for processing parking data, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining parking data of a target user in a set time period, wherein the parking data comprises position information of a passing port of a parking lot in each time in the set time period; counting according to the position information of the passing ports of the parking lots in each time, and determining the passing times of each parking lot passing in the set time length; according to the passing times of each parking lot in the set time length, determining a target parking lot with the passing times exceeding a first time number threshold; and determining the target parking lot as a fixed parking lot of the target user. The application can solve the problem that the data of the user determining the owned fixed parking lot is inaccurate due to incomplete data at present.
Description
Technical Field
The present application relates to the field of intelligent driving, and more particularly, to a method and apparatus for processing parking data, an electronic device, and a storage medium.
Background
An operator holder of a mass production vehicle (e.g., a vehicle enterprise) needs to know which users have fixed parking lots and/or owned parking spaces in order to push related messages for users who have fixed parking lots and/or owned parking spaces when designing related products or related activities for the parking lots of the users.
The statistics of which users have fixed parking lots and/or have parking spaces can be performed through questionnaires, but the method is low in efficiency, and the statistics result can be large in error due to insufficient data.
Disclosure of Invention
In view of the above, the embodiments of the present application provide a method, an apparatus, an electronic device and a storage medium for processing parking data, so as to improve the above-mentioned problems.
According to an aspect of the embodiment of the present application, there is provided a method for processing parking data, including: obtaining parking data of a target user in a set time period, wherein the parking data comprises position information of a passing port of a parking lot in each time in the set time period; counting according to the position information of the passing ports of the parking lots in each time, and determining the passing times of each parking lot passing in the set time length; according to the passing times of each parking lot in the set time length, determining a target parking lot with the passing times exceeding a first time number threshold; and determining the target parking lot as a fixed parking lot of the target user.
According to an aspect of an embodiment of the present application, there is provided a processing apparatus for parking data, including: the first acquisition module is used for acquiring parking data of a target user in a set time length, wherein the parking data comprises position information of a passing port which enters and exits the parking lot for each time in the set time length. The passing number determining module is used for counting according to the position information of the passing openings of the parking lots in each time and determining the passing number of the parking lots passing in the set time length. And the target parking lot determining module is used for determining the target parking lot with the passing times exceeding the first time threshold according to the passing times of each parking lot passing in the set time length. And the fixed parking lot determining module is used for determining the target parking lot as the fixed parking lot of the target user.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: a processor; and a memory having stored thereon computer readable instructions which, when executed by the processor, implement a method of processing parking data as described above.
According to an aspect of an embodiment of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor, implement a method of processing parking data as described above.
According to an aspect of an embodiment of the present application, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement a method of processing parking data as described above.
According to the scheme, the number of times that the target user passes through each parking lot in the set time is determined by acquiring the position information of the passing port of the target user entering the parking lot in the set time and then counting according to the position information of the passing port of the target user entering the parking lot in each time; and then determining a target parking lot with the passing times exceeding a first time threshold according to the passing times of each parking lot in the set time length, wherein the target parking lot is a fixed parking lot of a target user. According to the scheme provided by the application, whether the target user has a fixed parking lot or not can be determined based on a large amount of parking data of the target user, so that the accuracy of a statistical result is ensured, and meanwhile, the statistical efficiency is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a schematic view of a hardware environment of a method for processing parking data according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating a method of processing parking data according to an embodiment of the present application.
FIG. 3 is a flowchart illustrating the specific steps of step 220 according to one embodiment of the present application.
Fig. 4 is a flowchart illustrating steps following step 230 according to an embodiment of the present application.
FIG. 5 is a flowchart illustrating steps following step 240, according to one embodiment of the present application.
FIG. 6 is a flowchart illustrating the specific steps of step 510, according to one embodiment of the present application.
FIG. 7 is a flowchart illustrating steps of step 520 according to one embodiment of the present application.
FIG. 8 is a flowchart illustrating steps of step 710 according to an embodiment of the present application
Fig. 9A-9B are schematic diagrams illustrating semantic alignment analysis according to an embodiment of the present application.
Fig. 10 is a block diagram of a processing device for parking data according to an embodiment of the present application.
Fig. 11 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application may be practiced without one or more of the specific details, or with other methods, apparatus, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 is a schematic view of a hardware environment including a vehicle 110, a terminal device 130, and a server 140, which is suitable for a method of processing parking data according to an embodiment of the present invention. A communication connection between the vehicle 110, the terminal device 130, and the server 140.
The parking data collected by the vehicle 110 is uploaded to the terminal 130 and the server 140, and can be stored directly in the vehicle-mounted system of the vehicle 110. When parking data of the target user 120 is acquired for the first time, the server 140 transmits a request for acquiring the parking data to the terminal device 130 or the in-vehicle system of the vehicle 110. Upon receiving the request, the terminal device 130 or the in-vehicle system of the vehicle 110 may pop-up the window to prompt the target user 120 whether to approve the request, and prompt the target user 120 to perform the operation by default when parking data is acquired next time. When the target user 120 agrees to acquire the parking data, the server 140 acquires the historical parking data from the vehicle 110, where the historical parking data may be the parking data stored in the on-board system of the vehicle 110, may be the parking data stored in the terminal device 130, or may be the parking data stored in the server 140. The terminal device 130 may be a smart phone, a tablet computer, a smart watch, etc., which is not specifically limited herein; the communication connection may be a communication connection of the same local area network or a bluetooth connection.
In the method for processing the parking data provided by the embodiment of the application, the electronic equipment (for example, the server 400) with processing capability can determine the number of passes of each parking lot in a set duration according to the parking data of the target user 120, and then determine the fixed parking lot of the user, so as to solve the problem that the data of the user for determining the fixed parking in possession is not accurate enough due to incomplete data at present. The following describes a method, an apparatus, an electronic device, and a storage medium for processing parking data according to specific embodiments of the present application.
Fig. 2 is a flow chart illustrating a method for processing parking data according to an embodiment of the present application, which may be performed by an electronic device with processing capability, for example, a server, a cloud server, etc., which is not particularly limited herein; but may also be performed by an onboard system of the vehicle. As shown in fig. 2, the method includes:
step 210, obtaining parking data of a target user in a set time period, wherein the parking data comprises position information of a passing port of a parking lot in each time in the set time period.
In some embodiments, the parking data of the target user within the set time period may be the parking data of the target user within the set time period under the same account.
In some embodiments, the same account may be logged in the on-board systems of different vehicles or clients of different terminal devices, and the parking data for the same account may be uploaded to a server for aggregation.
The entrance of the parking lot may be the entrance of the parking lot or the exit of the parking lot in some embodiments. The location information of the entrance of the parking lot may include longitude and latitude information of the entrance, GNSS (Global Navigation Satellite System, global satellite navigation system) signals of the entrance, path information of the odometer, and the like.
The parking data in the set time length can be parking data in 4 weeks or parking data in 4 months, and when the time length is set, the set time length with large range can be set so as to ensure that the acquired parking data is rich enough. The time length may be set according to actual needs, and is not particularly limited herein.
The method for obtaining the parking data of the target user within the set time period can be selected by a person skilled in the art according to the need, and is not limited and described in detail herein.
Step 220, counting according to the position information of the passing ports of the parking lots, and determining the passing times of the parking lots in a set duration.
In some embodiments, the number of times of entering and exiting the entrance of the parking lot with the same location information may be counted according to the location information of the entrance entering and exiting the parking lot within a set period of time, for example, there is an entrance a and an entrance B, and if the location information of the entrance a is the same as the location information of the entrance B, the number of times of entering and exiting the entrance a and the entrance B by the target user is counted.
In some embodiments, a plurality of different sets of parking lots may be preset, and the counted times of the target user entering and exiting the communication ports of each parking lot within the set duration are put into the corresponding sets, so as to facilitate the subsequent determination of the target parking lot.
In some embodiments, referring to fig. 3, step 220 may include:
step 310, determining a cluster category to which each entrance belongs according to the coordinate information of the entrance entering and exiting the parking lot each time, wherein one cluster category corresponds to one parking lot, and the distance between the entrance belonging to the same cluster category is smaller than a distance threshold.
The location information may include coordinate information of the corresponding gate. In some embodiments, since the GNSS signal will disappear after the vehicle enters the parking lot, the coordinate information of the entrance may be calculated by the coordinates of the point where the GNSS signal disappears (the entrance point of the underground parking lot), or the coordinates of the point where the GNSS signal reappears (the exit point of the underground parking lot).
In other embodiments, since the GNSS signals of the vehicle outside the door drift, the error of the coordinates of the entrance is relatively large by using the GNSS signals alone as the coordinates of the entrance, and the coordinates of the entrance are determined by combining the GNSS signals of the entrance and the path information of the vehicle, so as to reduce the error.
In some embodiments, determining the clustering category to which each traffic port belongs may be performed according to the distance between the traffic ports, and the distance between the two traffic ports may be calculated according to the longitude and latitude information of the traffic ports of any two parking lots in the parking data of the target user, and then the traffic ports with the distance between any two traffic ports smaller than the distance threshold are collected in the same collection, where one collection corresponds to one clustering category, that is, corresponds to one parking lot. If the parking lot is an above-ground parking lot, the distance threshold may be 100 meters; if the parking lot is an underground parking lot, the distance threshold may be 50 meters, and the distance threshold may be set according to actual needs, which is not limited herein.
In other embodiments, the coordinate information of the traffic ports entering and exiting the parking lot each time may be subjected to cluster analysis, so as to obtain the cluster category to which each traffic port belongs. Cluster analysis refers to grouping a collection of data objects into multiple classes that are made up of similar objects. Each cluster category may correspond to a parking lot, and communication ports with a distance less than a distance threshold may be assigned to the same cluster category. The method of cluster analysis is not limited in this regard.
Step 320, counting the number of the traffic openings in each cluster type to obtain the number of times of traffic in each parking lot within a set duration.
In some embodiments, since the traffic ports of the target user having the same position information are collected in the same category, when the number of traffic ports in each cluster category in the set duration is counted, the number of times of the target user passing through each parking lot in the set duration can be obtained. For example, the traffic ports a, B, C and D have the same position information and are collected in the same cluster category, and when the number of traffic ports in the cluster category is counted, the number of passes of the target user through the parking lot within the set duration can be obtained to be 4.
And 230, determining a target parking lot with the passing times exceeding the first time threshold according to the passing times of each parking lot in the set time length.
In some embodiments, a parking lot that is accessed more than a first number threshold number of times in a set period of time is referred to as a target parking lot. The first time threshold may be stored in the server in advance, set empirically by a developer or calculated from historical data, and is not particularly limited herein.
Step 240, determining the target parking lot as a fixed parking lot for the target user.
The fixed parking lot refers to a certain parking lot or a plurality of parking lots in which a target user is fixedly accessed, for example, a parking lot of a cell in which the target user is located or a parking lot of an office building in which the target user is located. In the embodiment of the application, the fixed parking lot can be determined by the target parking lot. When the number of the target parking lots is one, the target parking lot can be directly determined as the fixed parking lot of the target user, when the number of the target parking lots is a plurality of the target parking lots, the plurality of the fixed parking lots can be determined as the fixed parking lots of the target user, and one or a preset number (for example, three) of the plurality of the target parking lots with the largest passing times in a set duration can be determined as the fixed parking lots of the target user.
According to the scheme, the number of passes of each parking lot in the set time is determined by acquiring the position information of the pass of each parking lot in the set time and then counting according to the position information of the pass of each parking lot; and then, according to the passing times of each parking lot passing in the set time length, determining a target parking lot with the passing times exceeding the first time number threshold value, wherein the target parking lot is a fixed parking lot of a target user. According to the scheme provided by the application, whether the target user has a fixed parking lot or not can be determined based on a large amount of parking data of the target user, so that the accuracy of the statistical result is improved, and meanwhile, the statistical efficiency is also improved.
Referring to fig. 4, in some embodiments, after step 230, the method further comprises:
step 410, counting the number of segments of the passing target parking lot in each time segment according to the passing time corresponding to the passing target parking lot in each time within the set duration; a time segment is a time period of each day.
And 420, if the number of segments exceeds the second number threshold, determining the building attribute of the target parking lot according to the corresponding relation between the time segments and the building attribute, wherein the building attribute comprises residential building and office building.
The number of times of passing through the target parking lot counted in each time segment is called the segment number. The time segments may be preset and stored in the server, and may be set according to the working time and the rest time, for example, 8 am to 12 am and 2 pm to 5 pm each day are set as the working time segments, and 5 pm to 12 pm are also set as the rest time period. The time segments may be set according to actual needs, and are not particularly limited herein.
In some embodiments, the correspondence between time segments and building attributes may be that the above-described time segments of 8 a.m. to 12 a.m. and 2 a.m. to 5 a.m. correspond to office buildings and 5 a.m. to 12 a.m. correspond to residential buildings. In some embodiments, the time segments and the building attributes may be stored in a table form in the server after being associated.
In some embodiments, the second time threshold is smaller than the first time threshold, for example, the first time threshold may be 12, the second time threshold may be 10, and the second time threshold may be set according to actual needs, which is not specifically limited herein. In other embodiments, the second time threshold and the first time threshold may be the same.
Referring to fig. 5, in some embodiments, after step 240, the method further comprises steps 510-540:
step 510, obtaining the traffic track data of the target user in the target parking lot each time within the set time length.
In some embodiments, the traffic trajectory data may include one or a combination of several of global satellite navigation signals, longitude and latitude information of each location point, radar information, path information of an odometer, a traffic trajectory graph, and a point cloud map.
Referring to fig. 6, in some embodiments, step 310 includes:
in step 610, attribute information of the target parking lot is acquired.
The attribute information of the target parking lot is used to characterize an attribute category of the target parking lot, for example, in some embodiments, the attribute information of the parking lot may be an above-ground parking lot and an underground parking lot, and in other embodiments, the attribute information of the parking lot may be an indoor parking lot and an outdoor parking lot.
Step 620, if the target parking lot is determined to be an underground parking lot according to the attribute information, acquiring traffic data acquired during the process that the vehicle of the target user passes through the target parking lot for each time within a set duration, wherein the traffic data comprises position images acquired at a plurality of position points and acquired sensing information of parking lot elements.
In some embodiments, the GNSS signals of the vehicle may disappear as it enters the underground parking garage. Whether the parking lot is an underground parking lot or not can be judged according to whether GNSS signals exist in the traffic track data of the target parking lot.
In other embodiments, it may be determined whether the target parking lot is an underground parking lot based on the position image of the entrance in the parking data and the radar information. When the position image of the passing opening shows that the two sides of the passing opening are walls and the radar information shows that the vehicles have barriers on the two sides of the passing opening, the target parking lot can be determined to be an underground parking lot.
In some embodiments, the plurality of location points may be location points corresponding to each parking space. The position image may be acquired by an image sensor of the vehicle.
In some embodiments, the parking lot element sensing information includes parking space lines, lane first, obstacles, arrow marks, speed bumps, pillars, etc., and the parking lot element sensing information may be acquired by an image sensor, an IMU (Inertial Measurement Unit ), a radar sensor, etc. on the vehicle.
In some embodiments, the traffic data of each parking lot may be stored in a corresponding cluster category, so as to update the traffic data of the corresponding parking lot later, and also facilitate the subsequent determination of whether the parking lot into which the target user enters any two times is the same parking lot.
And 630, performing track synthesis according to the position image and the parking lot element perception information acquired in the process of passing in the target parking lot each time, and obtaining the passing track data in the target parking lot each time.
In some embodiments, each time the target user enters the target parking lot and parks, the server may generate a point cloud base map at the cloud end according to the traffic data of the target user in the target parking lot, where the point cloud base map is generated according to the location information of the plurality of location points and the movement track of the target user. After the target parking lot is determined, a point cloud base map of the target parking lot is acquired at the cloud end, and then the position image acquired in the process of passing in the target parking lot and the sensing information of the elements of the parking lot are added to the point cloud base map, so that the passing track data of each time in the target parking lot is obtained.
In other embodiments, a track map of the target user in the target parking lot can be correspondingly generated according to the traffic data of the target user in the target parking lot each time, and then the position image and the parking lot element perception information acquired in the process of passing in the target parking lot each time are added into the corresponding track map, so that the traffic track data of the target parking lot each time is obtained.
In some embodiments, step 510 may further comprise:
if the target parking lot is determined to be an overground parking lot according to the attribute information, acquiring global satellite navigation signals acquired by the target user in the process of passing through the target parking lot each time within a set duration. And carrying out track synthesis according to the global satellite navigation signals acquired in the process of passing in the target parking lot each time, and obtaining the passing track data in the target parking lot each time.
In some embodiments, it may be determined whether the target parking lot is an above-ground parking lot based on the position image of the entrance in the parking data and the radar information. When the position image of the passing opening shows that the two sides of the passing opening are not walls and the radar information shows that the vehicles do not have barriers on the two sides of the passing opening, the target parking lot can be determined to be an on-ground parking lot.
In some embodiments, after each time the target user parks the vehicle in the target parking lot, the server generates a point cloud base map at the cloud end according to the traffic data of the target user in the target parking lot, where the point cloud base map is generated according to the position information of a plurality of position points in the traffic data and the moving track of the target user in the process of passing in the target parking lot. After the target parking lot is determined, a point cloud base map of the target parking lot is acquired at the cloud end, and then the global satellite navigation signals acquired in the process of passing the target user in the target parking lot are combined with the point cloud base map, so that the passing track data of each time in the target parking lot is obtained.
With continued reference to fig. 5, step 520 counts the number of parking times for each parking space in the target parking lot of the target user according to the traffic track data.
In some embodiments, statistics may be made based on location information of parking spaces in the traffic trajectory data within the target parking lot each time.
Referring to fig. 7, in some embodiments, step 520 may include steps 710-720:
step 710, carrying out parking space clustering according to the passing track in the target parking lot and the position images acquired near the parking spaces; the traffic track data comprises a traffic track and a position image acquired near the parking space.
In some embodiments, each parking space clustering category corresponds to one parking space, and the parking space clustering can be performed according to whether the traffic track in the target parking lot and the position image acquired near the parking space are the same or not. The distance between the target users in the parking spaces of the target parking lot can be calculated, and if the distance is smaller than the space distance threshold, the target users are considered to be parked in the same space, so that the space clustering can be performed. It can be appreciated that any two car positions with a car position distance less than the car position distance threshold are added to the same car position cluster category. The parking space distance threshold may be set according to real-time requirements, and is not particularly limited herein.
And 720, counting the parking times in each parking place cluster to obtain the parking times of each parking place in the target user target parking lot.
In some embodiments, a parking space set may be set according to a parking space set, different sets correspond to different parking space cluster categories, the number of times of parking of a target user in each parking space of a target parking space within a set duration is stored in the corresponding parking space set, and the number of times of parking in each parking space cluster is counted and can be directly obtained in the corresponding set. For example, in 4 months, the parking times of the target user in each parking space in a parking space of an office building are counted, the parking times of the target user in each parking space of the parking space are respectively stored in a corresponding parking space set and uploaded to a cloud end, so that the parking times of the target user are conveniently obtained, and the later updating of the parking times is also facilitated.
In some embodiments, as shown in fig. 8, step 720 may include steps 810-840:
step 810, selecting traffic track data corresponding to any two traffic, wherein the traffic track data corresponding to any two traffic comprises first traffic track data and second traffic track data.
The first traffic trace data and the second traffic trace data are different times of traffic trace data. It will be appreciated that.
Step 820, calculating the track overlap ratio between the first traffic track in the first traffic track data and the second traffic track in the second traffic track data.
In the present embodiment, the two-pass trajectories to be calculated are named as "first-pass trajectory" and "second-pass trajectory" to distinguish the two-pass trajectories.
In some embodiments, the coordinates of each position point in the first passing track and the second passing track may be compared, and if two or more continuous coordinate points in the first passing track a and two or more continuous coordinate points in the second passing track b are respectively compared, the two or more continuous coordinate points in a and b are corresponding to the same, or the distance between the corresponding coordinate points in a and b is smaller than a coordinate distance threshold, for example, 6 meters, then it is indicated that the passing tracks corresponding to the two or more coordinate points are coincident. And respectively calculating the proportion of the coincident passing tracks in the first passing track and the second passing track, and then calculating the average value of the proportion to obtain the track coincidence degree between the first passing track and the second passing track. The coordinate distance threshold is set according to actual needs, and is not particularly limited here.
In some embodiments, the elements in the first passing track and the elements in the second passing track may be subjected to feature matching, for example, feature elements such as a parking space line, an obstacle, a road sign, a lane line and the like in the first passing track and feature elements in the second passing track are matched, then the number of feature elements successfully matched and the number of all feature elements are counted, and a ratio between the number of feature elements successfully matched and the number of all feature elements is calculated, where the ratio is the track overlap ratio between the first passing track and the second passing track.
In other embodiments, the lanes of the first traffic track and the second traffic track may be respectively drawn with the same drawing coordinate system and the same drawing proportion to generate a plurality of track pictures with the same resolution and the same size, then the two or more track pictures are processed, for example, the two or more track pictures are converted into binary gray level pictures, and are matched according to the pixel positions shared by all the track lines in the binary gray level pictures, finally the track overlapping portion is determined based on the matched picture information, and then the ratio between the number of pictures of the track overlapping portion and the number of pictures of all the track is calculated, wherein the ratio is the track overlapping ratio between the first traffic track and the second traffic track.
In other embodiments, the first coordinate system and the second coordinate system may be respectively constructed according to the starting points in the first passing track and the second passing track, that is, the first passing track and the second passing track are respectively expressed by corresponding coordinate systems, that is, the coordinates of each position point in the first passing track and the second passing track are respectively expressed in the first coordinate system and the second coordinate system. Then, the first coordinate system and the second coordinate system are overlapped, then the rotation angle and the translation distance from the first passing track to the second passing track are calculated according to the overlapped first coordinate system and second coordinate system, and finally the track overlap ratio between the first passing track and the second passing track is calculated according to the calculated rotation angle and translation distance.
In other embodiments, the fraiche distance, the hausdorff distance, and the like between the traffic trajectories may be calculated, and the calculated distance may be used as the trajectory overlap ratio between the first traffic trajectory and the second traffic trajectory.
In some embodiments, the first and second traffic trajectories may be a graph of the travel trajectories of the target user.
In step 830, if the track overlap ratio exceeds the overlap ratio threshold, semantic alignment analysis is performed on the first position image in the first traffic track data and the second position image in the second traffic track data.
In some embodiments, the position image may include the position of each parking space, the position of the lane line, and the positions of the obstacle and the deceleration strip, and the position image may be acquired by an image sensor and a radar sensor of the vehicle.
The semantic alignment analysis is to align and match each element in the first position image and the second position image. In some embodiments, elements in the first position image and the second position image may be characterized to extract feature vectors of the elements, and semantic alignment may be performed according to the feature vectors of the elements.
In some embodiments, if the track overlap does not exceed the overlap threshold, then the process returns to re-execute step 510 and subsequent steps. The overlap ratio threshold value may be set according to actual needs, and is not particularly limited here.
Fig. 9A-9B are schematic diagrams illustrating semantic alignment analysis according to an embodiment of the present application. As shown in fig. 9A-9B, with the starting points of the first traffic track X and the second traffic track Y (it can be understood that the starting points are traffic ports of the parking lot) as references, elements at positions of the first position image and the second position image are aligned, finally, whether the first position image and the second position image are semantically matched or not can be judged according to the aligned first traffic track and second traffic track, fig. 9A indicates that the first traffic track X and the second traffic track Y are aligned, and whether the first traffic track X and the second traffic track Y are berthed into the parking lot is the same or not is judged according to the aligned first traffic track X and second traffic track Y according to the semantic alignment of the first position image and the aligned second traffic track Y.
With continued reference to fig. 8, in step 840, if it is determined that the first position image and the second position image are semantically matched, it is determined that the parking space corresponding to the first traffic track data and the parking space corresponding to the second traffic track data belong to the same parking space cluster.
In some embodiments, whether the first location image and the second location image are semantically matched may be determined based on whether the same element is included in the first location image and the second location image and the number of the same elements exceeds a number threshold.
When the first position image is matched with the second position image semantically, the parking space corresponding to the first passing track data and the parking space corresponding to the second passing track data can be determined to be the same parking space. It can be understood that the parking space corresponding to the first traffic track data is the end position of the first traffic track data.
With continued reference to fig. 5, in step 530, a target parking space with a parking number exceeding the third number threshold is determined according to the parking number of each parking space in the target user target parking lot.
The target parking space is a parking space in which the parking times of the target user in the target parking lot exceed a third time threshold.
In some embodiments, the third time threshold may be different from or the same as both the first time threshold and the second time threshold. It can be appreciated that setting the thresholds of the number of times of different values can improve the accuracy of determining whether the target user owns the fixed parking lot or the fixed parking space. For example, the third-order threshold may be 10 or 18, and may be set according to actual needs, and is not particularly limited here.
In some embodiments, since the target user may park into a temporary parking space when the parking space of the target user is occupied or used by a visitor, and the statistics of the parking times of each parking space in the target parking space of the target user may cause a problem in statistics of the parking times of certain parking spaces due to excessive data, the third time threshold may be determined according to a fixed formula, where the third time is S, the parking times of the target parking space within a set period of time is N, and the third time threshold S is: s=n×0.8.
Step 540, determining the target parking space as the fixed parking space of the target user in the target parking lot.
In this embodiment, according to the traffic track data of the target user entering the target parking lot each time within the set duration, whether the parking spaces in the target parking lot are the same for any two times can be judged, then the number of times of parking in the same parking space is counted, and the parking space with the number of times of parking exceeding the third number threshold is determined as the fixed parking space of the target user in the target parking lot. The user with the parking space can be determined, and a series of related parking space services can be conveniently provided for the target user.
Fig. 10 is a block diagram of a processing apparatus for parking data according to an embodiment of the present application, the processing apparatus 1000 for parking data including: the first acquisition module 1010, the number of passes determination module 1020, the target parking lot determination module 1030, and the fixed parking lot determination module 1040.
The first obtaining module 1010 is configured to obtain parking data of a target user within a set duration, where the parking data includes position information of a pass through the parking lot each time the target user enters and exits within the set duration. The number of passes determining module 1020 is configured to count according to the position information of the passing ports of the parking lots, and determine the number of passes of each parking lot within a set duration. The target parking lot determining module 1030 is configured to determine a target parking lot whose number of passes exceeds a first threshold according to the number of passes of each parking lot in the set duration. The fixed parking lot determination module 1040 is configured to determine the target parking lot as a fixed parking lot of the target user.
In some implementations, the location information includes coordinate information of the corresponding gate, and the number of communications determination module 1020 includes: the system comprises a clustering type determining unit, a distance determining unit and a distance determining unit, wherein the clustering type determining unit is used for determining the clustering type of each passing port according to the coordinate information of the passing port entering and exiting the parking lot each time, one clustering type corresponds to one parking lot, and the distance between the passing ports belonging to the same clustering type is smaller than a distance threshold; and the communication frequency determining unit is used for counting the number of the traffic ports in each clustering type to obtain the number of the traffic times of each parking lot passing in the set duration.
In some embodiments, the apparatus 1000 for processing parking data further includes: the segmentation number determining module is used for counting the segmentation number of the passing target parking lot in each time segment according to the passing time corresponding to the passing target parking lot in each time within the set duration, wherein the time segment is one time segment in each day; and the building attribute determining module is used for determining the building attribute of the target parking lot according to the corresponding relation between the time segment and the building attribute if the segmentation number exceeds the second number threshold, wherein the building attribute comprises a residential building and an office building.
In some embodiments, the apparatus 1000 for processing parking data further includes: the traffic track data acquisition module is used for acquiring traffic track data of the target user in the target parking lot every time within a set time length; the parking number counting module is used for counting the parking number of each parking space in the target parking lot of the target user according to the traffic track data; the target parking space determining module is used for determining target parking spaces with the parking times exceeding a third time threshold according to the parking times of all the parking spaces in the target parking space of the target user; and the fixed parking space determining module is used for determining the target parking space as the fixed parking space of the target user in the target parking lot.
In some embodiments, the traffic trajectory data acquisition module comprises: the attribute information acquisition unit is used for acquiring attribute information of the target parking lot; the communication data acquisition unit is used for acquiring traffic data acquired in the process that the vehicle of the target user passes through the target parking lot for each time within a set time period if the target parking lot is determined to be the underground parking lot according to the attribute information, wherein the traffic data comprises position images acquired at a plurality of position points and acquired parking lot element perception information; the first traffic track data determining unit is used for carrying out track synthesis according to the position images acquired in the process of passing in the target parking lot each time and the parking lot element perception information to obtain traffic track data in the target parking lot each time.
In some embodiments, the traffic trajectory data acquisition module further comprises: the global satellite navigation signal acquisition unit is used for acquiring global satellite navigation signals acquired in the process that the vehicles of the target user pass through the target parking lot each time within a set duration if the target parking lot is determined to be the ground parking lot according to the attribute information; and the second passing track data determining unit is used for carrying out track synthesis according to the global satellite navigation signals acquired in the process of passing in the target parking lot each time to obtain passing track data in the target parking lot each time.
In some embodiments, the traffic trajectory data includes a traffic trajectory and a position image acquired near the parking space, and the parking number determination module includes: the parking space clustering unit is used for carrying out parking space clustering according to the passing track in the target parking lot and the position images acquired near the parking spaces; the parking number determining unit is used for counting the parking number in each parking position cluster to obtain the parking number of each parking position in the target parking lot of the target user.
In some embodiments, the parking spot clustering unit is further configured to: the traffic track data selecting unit is used for selecting traffic track data corresponding to any two times of traffic, wherein the traffic track data corresponding to any two times of traffic comprises first traffic track data and second traffic track data; the track contact ratio calculation unit is used for calculating the track contact ratio between the first passing track in the first passing track data and the second passing track in the second passing track data; the semantic alignment analysis unit is used for carrying out semantic alignment analysis on the first position image in the first passing track data and the second position image in the second passing track data if the track overlap ratio exceeds the overlap ratio threshold value; and the determining unit is used for determining that the parking space corresponding to the first traffic track data and the parking space corresponding to the second traffic track data belong to the same parking space cluster if the first position image and the second position image are determined to be matched in a semantic manner.
Fig. 11 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application. It should be noted that, the computer system 1100 of the electronic device shown in fig. 11 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 11, the computer system 1100 includes a central processing unit (Central Processing Unit, CPU) 1101 that can perform various appropriate actions and processes, such as performing the method in the above-described embodiment, according to a program stored in a Read-Only Memory (ROM) 1102 or a program loaded from a storage portion 11011 into a random access Memory (Random Access Memory, RAM) 1103. In the RAM 1103, various programs and data required for system operation are also stored. The CPU1101, ROM1102, and RAM 1103 are connected to each other by a bus 1104. An Input/Output (I/O) interface 1105 is also connected to bus 1104.
The following components are connected to the I/O interface 1105: an input section 1106 including a keyboard, a mouse, and the like; an output portion 1107 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and a speaker; a storage section 1108 including a hard disk or the like; and a communication section 1109 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 1109 performs communication processing via a network such as the internet. The drive 1110 is also connected to the I/O interface 1105 as needed. Removable media 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed as needed in drive 1110, so that a computer program read therefrom is installed as needed in storage section 1108.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 1109, and/or installed from the removable media 1111. When executed by a Central Processing Unit (CPU) 1101, performs the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable storage medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer readable storage medium carries computer readable instructions which, when executed by a processor, implement the method of any of the above embodiments.
According to an aspect of the present application, there is also provided an electronic apparatus including: a processor; a memory having stored thereon computer readable instructions which, when executed by a processor, implement the method of any of the embodiments described above.
According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method of any of the embodiments described above.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (7)
1. A method of processing parking data, comprising:
obtaining parking data of a target user in a set time period, wherein the parking data comprises position information of a passing port of a parking lot in each time in the set time period;
counting according to the position information of the passing ports of the parking lots in each time, and determining the passing times of each parking lot passing in the set time length;
according to the passing times of each parking lot in the set time length, determining a target parking lot with the passing times exceeding a first time number threshold;
Determining the target parking lot as a fixed parking lot of the target user;
acquiring the traffic track data of the target user in the target parking lot each time within the set time length;
counting the parking times of all the parking spaces in the target parking space of the target user according to the traffic track data, wherein the traffic track data comprises a traffic track and position images acquired near the parking spaces, and the counting the parking times of all the parking spaces in the target parking space of the target user according to the traffic track data comprises the following steps:
selecting traffic track data corresponding to any two traffic, wherein the traffic track data corresponding to any two traffic comprises first traffic track data and second traffic track data;
calculating the track coincidence ratio between a first passing track in the first passing track data and a second passing track in the second passing track data;
if the track overlap ratio exceeds an overlap ratio threshold value, carrying out semantic alignment analysis on a first position image in the first passing track data and a second position image in the second passing track data;
if the first position image and the second position image are determined to be semantically matched, determining that the parking space corresponding to the first traffic track data and the parking space corresponding to the second traffic track data belong to the same parking space cluster;
Counting the parking times in each parking space cluster to obtain the parking times of each parking space in the target parking lot of the target user;
according to the parking times of all the parking spaces in the target parking lot of the target user, determining a target parking space with the parking times exceeding a third time threshold;
and determining the target parking space as a fixed parking space of the target user in the target parking lot.
2. The method of claim 1, wherein the location information includes coordinate information of the corresponding gate;
the counting is performed according to the position information of the passing ports of the parking lots, and the passing times of the parking lots passing in the set time length are determined, including:
determining the clustering type of each passing port according to the coordinate information of the passing ports entering and exiting the parking lot each time, wherein one clustering type corresponds to one parking lot, and the distance between the passing ports belonging to the same clustering type is smaller than a distance threshold;
and counting the number of the traffic openings in each cluster type to obtain the number of times of traffic in each parking lot passing in the set time length.
3. The method according to claim 2, wherein after the target parking lot whose number of passes exceeds the first number threshold is determined based on the number of passes of each parking lot within the set period of time, the method further comprises:
Counting the segmentation times of passing the target parking lot in each time segment according to the passing time corresponding to the target parking lot passing each time in the set time length; the time segment is one time period of each day;
and if the segmentation times exceeds a second time threshold, determining the building attribute of the target parking lot according to the corresponding relation between the time segmentation and the building attribute, wherein the building attribute comprises residential building and office building.
4. The method of claim 1, wherein the obtaining traffic trajectory data of the target user in the target parking lot each time within the set period of time comprises:
acquiring attribute information of the target parking lot;
if the target parking lot is determined to be an underground parking lot according to the attribute information, acquiring traffic data acquired in the process that the vehicle of the target user passes through the target parking lot for each time within the set time period, wherein the traffic data comprises position images acquired at a plurality of position points and acquired sensing information of parking lot elements;
and track synthesis is carried out according to the position images acquired in the process of passing in the target parking lot each time and the parking lot element perception information, so as to obtain the passing track data in the target parking lot each time.
5. The method of claim 4, wherein the obtaining the traffic trajectory data of the target user in the target parking lot each time within the set time period further comprises:
if the target parking lot is determined to be an overground parking lot according to the attribute information, acquiring global satellite navigation signals acquired in the process that the vehicles of the target user pass through the target parking lot each time within the set duration;
and carrying out track synthesis according to the global satellite navigation signals acquired in the process of passing in the target parking lot each time, and obtaining the passing track data in the target parking lot each time.
6. A device for processing parking data, the device comprising:
the first acquisition module is used for acquiring parking data of a target user in a set time length, wherein the parking data comprises position information of a passing port which enters and exits a parking lot for each time in the set time length;
the passing number determining module is used for counting according to the position information of the passing ports of the parking lots in and out of each time and determining the passing number of each parking lot passing in the set time length;
the target parking lot determining module is used for determining a target parking lot with the passing times exceeding a first time threshold according to the passing times of each parking lot passing in the set time length;
A fixed parking lot determining module for determining the target parking lot as a fixed parking lot of the target user; the passing track data acquisition module is used for acquiring the passing track data of the target user in the target parking lot each time within the set duration;
the parking number counting module is configured to count the parking number of each parking space in the target parking space according to the traffic track data, where the traffic track data includes a traffic track and a position image collected near the parking space, and the counting module is configured to count the parking number of each parking space in the target parking space according to the traffic track data, and includes:
the system comprises a traffic track data selection unit, a traffic track data processing unit and a traffic control unit, wherein the traffic track data selection unit is used for selecting traffic track data corresponding to any two-time traffic, and the traffic track data corresponding to any two-time traffic comprises first traffic track data and second traffic track data;
the track contact ratio calculation unit is used for calculating the track contact ratio between the first passing track in the first passing track data and the second passing track in the second passing track data;
the semantic alignment analysis unit is used for carrying out semantic alignment analysis on the first position image in the first passing track data and the second position image in the second passing track data if the track overlap ratio exceeds an overlap ratio threshold value;
The determining unit is used for determining that the parking space corresponding to the first passing track data and the parking space corresponding to the second passing track data belong to the same parking space cluster if the first position image and the second position image are determined to be matched in a semantic manner;
the parking number determining unit is used for counting the parking number in each parking space cluster to obtain the parking number of each parking space in the target parking space of the target user;
the target parking space determining module is used for determining a target parking space with the parking times exceeding a third time threshold according to the parking times of each parking space in the target parking lot of the target user;
and the fixed parking space determining module is used for determining the target parking space as the fixed parking space of the target user in the target parking lot.
7. An electronic device, the electronic device comprising:
one or more processors;
a memory electrically connected to the one or more processors;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-5.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000057003A1 (en) * | 1999-03-23 | 2000-09-28 | Koetter Wilfried | Array for securing a vehicle parking area |
| JP2010100267A (en) * | 2008-10-27 | 2010-05-06 | Mitsubishi Motors Corp | Parking assistance device |
| JP2012118765A (en) * | 2010-12-01 | 2012-06-21 | Mitsubishi Heavy Ind Ltd | Parking lot usage management system, data management apparatus, usage control apparatus, control method, and program |
| WO2013076342A1 (en) * | 2011-11-25 | 2013-05-30 | Mayor Agell Jaume | System for accessing parking spaces |
| CN103514641A (en) * | 2012-06-17 | 2014-01-15 | 余姚市精诚高新技术有限公司 | Intelligent parking lot management system and management method |
| CN106157625A (en) * | 2016-08-04 | 2016-11-23 | 蔚来汽车有限公司 | Fixing parking stall recognition methods |
| JP2018084906A (en) * | 2016-11-22 | 2018-05-31 | 株式会社日立システムズ | Parking lot reservation system |
| CN108230725A (en) * | 2016-12-09 | 2018-06-29 | 深圳市易行网交通科技有限公司 | Method and apparatus are recommended in parking |
| CN109466546A (en) * | 2018-11-20 | 2019-03-15 | 广州小鹏汽车科技有限公司 | Automatic parking method and system |
| CN110246367A (en) * | 2019-06-29 | 2019-09-17 | 惠州市智凯车联电子科技有限公司 | Parking space information method for pushing, device and electronic equipment |
| KR20210076886A (en) * | 2020-06-04 | 2021-06-24 | 베이징 바이두 넷컴 사이언스 앤 테크놀로지 코., 엘티디. | Parking model generation method and device, electronic device and storage medium |
| CN113570899A (en) * | 2021-07-16 | 2021-10-29 | 广州小鹏自动驾驶科技有限公司 | Parking lot list generation method and device, service equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9123035B2 (en) * | 2011-04-22 | 2015-09-01 | Angel A. Penilla | Electric vehicle (EV) range extending charge systems, distributed networks of charge kiosks, and charge locating mobile apps |
-
2022
- 2022-03-29 CN CN202210322995.8A patent/CN114842666B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000057003A1 (en) * | 1999-03-23 | 2000-09-28 | Koetter Wilfried | Array for securing a vehicle parking area |
| JP2010100267A (en) * | 2008-10-27 | 2010-05-06 | Mitsubishi Motors Corp | Parking assistance device |
| JP2012118765A (en) * | 2010-12-01 | 2012-06-21 | Mitsubishi Heavy Ind Ltd | Parking lot usage management system, data management apparatus, usage control apparatus, control method, and program |
| WO2013076342A1 (en) * | 2011-11-25 | 2013-05-30 | Mayor Agell Jaume | System for accessing parking spaces |
| CN103514641A (en) * | 2012-06-17 | 2014-01-15 | 余姚市精诚高新技术有限公司 | Intelligent parking lot management system and management method |
| CN106157625A (en) * | 2016-08-04 | 2016-11-23 | 蔚来汽车有限公司 | Fixing parking stall recognition methods |
| JP2018084906A (en) * | 2016-11-22 | 2018-05-31 | 株式会社日立システムズ | Parking lot reservation system |
| CN108230725A (en) * | 2016-12-09 | 2018-06-29 | 深圳市易行网交通科技有限公司 | Method and apparatus are recommended in parking |
| CN109466546A (en) * | 2018-11-20 | 2019-03-15 | 广州小鹏汽车科技有限公司 | Automatic parking method and system |
| CN110246367A (en) * | 2019-06-29 | 2019-09-17 | 惠州市智凯车联电子科技有限公司 | Parking space information method for pushing, device and electronic equipment |
| KR20210076886A (en) * | 2020-06-04 | 2021-06-24 | 베이징 바이두 넷컴 사이언스 앤 테크놀로지 코., 엘티디. | Parking model generation method and device, electronic device and storage medium |
| CN113570899A (en) * | 2021-07-16 | 2021-10-29 | 广州小鹏自动驾驶科技有限公司 | Parking lot list generation method and device, service equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 停车场管理系统的设计与优化;董加敏;;河南师范大学学报(自然科学版)(第01期);全文 * |
| 基于物联网的车位引导系统设计;赵盼等;《池州学院学报》;20200628(第03期);全文 * |
| 智能车位预定与场内停车导航系统的设计与开发;刘鑫等;《智能城市》;20180514(第09期);全文 * |
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