CN113033978A - Parking risk determination method, position recommendation method and device and electronic equipment - Google Patents
Parking risk determination method, position recommendation method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a parking risk determining method which comprises the steps of obtaining a plurality of position ranges of candidate points, determining ticket rate of each position range in a plurality of preset historical time periods, and determining parking risks corresponding to the candidate points based on the ticket rate. Therefore, the parking risks corresponding to the candidate points are determined according to the ticket penalty rates of the position ranges of the candidate points in the preset historical time periods, the calculation accuracy of the parking risks of the candidate points is improved, convenience is provided for vehicle parking, and the improvement of the parking experience of a driver is facilitated.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a parking risk determination method, a position recommendation method, a device and electronic equipment.
Background
The ticket rate of the parking spot reflects the risk of illegal parking at the current parking spot, and the timely understanding of the parking risk at the parking spot has important significance for the rapid parking of a driver and the improvement of traffic conditions. The existing ticket rate is usually determined according to the number of tickets within a certain time and a certain range, the information coverage range is small, and the calculation accuracy still needs to be improved. Therefore, a parking risk determination method is needed to improve the accuracy of parking risk determination at a parking spot, thereby facilitating vehicle parking and improving the parking experience of a driver.
Disclosure of Invention
In view of this, embodiments of the present invention provide a parking risk determining method, a position recommending method, a device, and an electronic device, so as to improve the calculation accuracy of parking risks and provide convenience for vehicle parking.
In a first aspect, an embodiment of the present invention provides a parking risk determining method, where the method includes:
acquiring a plurality of position ranges of candidate points, wherein the position ranges are mutually contained;
determining a ticket rate for each of the location ranges over a plurality of preset historical time periods;
and determining the parking risk corresponding to the candidate point based on the ticket rate.
Further, the determining the parking risk corresponding to the candidate point based on the ticket rate comprises:
determining a ticket rate for each location range;
and determining the parking risk of the corresponding candidate point according to the ticket rate of each position range and the corresponding position weight coefficient.
Further, the step of determining the parking risk of the corresponding candidate point according to the ticket penalty rate of each position range and the corresponding position weight coefficient specifically includes:
and carrying out weighted summation on the ticket rate of each position range and the corresponding position weight coefficient, and determining the result of the weighted summation as the parking risk of the corresponding candidate point.
Further, the position weight coefficient is set according to the distance between the center of the position range and the candidate point, and the position weight coefficient corresponding to the position range closest to the candidate point is the largest.
Further, the determining the ticket rate for each location range includes:
determining a ticket rate of a target location range within different preset historical time periods, the target location range being one of the plurality of location ranges;
and determining the ticket rate corresponding to the target position range according to the ticket rate of each preset historical time period and the corresponding time weight coefficient.
Further, the step of determining the ticket rate corresponding to the target position range according to the ticket rate of each preset historical time period and the corresponding time weight coefficient specifically includes:
and carrying out weighted summation on the ticket rate in each preset historical time period and the corresponding time weight coefficient, and determining the result of the weighted summation as the ticket rate of the corresponding target position range.
Further, the time weight coefficient is set according to the time length of the preset historical time period from the current time, and the time weight coefficient corresponding to the preset historical time period with the shortest time length from the current time is the largest.
Further, the determining the ticket rate of each position range in different preset historical time periods comprises:
acquiring the number of tickets and the number of parking times of each position range in different preset historical time periods;
and determining the ratio of the number of the tickets of each position range in each preset historical time period to the parking times as the ticket rate of the corresponding position range in the corresponding preset historical time period.
In a second aspect, an embodiment of the present invention provides a parking risk determination apparatus, where the apparatus includes:
an acquisition unit configured to acquire a plurality of position ranges of candidate points, the plurality of position ranges including each other;
the processing unit is used for determining the ticket rate of the position range in a plurality of preset historical time periods;
and the determining unit is used for determining the parking risk corresponding to the candidate point based on the ticket rate.
In a third aspect, an embodiment of the present invention provides a location recommendation method, where the method includes:
acquiring at least one position information in the order information;
determining a candidate point set according to the position information, wherein the candidate point set comprises at least one candidate point;
acquiring a plurality of position ranges of each candidate point, wherein the position ranges are mutually contained;
determining a ticket rate of each position range of each candidate point in a plurality of preset historical time periods;
determining parking risks corresponding to the candidate points based on the ticket rate;
and screening candidate points in the candidate point set based on the parking risk, and determining a target recommended point.
In a fourth aspect, an embodiment of the present invention provides a position recommendation apparatus, where the apparatus includes:
the starting unit is used for acquiring at least one piece of position information in the order information;
a set unit, configured to determine a candidate point set according to the location information, where the candidate point set includes at least one candidate point;
an acquisition unit configured to acquire a plurality of position ranges of each candidate point, the plurality of position ranges including each other;
the processing unit is used for determining the ticket rate of each position range of each candidate point in a plurality of preset historical time periods;
the determining unit is used for determining the parking risk corresponding to the candidate point based on the ticket rate;
and the target unit is used for screening the candidate points in the candidate point set based on the parking risk and determining target recommended points.
In a fifth aspect, embodiments of the present invention provide a computer program product comprising a computer program/instructions which, when executed by a processor, implement the method as defined in any one of the above.
In a sixth aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer program instructions, and the processor executes the one or more computer program instructions to implement the method described above.
In a seventh aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the method described above.
According to the technical scheme of the embodiment of the invention, the ticket rate of each position range in a plurality of preset historical time periods is determined by acquiring a plurality of position ranges of the candidate points, and the parking risk corresponding to the candidate points is determined based on the ticket rate, so that the calculation accuracy of the parking risk of the candidate points is improved, convenience is provided for vehicle parking, and the parking experience of a driver is improved.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a parking risk determination method of an embodiment of the present invention;
FIG. 2 is a schematic illustration of a range of positions for an embodiment of the present invention;
FIG. 3 is another schematic illustration of a range of positions for an embodiment of the present invention;
FIG. 4 is another schematic illustration of a range of positions for an embodiment of the present invention;
FIG. 5 is another schematic illustration of a range of positions for an embodiment of the present invention;
FIG. 6 is another schematic illustration of a range of positions for an embodiment of the present invention;
FIG. 7 is a flow chart for determining ticket rates for each location range for each preset historical time period;
FIG. 8 is a flow chart for determining candidate point parking risk based on ticket rate;
FIG. 9 is a flow chart of a location recommendation method of an embodiment of the present invention;
FIG. 10 is a schematic view of a parking risk determination device according to an embodiment of the present invention;
FIG. 11 is a schematic diagram of a position recommendation device of an embodiment of the present invention;
fig. 12 is a schematic diagram of an electronic device of an embodiment of the invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout the description, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified.
Fig. 1 is a flowchart of a parking risk determination method according to an embodiment of the present invention. As shown in fig. 1, the parking risk determining method of the present embodiment includes the following steps:
in step S100, a plurality of position ranges of the candidate points are obtained, and the plurality of position ranges are mutually included.
In this embodiment, as shown in fig. 2 to 6, the position range may be a closed space range formed by expanding the candidate points inward and outward, or may be a closed road network range extending layer by layer along the road section direction of the candidate points including the candidate points.
Alternatively, the spatial range in the present embodiment may be a location area around which an arbitrary regular or irregular contour is formed. The road network range may be a position region formed by extending along one side of the road segment direction or extending to both sides of the road segment direction by the same or different preset distances.
Optionally, to facilitate setting of the position range, the position range in this embodiment may be a space range corresponding to a preset radius range that is correspondingly expanded outward with the candidate point as a center, or may be a road network range corresponding to a road segment where the candidate point is located and that is extended to both sides by the same preset distance with the candidate point as a starting point.
Alternatively, the number of the position ranges in the present embodiment may be set according to actual situations. As shown in fig. 4, taking 3 spatial ranges as an example, the plurality of position ranges of the present embodiment may include spatial ranges S1, S2, and S3. The spatial ranges S1, S2, and S3 are all centered on the candidate point I0(0, 0), and extend all around by radii of 10 m, 20 m, and 30 m to form a circular location area. Where spatial range S1 is a subset of S2, S2 is a subset of S3, spatial range S1 and spatial range S2 each cover only a partial region of link L, and spatial range S3 covers both a partial region of link L and a partial region of link R. Or, as shown in fig. 6, taking 3 road network ranges as an example, L is used to represent the road segment where the candidate point is located, and the plurality of position ranges of this embodiment may include road network ranges L1, L2, and L3. The road network ranges L1, L2, and L3 all use the candidate point 0 as a center, and respectively use the candidate point as a starting point to simultaneously extend 10 meters, 20 meters, and 30 meters to both sides to form corresponding position areas, the circular road network span ranges corresponding to the road network ranges L1, L2, and L3 are 20 meters, 40 meters, and 60 meters, respectively, that is, the road network range L1 is a subset of L2, and L2 is a subset of L3.
It should be understood that the ticket rate is present on different road segments. The road network range of the present embodiment corresponds to a distance range on a single road segment, and the position range corresponding to the spatial range may include one or more road segments (for example, the position range S1 and the position range S2 in fig. 4 only include the road segment L, and the position range S3 includes the road segment L and the road segment R). When only one road segment exists in the space range, the parking risk in the space range is the same as the parking risk in the corresponding road network range.
In step S200, ticket rates for respective location ranges over a plurality of preset historical time periods are determined.
In this embodiment, when the position range is a spatial range, the ticket rate of each spatial range in a plurality of preset historical time periods is determined respectively. And when the position range is a road network range, determining the ticket rate of each road network range in a plurality of preset historical time periods respectively.
Alternatively, the plurality of preset history time periods in the present embodiment may include T1, T2, and T3. Wherein, T1, T2 and T3 correspond to time intervals corresponding to 0-30 days, 0-60 days and 0-90 days from the current time respectively. In determining the ticket rates for each location range over a plurality of preset historical time periods, the ticket rates for each spatial range or road network range over 0-30 days, 0-60 days, and 0-90 days are determined, respectively.
In step S300, a parking risk corresponding to the candidate point is determined based on the ticket rate.
In this embodiment, after determining the ticket rates of the position ranges within the preset historical time periods, the parking risk of the corresponding candidate point is determined based on the determined ticket rates. Therefore, the ticket rate of the position ranges of the candidate points in the preset historical time periods is obtained, and the parking risk corresponding to the candidate points is determined based on the ticket rate, so that the calculation accuracy of the parking risk of the candidate points is improved, convenience is provided for vehicle parking, and the parking experience of a driver is improved.
Figure 7 is a flow chart for determining ticket rates for respective location ranges over respective preset historical time periods. As shown in fig. 7, the determining the ticket rate of each position range in a plurality of preset historical time periods in the present embodiment includes:
in step S210, the number of tickets and the number of parking times for each position range in different preset historical time periods are acquired.
In this embodiment, the number of parking times and the number of tickets in each location range in different preset time periods are the number of parking times and the number of tickets occurring on all road segments in each location range, respectively.
Optionally, the number of parking times in this embodiment refers to the number of all vehicles parked in all road segments within each location range within each preset time period. And when the same vehicle stops every time within the corresponding position range and the preset historical time period, the number of times of parking is increased by 1 time. In addition, the number of tickets in the present embodiment refers to the number of tickets generated by illegal parking or other illegal operations of all vehicles parked in all road segments within each position range within each preset time period.
Further, the number of parking times and the number of tickets in the embodiment may be determined according to data collected by a traffic camera or a traffic data collection device within a location range.
In step S220, the ratio of the number of tickets in each position range in each preset historical time period to the number of parking times is determined as the ticket rate of the corresponding position range in the corresponding preset historical time period.
Alternatively, taking the ticket rate of a position range within a preset historical time as an example, the number of parking is N, the number of tickets is M, and the ticket rate is the ratio of M to N. And based on the method, the ticket rate of each position range in different preset time periods is calculated respectively.
FIG. 8 is a flow chart for determining candidate point parking risk based on ticket rate. As shown in fig. 8, the present embodiment includes the following steps in determining the parking risk corresponding to the candidate point based on the ticket rate.
In step S310, a ticket rate for each location range is determined.
In this embodiment, the ticket rate of each location range is used to characterize the parking risk in the corresponding location range.
Optionally, in this embodiment, the ticket rate of each location range is determined according to the ticket rate of each location range in a plurality of preset historical time periods. And the ticket rate of each position range in a plurality of preset historical time periods is used for representing the parking risk of each position range in each preset historical time period.
Optionally, in this embodiment, each preset historical time period is provided with a corresponding time weight coefficient. Further, the following step S311 and step S312 are included in determining the ticket rate for each location range.
In step S311, the ticket rates of the target position range over different preset historical time periods are determined. Wherein the target position range is one of a plurality of position ranges.
In step S312, the ticket rate corresponding to the target position range is determined according to the ticket rate of each preset historical time period and the corresponding time weighting factor.
Optionally, each time weight coefficient in this embodiment is based on a time between a preset historical time period and the current timeAnd setting the length, wherein the time weight coefficient corresponding to the preset historical time period with the shortest time length from the current time is the largest, and the time weight coefficient corresponding to the preset historical time period with the longest time length from the current time is the smallest. For example, the time weighting coefficients corresponding to the preset history time periods T1, T2, and T3 are x, respectively11、x12And x13Then the relationship of each time weight coefficient is x11>x12>x13。
Further, in this embodiment, the step of determining the ticket rate corresponding to the target position range according to the ticket rate of each preset historical time period and the corresponding time weighting coefficient specifically includes: and carrying out weighted summation on the ticket rate in each preset historical time period and the corresponding time weight coefficient, and determining the result of the weighted summation as the ticket rate of the corresponding target position range. Therefore, the penalty rate of each position range is calculated by considering time attenuation and converting the change of the penalty rate generated by the time attenuation factor into different time attenuation coefficients, so that the calculation result of the penalty rate in each position range is more practical, and the calculation accuracy of the parking risk of the candidate point is improved. Meanwhile, the time weight coefficient in the embodiment can be set and adjusted according to the actual use condition, and the calculation accuracy of the parking risk of the candidate point is further improved.
Specifically, at the ticket rate a of the spatial range S11The calculation of (2) is explained as an example, and the calculation method of the ticket rate in the other position range (which may be a spatial range or a road network range) is the same as this. Wherein the ticket rate generated by the space range S1 in the preset historical time period T1 is a11The ticket rate generated in the preset history period T2 is a12The ticket rate generated in the preset history period T3 is a13. At this time, the ticket rate corresponding to the spatial range S1 is calculated as follows:
a1＝x11×a11+x12×a12+x13×a13
it should be understood that the number of the preset historical time periods in the present embodiment may also be set to one, and the ticket rate in each position range is determined by the ratio of the number of tickets in the preset historical time periods to the number of parking times. This can improve the calculation speed of the parking risk at the candidate point.
In step S320, the parking risk of the corresponding candidate point is determined according to the ticket rate of each position range and the corresponding position weight coefficient.
Optionally, each position range in this embodiment is provided with a corresponding position weight coefficient. Each position weight coefficient is set according to the distance between the center of the position range and the candidate point, the position weight coefficient corresponding to the position range closest to the candidate point is the largest, and the position weight coefficient corresponding to the position range farthest from the candidate point is the smallest. For example, taking the position range as the spatial range as an example, the spatial ranges S1, S2, and S3 correspond to different position weight coefficients w1、w2And w3. The spatial range S1 is closest to the candidate point as a whole, the spatial range S3 is farthest from the candidate point as a whole, and the relationship between the position weight coefficients corresponding to the spatial ranges is w1>w2>w3. For another example, taking the position range as the road network range, the road network ranges L1, L2, and L3 correspond to different position weight coefficients v1、v2And v3. The entire road network range L1 is closest to the candidate points, the entire road network range L3 is farthest from the candidate points, and the relationship between the position weight coefficients corresponding to the spatial ranges is v1>v2>v3。
Further, the determining of the parking risk of the corresponding candidate point according to the ticket penalty rate of each location range and the corresponding location weight coefficient in this embodiment specifically includes: and carrying out weighted summation on the ticket rate of each position range and the corresponding position weight coefficient, and determining the result of the weighted summation as the parking risk of the corresponding candidate point. Therefore, calculation of the parking risk of the candidate point is achieved through the ticket rate of each position range and the corresponding position weight coefficient, the calculation result of the parking risk of the candidate point is more practical, and calculation accuracy is higher. Meanwhile, the position weight coefficient in the embodiment can be set and adjusted according to the actual use condition, and the calculation accuracy of the parking risk of the candidate point is further improved.
Specifically, when the position range is the spatial range, the parking risk of the candidate point is recorded as a, and the ticket rates corresponding to the spatial ranges S1, S2, and S3 are sequentially a1、a2And a3. At this time, the parking risk a of the candidate point is calculated as follows:
a＝w1×a1+w2×a2+w3×a3
when the position range is the road network range, the parking risk of the candidate point is recorded as a, and the ticket rate corresponding to the road network ranges L1, L2 and L3 is alpha in sequence1、α2And alpha3. At this time, the parking risk a of the candidate point is calculated as follows:
a＝v1×α1+v2×α2+v3×α3
the technical scheme of the embodiment of the invention comprises the steps of sequentially calculating the ticket penalty rate of each position range in each preset historical time period, determining the ticket penalty rate of each position range on the basis of the ticket penalty rate of each position range in each preset historical time period and the time weight coefficient corresponding to each preset historical time period, and determining the parking risk of the candidate point on the basis of the ticket penalty rate of each position range and the position weight coefficient corresponding to each position range. Therefore, the calculation accuracy of the parking risk of the candidate points is improved, convenience is provided for parking of the vehicle, and the parking experience of a driver is improved.
It should be noted that the parking risk determination method according to the embodiment of the present invention can be applied to any occasions with parking requirements.
In the following, taking the pick-up and delivery service process in the network appointment service as an example, the driver needs to go to the vicinity of the designated location to pick-up or deliver the passenger to the vicinity of the designated location. In order to avoid the influence caused by illegal parking, the driver can be guided to drive the vehicle to the recommended boarding point or the recommended alighting point for parking by determining the parking risk of each relevant position and recommending the proper boarding point or alighting point to the network car booking vehicle or the driver according to the parking risk.
Fig. 9 is a flowchart of a location recommendation method according to an embodiment of the present invention. As shown in fig. 9, the position recommendation method of the present embodiment includes the following steps:
in step S410, at least one piece of location information in the order information is acquired.
In this embodiment, the location information in the order information includes first location information and second location information. The first position information is used for representing a starting position in the order information, and the second position information is used for representing an arrival position in the order information.
Optionally, in this embodiment, a process of determining a recommended point according to the first location information in the order information is taken as an example to describe the location recommendation method in this embodiment, and the location recommendation method further includes the following steps.
In step S420, a candidate point set is determined according to the location information, and the candidate point set includes at least one candidate point.
In this embodiment, a plurality of candidate points are determined based on the first position information. The plurality of candidate points are all positions within a certain preset distance range around the first position information.
In step S430, a plurality of position ranges of each candidate point are obtained, and the plurality of position ranges are mutually included.
The position range in this embodiment may be a plurality of mutually included spatial ranges or road network ranges corresponding to the candidate points. The spatial range refers to a position area obtained by outwards expanding a preset radius range by taking the candidate point as a center. The road network range refers to a road section range which respectively extends from the candidate point as a center to two ends of the road section along the road section where the candidate point is located by a preset distance.
It should be understood that the actual vehicle parking violation and the generation of the ticket occur on a specific road segment. The road network range of the present embodiment corresponds to different road segments in one road, and the spatial range may include at least one road segment within the location area.
In step S440, the ticket rates of the position ranges of the candidate points within the preset history time periods are determined.
In this embodiment, when the position range is a spatial range, the ticket rate of each spatial range in a plurality of preset historical time periods is determined respectively. And when the position range is a road network range, determining the ticket rate of each road network range in a plurality of preset historical time periods respectively.
Optionally, in the embodiment, when determining the ticket rate of each position range of each candidate point in multiple preset time periods, first, the number of tickets and the number of parking times of each position range in different preset historical time periods are obtained, and a ratio of the number of tickets and the number of parking times of each position range in different preset time periods is determined as the ticket rate of the corresponding position range in the corresponding preset historical time period. And the parking times and the number of the tickets of all vehicles on the corresponding road section in each position range are respectively the parking times and the number of the tickets of all vehicles in the corresponding road section in each position range. Therefore, the ticket rate of each position range of each candidate point in different preset historical time periods is determined.
Optionally, the existing network car booking service system monitors order related information such as order travel information, travel path information and violation information of all vehicles in the operation process. Therefore, in order to conveniently acquire the number of parking times and the number of tickets of the plurality of position ranges corresponding to the candidate points in different preset historical time periods, in this embodiment, the number of parking times and the number of tickets can be acquired by querying or calling monitoring data on the network car booking service platform.
In step S450, a parking risk corresponding to the candidate point is determined based on the ticket rate.
In this embodiment, after determining the ticket rates of the position ranges within the preset historical time periods, the parking risk of the corresponding candidate point is determined based on the determined ticket rates. Therefore, the ticket rate of the position ranges of the candidate points in the preset historical time periods is obtained, and the parking risk corresponding to the candidate points is determined based on the ticket rate, so that the calculation accuracy of the parking risk of the candidate points is improved, convenience is provided for vehicle parking, and the parking experience of a driver is improved.
Optionally, when determining the parking risk corresponding to the candidate point based on the ticket rate, first, the ticket rate of each candidate point in each position range is determined according to the ticket rate of each position range of each candidate point in each preset historical time period, and a weighted summation operation is performed according to the ticket rate of each position unit and the position weight coefficients corresponding to different position ranges, so as to determine the parking risk corresponding to the candidate point. Therefore, calculation of the parking risk of the candidate point is achieved through the ticket rate of each position range and the corresponding position weight coefficient, the calculation result of the parking risk of the candidate point is more practical, and calculation accuracy is higher.
Further, the position weight coefficient in this embodiment may be set and adjusted according to the actual use condition. Specifically, the position weight coefficient in this embodiment is set according to the distance between the center of the position range and the candidate point, the position weight coefficient corresponding to the position range closest to the candidate point is the largest, and the position weight coefficient corresponding to the position range farthest from the candidate point is the smallest. Therefore, the calculation accuracy of the parking risk of the candidate point is further improved.
Further, when determining the ticket rate of each candidate point in each position range, determining the ticket rate of each position range according to the ticket rate of each position range in a plurality of preset historical time periods. And the ticket rate of each position range in a plurality of preset historical time periods is used for representing the parking risk of each position range in each preset historical time period.
Further, optionally, in this embodiment, each preset historical time period is provided with a corresponding time weight coefficient. And each time weight coefficient is set according to the time length of the preset historical time period from the current time, the time weight coefficient corresponding to the preset historical time period with the shortest time length from the current time is the largest, and the time weight coefficient corresponding to the preset historical time period with the longest time length from the current time is the smallest. Based on the method, weighted summation is carried out on the ticket rate in each preset historical time period and the corresponding time weight coefficient, and the result of the weighted summation is determined as the ticket rate of the corresponding position range. Therefore, the penalty rate of each position range is calculated by considering time attenuation and converting the change of the penalty rate generated by the time attenuation factor into different time attenuation coefficients, so that the calculation result of the penalty rate in each position range is more practical, and the calculation accuracy of the parking risk of the candidate point is improved. Meanwhile, the time weight coefficient in the embodiment can be set and adjusted according to the actual use condition, and the calculation accuracy of the parking risk of the candidate point is further improved.
In step S460, candidate points in the candidate point set are screened based on the parking risk, and a target recommended point is determined.
Optionally, the present embodiment includes the following steps when determining the target recommended point based on the parking risk.
In step S461, a target recommended point set is determined based on the parking risk and the attribute information of each candidate point.
In this embodiment, the parking risk and attribute information of each candidate point is input to a pre-configured recall model, and a target recommended point set is determined according to a result output by the recall model. And the target recommendation point set comprises at least one candidate point.
Optionally, to ensure the recall rate of the recall model and the determination speed and efficiency of the candidate points, the attribute information in this embodiment includes one or more items of heat information, violation information, and road crossing information. The recall model may be a linear model, a logistic regression model, or the like. Therefore, the parking risk and the attribute information of each candidate point are subjected to weighted calculation, candidate points in the candidate point set are subjected to coarse screening according to the weighted calculation result, a certain number of candidate points are obtained, and a target recommendation point set is determined.
In step S462, a target recommendation point is determined from the set of target recommendation points.
In this embodiment, the feature information of each candidate point in the target recommendation point set is input to a recommendation model trained in advance, and the recommendation model outputs an evaluation score corresponding to each candidate point. And sorting the candidate points in the candidate point set according to the evaluation scores, and recommending the candidate points corresponding to the top P evaluation scores as target recommendation points. The characteristic information of each candidate point comprises parking risks of a corresponding position range of each candidate point, positions of the candidate points, the number of tickets of each position area in each preset time period and parking times. Therefore, the target recommended points are determined according to the parking risks of the candidate points and the recommendation model, so that a driver can determine the best boarding points quickly, and the parking experience of the driver and the order use experience of passengers are improved.
Optionally, in this embodiment, the feature information of each candidate point includes a distance from the actual vehicle-loading position to the candidate point, whether there is a road crossing between the actual vehicle-loading position and the candidate point, and a parking risk of the candidate point, where each feature information corresponds to a different weight value, and the evaluation score of each candidate point is determined according to the different feature information and the corresponding weight value.
Optionally, the recommendation model in this embodiment may adopt a GBDT model, a DNN model, or a CNN model. Moreover, the recommendation model in this embodiment may be trained based on historical order information. Specifically, when a recommendation model is trained, a candidate point set is determined according to a predetermined recall model and first position information in history order information, and then positive samples and negative samples in a history order are determined according to feature information corresponding to each candidate point in the candidate point set. The characteristic information of each candidate point comprises the distance from the actual vehicle-loading position to the candidate point, whether a road-crossing exists between the actual vehicle-loading position and the candidate point or not and the parking risk of the candidate point, each characteristic information corresponds to different weight values respectively, the evaluation score of each candidate point is determined according to different characteristic information and the corresponding weight values, the corresponding candidate points are ranked according to the sequence of the evaluation scores from large to small, the candidate points corresponding to the P candidate points at the top in the ranking result are used as positive samples, other candidate points in the candidate point set are used as negative samples to train the selected recommendation model, and the trained recommendation model is applied to the position recommendation method.
Alternatively, the number of the target recommendation points in this embodiment may be set to 3, that is, the value of P may be set to 3. And the candidate point corresponding to the highest evaluation score is the default vehicle getting-on point. Therefore, by recommending a certain number of target recommendation points, a user can conveniently select the target recommendation points according to the actual situation, and the flexibility of position recommendation is improved. Meanwhile, the default boarding point is set, so that a user can conveniently and quickly determine the target boarding point.
Optionally, the target recommended point in this embodiment may be displayed through a user terminal (a driver terminal and/or a passenger terminal) so as to facilitate the driver or the passenger to view and adjust the travel route in time.
Further, when the user issues a selection instruction through the user terminal, the target boarding point is determined from the target recommendation points according to the selection instruction of the user. And when no operation occurs on the user terminal, determining the default boarding point as the target boarding point. The user terminal of the embodiment may be a driver terminal or a passenger terminal. When the driver terminal and the passenger terminal simultaneously issue the selection instruction, the selection instruction of the passenger terminal is preferentially adopted to determine the target boarding point. Therefore, a user can conveniently and quickly determine the target getting-on point, and the order use experience of passengers is improved.
According to the technical scheme, at least one position information in the order information is obtained, a candidate point set is determined according to the position information, a plurality of position ranges of each candidate point are obtained, a penalty rate of each position range of each candidate point in a plurality of preset historical time periods is determined, a parking risk corresponding to each candidate point is determined based on the penalty rate, a target recommendation point set is determined based on the parking risk and attribute information of each candidate point, and a target recommendation point is determined from the target recommendation point set.
Fig. 10 is a schematic diagram of a parking risk determination device according to an embodiment of the present invention, and as shown in fig. 10, the parking risk determination device 1 according to the embodiment includes an acquisition unit 11, a processing unit 12, and a determination unit 13. The obtaining unit 11 is configured to obtain a plurality of position ranges of the candidate points, where the position ranges are mutually included. The processing unit 12 is arranged to determine ticket rates for the location range over a plurality of preset historical time periods. The determination unit 13 is configured to determine a parking risk corresponding to the candidate point based on the ticket rate.
Fig. 11 is a schematic diagram of a position recommendation device according to an embodiment of the present invention. As shown in fig. 11, the position recommendation apparatus 2 of the present embodiment includes a start unit 21, an aggregation unit 22, an acquisition unit 23, a processing unit 24, a determination unit 25, and a target unit 26. The starting unit 21 is configured to obtain at least one piece of location information in the order information. The aggregation unit 22 is configured to determine a candidate point set according to the location information, where the candidate point set includes at least one candidate point. The acquisition unit 23 is configured to acquire a plurality of position ranges of each candidate point, and the plurality of position ranges are mutually included. The processing unit 24 is adapted to determine a ticket rate for each position range of each candidate point over a plurality of preset historical time periods. The determination unit 25 is configured to determine a parking risk corresponding to the candidate point based on the ticket rate. The target unit 26 is configured to filter candidate points in the candidate point set based on the parking risk, and determine a target recommended point.
Fig. 12 is a schematic diagram of an electronic device of an embodiment of the invention. As shown in fig. 12, the electronic device of the present embodiment is a general-purpose data processing apparatus, which includes a general-purpose computer hardware structure, and at least includes a processor 31 and a memory 32. The processor 31 and the memory 32 are connected by a bus 33. The memory 32 is adapted to store instructions or programs executable by the processor 31. The processor 31 may be a stand-alone microprocessor or may be a collection of one or more microprocessors. Thus, the processor 31 implements the processing of data and the control of other devices by executing instructions stored by the memory 32 to perform the method flows of embodiments of the present invention as described above. The bus 33 connects the above components together, and also connects the above components to a display controller 34, a display device, and an input/output (I/O) device 35. Input/output (I/O) devices 35 may be a mouse, keyboard, modem, network interface, touch input device, motion sensing input device, printer, and other devices known in the art. Typically, the input/output device 35 is connected to the system through an input/output (I/O) controller 36.
The memory 32 may store, among other things, software components such as an operating system, communication modules, interaction modules, and application programs. Each of the modules and applications described above corresponds to a set of executable program instructions that perform one or more functions and methods described in embodiments of the invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus (device) or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may employ a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations of methods, apparatus (devices) and computer program products according to embodiments of the application. It will be understood that each flow in the flow diagrams can be implemented by computer program instructions.
Another embodiment of the invention relates to a computer program product comprising computer programs/instructions for implementing some or all of the steps of the above-described method embodiments when executed by a processor. These computer programs/instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows. These computer programs/instructions may also be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
Another embodiment of the invention is directed to a non-transitory storage medium storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiment of the invention discloses A1 and a parking risk determination method, wherein the method comprises the following steps:
acquiring a plurality of position ranges of candidate points, wherein the position ranges are mutually contained;
determining a ticket rate for each of the location ranges over a plurality of preset historical time periods;
and determining the parking risk corresponding to the candidate point based on the ticket rate.
A2, the method according to A1, wherein the determining the parking risk corresponding to the candidate point based on the ticket rate includes:
determining a ticket rate for each location range;
and determining the parking risk of the corresponding candidate point according to the ticket rate of each position range and the corresponding position weight coefficient.
A3, the method according to a2, wherein the determining the parking risk of the corresponding candidate point according to the ticket rate of each position range and the corresponding position weight coefficient specifically includes:
and carrying out weighted summation on the ticket rate of each position range and the corresponding position weight coefficient, and determining the result of the weighted summation as the parking risk of the corresponding candidate point.
A4, the method according to A2 or A3, wherein the position weight coefficient is set according to the distance between the center of the position range and the candidate point, and the position weight coefficient corresponding to the position range closest to the candidate point is the largest.
A5, the method of a2, wherein the determining ticket rates for each location range includes:
determining a ticket rate of a target location range within different preset historical time periods, the target location range being one of the plurality of location ranges;
and determining the ticket rate corresponding to the target position range according to the ticket rate of each preset historical time period and the corresponding time weight coefficient.
A6, the method according to a5, wherein the determining of the ticket rate of the corresponding target position range according to the ticket rate of each preset historical time period and the corresponding time weighting coefficient specifically includes:
and carrying out weighted summation on the ticket rate in each preset historical time period and the corresponding time weight coefficient, and determining the result of the weighted summation as the ticket rate of the corresponding target position range.
A7, the method according to A5 or A6, wherein the time weighting factor is set according to the time length of the preset historical time period from the current time, and the time weighting factor corresponding to the preset historical time period with the shortest time length from the current time is the largest.
A8, the method according to A5, wherein the determining ticket rates for each position range within different preset historical time periods includes:
acquiring the number of tickets and the number of parking times of each position range in different preset historical time periods;
and determining the ratio of the number of the tickets of each position range in each preset historical time period to the parking times as the ticket rate of the corresponding position range in the corresponding preset historical time period.
The embodiment of the invention also discloses B1 and a parking risk determining device, wherein the device comprises:
an acquisition unit configured to acquire a plurality of position ranges of candidate points, the plurality of position ranges including each other;
the processing unit is used for determining the ticket rate of the position range in a plurality of preset historical time periods;
and the determining unit is used for determining the parking risk corresponding to the candidate point based on the ticket rate.
The embodiment of the invention also discloses C1 and a position recommendation method, wherein the method comprises the following steps:
acquiring at least one position information in the order information;
determining a candidate point set according to the position information, wherein the candidate point set comprises at least one candidate point;
acquiring a plurality of position ranges of each candidate point, wherein the position ranges are mutually contained;
determining a ticket rate of each position range of each candidate point in a plurality of preset historical time periods;
determining parking risks corresponding to the candidate points based on the ticket rate;
and screening candidate points in the candidate point set based on the parking risk, and determining a target recommended point.
The embodiment of the invention also discloses D1 and a position recommending device, wherein the device comprises:
the starting unit is used for acquiring at least one piece of position information in the order information;
a set unit, configured to determine a candidate point set according to the location information, where the candidate point set includes at least one candidate point;
an acquisition unit configured to acquire a plurality of position ranges of each candidate point, the plurality of position ranges including each other;
the processing unit is used for determining the ticket rate of each position range of each candidate point in a plurality of preset historical time periods;
the determining unit is used for determining the parking risk corresponding to the candidate point based on the ticket rate;
and the target unit is used for screening the candidate points in the candidate point set based on the parking risk and determining target recommended points.
An embodiment of the invention also discloses E1, a computer program product, wherein the computer program product comprises computer programs/instructions which, when executed by a processor, implement the method as described in any of a1-a8 or a 10.
The embodiment of the invention also discloses F1, an electronic device, comprising a memory and a processor, wherein the memory is used for storing one or more computer program instructions, and the processor executes the one or more computer program instructions to realize the method according to any one of A1-A8 or A10.
The embodiment of the invention also discloses G1 and a computer readable storage medium, wherein the computer readable storage medium stores a computer program which realizes the method of any one of A1-A8 or A10 when being executed by a processor.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A parking risk determination method, characterized in that the method comprises:
acquiring a plurality of position ranges of candidate points, wherein the position ranges are mutually contained;
determining a ticket rate for each of the location ranges over a plurality of preset historical time periods;
and determining the parking risk corresponding to the candidate point based on the ticket rate.
2. The method of claim 1, wherein the determining the parking risk corresponding to the candidate point based on the citation rate comprises:
determining a ticket rate for each location range;
and determining the parking risk of the corresponding candidate point according to the ticket rate of each position range and the corresponding position weight coefficient.
3. The method of claim 2, wherein determining a ticket rate for each range of locations comprises:
determining a ticket rate of a target location range within different preset historical time periods, the target location range being one of the plurality of location ranges;
and determining the ticket rate corresponding to the target position range according to the ticket rate of each preset historical time period and the corresponding time weight coefficient.
4. The method according to claim 3, wherein the time weighting factor is set according to the time length of the preset historical time period from the current time, and the time weighting factor corresponding to the preset historical time period with the shortest time length from the current time is the largest.
5. A parking risk determination apparatus, characterized in that the apparatus comprises:
an acquisition unit configured to acquire a plurality of position ranges of candidate points, the plurality of position ranges including each other;
the processing unit is used for determining the ticket rate of the position range in a plurality of preset historical time periods;
and the determining unit is used for determining the parking risk corresponding to the candidate point based on the ticket rate.
6. A method for location recommendation, the method comprising:
acquiring at least one position information in the order information;
determining a candidate point set according to the position information, wherein the candidate point set comprises at least one candidate point;
acquiring a plurality of position ranges of each candidate point, wherein the position ranges are mutually contained;
determining a ticket rate of each position range of each candidate point in a plurality of preset historical time periods;
determining parking risks corresponding to the candidate points based on the ticket rate;
and screening candidate points in the candidate point set based on the parking risk, and determining a target recommended point.
7. A location recommendation device, the device comprising:
the starting unit is used for acquiring at least one piece of position information in the order information;
a set unit, configured to determine a candidate point set according to the location information, where the candidate point set includes at least one candidate point;
an acquisition unit configured to acquire a plurality of position ranges of each candidate point, the plurality of position ranges including each other;
the processing unit is used for determining the ticket rate of each position range of each candidate point in a plurality of preset historical time periods;
the determining unit is used for determining the parking risk corresponding to the candidate point based on the ticket rate;
and the target unit is used for screening the candidate points in the candidate point set based on the parking risk and determining target recommended points.
8. A computer program product, characterized in that the computer program product comprises computer programs/instructions which, when executed by a processor, implement the method according to any of claims 1-4 or 6.
9. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any of claims 1-4 or 6.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1-4 or 6.
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| CN202110262219.9A CN113033978A (en) | 2021-03-10 | 2021-03-10 | Parking risk determination method, position recommendation method and device and electronic equipment |
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