CN115775449A - Vehicle type detection method and device - Google Patents
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Abstract
The application discloses a vehicle type detection method and device, which are used for solving the problems that an entity label is relied on and an applicable scene is limited in vehicle type detection. The method provided in the present application comprises: the method comprises the steps of obtaining vehicle passing data within set time, determining a plurality of travel paths of a vehicle to be detected within the set time according to the vehicle passing data, analyzing the travel paths to obtain travel characteristics of the vehicle to be detected within a set area, and determining the type of the vehicle to be detected according to the travel characteristics.
Description
Technical Field
The present application relates to the field of data analysis and processing, and in particular, to a method and an apparatus for detecting a vehicle type.
Background
With the increasing perfection of the vehicle passing data acquisition system by the traffic management department, the big data analysis based on the vehicle passing data is concerned by the traffic management department greatly. Through the data of passing the vehicle, the trip characteristics of analysis vehicle are classified, can know the current traffic condition of city traffic to in time discover the problem and make the scheme.
However, in the current technology, the classification monitoring of the vehicle depends on an entity tag, or a plurality of hardware devices are arranged in a limited scene, and the implementation is complex.
Disclosure of Invention
The embodiment of the application provides a vehicle type detection method and device, and aims to solve the problems that vehicle classification depends on entity labels and application scenes are limited.
In a first aspect, an embodiment of the present application provides a vehicle type detection method, including:
obtaining vehicle passing data within a set time length, wherein the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
determining a plurality of travel paths of the vehicle to be detected within the set time length according to the vehicle passing data, wherein each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes;
when the number of similar paths in the plurality of travel paths is larger than a set threshold value, determining that the vehicle to be detected is a commuting vehicle;
the first travel path and the second travel path are any two similar paths in the plurality of travel paths, the starting points of the first travel path and the second travel path are the same, and the ending points of the first travel path and the second travel path are the same.
Based on the scheme, when the vehicle type is detected, a plurality of travel paths of the vehicle to be detected in the set time length are determined based on the vehicle passing data, and the vehicle to be detected is determined to be a commuting vehicle according to the number of similar paths in the travel paths. The vehicle passing data are acquired through the bayonet device and/or the electric alarm electronic monitoring device of the road section of the intersection, and the vehicle passing data of the vehicle to be detected are not acquired through the entity tag, so that the convenience and the practicability of vehicle detection can be improved.
In some embodiments, the number of monitoring ports in the plurality of travel paths is less than a number threshold.
In some embodiments, the first travel path and the second travel path comprise at least one identical monitoring port in addition to the starting point and the ending point. Illustratively, the number of monitoring ports in the plurality of travel paths is greater than a number threshold. Based on the scheme, the similar paths are determined through the monitoring ports, so that the accuracy of determining the similar paths is improved.
In some embodiments, the set duration includes N days, and the times generated by the travel routes are working days.
In some embodiments, each of the plurality of travel routes includes a monitoring time interval between two adjacent monitoring ports that is less than a time interval threshold.
Based on the scheme, the multiple travel paths of the vehicle within the set time length are determined according to the vehicle passing data of the vehicle to be detected, the accuracy of determining the travel paths can be improved by setting the time interval threshold, and the accuracy of detecting the vehicle type is further improved.
In a second aspect, a vehicle type detection method provided in an embodiment of the present application includes:
obtaining vehicle passing data within a set time length, wherein the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
obtaining a plurality of travel paths of a vehicle to be detected according to the vehicle passing data, wherein each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes;
determining the proportion of the number of times of generating set behavior patterns in a set area of the vehicle to be detected in the number of travel paths according to the travel paths, wherein the set behavior patterns are stay in the set area, appear in the set area or pass through the set area;
and when the proportion meets the judgment condition of the vehicle type corresponding to the set area, determining the vehicle type of the vehicle to be detected as the vehicle type corresponding to the set area.
Based on the scheme, a plurality of travel paths of the vehicle to be detected are obtained through vehicle passing data, the proportion of the times of generating the set behavior patterns in the set area of the vehicle to be detected to the number of the travel paths is determined according to the travel paths, and the vehicle type of the vehicle to be detected is determined. According to the scheme, various vehicle types can be detected and classified, the problem that the use scene is limited is avoided, vehicle data are acquired according to the electronic monitoring equipment, and the practicability of vehicle detection is improved.
In some embodiments, the vehicle types include one or more of a transit car, a shopping cart, a home car, a high-frequency trip car, and a foreign car.
Based on the scheme, the method can be used for detecting and classifying various vehicles, the use scene is not limited, and the practicability of the scheme is improved.
In some embodiments, the vehicle type is a transit vehicle, the set area corresponding to the transit vehicle is an administrative area, and the set behavior pattern is present in the administrative area; or,
the vehicle type is a shopping cart, the set area corresponding to the shopping cart is a shopping mall, and the set behavior mode is that the vehicle stays in the shopping mall; or,
the vehicle type is a parent vehicle, a set region corresponding to the parent vehicle is a school, and the set behavior mode is that the vehicle passes through the school; or,
the vehicle type is a high-frequency trip vehicle, a set area corresponding to the high-frequency trip vehicle is an administrative area, and the set behavior pattern appears in the administrative area; or,
the vehicle type is a foreign vehicle, the set area corresponding to the foreign vehicle is an administrative area, and the set behavior pattern appears in the administrative area.
Through the scheme, different behavior mode settings are carried out on the vehicle according to different vehicle types in different areas, and the method and the device are suitable for various scenes.
In some embodiments, determining, according to the plurality of travel routes, a ratio of the number of times that the vehicle to be detected generates the set behavior pattern in the set area to the number of travel routes includes:
and determining the proportion of the number of times of generating the set behavior pattern in the set area of the vehicle to be detected in the number of travel paths according to at least one travel path with the generation time interval in the statistical time interval corresponding to the vehicle type in the travel paths.
Based on the scheme, the type of the vehicle to be detected is determined according to the proportion of the times of generating the set behavior patterns in the set area of the vehicle to be detected to the number of the travel routes, the vehicle can be accurately detected according to the different behavior patterns and the times proportion, and the detection accuracy is improved.
In a third aspect, an embodiment of the present application provides a vehicle type detection apparatus, including:
the system comprises an acquisition module, a monitoring module and a display module, wherein the acquisition module is used for acquiring vehicle passing data within a set time length, and the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
the processing module is used for determining a plurality of travel paths of the vehicle to be detected within the set time length according to the vehicle passing data acquired by the acquisition module, and each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes; when the number of the similar paths in the travel paths is larger than a set threshold value, determining that the vehicle to be detected is a commuting vehicle;
the first travel path and the second travel path are any two similar paths in the plurality of travel paths, the starting points of the first travel path and the second travel path are the same, and the ending points of the first travel path and the second travel path are the same.
In some embodiments, the first travel route and the second travel route comprise at least one identical monitoring port in addition to the starting point and the ending point.
In some embodiments, the set duration includes N days, and the times generated by the travel routes are working days.
In some embodiments, each of the travel routes includes a monitoring time interval between two adjacent monitoring ports smaller than a time interval threshold.
In some embodiments, the processing module is specifically configured to screen the vehicle passing data according to the monitoring time, and after the vehicle passing data is subjected to data screening in the set area, the vehicle passing data needs to be screened again according to the statistical time period, and then the vehicle passing data is subjected to vehicle passing data merging to generate the travel path of the vehicle to be detected.
In some embodiments, the processing module is specifically configured to count the number of times of a starting point and an important point of each travel path, and determine a high-frequency starting point and a high-frequency ending point. And the processing module performs loop statistics on each path to determine a reference path, wherein the starting point of the reference path is a high-frequency starting point, the key point is a high-frequency end point, and the number of point positions in the path is the largest.
In some embodiments, the processing module is configured to perform similar path learning on the plurality of travel paths and the reference path, and when the plurality of paths cannot perform similar path learning on the reference path, perform similar path learning on paths other than the reference path, and add the similar paths into the similar path package.
In some embodiments, the processing module is specifically configured to calculate a trip rate of the vehicle to be detected, the number of similar paths in the similar path package, and a similar path proportion. The trip rate is the proportion of the days of vehicle trip in a set time, and the similar paths are the first trip path and the second trip path, and at least one common monitoring port is arranged except for the same starting point and the same end point; and calculating a similar path proportion, wherein the similar path proportion is the ratio of the number of similar paths to the total travel path number. And when the similar path proportion is larger than a set threshold value, determining that the vehicle to be detected is a commuting vehicle.
In a fourth aspect, an embodiment of the present application provides another vehicle type detecting device, including:
the system comprises an acquisition module, a monitoring module and a display module, wherein the acquisition module is used for acquiring vehicle passing data within a set time length, and the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
the processing module is used for determining a plurality of travel paths of the vehicle to be detected according to the vehicle passing data acquired by the acquisition module, and each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes; determining the proportion of the number of times of generating set behavior patterns in a set area of the vehicle to be detected in the number of travel paths according to the travel paths, wherein the set behavior patterns are stay in the set area, appear in the set area or pass through the set area; and when the proportion meets the judgment condition of the vehicle type corresponding to the set area, determining the vehicle type of the vehicle to be detected as the vehicle type corresponding to the set area.
In some embodiments, the vehicle types include one or more of a transit car, a shopping cart, a home car, a high-frequency trip car, and a foreign car.
In some embodiments, the vehicle type is a transit vehicle, the set area corresponding to the transit vehicle is an administrative area, and the set behavior pattern is a passing through of the administrative area; or,
the vehicle type is a shopping cart, the set area corresponding to the shopping cart is a shopping mall, and the set behavior mode is stay in the shopping mall; or,
the vehicle type is a parent vehicle, a set region corresponding to the parent vehicle is a school, and the set behavior mode appears in the school; or,
the vehicle type is a high-frequency trip vehicle, a set region corresponding to the high-frequency trip vehicle is an administrative region, and the set behavior mode appears in the administrative region; or,
the vehicle type is a foreign vehicle, the set area corresponding to the foreign vehicle is an administrative area, and the set behavior pattern appears in the administrative area.
In some embodiments, the processing module, when determining, according to the multiple travel routes, a ratio of the number of times that the vehicle to be detected generates the set behavior pattern in the set area to the number of travel routes, is specifically configured to:
and determining the proportion of the times of generating the set behavior pattern in the set area of the vehicle to be detected in the number of the travel paths according to at least one travel path with the generation time interval positioned in the statistical time interval corresponding to the vehicle type in the travel paths.
In some embodiments, the processing module is further configured to screen the vehicle passing data according to the monitoring time, and after the vehicle passing data is subjected to data screening in the set area, the vehicle passing data needs to be screened again according to the statistical time period, and then the vehicle passing data is subjected to vehicle passing data merging to generate the travel path of the vehicle to be detected.
In a fifth aspect, an applied embodiment provides a vehicle type detecting device including: a memory and a processor;
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the vehicle type detection method of the first aspect or the second aspect according to the obtained program.
In a sixth aspect, the present application provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the vehicle type detection method according to the first aspect or the second aspect.
In addition, for technical effects brought by any one implementation manner of the third aspect to the sixth aspect, reference may be made to technical effects brought by different implementation manners of the first aspect and the second aspect, which are not described herein again.
Drawings
Fig. 1 is a schematic system architecture diagram of a vehicle type detection method according to an embodiment of the present application;
FIG. 2 is a flow chart of a vehicle type detection method provided by an embodiment of the present application;
fig. 3 is a schematic diagram of an arrangement position of an electronic monitoring device on an intersection road section according to an embodiment of the present application;
fig. 4 is a flowchart of a vehicle type detection method for a commuter vehicle according to an embodiment of the present disclosure;
fig. 5 is a flowchart of determining a commute vehicle based on a travel path according to an embodiment of the present application;
FIG. 6 is a flow chart of another vehicle type detection method provided by the embodiments of the present application;
FIG. 7 is a schematic diagram of an apparatus for a vehicle type detection method according to an embodiment of the present application;
fig. 8 is a schematic device diagram of another vehicle type detection method according to an embodiment of the present application.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 illustrates a system architecture to which the embodiments of the present application are applicable, which may include one or more servers 100, where the servers 100 may include a processor 110, a communication interface 120, and a memory 130.
The communication interface 120 is used for communicating with the electronic monitoring devices such as the gate of the intersection section and the electric alarm, and receiving and transmitting information transmitted by the electronic monitoring devices such as the gate of the intersection section and the electric alarm, so as to realize communication.
The processor 110 is a control center of the server 100, connects various parts of the entire server 100 using various interfaces and routes, performs various functions of the server 100 and processes data by operating or executing software programs and/or modules stored in the memory 130 and calling data stored in the memory 130. Optionally, processor 110 may include one or more processing units.
The memory 130 may be used to store software programs and modules, and the processor 110 executes various functional applications and data processing by operating the software programs and modules stored in the memory 130. The memory 130 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to a business process, and the like. Further, the memory 130 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
It should be noted that the structure shown in fig. 1 is only an example, and the embodiment of the present invention is not limited thereto.
The present embodiment provides a vehicle type detection method, fig. 2 exemplarily shows a flow of the vehicle type detection method, and the flow may be performed by an apparatus of the vehicle type detection method, the apparatus may be located in the server 100 shown in fig. 1, for example, the apparatus may be a processor, or the server 100, and the following description takes the server 100 as an example, and for convenience of description, the following description of the server 100 does not illustrate numerical references.
And 201, acquiring vehicle passing data of the vehicle to be detected within a set time length.
The vehicle passing data is used for representing the condition that the vehicle passes through a monitoring port in a set area. The monitoring ports may also be referred to as monitoring points, or simply points. For example, the vehicle passing data may include one or more of a monitoring port, a monitoring time, a license plate number, and the like of the vehicle to be detected.
In the embodiment of the application, electronic monitoring equipment such as a gate device or an electric alarm and the like of the road section of the intersection is used as a monitoring gate for acquiring vehicle passing data, and the vehicle passing data is acquired through the gate device of the road section of the intersection and the electronic monitoring equipment such as the electric alarm and the like. The electric alarm or the gate device may include an inductive detector and an electric alarm detector, wherein the inductive detector is generally arranged at a position 30m away from the stop line, the detection data of the inductive detector generally comprises the traffic flow (traffic passing data) of the entrance way and the occupancy, and the data output interval is one signal period. The electric police detector is generally arranged on an entrance way of each intersection in each direction, and the electric police shall be a multifunctional electric police integrating the bayonet function, so that the function of beating the bus when the bus is closed can be realized. The multifunctional electric police is generally arranged at a position 18-23m away from a stop line, the output data of the multifunctional electric police is the license plate number and the passing time of each vehicle passing through the stop line, and the data output interval is one signal period. In some embodiments, referring to fig. 3, the arrangement position of the electronic monitoring device on the road section of the intersection is exemplarily described.
The vehicle passing data monitored by the electric police or the gate device comprise vehicle passing data of a plurality of vehicles, and each vehicle can be classified according to the embodiment of the application. When the vehicle classification is described in the embodiment of the application, only the vehicle to be detected is taken as an example for explanation.
In some embodiments, the set duration may be one week, half month, one month, three months, N weeks, and so forth.
202, generating a plurality of travel paths of the vehicle to be detected within the set time length according to the vehicle passing data. Each trip path is generated according to vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes.
In some embodiments, the server may filter and clean the vehicle passing data of the vehicle to be detected based on the type of the detected vehicle to which the server is directed before determining the travel path of the vehicle to be detected. For example, when determining whether the vehicle to be detected is a commuter, the vehicle passing data of the non-morning and night trip time period and the vehicle passing data of the non-working day can be filtered.
In some embodiments, before determining the travel path of the vehicle to be detected, the server may further perform data screening on the vehicle-passing data, delete abnormal data, or perform position complementing on the vehicle-passing data.
For example, when data screening is performed on the vehicle passing data, the vehicle passing data can be screened for each vehicle to be detected according to the vehicle number to be detected, and travel data corresponding to each vehicle to be detected is generated.
Illustratively, the anomaly data may include one or more of:
(1) The vehicle passing data which are not in all possible points in the travel path of the vehicle to be detected within the set time length, for example, the vehicle passing data which are not between two adjacent points appear in the vehicle passing data, and the vehicle passing data are abnormal data.
(2) And the time of the passing data of the corresponding point location is not in the time interval of the passing data of two adjacent point locations.
(3) And repeatedly generating the vehicle passing data at the same point.
In some embodiments, when the vehicle to be detected has a plurality of passing data at a certain point, the abnormal data may be deleted according to the passing time of two points adjacent to the certain point. The reason for the abnormal data may be that electronic monitoring equipment such as a gate equipment and an electric alarm at the intersection section incorrectly identifies the license plate number and the like.
In some embodiments, when another point location exists between two adjacent point locations in the trip data of the vehicle to be detected, and the vehicle to be detected does not have vehicle passing data at the point location, the vehicle passing data can be complemented, so as to complement the vehicle passing data at the point location.
In some embodiments, the server may perform travel analysis on the vehicle to be detected according to the vehicle passing data, and may include travel starting time, starting location, destination time, destination address, and passing monitoring point location of the passing travel of the vehicle to be detected each time. For example, the server may collect vehicle passing data of 8 weeks of the vehicle to be detected, and analyze a travel route of each day in each week. The travel path obtained through analysis can be recorded in a linked list data form to obtain travel link data. For example, the travel link data may include { number plate number, number plate type, start time, end time, path trajectory }. Illustratively, the detection time interval between two adjacent monitoring points in each travel path in the determined travel paths is smaller than a time interval threshold, such as 30 minutes, or 60 minutes, and so on. Based on this, when the travel path is determined, the server may determine whether each two travel links of the vehicle to be detected may be merged according to the start time and the end time of the travel links and the adjacency condition of the monitoring port after determining the travel links. For example, the time difference between the start time of one trip link and the end time of another trip link is less than 60 minutes, and the end position of one occurring link is the start position of another trip link, two trip links may be combined into one trip link.
And 203, analyzing the travel characteristics of the vehicle to be detected in the set area according to the travel path, and determining the vehicle type of the vehicle to be detected according to the travel characteristics.
The vehicle types may include, for example, commuter cars, transit cars, shopping carts, family carts, high-frequency travel carts, or foreign carts, among others.
A commuter vehicle is a vehicle which runs on a usually fixed path in the morning and evening peak hours of a working day. For example, the application may determine whether the vehicle is a commuter vehicle according to the proportion of similar travel paths for a set time period, such as two consecutive months.
The transit vehicle is a vehicle which usually passes through a set area within a set time length. For example, the number of times of passing through a certain setting area is large or the number of routes passing through a certain setting area is large.
Shopping carts are typically vehicles that stay in a business establishment area for a set length of time on non-work days for a longer period of time.
The family car, the vehicle that often appears in school district during the time period of school of the school and leaving on weekdays within the set duration.
High-frequency traveling vehicles, or non-low-frequency vehicles, are frequently traveling in a set area.
Foreign vehicles, foreign license plate vehicles that often appear in set areas.
Further, the server can judge whether the vehicle to be detected meets the judgment condition of the vehicle type according to the travel characteristics of the vehicle, and determine the vehicle type of the vehicle to be detected. The trip route of waiting to detect the vehicle is obtained through collecting the data of waiting to detect the vehicle to this embodiment, carries out trip characteristic analysis to the trip route of waiting to detect the vehicle, judges the vehicle type of waiting to detect the vehicle. The application adopts the trip data of the vehicle obtained through the existing electronic monitoring equipment, does not need to set the entity label to classify the vehicle, has unlimited application scene and can be applied to various occasions.
Based on the above description, in order to facilitate understanding of the embodiment of the present application, as shown in fig. 4, there is provided a flow of a vehicle type detection method of a commuter vehicle:
401-402, see 201-202, and will not be described in detail here.
403, when the occupation ratio of the similar paths in the plurality of travel paths is greater than a set threshold, determining that the vehicle to be detected is a commuting vehicle. When two travel paths have at least the same starting point and the same ending point (or simply called end point), the two travel paths may be considered to be similar paths. For example, the first travel path and the second travel path are any two similar paths in the plurality of travel paths, the starting points of the first travel path and the second travel path are the same, and the ending points of the first travel path and the second travel path are the same. The similar path proportion can be understood as the ratio of the number of similar paths to the total travel path number.
In some embodiments, when determining a similar path of the plurality of travel paths, the travel paths may be divided into two groups according to the number of monitoring points included in the travel paths. And respectively determining whether the conditions of the commuting vehicles are met or not according to the two groups of travel paths. The first group is the paths with the number of monitoring points in the travel path larger than the number threshold. The second group is paths of which the number of monitoring points included in the travel path is less than or equal to a number threshold value.
In a possible implementation manner, when determining the type of the commuting vehicle, the commuting vehicle is divided into two sets of travel paths according to the number of monitoring points included in the travel paths, and whether the conditions of the commuting vehicle are met is determined for the two sets of travel paths respectively, which may be specifically implemented as follows, for example, as shown in fig. 5.
501, obtaining a travel route belonging to a first group in a plurality of travel routes of a vehicle to be detected, and determining the travel rate of the vehicle to be detected.
When the travel rate is determined to be greater than travel rate threshold 1, execution continues to 502, otherwise, execution is 506.
The trip rate refers to the ratio of the number of days of the vehicle to be detected to the number of days of a working day within a set time. And when the ratio is greater than the set threshold value, the number of days for the vehicle to be detected to travel in the set time length meets the condition of the commuter vehicle. When the travel days of the vehicle to be detected are calculated, if the travel path of the vehicle to be detected is detected in a certain day, the vehicle to be detected is determined to travel in the certain day.
For example, if the trip rate threshold 1 is 30%, if it is determined that the trip rate of the vehicle to be detected in the set time period is greater than 30%, the process continues to be executed 502.
502, a reference path is determined.
Illustratively, the server counts the number of the same starting points and the same end points in each travel path to determine the high-frequency starting points and the high-frequency end points. The high-frequency starting point is a starting point with the largest number of times of starting points in each travel path of the vehicle to be detected in statistics, and the high-frequency end point is an end point with the largest number of times of end points in each travel path of the vehicle to be detected in statistics. The server further determines a reference path according to the determined high-frequency starting point and the determined high-frequency end point. The reference path may be a path having a starting point as a high-frequency starting point and an end point as a high-frequency end point, and the maximum number of bits in the path.
503, similar path learning is performed with the reference path, and the conditions for performing similar path learning are as follows: at least N1 point positions are the same in the initial M1 point positions of the two paths, and at least N2 point positions are the same in the ending M2 point positions, wherein M1, N1, M2 and N2 are positive integers, M1 is larger than N1, and M2 is larger than N2. For example, at least 1 point location of the first 3 point locations of the two paths is the same, and at least 1 point location of the last 3 point locations is the same, then the two paths are similar paths.
In some embodiments, when a certain travel route is similar to the reference route, the similar route may be put into the similar route package of the reference route.
504, for the travel routes not meeting 503, similar route learning is performed with the travel routes other than the reference route, and the method for performing similar route learning is similar to the step 503, and is not repeated here.
And 505, counting the number and the proportion of the similar paths, and determining whether the vehicle to be detected is the commuter vehicle according to the maximum number of the similar paths.
For example, the server may count the number of travel paths in each similar path packet, and determine that the vehicle to be detected is a commuting vehicle if the ratio of the number of travel paths of a certain similar path packet to the total number of travel paths is greater than a set threshold.
And 506, acquiring the travel path belonging to the second group in the plurality of travel paths of the vehicle to be detected, and determining the travel rate of the vehicle to be detected.
And when the travel rate is determined to be greater than the travel rate threshold value 1, continuing to execute 506, otherwise, the vehicle to be detected is not a commuter vehicle.
And 507, determining similar path proportion in the traveling paths belonging to the second group, and determining whether the vehicle to be detected is a commuting vehicle or not according to the similar path proportion. Similar paths may be determined in a manner similar to 502-504 and will not be described in further detail herein.
In some embodiments, the quantity threshold may be 3. Then at 507, the travel route with the same start point and the same end point may be directly counted. And when the maximum number ratio of the travel routes with the same starting point and the same key point is greater than a set threshold value, determining that the vehicle to be detected is a commuting vehicle.
The manner of detection of other types of vehicles than commuters is described below in connection with the embodiments. Referring to fig. 6, a flowchart of another vehicle type detection method provided in the embodiment of the present application specifically includes:
601, the server collects vehicle passing data of the to-be-detected vehicle in a set area within a set time length.
For the description of the passing data, refer to the description in the example corresponding to fig. 3, and are not repeated here.
In some embodiments, the set duration may be N days, and the set area may be an administrative area, a shopping mall, or a school.
After the vehicle passing data is obtained, the server can perform data screening on the vehicle passing data, delete abnormal data or perform bit supplementing on the vehicle passing data. For details, reference may be made to the related description in 202, which is not described herein again.
And 602, generating a travel path of the vehicle to be detected according to the vehicle passing data and the vehicle type detected this time.
In some embodiments, the server may select, from the vehicle passing data, vehicle passing data meeting the travel time condition of the vehicle type according to the vehicle type detected this time. For example, for a family car, the time of the screened car passing data is located in the morning and evening peak time period.
603, judging the vehicle type of the vehicle to be detected according to the behavior mode of the vehicle to be detected.
Determining the times and/or the proportion of a set behavior mode generated by a vehicle to be detected in a set area according to a travel path of the vehicle to be detected, wherein the proportion is the ratio of the times of the set behavior mode to the travel path, and the set behavior mode comprises at least one of the following modes: pass through the defined area, stop at the defined area, or appear in the defined area.
In one possible implementation, the type of vehicle is a transit vehicle, the set area of the transit vehicle is a administrative district, and the behavior pattern of the type of vehicle can be passed through the administrative district. As an example, the set time of the transit vehicle statistics is 30 days, that is, the transit data within 30 days is counted, which is shown in table 1. Then, data screening can be carried out on the vehicle passing data according to the license plate number, abnormal data are deleted, or position complementing is carried out on the vehicle passing data, and a travel path corresponding to a vehicle is formed; and at least two travel paths meeting the merging requirement are merged into one travel path. And determining the number ratio of the travel paths passing through the administrative area in the plurality of travel paths of the vehicle, and determining that the vehicle to be detected is a transit vehicle when the ratio reaches a threshold value 3. For example, the threshold 3 may be 50%. See table 1 for details.
TABLE 1
In another possible implementation, the vehicle type is a shopping cart, the set area of the shopping cart is a shopping mall, and the behavior pattern of the vehicle type can be staying in the shopping mall area. For example, referring to table 1, the set time period of the shopping cart statistics is 12 weeks, that is, the data of passing the cart in 12 weeks is counted. And then, data screening can be carried out on the vehicle passing data according to the license plate number, abnormal data can be deleted, or the position of the vehicle passing data can be complemented, so that a travel path corresponding to the vehicle can be formed. When the shopping cart is subjected to data screening on the vehicle passing data, the vehicle passing data of a non-working day can be selected, and the travel route is determined according to the screened vehicle passing data. In some embodiments, the travel paths may be determined first, and then travel paths other than the travel path on the working day are screened from the determined travel paths, so as to further determine whether the vehicle to be detected is a shopping cart. In some embodiments, at least two travel paths meeting the merging requirement may be merged into one travel path. Further, it may be determined that the end point or the starting point included in the plurality of travel paths of the vehicle is a shopping mall area, and the week ratio of the vehicle staying in the shopping mall area is determined. The week occupation ratio is the ratio of the number of weeks that the vehicle stays in the shopping mall area to the total number of weeks in the statistical duration. The server may determine that the vehicle is a shopping cart upon determining that the duty cycle at which the vehicle is located in the mall area reaches a threshold of 4. For example, the threshold 4 may be 60%.
In yet another possible implementation, the vehicle type is a parent vehicle, the designated area of the parent vehicle is school, and the behavior pattern of the vehicle type may be present in the school area. For example, referring to table 1, the preset time period of the parent vehicle statistics is 30 days, that is, the vehicle passing data within 30 days is counted. And then, data screening can be carried out on the vehicle passing data according to the license plate number, abnormal data can be deleted, or the position of the vehicle passing data can be supplemented, so that a travel path corresponding to the vehicle can be formed. When the data screening is carried out on the vehicle passing data by the family car, the vehicle passing data of the working day can be selected, and the travel route is determined according to the screened vehicle passing data. In some embodiments, the travel paths may be determined first, and then the travel path of a working day (or a morning and evening study time period of the working day) is screened out from the determined travel paths, so as to further determine whether the vehicle to be detected is a parent vehicle. Further, at least two travel paths meeting the merging requirement can be merged into one travel path. Further, the number of times of occurrence in the school zone in the multiple travel paths of the vehicle can be determined, and when the number of times reaches a threshold value 5, the vehicle is determined to be a family car. For example, the threshold 5 may be 20.
In another possible implementation manner, the vehicle type is a high-frequency trip vehicle, the set area of the high-frequency trip vehicle is a administrative area, and the behavior pattern of the vehicle of the type may appear in the administrative area. As an example, referring to table 1, the set duration of the hf trip vehicle statistics is 60 days, that is, the vehicle passing data within 60 days is counted. And then, data screening can be carried out on the vehicle passing data according to the license plate number, abnormal data can be deleted, or the position of the vehicle passing data can be complemented, so that a travel path corresponding to the vehicle can be formed. Optionally, at least two travel paths meeting the merging requirement may be merged into one travel path. And determining the number of travel paths of the vehicle passing through the administrative district in the plurality of travel paths, namely determining the number of times of the vehicle appearing in the administrative district. And if the number of times of the vehicle appearing in the administrative area reaches the threshold value 6, determining that the vehicle to be detected is a high-frequency driving vehicle. For example, the threshold 6 may be 50.
In another possible implementation manner, the vehicle type is a local-use foreign vehicle (referred to as a foreign vehicle for short), the set area of the foreign vehicle is a administrative area, and the behavior pattern of the vehicle type can appear in the administrative area. As an example, the set time period counted by the foreign vehicle is 30 days, that is, the vehicle passing data in 30 days is counted. And then, data screening can be carried out on the vehicle passing data according to the license plate number, abnormal data can be deleted, or the position of the vehicle passing data can be supplemented, so that a travel path corresponding to the vehicle can be formed. The license plate number is a foreign license plate number. Optionally, at least two travel paths meeting the merging requirement are merged into one travel path. And determining the number of times of the vehicle appearing in the administrative district according to the plurality of travel routes of the vehicle, and if the number of times reaches a threshold value 7, determining that the vehicle is a local vehicle. For example, the threshold 7 may be 15. Or determining the ratio of the number of days appearing in the administrative area in the plurality of travel paths of the vehicle to the number of days included in the set time length, and determining that the vehicle to be detected is an outside vehicle when the ratio reaches a threshold value 8. For example, the set threshold 8 may be 55%.
In some embodiments, after the vehicle type to be detected is targeted, the association relationship between the vehicle to be detected and the vehicle type tag may be saved. For example, the association may be saved in the format of a persistent table name. The association relationship may be an association relationship between a license plate number of the vehicle and the type tag. It should be noted that the same vehicle may have only one type tag, or may have a plurality of type tags. Such as a commuter car, may also be a home keeper car.
Through the detection to the vehicle on duty, be favorable to discerning the trunk road on duty in this application embodiment, and then can carry out key management to the trunk road on duty, reduce the condition that the morning and evening peak is blocked up to appear. Through the detection to the car of family's keeper, help sending the notice to the head of a family car under special circumstances, perhaps when the gathering of car of family takes place in certain school's region, can in time provide the early warning to the regional traffic management of school. When the traffic flow of the shopping mall areas in holidays and non-workdays is large, through detection of the shopping vehicles, notification messages or suggestions can be sent to the shopping vehicles related to the shopping mall in advance when the occupancy of the shopping vehicles in the shopping mall areas reaches a threshold value, the traffic flow can be evacuated in time, and the purpose of reducing congestion is achieved. Through the detection of the passing vehicles, when special conditions occur in the area identified by the passing vehicles, notification messages can be sent to the passing vehicles in the area, and the situation that congestion is aggravated is avoided.
Based on the same technical concept, fig. 7 exemplarily shows a structure of an apparatus 600 for a vehicle type detecting method provided in the embodiment of the present application, which may execute a flow of the vehicle type detecting method shown in fig. 2, fig. 3, or fig. 4, and the apparatus may be located in the server 100 shown in fig. 1, or may be located in the server 100.
As shown in fig. 7, the apparatus specifically includes:
the acquiring module 701 is configured to acquire vehicle passing data of a vehicle to be detected for a set duration, where the vehicle passing data includes a monitoring port, monitoring time, and a license plate number of the vehicle to be detected.
A processing module 702, configured to determine, according to the vehicle passing data acquired by the acquisition module 701, multiple travel paths of the vehicle to be detected within the set time duration, where each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes; and when the number of the similar paths in the plurality of travel paths is determined to be larger than a set threshold value, determining that the vehicle to be detected is a commuting vehicle.
The first travel path and the second travel path are any two similar paths in the travel paths, the starting points of the first travel path and the second travel path are the same, and the ending points of the first travel path and the second travel path are the same.
In some embodiments, the first travel path and the second travel path comprise at least one identical monitoring port in addition to the starting point and the ending point.
In some embodiments, the set duration includes N days, and the time generated by the travel routes is a working day.
In some embodiments, each of the travel routes includes a monitoring time interval between two adjacent monitoring ports smaller than a time interval threshold.
Based on the same technical concept, fig. 8 exemplarily shows a structure of an apparatus 800 for a vehicle type detection method provided in the embodiment of the present application, which may execute the flow of the vehicle type detection method shown in fig. 6, and the apparatus may be located in the server 100 shown in fig. 1, or may be located in the server 100.
As shown in fig. 8, the apparatus specifically includes:
the obtaining module 801 is configured to obtain vehicle passing data within a set time period, where the vehicle passing data is used to represent a situation that a vehicle passes through a monitoring port in a set area.
A processing module 802, configured to determine multiple travel paths of a vehicle to be detected according to the vehicle passing data acquired by the acquisition module, where each travel path is generated according to vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes; determining the proportion of the times of generating set behavior patterns in a set area of the vehicle to be detected to the number of the travel paths according to the travel paths, wherein the set behavior patterns are staying in the set area, appearing in the set area or passing through the set area; and when the proportion meets the judgment condition of the vehicle type corresponding to the set area, determining the vehicle type of the vehicle to be detected as the vehicle type corresponding to the set area.
In some embodiments, the vehicle types include one or more of a transit car, a shopping cart, a home car, a high-frequency trip car, and a foreign car.
In some embodiments, the vehicle type is a transit vehicle, the set area corresponding to the transit vehicle is an administrative area, and the set behavior pattern is passed through the administrative area; or,
the vehicle type is a shopping cart, the set area corresponding to the shopping cart is a shopping mall, and the set behavior mode is stay in the shopping mall; or,
the vehicle type is a parent vehicle, a set region corresponding to the parent vehicle is a school, and the set behavior mode appears in the school; or,
the vehicle type is a high-frequency trip vehicle, a set area corresponding to the high-frequency trip vehicle is an administrative area, and the set behavior pattern appears in the administrative area;
the vehicle type is a foreign vehicle, a set region corresponding to the foreign vehicle is an administrative region, and the set behavior pattern appears in the administrative region.
In some embodiments, the processing module 802, when determining, according to the multiple travel routes, a ratio of a number of times that the vehicle to be detected generates the set behavior pattern in the set area to the number of travel routes, is specifically configured to:
and determining the proportion of the times of generating the set behavior pattern in the set area of the vehicle to be detected in the number of the travel paths according to at least one travel path with the generation time interval positioned in the statistical time interval corresponding to the vehicle type in the travel paths.
Based on the same technical concept, the embodiment of the present application further provides a vehicle type detecting device, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the vehicle type detection method according to the obtained program.
Based on the same technical concept, embodiments of the present application further provide a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer reads and executes the computer-readable instructions, the computer is caused to execute the vehicle type detection method based on the passing vehicle data.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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 and/or block diagram block or blocks.
These computer program instructions may also 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 and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A vehicle type detection method, characterized by comprising:
obtaining vehicle passing data within a set time length, wherein the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
determining a plurality of travel paths of a vehicle to be detected in the set time length according to the vehicle passing data, wherein each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes;
when the number of similar paths in the plurality of travel paths is larger than a set threshold value, determining that the vehicle to be detected is a commuting vehicle;
the first travel path and the second travel path are any two similar paths in the travel paths, the starting points of the first travel path and the second travel path are the same, and the ending points of the first travel path and the second travel path are the same.
2. The method of claim 1, wherein a first travel path and said second travel path include at least one identical monitoring port in addition to said starting point and ending point.
3. The method of claim 1 or 2, wherein any one of the plurality of travel routes includes a monitoring time interval between two adjacent monitoring ports that is less than a time interval threshold.
4. A vehicle type detection method characterized by comprising:
obtaining vehicle passing data within a set time length, wherein the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
obtaining a plurality of travel paths of a vehicle to be detected according to the vehicle passing data, wherein each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes;
determining the proportion of the times of generating set behavior patterns in a set area of the vehicle to be detected to the number of the travel paths according to the travel paths, wherein the set behavior patterns are staying in the set area, appearing in the set area or passing through the set area;
and when the proportion meets the judgment condition of the vehicle type corresponding to the set area, determining the vehicle type of the vehicle to be detected as the vehicle type corresponding to the set area.
5. The method of claim 4, wherein the vehicle types include one or more of a transit vehicle, a shopping cart, a family vehicle, a high-frequency trip vehicle, and an outside vehicle;
the vehicle type is a transit vehicle, a set region corresponding to the transit vehicle is an administrative region, and the set behavior mode is that the transit vehicle appears in the administrative region; or,
the vehicle type is a shopping cart, a set area corresponding to the shopping cart is a shopping mall, and the set behavior mode is that the vehicle stays in the shopping mall; or,
the vehicle type is a parent vehicle, a set region corresponding to the parent vehicle is a school, and the set behavior mode is that the vehicle passes through the school; or,
the vehicle type is a high-frequency trip vehicle, a set region corresponding to the high-frequency trip vehicle is an administrative region, and the set behavior mode appears in the administrative region;
the vehicle type is a foreign vehicle, the set area corresponding to the foreign vehicle is an administrative area, and the set behavior pattern appears in the administrative area.
6. The method according to claim 4 or 5, wherein determining the proportion of the number of times of generating the set behavior pattern in the set area of the vehicle to be detected to the number of travel routes according to the travel routes comprises:
and determining the proportion of the times of generating the set behavior pattern in the set area of the vehicle to be detected in the number of the travel paths according to at least one travel path with the generation time interval positioned in the statistical time interval corresponding to the vehicle type in the travel paths.
7. A vehicle type detection device characterized by comprising:
the system comprises an acquisition module, a monitoring module and a display module, wherein the acquisition module is used for acquiring vehicle passing data within a set time length, and the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
the processing module is used for determining a plurality of travel paths of the vehicle to be detected within the set time length according to the vehicle passing data acquired by the acquisition module, and each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes; when the number of similar paths in the plurality of travel paths is determined to be larger than a set threshold value, determining that the vehicle to be detected is a commuting vehicle;
the first travel path and the second travel path are any two similar paths in the travel paths, the starting points of the first travel path and the second travel path are the same, and the ending points of the first travel path and the second travel path are the same.
8. A vehicle type detection device characterized by comprising:
the system comprises an acquisition module, a monitoring module and a display module, wherein the acquisition module is used for acquiring vehicle passing data within a set time length, and the vehicle passing data is used for representing the condition that a vehicle passes through a monitoring port in a set area;
the processing module is used for determining a plurality of travel paths of the vehicle to be detected according to the vehicle passing data acquired by the acquisition module, wherein each travel path is generated according to the vehicle passing data of at least two monitoring ports through which the vehicle to be detected passes; determining the proportion of the number of times of generating set behavior patterns in a set area of the vehicle to be detected in the number of travel paths according to the travel paths, wherein the set behavior patterns are stay in the set area, appear in the set area or pass through the set area; and when the proportion meets the judgment condition of the vehicle type corresponding to the set area, determining the vehicle type of the vehicle to be detected as the vehicle type corresponding to the set area.
9. A vehicle type detection device characterized by comprising:
a memory and a processor;
a memory for storing program instructions;
a processor for calling program instructions stored in said memory and executing the method of any one of claims 1 to 6 in accordance with the obtained program.
10. A computer readable storage medium having stored thereon computer instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1 to 6.
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