CN112614345A - Vehicle speed calculation method, device, equipment and storage medium - Google Patents
Vehicle speed calculation method, device, equipment and storage medium Download PDFInfo
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- CN112614345A CN112614345A CN202011476058.5A CN202011476058A CN112614345A CN 112614345 A CN112614345 A CN 112614345A CN 202011476058 A CN202011476058 A CN 202011476058A CN 112614345 A CN112614345 A CN 112614345A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Abstract
The invention relates to the technical field of big data, is applied to the field of intelligent traffic, and discloses a vehicle speed calculation method, a vehicle speed calculation device, vehicle speed calculation equipment and a vehicle speed storage medium, which are used for solving the problem of difficulty in obtaining road running conditions and improving the accuracy rate of monitoring the road running conditions. The vehicle speed calculation method includes: acquiring vehicle track data and vehicle basic information of a plurality of vehicles; classifying the vehicles according to the vehicle track data or the vehicle basic information to obtain vehicle categories; performing deviation rectification processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data to obtain a plurality of effective vehicle track data; the method comprises the steps of calculating a plurality of vehicle type speed data based on a plurality of effective vehicle track data and a plurality of vehicle types, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method, a device, equipment and a storage medium for calculating vehicle speed.
Background
With the increase of consumption level, automobiles are more and more popular, and the mass of automobiles are brought into the market along with the problems of urban road planning, road traffic control and the like. The road operation condition is used as one of traffic basic parameters, is not only a basis for road traffic control and traffic flow induction, but also an important basis for dividing road service level grades and planning urban roads.
Currently, the road operation condition is usually obtained by using hardware equipment such as video monitoring and the like to obtain the road operation condition of a certain area of a certain road, but the hardware equipment such as video monitoring and the like adopted in the mode is large and the hardware is difficult to deploy, and the corresponding overall road operation condition cannot be accurately presented according to the road operation condition of a specific area.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for calculating the speed of a vehicle, solves the problem of difficulty in acquiring the running condition of a road, and improves the accuracy rate of monitoring the running condition of the road.
A first aspect of the invention provides a vehicle speed calculation method, including: the method comprises the steps of obtaining vehicle track data and basic vehicle information of a plurality of vehicles, wherein the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points; classifying a plurality of vehicles according to the plurality of vehicle track data or the plurality of vehicle basic information to obtain a plurality of vehicle categories; performing deviation rectification processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data to obtain a plurality of effective vehicle track data; and calculating to obtain a plurality of vehicle type speed data based on the plurality of effective vehicle track data and the plurality of vehicle types, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition.
Optionally, in a first implementation manner of the first aspect of the present invention, the classifying the plurality of vehicles according to the plurality of vehicle trajectory data or the plurality of pieces of vehicle basic information to obtain a plurality of vehicle categories includes: when the corresponding vehicles are classified according to the vehicle track data, the corresponding vehicle types are obtained from the vehicle platforms corresponding to the target vehicle track data, and a plurality of vehicle types are obtained; when corresponding vehicles are classified according to the basic information of each vehicle, target license plate number data, target vehicle color data and target vehicle type data are extracted from the basic information of the target vehicle; classifying based on the target license plate number data, the target vehicle color data and the target vehicle type data to generate corresponding vehicle types, and obtaining a plurality of vehicle types.
Optionally, in a second implementation manner of the first aspect of the present invention, the performing, based on each group of vehicle location points, a deviation rectification process on the corresponding vehicle trajectory data to generate corresponding effective vehicle trajectory data, and obtaining a plurality of effective vehicle trajectory data includes: reading a plurality of corresponding vehicle position points from the target vehicle track data, wherein the plurality of vehicle position points form a group of vehicle position points, and each vehicle position point corresponds to a moment; determining a plurality of first position points to be deleted based on the plurality of vehicle position points and preset road data, and deleting the first position points to be deleted to obtain a plurality of vehicle track data after primary deviation rectification, wherein each vehicle track data after primary deviation rectification comprises a plurality of vehicle position points after deviation rectification; and determining a plurality of second position points to be deleted based on the plurality of corrected vehicle position points, and deleting the plurality of second position points to be deleted to obtain a plurality of effective vehicle track data.
Optionally, in a third implementation manner of the first aspect of the present invention, the determining a plurality of first position points to be deleted based on the plurality of vehicle position points and preset road data, and deleting the first position points to be deleted to obtain a plurality of primarily corrected vehicle trajectory data, where each of the initially corrected vehicle trajectory data includes a plurality of corrected vehicle position points includes: calculating the vertical distance between each vehicle position point and preset road data to obtain a corresponding position point distance, and judging whether the distance of each position point is greater than a distance threshold value; if the distance between the target position points is greater than the distance threshold, determining that the vehicle position points corresponding to the distance between the target position points are first position points to be deleted to obtain a plurality of first deletion position points; and deleting the first position points to be deleted to obtain a plurality of vehicle track data after primary deviation rectification, wherein each vehicle track data after primary deviation rectification comprises a plurality of vehicle position points after deviation rectification.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the determining, based on the plurality of corrected vehicle position points, a plurality of second position points to be deleted, and deleting the plurality of second position points to be deleted to obtain a plurality of effective vehicle trajectory data includes: calculating corresponding displacement data based on every two adjacent vehicle position points after deviation rectification to obtain a plurality of displacement data; judging whether each displacement data is larger than a displacement threshold value; if the target displacement data is larger than the displacement threshold, determining two adjacent vehicle position points after deviation correction of the target as second position points to be deleted to obtain a plurality of second position points to be deleted; and deleting the second position points to be deleted to obtain a plurality of effective vehicle track data.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the calculating, based on the plurality of valid vehicle trajectory data and the plurality of vehicle categories, a plurality of vehicle category speed data, where each vehicle category speed data is used for monitoring and analyzing a road running condition, includes: calculating corresponding vehicle speed data based on each effective vehicle track data to obtain a plurality of vehicle speed data; classifying the plurality of vehicle speed data based on the plurality of vehicle categories to obtain a plurality of classified vehicle speed data; performing abnormal data elimination processing on the classified vehicle speed data to obtain abnormal vehicle speed data; and calculating corresponding vehicle type speed data based on the vehicle speed data after each abnormal processing and the corresponding vehicle speed data quantity to obtain a plurality of vehicle type speed data, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the performing abnormal data elimination processing on the plurality of classified vehicle speed data to obtain a plurality of vehicle speed data after abnormal processing includes: constructing a normal distribution graph based on the plurality of classified vehicle speed data to obtain a speed normal distribution graph; determining a plurality of abnormally classified vehicle speed data based on the speed normal distribution map, and eliminating the plurality of abnormally classified vehicle speed data to obtain a plurality of abnormally processed vehicle speed data, wherein the plurality of abnormally classified vehicle speed data are a plurality of classified vehicle speed data corresponding to traffic accident road conditions or traffic jam road conditions.
A second aspect of the present invention provides a vehicle speed calculation device, including: the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring vehicle track data and basic vehicle information of a plurality of vehicles, the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points; the classification module is used for classifying a plurality of vehicles according to the plurality of vehicle track data or the plurality of vehicle basic information to obtain a plurality of vehicle categories; the deviation correcting module is used for performing deviation correcting processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data and obtain a plurality of effective vehicle track data; and the calculation module is used for calculating to obtain a plurality of vehicle type speed data based on the effective vehicle track data and the vehicle types, and each vehicle type speed data is used for monitoring and analyzing the road running condition.
Optionally, in a first implementation manner of the second aspect of the present invention, the classification module is specifically configured to: when the corresponding vehicles are classified according to the vehicle track data, the corresponding vehicle types are obtained from the vehicle platforms corresponding to the target vehicle track data, and a plurality of vehicle types are obtained; when corresponding vehicles are classified according to the basic information of each vehicle, target license plate number data, target vehicle color data and target vehicle type data are extracted from the basic information of the target vehicle; classifying based on the target license plate number data, the target vehicle color data and the target vehicle type data to generate corresponding vehicle types, and obtaining a plurality of vehicle types.
Optionally, in a second implementation manner of the second aspect of the present invention, the deviation rectifying module includes: the reading unit is used for reading a plurality of corresponding vehicle position points from the target vehicle track data, the plurality of vehicle position points form a group of vehicle position points, and each vehicle position point corresponds to one moment; the initial deviation rectifying unit is used for determining a plurality of first position points to be deleted based on the plurality of vehicle position points and preset road data, deleting the first position points to be deleted and obtaining a plurality of initially corrected vehicle track data, wherein each initially corrected vehicle track data comprises a plurality of corrected vehicle position points; and the secondary deviation rectifying unit is used for determining a plurality of second position points to be deleted based on the plurality of corrected vehicle position points and deleting the plurality of second position points to be deleted to obtain a plurality of effective vehicle track data.
Optionally, in a third implementation manner of the second aspect of the present invention, the primary deviation rectifying unit is specifically configured to: calculating the vertical distance between each vehicle position point and preset road data to obtain a corresponding position point distance, and judging whether the distance of each position point is greater than a distance threshold value; if the distance between the target position points is greater than the distance threshold, determining that the vehicle position points corresponding to the distance between the target position points are first position points to be deleted to obtain a plurality of first deletion position points; and deleting the first position points to be deleted to obtain a plurality of vehicle track data after primary deviation rectification, wherein each vehicle track data after primary deviation rectification comprises a plurality of vehicle position points after deviation rectification.
Optionally, in a fourth implementation manner of the second aspect of the present invention, the secondary deviation rectifying unit is specifically configured to: calculating corresponding displacement data based on every two adjacent vehicle position points after deviation rectification to obtain a plurality of displacement data; judging whether each displacement data is larger than a displacement threshold value; if the target displacement data is larger than the displacement threshold, determining two adjacent vehicle position points after deviation correction of the target as second position points to be deleted to obtain a plurality of second position points to be deleted; and deleting the second position points to be deleted to obtain a plurality of effective vehicle track data.
Optionally, in a fifth implementation manner of the second aspect of the present invention, the calculation module includes: the first calculation unit is used for calculating corresponding vehicle speed data based on each effective vehicle track data to obtain a plurality of vehicle speed data; a classification unit configured to classify the plurality of vehicle speed data based on the plurality of vehicle categories to obtain a plurality of classified vehicle speed data; the rejecting unit is used for rejecting abnormal data from the classified vehicle speed data to obtain a plurality of vehicle speed data subjected to abnormal processing; and the calculating unit is used for calculating corresponding vehicle type speed data based on each abnormal processed vehicle speed data and the corresponding vehicle speed data quantity to obtain a plurality of vehicle type speed data, and each vehicle type speed data is used for monitoring and analyzing the road running condition.
Optionally, in a sixth implementation manner of the second aspect of the present invention, the rejecting unit is specifically configured to: constructing a normal distribution graph based on the plurality of classified vehicle speed data to obtain a speed normal distribution graph; determining a plurality of abnormally classified vehicle speed data based on the speed normal distribution map, and eliminating the plurality of abnormally classified vehicle speed data to obtain a plurality of abnormally processed vehicle speed data, wherein the plurality of abnormally classified vehicle speed data are a plurality of classified vehicle speed data corresponding to traffic accident road conditions or traffic jam road conditions.
A third aspect of the present invention provides a vehicle speed calculation apparatus comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line; the at least one processor invokes the instructions in the memory to cause the vehicle speed calculation device to execute the vehicle speed calculation method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to execute the above-described method of calculating a vehicle speed.
According to the technical scheme, vehicle track data and vehicle basic information of a plurality of vehicles are obtained, the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points; classifying a plurality of vehicles according to the plurality of vehicle track data or the plurality of vehicle basic information to obtain a plurality of vehicle categories; performing deviation rectification processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data to obtain a plurality of effective vehicle track data; and calculating to obtain a plurality of vehicle type speed data based on the plurality of effective vehicle track data and the plurality of vehicle types, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition. In the embodiment of the invention, the vehicles are classified according to the track data of the vehicles, then the deviation rectification processing is carried out on the track data of the vehicles, the speed of each vehicle category is calculated according to the vehicle track data after the deviation rectification, the running condition of the road can be obtained, the problem that the road running condition is difficult to obtain is solved, and the accuracy of monitoring the road running condition is improved. The method can be applied to the field of intelligent traffic, so that the construction of an intelligent city is promoted.
Drawings
FIG. 1 is a schematic diagram of an embodiment of a method for calculating a vehicle speed according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another embodiment of a method for calculating a vehicle speed according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an embodiment of a vehicle speed calculation device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of another embodiment of the vehicle speed calculation device in the embodiment of the invention;
fig. 5 is a schematic diagram of an embodiment of a vehicle speed calculation device in the embodiment of the invention.
Detailed Description
The embodiment of the invention provides a vehicle speed calculation method, a vehicle speed calculation device, vehicle speed calculation equipment and a storage medium, which are used for solving the problem of difficulty in obtaining a road running condition and improving the accuracy rate of monitoring the road running condition.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a detailed flow of an embodiment of the present invention is described below, and referring to fig. 1, an embodiment of a method for calculating a vehicle speed in an embodiment of the present invention includes:
101. the method comprises the steps of obtaining vehicle track data and basic vehicle information of a plurality of vehicles, wherein the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points;
the server obtains a plurality of vehicle track data which are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, each vehicle track data at least comprises a group of vehicle position points, and each group of vehicle position points comprises a plurality of vehicle position points.
The bus track data is track data reported by a bus company platform, the taxi track data is track data reported by taxi companies and network appointment company platforms, and the large-scale vehicle track data is track data reported by long-distance passenger companies, hazardous chemical substance long-distance vehicle companies and hazardous chemical substance vehicle company platforms. When the platform does not exist, the user docks the vehicle-mounted equipment and accesses the vehicle-mounted equipment into the Internet of vehicles international standard protocol, so that vehicle track data (Internet map vehicle track data) are reported to the server, or the vehicle track data (Internet map vehicle track data) are reported at regular time through software of the mobile terminal. When any platform or any terminal reports the vehicle track data, the server receives the data. The basic information of the vehicle is basic information of the vehicle, such as a license plate number of the vehicle, a color of the vehicle, or a type of the vehicle.
It should be noted that, in the present embodiment, the vehicle trajectory data and the vehicle basic information are acquired every 3 seconds.
It is understood that the execution subject of the present invention may be a vehicle speed calculation device, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
102. Classifying the vehicles according to the vehicle track data or the vehicle basic information to obtain vehicle categories;
the server classifies the vehicles according to the vehicle track data to obtain vehicle types. Or classifying the vehicles according to a plurality of pieces of vehicle basic information so as to obtain a plurality of vehicle categories.
Since the associated road speed dictates that the speed limits for different vehicles are different, e.g. car and bus, an important step before calculating the vehicle class speed data is to classify the vehicles to obtain a plurality of vehicle classes.
For example, in one embodiment, assuming that the vehicle trajectory data a1 is bus trajectory data, the server determines that the vehicle category of the vehicle is bus; in another embodiment, assuming that the vehicle type of the vehicle basic information a2 is passenger car, the color of the vehicle is yellow, and the license plate number is jing a · C12345, the server determines that the vehicle category of the vehicle is public transport.
103. Performing deviation rectification processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data to obtain a plurality of effective vehicle track data;
the server carries out deviation rectification processing on each vehicle track data according to the group of vehicle position points corresponding to each vehicle track data so as to generate a plurality of effective vehicle track data,
each set of vehicle position points may include a part of abnormal position points, and the abnormal position points may be points that are not on the preset road data or may be position points that do not match the actual position, for example, the distance between the vehicle position point B1 and the vehicle position point B2 in one set of vehicle position points is greater than the distance threshold, and one of the two points is an abnormal position point. The server carries out deviation rectification processing on the plurality of vehicle track data, namely abnormal position points are filtered out, and therefore a plurality of effective vehicle track data are obtained.
For example, if the number of the C2 groups of vehicle position points corresponding to the vehicle trajectory data C1 is 10, and the vehicle position points C3 and C4 are significantly deviated from the road data, the two vehicle position points are deleted, and the distance between the vehicle position points C7 and C8 is much greater than the distance threshold, the two vehicle position points are deleted, so that the deviation rectification processing of the vehicle trajectory data C1 is completed.
104. And calculating to obtain a plurality of vehicle type speed data based on the plurality of effective vehicle track data and the plurality of vehicle types, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition.
The server calculates a plurality of vehicle category speed data for road analysis based on the plurality of valid vehicle trajectory data and the plurality of vehicle category data.
According to the related road speed regulation, the server needs to calculate the speed of each vehicle type respectively so as to obtain a plurality of vehicle type speed data for road analysis, and the method can be understood as that the server calculates the speed data of the bus vehicle type, the speed data of the taxi vehicle, the speed data of the coach vehicle and/or the speed data of the internet map vehicle respectively according to a plurality of effective vehicle track data and a plurality of vehicle types.
It should be noted that, in this embodiment, the vehicle category data may be used as a basis for road traffic control and traffic flow guidance, and may also be used for road service level classification and urban road planning, which all belong to the category of road analysis.
The specific application is as follows: calculating a category speed difference value between the category speed data of the target vehicle and preset vehicle category speed data at the target moment; judging whether the category speed difference is greater than a difference threshold value; if the difference is larger than the preset threshold, the problem occurs in the operation condition of the corresponding road.
For example, assume that the difference is preset to 3km/h, and that the preset bus class speed data is 40km/h in road A at 4:00 PM. And 4:00 in afternoon on 17 th 9 th, in the road A, calculating the obtained bus class speed data to be 10km/h, calculating the class speed difference value between the bus class speed data and the road A to be 30km/h, and judging that the class speed difference value is greater than the difference threshold value by the server, so that the operation problem of the operation condition of the road A is shown, and at the moment, workers need to be dispatched to carry out traffic control or traffic flow induction and the like.
In other embodiments, multiple vehicle category speed data may be integrated together to obtain the final vehicle average speed data, and the final vehicle average speed data is used for monitoring and analyzing the road running condition. The specific application is as follows: calculating the difference between the final vehicle average speed data at the target moment and the preset vehicle average speed data to obtain an average speed difference; and judging whether the average speed difference is greater than the average speed threshold value, and if so, indicating that the running condition of the corresponding road has a problem.
In the embodiment of the invention, the vehicles are classified according to the track data of the vehicles, then the deviation rectification processing is carried out on the track data of the vehicles, the speed of each vehicle category is calculated according to the vehicle track data after the deviation rectification, the running condition of the road can be obtained, the problem that the road running condition is difficult to obtain is solved, and the accuracy of monitoring the road running condition is improved. The method can be applied to the field of intelligent traffic, so that the construction of an intelligent city is promoted.
Referring to fig. 2, another embodiment of the method for calculating a vehicle speed according to the embodiment of the present invention includes:
201. the method comprises the steps of obtaining vehicle track data and basic vehicle information of a plurality of vehicles, wherein the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points;
the server obtains a plurality of vehicle track data which are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, each vehicle track data at least comprises a group of vehicle position points, and each group of vehicle position points at least comprises a plurality of vehicle position points.
The bus track data is track data reported by a bus company platform, the taxi track data is track data reported by taxi companies and network appointment company platforms, and the large-scale vehicle track data is track data reported by long-distance passenger companies, hazardous chemical substance long-distance vehicle companies and hazardous chemical substance vehicle company platforms. When the platform does not exist, the user docks the vehicle-mounted equipment and accesses the vehicle-mounted equipment into the Internet of vehicles international standard protocol, so that vehicle track data (Internet map vehicle track data) are reported to the server, or the vehicle track data (Internet map vehicle track data) are reported at regular time through software of the mobile terminal. When any platform or any terminal reports the vehicle track data, the server receives the data. The basic information of the vehicle is basic information of the vehicle, such as a license plate number of the vehicle, a color of the vehicle, or a type of the vehicle.
It should be noted that, in the present embodiment, the vehicle trajectory data and the vehicle basic information are acquired every 3 seconds.
202. Classifying the vehicles according to the vehicle track data or the vehicle basic information to obtain vehicle categories;
the server classifies the vehicles according to the vehicle track data to obtain vehicle types. Or classifying the vehicles according to a plurality of pieces of vehicle basic information so as to obtain a plurality of vehicle categories.
Since the associated road speed dictates that the speed limits for different vehicles are different, e.g. car and bus, an important step before calculating the vehicle class speed data is to classify the vehicles to obtain a plurality of vehicle classes.
For example, in one embodiment, assuming that the vehicle trajectory data a1 is bus trajectory data, the server determines that the vehicle category of the vehicle is bus; in another embodiment, assuming that the vehicle type of the vehicle basic information a2 is passenger, the color of the vehicle is yellow, and the license plate number is kyo a · C12345, the server determines that the vehicle category of the vehicle is public transport.
Specifically, when vehicle classification is performed according to each vehicle track data, the server acquires a corresponding vehicle type from a vehicle platform corresponding to the target vehicle track data to obtain a plurality of vehicle types; when vehicle classification is carried out according to each piece of vehicle basic information, the server extracts and obtains target license plate number data, target vehicle color data and target vehicle type data from corresponding target vehicle basic information; the category of the target vehicle is then determined based on the target license plate number data, the target vehicle color data, and the target vehicle type data, resulting in a plurality of vehicle categories.
The vehicle platform is a platform to which vehicle trajectory data is to be published, such as a public transport company platform, a taxi platform, and a large-scale vehicle platform, wherein the taxi platform includes a contract vehicle platform, the large-scale vehicle platform includes a long-distance passenger vehicle platform and a hazardous chemical substance vehicle platform, and the server determines a corresponding vehicle category through the platform to which the vehicle trajectory data is published.
203. Reading a plurality of corresponding vehicle position points from the target vehicle track data, wherein the plurality of vehicle position points form a group of vehicle position points, and each vehicle position point corresponds to a moment;
the server reads, from the target vehicle trajectory data, a plurality of vehicle position points constituting a group of vehicle position points, each of which corresponds to a time, for example, in the vehicle trajectory data D1, 7: 11: 15 corresponds to vehicle position points d1, 7: 11: 17 corresponds to vehicle position points d2 and 7: 11: the server obtains 19 a vehicle position point d1, d2 and d3 corresponding to the vehicle position point d 3.
204. Determining a plurality of first position points to be deleted based on the plurality of vehicle position points and preset road data, and deleting the first position points to be deleted to obtain a plurality of vehicle track data after primary deviation rectification, wherein each vehicle track data after primary deviation rectification comprises a plurality of vehicle position points after deviation rectification;
specifically, the server calculates the vertical distance between each vehicle position point and preset road data to obtain a corresponding position point distance, and judges whether the distance of each position point is greater than a distance threshold value; if the distance between the target position points is greater than the distance threshold, the server determines that the vehicle position point corresponding to the distance between the target position points is a first position point to be deleted, and a plurality of first deletion position points are obtained; the server deletes the first position points to be deleted to obtain a plurality of vehicle track data after initial deviation correction, wherein each vehicle track data after initial deviation correction comprises a plurality of vehicle position points after deviation correction.
It should be noted that, in this embodiment, the road data may be mapped into a straight line, a vertical distance between the vehicle position point and the preset road data is obtained through calculation, and it is determined whether the vertical distance is greater than a distance threshold, if the vertical distance is greater than the distance threshold, the vehicle position point is determined as a first position point to be deleted, a first position point to be deleted is obtained, and finally, the vehicle position point is deleted, so as to obtain the vehicle trajectory data after the initial deviation correction. In other embodiments, a triangle area formed by any two vehicle position points and preset road data can be calculated, whether the triangle area is larger than an area threshold value or not is judged, if so, the two vehicle position points are confirmed as first position points to be deleted, and the two vehicle position points are deleted, so that a plurality of pieces of initial vehicle track data after deviation correction are obtained.
205. Determining a plurality of second position points to be deleted based on the plurality of corrected vehicle position points, and deleting the plurality of second position points to be deleted to obtain a plurality of effective vehicle track data;
specifically, the server calculates corresponding displacement data based on every two adjacent vehicle position points after rectification to obtain a plurality of displacement data; the server judges whether each displacement data is larger than a displacement threshold value; if the target displacement data is larger than the displacement threshold, the server determines two adjacent vehicle position points after deviation correction of the target as second position points to be deleted to obtain a plurality of second position points to be deleted; and the server deletes the second position points to be deleted to obtain effective vehicle track data.
In the embodiment, the vehicle track data and the vehicle basic information are acquired once every three seconds, the displacement threshold value is determined to be 167m according to the maximum speed of the vehicle, and the service determines the second position point to be deleted according to the displacement threshold value. And assuming that the calculated displacement data is 168m, determining the two adjacent corrected vehicle position points as second position points to be deleted, and deleting the two corrected position points to obtain effective vehicle track data.
206. And calculating to obtain a plurality of vehicle type speed data based on the plurality of effective vehicle track data and the plurality of vehicle types, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition.
The server calculates a plurality of vehicle category speed data for road analysis based on the plurality of valid vehicle trajectory data and the plurality of vehicle category data.
According to the related road speed regulation, the server needs to calculate the speed of each vehicle type respectively so as to obtain a plurality of vehicle type speed data for road analysis, and the method can be understood as that the server calculates the speed data of the bus vehicle type, the speed data of the taxi vehicle, the speed data of the coach vehicle and/or the speed data of the internet map vehicle respectively according to a plurality of effective vehicle track data and a plurality of vehicle types.
It should be noted that the vehicle category data may be used as a basis for road traffic control and traffic flow guidance, and may also be used for road service level classification and urban road planning, which all belong to the category of road analysis.
Specifically, the server calculates corresponding vehicle speed data based on each effective vehicle track data to obtain a plurality of vehicle speed data; the server classifies the plurality of vehicle speed data based on the plurality of vehicle categories to obtain a plurality of classified vehicle speed data; the server carries out abnormal data elimination processing on the plurality of classified vehicle speed data to obtain a plurality of vehicle speed data after abnormal processing; and the server calculates corresponding vehicle type speed data based on each abnormal processed vehicle speed data and the corresponding vehicle speed data quantity to obtain a plurality of vehicle type speed data, and each vehicle type speed data is used for monitoring and analyzing the road running condition.
The server calculates the vehicle speed data of each vehicle, and then classifies the speed data according to vehicle types to obtain bus vehicle speed data, taxi vehicle speed data, coach vehicle speed data and internet map vehicle speed data, wherein each speed data comprises a plurality of vehicle speed data; then the server eliminates abnormal data based on each category of vehicle speed data to obtain a plurality of vehicle speed data after abnormal processing, and finally obtains a plurality of vehicle category speed data based on the vehicle speed data after abnormal processing, wherein the specific calculation mode is as follows:
wherein f (v)i) For the vehicle category i speed data, viFor the sum of a plurality of the same category of abnormality processed vehicle speed data,
the number of corresponding vehicle categories.
The server carries out abnormal data elimination processing on the plurality of classified vehicle speed data, and the specific process of obtaining the plurality of vehicle speed data after abnormal processing is as follows:
the server constructs a normal distribution graph based on the classified vehicle speed data to obtain a speed normal distribution graph; the server determines a plurality of abnormally classified vehicle speed data based on the speed normal distribution map, eliminates the plurality of abnormally classified vehicle speed data, and obtains a plurality of abnormally processed vehicle speed data, wherein the plurality of abnormally classified vehicle speed data are a plurality of classified vehicle speed data corresponding to traffic accident road conditions or traffic jam road conditions.
In the velocity normal distribution map, each piece of classified vehicle velocity data exists in the form of a point, and a point corresponding to abnormal classified vehicle velocity data is significantly deviated from a point corresponding to other normal classified vehicle velocity data, so that the abnormal classified vehicle velocity data can be obtained by eliminating the points significantly deviated from other normal classified vehicle velocity data.
In the embodiment of the invention, the vehicles are classified according to the track data of the vehicles, then the deviation rectification processing is carried out on the track data of the vehicles, the speed of each vehicle category is calculated according to the vehicle track data after the deviation rectification, the running condition of the road can be obtained, the problem that the road running condition is difficult to obtain is solved, and the accuracy of monitoring the road running condition is improved. The method can be applied to the field of intelligent traffic, so that the construction of an intelligent city is promoted.
With reference to fig. 3, the method for calculating the vehicle speed according to the embodiment of the present invention is described above, and a device for calculating the vehicle speed according to the embodiment of the present invention is described below, where an embodiment of the device for calculating the vehicle speed according to the embodiment of the present invention includes:
the system comprises an acquisition module 301, a storage module and a display module, wherein the acquisition module 301 is used for acquiring vehicle track data and basic vehicle information of a plurality of vehicles, the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points;
a classification module 302, configured to classify a plurality of vehicles according to the plurality of vehicle trajectory data or the plurality of vehicle basic information to obtain a plurality of vehicle categories;
the deviation rectifying module 303 is configured to perform deviation rectifying processing on the corresponding vehicle track data based on each group of vehicle position points, generate corresponding effective vehicle track data, and obtain a plurality of effective vehicle track data;
and the calculating module 304 is configured to calculate a plurality of vehicle category speed data based on the plurality of effective vehicle trajectory data and the plurality of vehicle categories, where each vehicle category speed data is used for monitoring and analyzing a road running condition.
In the embodiment of the invention, the vehicles are classified according to the track data of the vehicles, then the deviation rectification processing is carried out on the track data of the vehicles, the speed of each vehicle category is calculated according to the vehicle track data after the deviation rectification, the running condition of the road can be obtained, the problem that the road running condition is difficult to obtain is solved, and the accuracy of monitoring the road running condition is improved. The method can be applied to the field of intelligent traffic, so that the construction of an intelligent city is promoted.
Referring to fig. 4, another embodiment of the apparatus for calculating a vehicle speed according to the embodiment of the present invention includes:
the system comprises an acquisition module 301, a storage module and a display module, wherein the acquisition module 301 is used for acquiring vehicle track data and basic vehicle information of a plurality of vehicles, the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points;
a classification module 302, configured to classify a plurality of vehicles according to the plurality of vehicle trajectory data or the plurality of vehicle basic information to obtain a plurality of vehicle categories;
the deviation rectifying module 303 is configured to perform deviation rectifying processing on the corresponding vehicle track data based on each group of vehicle position points, generate corresponding effective vehicle track data, and obtain a plurality of effective vehicle track data;
and the calculating module 304 is configured to calculate a plurality of vehicle category speed data based on the plurality of effective vehicle trajectory data and the plurality of vehicle categories, where each vehicle category speed data is used for monitoring and analyzing a road running condition.
Optionally, the classification module 302 may be further specifically configured to:
when the corresponding vehicles are classified according to the vehicle track data, the corresponding vehicle types are obtained from the vehicle platforms corresponding to the target vehicle track data, and a plurality of vehicle types are obtained;
when corresponding vehicles are classified according to the basic information of each vehicle, target license plate number data, target vehicle color data and target vehicle type data are extracted from the basic information of the target vehicle;
classifying based on the target license plate number data, the target vehicle color data and the target vehicle type data to generate corresponding vehicle types, and obtaining a plurality of vehicle types.
Optionally, the deviation rectifying module 303 includes:
a reading unit 3031, configured to read a plurality of corresponding vehicle location points from the target vehicle trajectory data, where the plurality of vehicle location points form a group of vehicle location points, and each vehicle location point corresponds to a time;
a primary deviation rectifying unit 3032, configured to determine a plurality of first position points to be deleted based on the plurality of vehicle position points and preset road data, and delete the first position points to be deleted, so as to obtain a plurality of primarily corrected vehicle trajectory data, where each of the initially corrected vehicle trajectory data includes a plurality of corrected vehicle position points;
and the secondary deviation rectifying unit 3033 is configured to determine a plurality of second position points to be deleted based on the plurality of corrected vehicle position points, and delete the plurality of second position points to be deleted to obtain a plurality of effective vehicle trajectory data.
Optionally, the primary deviation rectifying unit 3032 may further specifically be configured to:
calculating the vertical distance between each vehicle position point and preset road data to obtain a corresponding position point distance, and judging whether the distance of each position point is greater than a distance threshold value;
if the distance between the target position points is greater than the distance threshold, determining that the vehicle position points corresponding to the distance between the target position points are first position points to be deleted to obtain a plurality of first deletion position points;
and deleting the first position points to be deleted to obtain a plurality of vehicle track data after primary deviation rectification, wherein each vehicle track data after primary deviation rectification comprises a plurality of vehicle position points after deviation rectification.
Optionally, the secondary deviation rectifying unit 3033 may be further specifically configured to:
calculating corresponding displacement data based on every two adjacent vehicle position points after deviation rectification to obtain a plurality of displacement data;
judging whether each displacement data is larger than a displacement threshold value;
if the target displacement data is larger than the displacement threshold, determining two adjacent vehicle position points after deviation correction of the target as second position points to be deleted to obtain a plurality of second position points to be deleted;
and deleting the second position points to be deleted to obtain a plurality of effective vehicle track data.
Optionally, the calculation module 304 includes:
a first calculation unit 3041 for calculating corresponding vehicle speed data based on each effective vehicle trajectory data, resulting in a plurality of vehicle speed data;
a classifying unit 3042 configured to classify the plurality of vehicle speed data based on the plurality of vehicle categories to obtain a plurality of classified vehicle speed data;
a rejecting unit 3043, configured to perform abnormal data rejection processing on the plurality of classified vehicle speed data to obtain a plurality of vehicle speed data after abnormal processing;
a second calculating unit 3044, configured to calculate corresponding vehicle category speed data based on each processed vehicle speed data and the corresponding number of vehicle speed data, so as to obtain a plurality of vehicle category speed data, where each vehicle category speed data is used for monitoring and analyzing a road running condition.
Optionally, the rejecting unit 3043 may be further specifically configured to:
constructing a normal distribution graph based on the plurality of classified vehicle speed data to obtain a speed normal distribution graph;
determining a plurality of abnormally classified vehicle speed data based on the speed normal distribution map, and eliminating the plurality of abnormally classified vehicle speed data to obtain a plurality of abnormally processed vehicle speed data, wherein the plurality of abnormally classified vehicle speed data are a plurality of classified vehicle speed data corresponding to traffic accident road conditions or traffic jam road conditions.
In the embodiment of the invention, the vehicles are classified according to the track data of the vehicles, then the deviation rectification processing is carried out on the track data of the vehicles, the speed of each vehicle category is calculated according to the vehicle track data after the deviation rectification, the running condition of the road can be obtained, the problem that the road running condition is difficult to obtain is solved, and the accuracy of monitoring the road running condition is improved. The method can be applied to the field of intelligent traffic, so that the construction of an intelligent city is promoted.
Fig. 3 and 4 above describe the vehicle speed calculating device in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the vehicle speed calculating device in the embodiment of the present invention is described in detail from the perspective of the hardware processing.
Fig. 5 is a schematic structural diagram of a vehicle speed computing device 500, which may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations in the computing device 500 for vehicle speed. Still further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the vehicle speed computing device 500.
The vehicle speed computing device 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and the like. Those skilled in the art will appreciate that the vehicle speed computing device configuration shown in fig. 5 does not constitute a limitation of the vehicle speed computing device and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
Further, the computer usable storage medium 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 the use of the blockchain node, and the like.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and which may also be a volatile computer readable storage medium, having stored therein instructions, which, when run on a computer, cause the computer to perform the steps of the method of calculating a speed of a vehicle.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of calculating a vehicle speed, characterized by comprising:
the method comprises the steps of obtaining vehicle track data and basic vehicle information of a plurality of vehicles, wherein the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points;
classifying a plurality of vehicles according to the plurality of vehicle track data or the plurality of vehicle basic information to obtain a plurality of vehicle categories;
performing deviation rectification processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data to obtain a plurality of effective vehicle track data;
and calculating to obtain a plurality of vehicle type speed data based on the plurality of effective vehicle track data and the plurality of vehicle types, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition.
2. The method of calculating a vehicle speed according to claim 1, wherein the classifying the plurality of vehicles according to the plurality of vehicle trajectory data or the plurality of vehicle basic information, and obtaining a plurality of vehicle categories comprises:
when the corresponding vehicles are classified according to the vehicle track data, the corresponding vehicle types are obtained from the vehicle platforms corresponding to the target vehicle track data, and a plurality of vehicle types are obtained;
when corresponding vehicles are classified according to the basic information of each vehicle, target license plate number data, target vehicle color data and target vehicle type data are extracted from the basic information of the target vehicle;
classifying based on the target license plate number data, the target vehicle color data and the target vehicle type data to generate corresponding vehicle types, and obtaining a plurality of vehicle types.
3. The method for calculating the vehicle speed according to claim 1, wherein the performing the deviation rectification processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data, and obtaining a plurality of effective vehicle track data comprises:
reading a plurality of corresponding vehicle position points from the target vehicle track data, wherein the plurality of vehicle position points form a group of vehicle position points, and each vehicle position point corresponds to a moment;
determining a plurality of first position points to be deleted based on the plurality of vehicle position points and preset road data, and deleting the first position points to be deleted to obtain a plurality of vehicle track data after primary deviation rectification, wherein each vehicle track data after primary deviation rectification comprises a plurality of vehicle position points after deviation rectification;
and determining a plurality of second position points to be deleted based on the plurality of corrected vehicle position points, and deleting the plurality of second position points to be deleted to obtain a plurality of effective vehicle track data.
4. The method for calculating the vehicle speed according to claim 3, wherein the determining a plurality of first position points to be deleted based on the plurality of vehicle position points and preset road data, and deleting the first position points to be deleted to obtain a plurality of initial vehicle trajectory data after rectification, each initial vehicle trajectory data after rectification comprising a plurality of rectified vehicle position points comprises:
calculating the vertical distance between each vehicle position point and preset road data to obtain a corresponding position point distance, and judging whether the distance of each position point is greater than a distance threshold value;
if the distance between the target position points is greater than the distance threshold, determining that the vehicle position points corresponding to the distance between the target position points are first position points to be deleted to obtain a plurality of first deletion position points;
and deleting the first position points to be deleted to obtain a plurality of vehicle track data after primary deviation rectification, wherein each vehicle track data after primary deviation rectification comprises a plurality of vehicle position points after deviation rectification.
5. The method for calculating the vehicle speed according to claim 3, wherein the determining a plurality of second to-be-deleted position points based on the plurality of corrected vehicle position points and deleting the plurality of second to-be-deleted position points to obtain a plurality of effective vehicle trajectory data comprises:
calculating corresponding displacement data based on every two adjacent vehicle position points after deviation rectification to obtain a plurality of displacement data;
judging whether each displacement data is larger than a displacement threshold value;
if the target displacement data is larger than the displacement threshold, determining two adjacent vehicle position points after deviation correction of the target as second position points to be deleted to obtain a plurality of second position points to be deleted;
and deleting the second position points to be deleted to obtain a plurality of effective vehicle track data.
6. The method according to any one of claims 1 to 6, wherein the step of calculating a plurality of vehicle category speed data based on the plurality of valid vehicle trajectory data and the plurality of vehicle categories, each vehicle category speed data being used for monitoring and analyzing road running conditions comprises:
calculating corresponding vehicle speed data based on each effective vehicle track data to obtain a plurality of vehicle speed data;
classifying the plurality of vehicle speed data based on the plurality of vehicle categories to obtain a plurality of classified vehicle speed data;
performing abnormal data elimination processing on the classified vehicle speed data to obtain abnormal vehicle speed data;
and calculating corresponding vehicle type speed data based on the vehicle speed data after each abnormal processing and the corresponding vehicle speed data quantity to obtain a plurality of vehicle type speed data, wherein each vehicle type speed data is used for monitoring and analyzing the road running condition.
7. The method according to claim 1, wherein the performing the abnormal data removing process on the plurality of classified vehicle speed data to obtain a plurality of vehicle speed data after the abnormal process includes:
constructing a normal distribution graph based on the plurality of classified vehicle speed data to obtain a speed normal distribution graph;
determining a plurality of abnormally classified vehicle speed data based on the speed normal distribution map, and eliminating the plurality of abnormally classified vehicle speed data to obtain a plurality of abnormally processed vehicle speed data, wherein the plurality of abnormally classified vehicle speed data are a plurality of classified vehicle speed data corresponding to traffic accident road conditions or traffic jam road conditions.
8. A vehicle speed calculation device characterized by comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring vehicle track data and basic vehicle information of a plurality of vehicles, the vehicle track data are bus track data, taxi track data, large vehicle track data and/or internet map vehicle track data, and one vehicle track data comprises a group of vehicle position points;
the classification module is used for classifying a plurality of vehicles according to the plurality of vehicle track data or the plurality of vehicle basic information to obtain a plurality of vehicle categories;
the deviation correcting module is used for performing deviation correcting processing on the corresponding vehicle track data based on each group of vehicle position points to generate corresponding effective vehicle track data and obtain a plurality of effective vehicle track data;
and the calculation module is used for calculating to obtain a plurality of vehicle type speed data based on the effective vehicle track data and the vehicle types, and each vehicle type speed data is used for monitoring and analyzing the road running condition.
9. A vehicle speed calculation device characterized by comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line;
the at least one processor invokes the instructions in the memory to cause the vehicle speed calculation device to perform the vehicle speed calculation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of calculating a vehicle speed according to any one of claims 1 to 7.
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