CN110648028B - Traffic big data cloud platform based on 5G network and use method thereof - Google Patents
Traffic big data cloud platform based on 5G network and use method thereof Download PDFInfo
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Abstract
The invention discloses a traffic big data cloud platform based on a 5G network and a using method thereof, and the traffic big data cloud platform comprises an input unit, a monitoring acquisition module, a data analysis module, a processor, a server, a route adjusting module and a display screen, wherein map data and traffic light position data are stored in the server, the input unit is used for inputting a destination name, and the input unit is used for positioning a real-time position of a vehicle and a destination position through a positioning device.
Description
Technical Field
The invention relates to the technical field of route navigation, in particular to a traffic big data cloud platform based on a 5G network and a using method thereof.
Background
Turning to cloud computing, which is a major change in the industry, the emergence of various cloud platforms is one of the most important links of the transition, and as the name suggests, such platforms allow developers to either run written programs in the "cloud", or use services provided in the "cloud", or both, and as for the name of such platforms, we can now hear more than one name, such as an on-demand platform, a platform-as-a-service, etc., but whatever it is called, there is a great potential for this new way of supporting applications.
The intelligent traffic big data analysis with the notice number of CN206282356U can accurately analyze the traffic information of urban roads in real time, but the intelligent traffic big data analysis cannot accurately analyze the road congestion under the condition of road congestion and cannot provide a proper driving route for a driver, so that a traffic big data cloud platform based on a 5G network and a using method thereof are provided.
Disclosure of Invention
The invention aims to input a destination name in an input unit, the input unit positions a vehicle real-time position and a destination position through a positioning device and transmits the vehicle real-time position and the destination position to a data analysis module, a monitoring and collecting module collects road information and vehicle information monitored in real time, the road information is transmitted to the data analysis module, the vehicle information is transmitted to a line adjustment module, the data analysis module performs analysis operation according to the road congestion condition, all route information is acquired through a navigation device according to the vehicle real-time position and the destination position acquired by the input unit, the route information comprises route data and road data, the data analysis module is further used for performing route planning operation on the route information and planning a driving route, and the acquired first recommendation sequence, second recommendation sequence and third recommendation sequence are transmitted to the line adjustment module through a processor, and the line adjustment module is used for performing route adjustment operation on the vehicle information, the first recommendation sequence, the second recommendation sequence and the third recommendation sequence and planning the route for multiple times.
The technical problem to be solved by the invention is as follows:
(1) How to calculate the average running speed of the vehicles, the traffic flow data and the time consumed by each vehicle passing through the road section through a data analysis module and determine the congestion condition of the road so as to solve the problem that the road congestion is difficult to accurately analyze in the prior art;
(2) How to calculate the congestion rate of each road section and the total length data of each route through a data analysis module and plan the driving route of the vehicle so as to solve the problem that a driver often walks wrong roads under the condition of congestion in the prior art;
(3) The driving route of the vehicle is secondarily planned through the evaluation rate of the overspeed times, the times of violating the traffic instructions and the occurrence of the traffic accidents of the vehicle in the last year by the route adjusting module, the age data of the driver is analyzed, and the age of the driver is segmented, so that the driving route is planned for three times, and the problem that the driver is not satisfied with the planned driving route in the prior art is solved.
The purpose of the invention can be realized by the following technical scheme: a traffic big data cloud platform based on a 5G network comprises an input unit, a monitoring and collecting module, a data analyzing module, a processor, a server, a route adjusting module and a display screen;
the system comprises a server, an input unit, a data analysis module, a positioning device and a data processing module, wherein map data and traffic light position data are stored in the server, the input unit is used for inputting a destination name, and the input unit is used for positioning a vehicle real-time position and a destination position through the positioning device and transmitting the vehicle real-time position and the destination position to the data analysis module;
the monitoring and collecting module is used for monitoring and collecting road information and vehicle information in real time, wherein the road information comprises road length data, driving speed data and driving quantity data, the vehicle information comprises violation data and driver age data, the road information is transmitted to the data analysis module, and the vehicle information is transmitted to the line adjusting module;
the data analysis module analyzes and operates accordingly to obtain a severely congested road section, a generally congested road section and a smooth road section in a first time period, all route information is obtained through the navigation device according to the real-time position and the destination position of the vehicle obtained by the input unit, the route information comprises route data and road section data, the data analysis module is further used for carrying out route planning operation on the route information, the obtained first recommendation sequence, the obtained second recommendation sequence and the obtained third recommendation sequence are transmitted to the route adjustment module through the processor, and the route adjustment module is used for carrying out route adjustment operation on the vehicle information, the obtained first recommendation sequence, the obtained second recommendation sequence and the obtained third recommendation sequence to obtain secondary route planning and tertiary route planning;
the display screen is used for displaying the driving route and the operation options.
As a further improvement of the invention: the specific operation process of the analysis operation is as follows:
the method comprises the following steps: acquiring road information and traffic light position data in a first time period, and marking the road length between every two traffic lights as Di, wherein i =1,2,3.. Eta.. N, the vehicle running speed data between every two traffic lights is marked as Xi, i =1,2,3.. Eta.. N and the vehicle running data between every two traffic lights are marked as Ci, i =1,2,3.. Eta.. N, and Di, xi and Ci are in one-to-one correspondence;
step two: according to the formula
The average vehicle speed between every two traffic lights in the first time period is determined and is substituted into the formula->
Ri represents the traffic flow between two traffic lights in a first time period, and the first time period represents the duration of one hour;
step three: according to a formula Ti = Di/PX, obtaining the average time of each vehicle passing, and judging the road congestion condition according to the average time and the traffic flow in the step two, specifically:
s1: when Ri is larger than or equal to H and Ti is larger than or equal to B, judging that the road is a severely congested road section;
s2: when Ri is larger than or equal to H and Ti is smaller than B, the road is judged to be a general congested road section;
s3: when Ri is less than H and Ti is more than or equal to B, judging that the road is a general congested road section;
s3: and when Ri is less than H and Ti is less than B, judging that the road is an unobstructed road section.
As a further improvement of the invention: the specific operation process of the route planning operation comprises the following steps:
e1: calibrating the path data of each route as Zl, i =1,2,3.. To-o, calibrating the total number of the routes in each route as Ln, and n =1,2,3.. To-m, wherein the calibration mode of the total number of the routes is as follows: the road between every two traffic lights is marked as a road section, meanwhile, the number of the monitored severely congested road sections is marked as Yb, b =1,2,3.. Eta.x, the number of the monitored general congested road sections is marked as Ub, b =1,2,3.. Eta.x, the number of the monitored unobstructed road sections is marked as Ob, b =1,2,3.. Eta.x, and the Yb, the Ub and the Ob correspond one to one;
e2: respectively dividing Yb, ub and Ob by Ln to obtain congestion rates A1o, A2o and A3o of each line, wherein o =1,2,3.. Once.p, and performing route recommendation sequencing according to the congestion rates A1o, A2o and A3o of each line and route data Zl, specifically:
EE1: the first recommendation order is: when A3o > A1o + A2o, and
selecting a route with the largest difference value, and selecting a route with small route data Zl value when a plurality of routes with the same difference value exist;
EE2: the second recommendation order is: when A3o = A1o + A2o, and
selecting a line with small distance data Zl value;
EE3: the third recommendation order is: when A3o < A1o + A2o, and
selecting a route with the smallest difference, and selecting a route with small distance data Zl value when a plurality of routes with the same difference exist, wherein Z Small For the minimum distance of the path, Z, in the path data Big (a) The distance of the selected route in the route data.
As a further improvement of the invention: the specific operation process of the route adjusting operation comprises the following steps:
k1: acquiring vehicle violation data in a second time period, marking the overspeed times of each vehicle as Fs, wherein s =1,2,3.. Once.q, marking the traffic instruction violation times of the vehicles as Gs, and s =1,2,3.. Once.q, marking the traffic accident times of the vehicles as Js, and s =1,2,3.. Once.q, wherein Fs, gs and Js are in one-to-one correspondence;
k2: according to formulas V1c = Fs 30/365, V2c = Gs 30/365 and V3c = Js 30/365, the occurrence coefficients of vehicle overspeed, violation of traffic instructions and traffic accidents are obtained, and secondary route planning is carried out according to the occurrence coefficients, specifically:
KK1: when V1c is less than V1, V2c is less than V2, and V3c =0, maintaining the recommendation sequence obtained in the route planning operation;
KK2: when any one of V1c and V2c is greater than V1 and V2, and V3c =0, then the ratio of the selected route to the shortest route is modified on the basis of the recommended sequence obtained in the route planning operation as follows:
KK3: when V1c is greater than or equal to V1, V2c is greater than or equal to V2, and V3c =0, the ratio of the selected route to the shortest route is modified on the basis of the recommended sequence obtained in the route planning operation:
KK4: when V3c > 0, then the ratio of the selected route to the shortest route is modified based on the recommended order derived in the route planning operation to:
k3: acquiring driver age data, calibrating a driver with the age of 18-30 years as a young driver, calibrating a driver with the age of 30-50 years as a middle-aged driver, calibrating a driver with the age of more than 50 years as a middle-aged driver, and planning a route for three times on the basis of secondary route planning according to the calibration of the young driver, the middle-aged driver and the middle-aged driver, wherein the specific steps are as follows:
WW1: when the driver is a middle-aged driver, maintaining the recommended route of the route secondary planning;
WW2: when the driver is a young driver, the ratio of the selected route to the shortest route is modified to be as follows on the basis of the recommended route of the route quadratic programming:
WW3: when the driver is a middle-aged driver, the ratio of the selected route to the shortest route is modified to be as follows on the basis of the recommended route of route quadratic programming:
as a further improvement of the invention: the driver can select the route by clicking route planning, route secondary planning and route tertiary planning, when clicking the route planning, the route planning is automatically carried out, the secondary and tertiary route planning are not carried out, when clicking the route secondary planning, the route planning and the route secondary planning are automatically carried out, the route tertiary planning is not carried out, and when clicking the route tertiary planning, the complete tertiary planning is automatically carried out.
As a further improvement of the invention: the processor downloads and refreshes the map data from the server every 1 minute, and the route adjusting module provides a return route of the original route and a prompt of a planned new route when the vehicle deviates the distance between 50 and 200 meters from the navigation route during the running of the vehicle.
As a further improvement of the invention: a use method of a traffic big data cloud platform based on a 5G network comprises the following steps:
GH1: the method comprises the steps that a destination name is input into an input unit, the input unit positions a vehicle real-time position and a destination position through a positioning device and transmits the vehicle real-time position and the destination position to a data analysis module, a monitoring acquisition module acquires road information and vehicle information monitored in real time, the road information is transmitted to the data analysis module, the vehicle information is transmitted to a line adjustment module, and the data analysis module performs analysis operation according to the road information to obtain a severely congested road section, a generally congested road section and a smooth road section in a first time period;
GH2: the method comprises the steps that according to the real-time position and the destination position of a vehicle obtained by an input unit, all route information is obtained through a navigation device, the route information comprises route data and road section data, a data analysis module is further used for carrying out route planning operation on the route information, the obtained first recommendation sequence, second recommendation sequence and third recommendation sequence are transmitted to a route adjustment module through a processor, and the route adjustment module is used for carrying out route adjustment operation on the vehicle information, the first recommendation sequence, the second recommendation sequence and the third recommendation sequence to obtain secondary route planning and tertiary route planning;
GH3: the method comprises the steps that a driver selects route planning on a display screen, the driver can select the route by clicking route planning, route secondary planning and route tertiary planning, when the route planning is clicked, the route planning is automatically carried out, the secondary and tertiary route planning is not carried out, when the route secondary planning is clicked, the route planning and the route secondary planning are automatically carried out, the route tertiary planning is not carried out, when the route tertiary planning is clicked, the complete tertiary planning is automatically carried out, and the planned route is transmitted to the display screen.
The invention has the beneficial effects that:
(1) The method comprises the steps that a destination name is input into an input unit, the input unit positions a vehicle real-time position and a destination position through a positioning device and transmits the vehicle real-time position and the destination position to a data analysis module, a monitoring acquisition module acquires road information and vehicle information monitored in real time, the road information is transmitted to the data analysis module, the vehicle information is transmitted to a line adjustment module, the data analysis module performs analysis operation according to the road information, the average driving speed and traffic flow data of the vehicle and the time consumed by each vehicle passing through a road section are calculated through the data analysis module, the road congestion condition is judged, the data are detailed, the traffic congestion condition of each road section can be carefully analyzed, driving route planning can be better performed, and the route planning accuracy is improved;
(2) The data analysis module is also used for carrying out route planning operation on the route information, calculating the congestion rate of each road section and the total length data of each route through the data analysis module, carrying out driving route planning on the vehicle, increasing the accuracy of route planning and facilitating judgment of the route by a driver;
(3) The obtained first recommendation sequence, the second recommendation sequence and the third recommendation sequence are transmitted to a route adjusting module through a processor, the route adjusting module is used for carrying out route adjusting operation on vehicle information, the first recommendation sequence, the second recommendation sequence and the third recommendation sequence, the driving route of the vehicle is secondarily planned through the route adjusting module according to the number of times of overspeed of the vehicle in the last year, the number of times of violation of traffic instructions and the evaluation rate of occurrence of traffic accidents, the age data of a driver is analyzed, the age of the driver is segmented, the driving route is planned for three times, different driving routes are provided for different drivers, the fact that the driver is irritated due to wrong selection of the route under the condition of traffic jam is avoided, and driving safety is improved.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a system block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
Referring to fig. 1, the invention relates to a traffic big data cloud platform based on a 5G network, which comprises an input unit, a monitoring and collecting module, a data analyzing module, a processor, a server, a route adjusting module and a display screen;
the system comprises a server, an input unit, a data analysis module, a positioning device and a data processing module, wherein the server stores map data and traffic light position data, the input unit is used for inputting a destination name, and the input unit is used for positioning a real-time position of a vehicle and a destination position through the positioning device and transmitting the real-time position of the vehicle and the destination position to the data analysis module;
the monitoring and collecting module is used for monitoring and collecting road information and vehicle information in real time, wherein the road information comprises road length data, driving speed data and driving quantity data, the vehicle information comprises violation data and driver age data, the road information is transmitted to the data analysis module, and the vehicle information is transmitted to the line adjusting module;
the data analysis module performs analysis operation according to the data analysis method, and the specific process is as follows:
the method comprises the following steps: acquiring road information and traffic light position data in a first time period, and marking the road length between every two traffic lights as Di, i =1,2,3.. Eta, and the vehicle running speed data between every two traffic lights as Xi, i =1,2,3.. Eta, and the vehicle running data between every two traffic lights as Ci, i =1,2,3.. Eta, and Di, xi and Ci are in one-to-one correspondence;
step two: according to the formula
The average vehicle speed between every two traffic lights in the first time period is determined and is substituted into the formula->
Ri represents the traffic flow between two traffic lights in a first time period, and the first time period represents the duration of one hour;
step three: according to a formula Ti = Di/PX, obtaining the average time of each vehicle passing, and judging the road congestion condition according to the average time and the traffic flow in the step two, specifically:
s1: when Ri is larger than or equal to H and Ti is larger than or equal to B, judging that the road is a severely congested road section;
s2: when Ri is larger than or equal to H and Ti is smaller than B, the road is judged to be a general congested road section;
s3: when Ri is less than H and Ti is more than or equal to B, judging that the road is a general congested road section;
s3: when Ri is less than H and Ti is less than B, the road is judged to be a smooth road section;
the data analysis module obtains a severely congested road section, a generally congested road section and a smooth road section in a first time period, and obtains all route information through the navigation device according to a real-time position and a destination position of a vehicle obtained by the input unit, wherein the route information comprises route data and road section data, the route data refers to a total route between the real-time position and the destination position of the vehicle, the road section data refers to a road mark between two traffic lights as one road section, the total route is a common owned road section, and the data analysis module is further used for performing route planning operation on the route information, and specifically comprises the following steps:
e1: calibrating the path data of each route as Zl, i =1,2,3.. To-o, calibrating the total number of the routes in each route as Ln, and n =1,2,3.. To-m, wherein the calibration mode of the total number of the routes is as follows: the road between every two traffic lights is marked as a road section, meanwhile, the number of the monitored severely congested road sections is marked as Yb, b =1,2,3.. Eta.x, the number of the monitored general congested road sections is marked as Ub, b =1,2,3.. Eta.x, the number of the monitored unobstructed road sections is marked as Ob, b =1,2,3.. Eta.x, and the Yb, the Ub and the Ob correspond one to one;
e2: respectively dividing Yb, ub and Ob by Ln to obtain congestion rates A1o, A2o and A3o of each line, wherein o =1,2,3.
EE1: the first recommendation order is: when A3o > A1o + A2o, and
selecting a route with the largest difference value, and selecting a route with small route data Zl value when a plurality of routes with the same difference value exist;
EE2: the second recommendation sequence is as follows: when A3o = A1o + A2o, and
selecting a line with small distance data Zl value;
EE3: the third recommendation order is: when A3o < A1o + A2o, and
selecting the difference valueSelecting the route with small distance data Zl value when a plurality of routes with the same difference value exist in the smallest route, wherein Z Small For the minimum distance of the path, Z, in the path data Big (a) Selecting a distance from the distance data;
the data analysis module transmits the obtained first recommendation sequence, the second recommendation sequence and the third recommendation sequence to the route adjustment module through the processor, and the route adjustment module is used for performing route adjustment operation on the vehicle information, the first recommendation sequence, the second recommendation sequence and the third recommendation sequence, and specifically comprises the following steps:
k1: acquiring vehicle violation data in a second time period, marking the overspeed times of each vehicle as Fs, wherein s =1,2,3.. Once.q, marking the number of times of vehicle violation traffic instructions as Gs, and s =1,2,3.. Once.q, marking the number of times of vehicle traffic accidents as Js, and s =1,2,3.. Once.q, wherein Fs, gs and Js are in one-to-one correspondence, and the second time period is the time length of one year;
k2: according to formulas V1c = Fs 30/365, V2c = Gs 30/365 and V3c = Js 30/365, the occurrence coefficients of vehicle overspeed, violation of traffic instructions and traffic accidents are obtained, and secondary route planning is carried out according to the occurrence coefficients, specifically:
KK1: when V1c is less than V1, V2c is less than V2, and V3c =0, maintaining the recommendation sequence obtained in the route planning operation;
KK2: when any one of V1c and V2c is greater than V1 and V2, and V3c =0, then the ratio of the selected route to the shortest route is modified on the basis of the recommended sequence obtained in the route planning operation as follows:
KK3: when V1c is larger than or equal to V1, V2c is larger than or equal to V2, and V3c =0, the ratio of the selected route to the shortest route is modified to be as follows on the basis of the recommended sequence obtained in the route planning operation:
KK4: when V3c > 0, then the route and shortest route will be selected based on the recommended order obtained in the route planning operationThe ratio is modified as follows:
k3: acquiring driver age data, calibrating a driver with the age of 18-30 years as a young driver, calibrating a driver with the age of 30-50 years as a middle-aged driver, calibrating a driver with the age of more than 50 years as a middle-aged driver, and planning a route for three times on the basis of secondary route planning according to the calibration of the young driver, the middle-aged driver and the middle-aged driver, wherein the specific steps are as follows:
WW1: when the driver is a middle-aged driver, maintaining the recommended route of the route secondary planning;
WW2: when the driver is a young driver, the ratio of the selected route to the shortest route is modified on the basis of the recommended route of the route quadratic programming as follows:
WW3: when the driver is a middle-aged driver, the ratio of the selected route to the shortest route is modified to be as follows on the basis of the recommended route of route quadratic programming:
in the route planning, a driver can select a route by clicking route planning, route secondary planning and route tertiary planning, when the route planning is clicked, the route planning is automatically carried out, the secondary and tertiary route planning is not carried out, when the route secondary planning is clicked, the route planning and the route secondary planning are automatically carried out, the route tertiary planning is not carried out, and when the route tertiary planning is clicked, the complete tertiary planning is automatically carried out;
the processor downloads and refreshes the map data from the server every 1 minute, and the route adjusting module provides a return route of the original route and a prompt of a planned new route when the vehicle deviates the distance between 50 and 200 meters from the navigation route during the running of the vehicle;
the display screen is used for displaying the driving route and the operation options.
When the invention works, a destination name is input in an input unit, the input unit positions a vehicle real-time position and a destination position through a positioning device and transmits the vehicle real-time position and the destination position to a data analysis module, a monitoring acquisition module acquires road information and vehicle information monitored in real time, the road information is transmitted to the data analysis module, the vehicle information is transmitted to a route adjustment module, the data analysis module performs analysis operation according to the road information and the vehicle information, a severe congestion road section, a general congestion road section and a smooth road section in a first time period are obtained, all route information is obtained through a navigation device according to the vehicle real-time position and the destination position obtained by the input unit, the route information comprises route data and road section data, the data analysis module is also used for performing route planning operation on the route information, the obtained first recommendation sequence, the second recommendation sequence and the third recommendation sequence are transmitted to a route adjusting module through a processor, the route adjusting module is used for carrying out route adjusting operation on the vehicle information, the first recommendation sequence, the second recommendation sequence and the third recommendation sequence to obtain secondary route planning and tertiary route planning, a driver carries out route planning selection on a display screen, the driver can carry out route selection by clicking the route planning, the secondary route planning and the tertiary route planning, when the route planning is clicked, the route planning is carried out automatically, the secondary route planning and the tertiary route planning are not carried out, when the secondary route planning is clicked, the route planning and the secondary route planning are carried out automatically, the tertiary route planning is not carried out, and when the tertiary route planning is clicked, the complete tertiary route planning is carried out automatically.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (4)
1. A traffic big data cloud platform based on a 5G network is characterized by comprising an input unit, a monitoring acquisition module, a data analysis module, a processor, a server, a route adjusting module and a display screen;
the system comprises a server, an input unit, a data analysis module, a positioning device and a data processing module, wherein map data and traffic light position data are stored in the server, the input unit is used for inputting a destination name, and the input unit is used for positioning a vehicle real-time position and a destination position through the positioning device and transmitting the vehicle real-time position and the destination position to the data analysis module;
the monitoring and collecting module is used for monitoring and collecting road information and vehicle information in real time, wherein the road information comprises road length data, driving speed data and driving quantity data, the vehicle information comprises violation data and driver age data, the road information is transmitted to the data analysis module, and the vehicle information is transmitted to the line adjusting module;
the data analysis module analyzes and operates accordingly to obtain a severely congested road section, a generally congested road section and a smooth road section in a first time period, all route information is obtained through the navigation device according to the real-time position and the destination position of the vehicle obtained by the input unit, the route information comprises route data and road section data, the data analysis module is further used for carrying out route planning operation on the route information, the obtained first recommendation sequence, the obtained second recommendation sequence and the obtained third recommendation sequence are transmitted to the route adjustment module through the processor, and the route adjustment module is used for carrying out route adjustment operation on the vehicle information, the obtained first recommendation sequence, the obtained second recommendation sequence and the obtained third recommendation sequence to obtain secondary route planning and tertiary route planning;
the display screen is used for displaying driving routes and operation options;
the specific operation process of the analysis operation is as follows:
the method comprises the following steps: acquiring road information and traffic light position data in a first time period, and marking the road length between every two traffic lights as Di, i =1,2,3.. Eta, and the vehicle running speed data between every two traffic lights as Xi, i =1,2,3.. Eta, and the vehicle running data between every two traffic lights as Ci, i =1,2,3.. Eta, and Di, xi and Ci are in one-to-one correspondence;
step two: according to the formula
Calculating the average running speed of the vehicle between every two traffic lights in the first time period, and substituting the average running speed into a formula
Ri represents the traffic flow between two traffic lights in a first time period, and the first time period represents the duration of one hour;
step three: according to a formula Ti = Di/PX, obtaining the average time of each vehicle passing through, and judging the congestion condition of the road according to the average time and the traffic flow in the second step, specifically:
s1: when Ri is larger than or equal to H and Ti is larger than or equal to B, judging that the road is a severely congested road section;
s2: when Ri is larger than or equal to H and Ti is smaller than B, the road is judged to be a general congested road section;
s3: when Ri is less than H and Ti is more than or equal to B, judging that the road is a general congested road section;
s3: when Ri is less than H and Ti is less than B, the road is judged to be a smooth road section;
the specific operation process of the route planning operation comprises the following steps:
e1: calibrating the path data of each route as Zl, i =1,2,3.. To-o, calibrating the total number of the routes in each route as Ln, and n =1,2,3.. To-m, wherein the calibration mode of the total number of the routes is as follows: the road between every two traffic lights is marked as a road section, meanwhile, the number of the monitored severely congested road sections is marked as Yb, b =1,2,3.. Eta.x, the number of the monitored general congested road sections is marked as Ub, b =1,2,3.. Eta.x, the number of the monitored unobstructed road sections is marked as Ob, b =1,2,3.. Eta.x, and the Yb, the Ub and the Ob correspond one to one;
e2: respectively dividing Yb, ub and Ob by Ln to obtain congestion rates A1o, A2o and A3o of each line, wherein o =1,2,3.. Once.p, and performing route recommendation sequencing according to the congestion rates A1o, A2o and A3o of each line and route data Zl, specifically:
EE1: the first recommendation order is: when A3o > A1o + A2o, and
selecting a route with the largest difference value, and selecting a route with small route data Zl value when a plurality of routes with the same difference value exist;
EE2: the second recommendation sequence is as follows: when A3o = A1o + A2o, and
selecting a line with small distance data Zl value;
EE3: the third recommendation order is: when A3o < A1o + A2o, and
selecting a route with the smallest difference, and selecting a route with small distance data Zl value when a plurality of routes with the same difference exist, wherein Z Small For the minimum distance of the path, Z, in the path data Big (a) Selecting a distance from the distance data;
the specific operation process of the route adjusting operation comprises the following steps:
k1: obtaining vehicle violation data in a second time period, marking the overspeed times of each vehicle as Fs, s =1,2,3.. Once.q, marking the number of times of vehicle violation traffic instructions as Gs, s =1,2,3.. Once.q, and marking the number of times of vehicle traffic accidents as Js, s =
1,2,3.. Q, wherein Fs, gs and Js correspond to one another;
k2: according to formulas V1c = Fs 30/365, V2c = Gs 30/365 and V3c = Js 30/365, occurrence coefficients of vehicle overspeed, violation of traffic instructions and traffic accidents are obtained, and secondary route planning is carried out according to the occurrence coefficients, specifically:
KK1: when V1c is less than V1, V2c is less than V2, and V3c =0, maintaining the recommendation sequence obtained in the route planning operation;
KK2: when any one of V1c and V2c is greater than V1 and V2, and V3c =0, then the ratio of the selected route to the shortest route is modified on the basis of the recommended sequence obtained in the route planning operation as follows:
KK3: when V1c is greater than or equal to V1, V2c is greater than or equal to V2, and V3c =0, the ratio of the selected route to the shortest route is modified on the basis of the recommended sequence obtained in the route planning operation:
KK4: when V3c > 0, then the ratio of the selected route to the shortest route is modified based on the recommended order derived in the route planning operation to:
k3: acquiring driver age data, calibrating a driver with the age of 18-30 years as a young driver, calibrating a driver with the age of 30-50 years as a middle-aged driver, calibrating a driver with the age of more than 50 years as a middle-aged driver, and planning a route for three times on the basis of secondary route planning according to the calibration of the young driver, the middle-aged driver and the middle-aged driver, wherein the specific steps are as follows:
WW1: when the driver is a middle-aged driver, maintaining the recommended route of the route secondary planning;
WW2: when the driver is a young driver, the ratio of the selected route to the shortest route is modified on the basis of the recommended route of the route quadratic programming as follows:
WW3: when the driver is a middle-aged driver, the ratio of the selected route to the shortest route is modified to be as follows on the basis of the recommended route of route quadratic programming:
2. the traffic big data cloud platform based on the 5G network as claimed in claim 1, wherein a driver can select a route by clicking route planning, route secondary planning and route tertiary planning, when clicking the route planning, the route planning is automatically performed without performing the secondary and tertiary route planning, when clicking the route secondary planning, the route planning and the route secondary planning are automatically performed without performing the route tertiary planning, and when clicking the route tertiary planning, the complete tertiary planning is automatically performed.
3. The traffic big data cloud platform based on the 5G network as claimed in claim 1, wherein the processor downloads and refreshes the map data from the server every 1 minute, and the route adjustment module provides a return route of the original route and a prompt of a new planned route when the vehicle deviates from the navigation route by a distance of 50-200 meters during the running of the vehicle.
4. A use method of a traffic big data cloud platform based on a 5G network is characterized by comprising the following steps:
GH1: the method comprises the steps that a destination name is input into an input unit, the input unit positions a vehicle real-time position and a destination position through a positioning device and transmits the vehicle real-time position and the destination position to a data analysis module, a monitoring acquisition module acquires road information and vehicle information monitored in real time, the road information is transmitted to the data analysis module, the vehicle information is transmitted to a line adjustment module, and the data analysis module performs analysis operation according to the road information to obtain a severely congested road section, a generally congested road section and a smooth road section in a first time period;
GH2: the method comprises the steps that according to the real-time position and the destination position of a vehicle obtained by an input unit, all route information is obtained through a navigation device, the route information comprises route data and road section data, a data analysis module is further used for carrying out route planning operation on the route information, the obtained first recommendation sequence, second recommendation sequence and third recommendation sequence are transmitted to a route adjustment module through a processor, and the route adjustment module is used for carrying out route adjustment operation on the vehicle information, the first recommendation sequence, the second recommendation sequence and the third recommendation sequence to obtain secondary route planning and tertiary route planning;
GH3: the method comprises the following steps that a driver selects route planning on a display screen, the driver can select the route by clicking route planning, route secondary planning and route tertiary planning, when the route planning is clicked, the route planning is automatically carried out, secondary and tertiary route planning is not carried out, when the route secondary planning is clicked, the route planning and the route secondary planning are automatically carried out, the route tertiary planning is not carried out, when the route tertiary planning is clicked, the complete tertiary planning is automatically carried out, and the planned route is transmitted to the display screen;
the specific operation process of the analysis operation is as follows:
the method comprises the following steps: acquiring road information and traffic light position data in a first time period, and marking the road length between every two traffic lights as Di, i =1,2,3.. Eta, and the vehicle running speed data between every two traffic lights as Xi, i =1,2,3.. Eta, and the vehicle running data between every two traffic lights as Ci, i =1,2,3.. Eta, and Di, xi and Ci are in one-to-one correspondence;
step two: according to the formula
Calculating the average running speed of the vehicle between every two traffic lights in the first time period, and substituting the average running speed into a formula
Ri represents the traffic flow between two traffic lights in a first time period, and the first time period represents the duration of one hour;
step three: according to a formula Ti = Di/PX, obtaining the average time of each vehicle passing, and judging the road congestion condition according to the average time and the traffic flow in the step two, specifically:
s1: when Ri is larger than or equal to H and Ti is larger than or equal to B, judging that the road is a severely congested road section;
s2: when Ri is larger than or equal to H and Ti is smaller than B, the road is judged to be a general congested road section;
s3: when Ri is less than H and Ti is more than or equal to B, judging that the road is a general congested road section;
s3: when Ri is less than H and Ti is less than B, the road is judged to be a smooth road section;
the specific operation process of the route planning operation comprises the following steps:
e1: calibrating the path data of each route as Zl, i =1,2,3.. To-o, calibrating the total number of the routes in each route as Ln, and n =1,2,3.. To-m, wherein the calibration mode of the total number of the routes is as follows: the road between every two traffic lights is marked as a road section, meanwhile, the number of the monitored heavily congested road sections is marked as Yb, b =1,2,3.. Eta.x, the number of the monitored generally congested road sections is marked as Ub, b =1,2,3.. Eta.x, the number of the monitored unobstructed road sections is marked as Ob, b =1,2,3.. Eta.x, and the Yb, the Ub and the Ob correspond to one another;
e2: respectively dividing Yb, ub and Ob by Ln to obtain congestion rates A1o, A2o and A3o of each line, wherein o =1,2,3.. Once.p, and performing route recommendation sequencing according to the congestion rates A1o, A2o and A3o of each line and route data Zl, specifically:
EE1: the first recommendation order is: when A3o > A1o + A2o, and
selecting a route with the largest difference value, and selecting a route with small route data Zl value when a plurality of routes with the same difference value exist;
EE2: the second recommendation sequence is as follows: when A3o = A1o + A2o, and
selecting a line with small distance data Zl value;
EE3: the third recommendation order is: when A3o < A1o + A2o, and
selecting a route with the smallest difference, and selecting a route with small distance data Zl value when a plurality of routes with the same difference exist, wherein Z Small For the minimum distance of the path, Z, in the path data Big (a) Routes selected for route dataA stroke distance;
the specific operation process of the route adjusting operation comprises the following steps:
k1: acquiring vehicle violation data in a second time period, marking the overspeed times of each vehicle as Fs, wherein s =1,2,3.. Once.q, marking the number of times of vehicle violation traffic instructions as Gs, and s =1,2,3.. Once.q, marking the number of times of vehicle traffic accidents as Js, and s =1,2,3.. Once.q, wherein Fs, gs and Js are in one-to-one correspondence;
k2: according to formulas V1c = Fs 30/365, V2c = Gs 30/365 and V3c = Js 30/365, the occurrence coefficients of vehicle overspeed, violation of traffic instructions and traffic accidents are obtained, and secondary route planning is carried out according to the occurrence coefficients, specifically:
KK1: when V1c is less than V1, V2c is less than V2, and V3c =0, maintaining the recommendation sequence obtained in the route planning operation;
KK2: when any one of V1c and V2c is greater than V1 and V2, and V3c =0, then the ratio of the selected route to the shortest route is modified on the basis of the recommended sequence obtained in the route planning operation as follows:
KK3: when V1c is larger than or equal to V1, V2c is larger than or equal to V2, and V3c =0, the ratio of the selected route to the shortest route is modified to be as follows on the basis of the recommended sequence obtained in the route planning operation:
KK4: when V3c > 0, then the ratio of the selected route to the shortest route is modified based on the recommended order derived in the route planning operation to:
k3: acquiring driver age data, calibrating a driver aged 18-30 years as a young driver, calibrating a driver aged 30-50 years as a middle-aged driver, calibrating a driver aged more than 50 years as a middle-aged driver, and planning a route for three times on the basis of secondary route planning according to the calibration of the young, middle-aged and middle-aged drivers, wherein the specific steps are as follows:
WW1: when the driver is a middle-aged driver, maintaining the recommended route of the route secondary planning;
WW2: when the driver is a young driver, the ratio of the selected route to the shortest route is modified on the basis of the recommended route of the route quadratic programming as follows:
WW3: when the driver is a middle-aged driver, the ratio of the selected route to the shortest route is modified to be as follows on the basis of the recommended route of route quadratic programming:
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