CN116758726B - Big data analysis method and system based on Internet of vehicles - Google Patents
Big data analysis method and system based on Internet of vehicles Download PDFInfo
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- CN116758726B CN116758726B CN202310418153.7A CN202310418153A CN116758726B CN 116758726 B CN116758726 B CN 116758726B CN 202310418153 A CN202310418153 A CN 202310418153A CN 116758726 B CN116758726 B CN 116758726B
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- 238000007405 data analysis Methods 0.000 title claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 35
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000007726 management method Methods 0.000 claims description 28
- 238000006243 chemical reaction Methods 0.000 claims description 3
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- 238000012549 training Methods 0.000 description 5
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention discloses a big data analysis method and a system based on the Internet of vehicles, which belong to the technical field of traffic management and comprise the following steps: s1: dividing a working area, and acquiring vehicle information entering the working area in real time based on the Internet of vehicles; s2: analyzing the vehicle history data of the target vehicle according to the real-time position of the target vehicle in the working area to obtain corresponding analysis data; s3: analyzing the road conditions in the working area according to the obtained analysis data to obtain traffic treatment schemes in each time period of each road section in the working area; s4: acquiring a traffic display model, and marking traffic congestion levels corresponding to each road section in the traffic display model; s5: traffic pre-management of each road section is carried out according to the obtained traffic processing scheme; the intelligent analysis is carried out by acquiring the data of each vehicle based on the Internet of vehicles, so that the traffic jam condition corresponding to each road is estimated in advance, and traffic management personnel can conveniently predict the traffic condition of each road section in advance.
Description
Technical Field
The invention belongs to the technical field of traffic management, and particularly relates to a big data analysis method and system based on the Internet of vehicles.
Background
With the development of society, the holding amount of automobiles is gradually increased, however, the increment of roads used for automobile driving is far less than the increasing speed of the holding amount of automobiles, so that the congestion phenomenon of modern urban roads is increasingly serious, and great difficulty is brought to urban management; in addition, when the existing traffic management department carries out traffic management, the traffic management department generally carries out dredging after congestion occurs, and large-area blockage possibly occurs in the waiting time to influence the traffic operation.
Therefore, in order to provide support for traffic management work, the increasingly severe current situation of congestion is practically relieved, normal and efficient operation of traffic in cities is ensured, and each traffic management department needs to be assisted in setting an auxiliary method to assist corresponding management personnel in traffic management.
Based on the method and the system, the invention provides a big data analysis method and a big data analysis system based on the Internet of vehicles.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a big data analysis method and a big data analysis system based on the Internet of vehicles, which are used for solving the problem of traffic management lag in the existing Internet of vehicles data analysis method.
The aim of the invention can be achieved by the following technical scheme:
a big data analysis method and system based on the Internet of vehicles comprises the following steps:
s1: dividing a working area, and acquiring vehicle information entering the working area in real time based on the Internet of vehicles;
further, the vehicle information includes a vehicle number, vehicle history data, a vehicle model number, a vehicle use.
The vehicle use is used for indicating that the vehicle is a private car, a net taxi, a truck and the like.
Further, the method for acquiring the vehicle history data in the vehicle information includes:
setting a collection period, acquiring vehicles entering and exiting the working area at the collection period, marking the vehicles as initial vehicles, automatically generating a unique code for each non-repeated vehicle, establishing a historical database, identifying the number of the vehicles entering the working area, and matching corresponding historical vehicle data from the historical database according to the identified vehicle number.
Further, the method for establishing the history database comprises the following steps:
acquiring a historical driving record of an initial vehicle in an acquisition limit period, extracting a driving route and a corresponding time period in the driving record, and integrating the obtained driving route and the corresponding time period into vehicle historical data; establishing a history database, marking the obtained vehicle history data with corresponding vehicle numbers, storing the vehicle numbers in the history database, and recording the driving route and the time period of each vehicle in real time in the running process, and supplementing the driving route and the time period to the corresponding vehicle history data in the history database;
for the vehicles entering the working area for the first time in the running process, corresponding codes are generated, the running route and the time period of the vehicles are recorded, the recorded running route and time period are integrated into vehicle history data, and the vehicle history data are stored in a history database.
S2: analyzing the vehicle history data of the target vehicle according to the real-time position of the target vehicle in the working area to obtain corresponding analysis data;
further, the method for performing the analysis includes:
corresponding initial data are extracted from vehicle historical data according to the position of the target vehicle, corresponding optional roads and corresponding road data of all optional roads are set according to the obtained initial data, the road data are analyzed to obtain corresponding effective values, and corresponding to-be-selected roads, corresponding to-be-selected values of all the to-be-selected roads and estimated time are set according to the obtained effective values.
Further, the method for calculating the candidate value of each selectable road comprises the following steps:
the effective value is marked as YZi, and the effective value is expressed according to a representative value formulaCalculating representative values of the optional roads, marking the optional roads as j, wherein j=1, 2, … …, m and m are positive integers, and +_according to a formula of the candidate values +_>And calculating a candidate value of each optional road, wherein alpha is a conversion coefficient.
Further, the method for determining the candidate road comprises the following steps:
setting a threshold value X1, marking an optional road with a candidate value not smaller than the threshold value X1 as a candidate road, and integrating the candidate value corresponding to the candidate road and the estimated time into analysis data.
S3: analyzing the road conditions in the working area according to the obtained analysis data to obtain traffic treatment schemes in each time period of each road section in the working area;
the road condition analysis method comprises the following steps:
establishing a traffic display model of a working area, inputting analysis data of each target vehicle into the traffic display model, obtaining analysis data corresponding to each road section in the working area, classifying according to estimated time in the analysis data, and obtaining a set of to-be-selected values corresponding to each time period of each road section;
and establishing a traffic scheme library, evaluating corresponding traffic congestion levels according to the obtained set of candidate values, and matching corresponding traffic processing schemes from the traffic scheme library according to the obtained traffic congestion levels.
S4: acquiring a traffic display model, and marking traffic congestion levels corresponding to each road section in the traffic display model;
s5: and carrying out traffic pre-management on each road section according to the obtained traffic processing scheme.
Compared with the prior art, the invention has the beneficial effects that:
the traffic management system has the advantages that the traffic jam conditions corresponding to the roads are estimated in advance by acquiring the data of the vehicles based on the Internet of vehicles, traffic management personnel can conveniently pre-judge the traffic conditions of the road sections in advance, the traffic management personnel can conveniently dispatch the corresponding traffic management personnel to reach the corresponding road sections for traffic management through establishing a traffic display model, and the dispatched traffic management personnel can conveniently select a proper traffic route to reach a destination through the following traffic conditions of the road sections; through the analyzed traffic jam level, the intelligent matching of corresponding traffic treatment schemes realizes the intelligent management of traffic in jurisdictions and ensures the efficient operation of traffic in jurisdictions.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a flowchart of step S2 of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a big data analysis method and system based on internet of vehicles includes:
s1: inputting a traffic area to be analyzed, marking the input traffic area range in a traffic information graph, realizing the division of working areas, and acquiring vehicle information entering the working areas in real time based on the Internet of vehicles;
the vehicle information is collected and integrated according to preset collection content, the preset collection content is analyzed by analyzing a route which a corresponding vehicle is likely to run next, such as a vehicle number, vehicle history data, a vehicle model and the like, and the vehicle history data is route data which is acquired based on the internet of vehicles and runs in a working area before the vehicle.
Specifically, the method for acquiring the vehicle history data in the vehicle information includes:
setting an acquisition limit period, only initially analyzing data in the acquisition limit period, setting a specific acquisition limit period by a corresponding manager, acquiring vehicles entering and exiting a working area in the acquisition limit period, marking the vehicles as initial vehicles, automatically setting a unique number for each initial vehicle, automatically generating a number for the vehicles entering the working area for the first time under the same subsequent condition, acquiring a historical driving record of the initial vehicles in the working area, and extracting a driving route and a corresponding time period in the historical driving record; integrating the obtained driving route and the corresponding time period into vehicle history data, establishing a history database, marking the obtained vehicle history data with corresponding vehicle numbers, storing the vehicle numbers in the history database, and recording the driving route and the time period of each vehicle in real time in the running process and supplementing the driving route and the time period to the corresponding vehicle history data in the history database; generating a code for the vehicle which just enters in the running process, recording the running route and the time period of the code, and storing the code into a history database as new vehicle history data; wherein the run-time refers to the period of time during which the present invention is applied;
when the vehicle history data of the vehicle entering the working area is required to be acquired, the number of the vehicle is identified, and the corresponding vehicle history data is matched from the history database according to the identified number.
S2: marking a vehicle entering a working area or traveling in the working area as a target vehicle, analyzing vehicle history data of the target vehicle according to the real-time position of the target vehicle in the working area, analyzing a road on which the target vehicle will travel in the target area, predicting the traveling time of the target vehicle on a corresponding road, namely, predicting when the target vehicle will reach the position on any position of the road, marking the corresponding road as a candidate road, and integrating a candidate value corresponding to the candidate road and the predicted time of the target vehicle on the corresponding road into analysis data;
the analysis data of each vehicle will be updated dynamically over time and with changes in vehicle position.
The method for analyzing the analysis data comprises the following steps:
extracting data which accords with the running of a current target vehicle from vehicle historical data according to the position of the target vehicle, marking the data as initial data, namely screening vehicle historical records comprising the current road according to the position, the running direction and the like of the target vehicle, counting optional roads which are arranged in the initial data and are arranged on the follow-up of the target vehicle, wherein the data are displayed more intuitively and conveniently in an analysis mode in a road distribution map mode, classifying the initial data according to the optional roads to obtain road data of the optional roads, namely the historical running times of the roads and time periods corresponding to the times, evaluating the effective value of the times according to the current time of the target vehicle and the time periods corresponding to the running times of the optional roads, setting the effective value of the corresponding running at different times, particularly establishing a corresponding effective analysis model based on a CNN network or a DNN network, establishing a corresponding training set in a manual mode for training, estimating the time reaching the optional roads according to the position and the time of the current target vehicle, carrying out intelligent evaluation through the prior art, namely estimating the effective value of the running times in the pre-estimated time of the current navigation software, analyzing the corresponding running times of the optional roads and the effective value of the corresponding running time of the optional roads on the estimated time of the road according to the estimated time of the estimated running time of the corresponding running time of the optional road;
marking the pass record in the optional road as i, wherein i=1, 2, … …, n and n are positive integers, and the pass record is one pass record; the obtained effective value is marked as YZi, and the effective value is calculated according to the formula of the representative valueCalculating representative values of the optional roads, marking the optional roads as j, wherein j=1, 2, … …, m and m are positive integers, and according to a formula of the candidate valuesAnd calculating a candidate value of each selectable road, wherein alpha is a conversion coefficient and is used for converting the corresponding probability value into the candidate value, specifically, the expert group is used for adjusting and setting, the selectable road with the candidate value not smaller than the threshold value X1 is marked as the candidate road, and the candidate value and the estimated time corresponding to the candidate road are integrated into analysis data.
S3: analyzing the road conditions in the working area according to the obtained analysis data to obtain traffic treatment schemes in each time period of each road section in the working area;
the method comprises the steps of establishing a traffic display model of a working area, wherein the traffic display model is a three-dimensional data model established based on a traffic information graph, inputting analysis data corresponding to each road section into the traffic display model, counting the analysis data of each vehicle of the road section on the corresponding road section in real time in the traffic display model, classifying each analysis data according to a corresponding preset time period and a corresponding estimated time, distributing the analysis data into the corresponding time period, and dynamically updating the estimated time along with the time variation, so that each analysis data is changed in different time periods; the method comprises the steps of analyzing the to-be-selected value corresponding to each analysis data in a period, judging the traffic congestion level in the period, presetting the traffic congestion level, and carrying out treatment when the traffic congestion level is in the corresponding traffic congestion level, such as sending corresponding traffic staff to go to the corresponding road section in advance to conduct traffic guidance, carrying out preparation in advance, guaranteeing the smooth of traffic, improving the traffic management efficiency, avoiding manual dredging after large-area congestion is generated, and being low in efficiency and influencing traffic operation.
Specifically, firstly determining the traffic congestion level, setting a traffic processing scheme corresponding to each traffic congestion level according to the traffic congestion level, setting the traffic processing scheme by means of expert group discussion, and setting up a traffic scheme library after finishing the set traffic processing scheme;
establishing a corresponding road condition analysis model based on a CNN network or a DNN network, establishing a corresponding training set in a manual mode for training, analyzing a corresponding set of candidate values of each road section through the road condition analysis model after successful training, obtaining the traffic congestion level of the road section in the period, and matching from a traffic scheme library according to the analyzed traffic congestion level to obtain a corresponding traffic processing scheme.
S4: acquiring a traffic display model, and marking traffic congestion levels corresponding to each road section in the traffic display model;
s5: and carrying out traffic pre-management on each road section according to the obtained traffic processing scheme.
The traffic management system has the advantages that the traffic jam conditions corresponding to the roads are estimated in advance by acquiring the data of the vehicles based on the Internet of vehicles, traffic management personnel can conveniently pre-judge the traffic conditions of the road sections in advance, the traffic management personnel can conveniently dispatch the corresponding traffic management personnel to reach the corresponding road sections for traffic management through establishing a traffic display model, and the dispatched traffic management personnel can conveniently select a proper traffic route to reach a destination through the following traffic conditions of the road sections; through the analyzed traffic jam level, the intelligent matching of corresponding traffic treatment schemes realizes the intelligent management of traffic in jurisdictions and ensures the efficient operation of traffic in jurisdictions.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (5)
1. The big data analysis method based on the Internet of vehicles is characterized by comprising the following steps of:
acquiring vehicle information entering a working area in real time, wherein the vehicle information comprises a real-time position;
analyzing the vehicle history data of the target vehicle according to the real-time position of the target vehicle in the working area to obtain analysis data;
analyzing the road condition in the working area according to the obtained analysis data to obtain a traffic processing scheme in the working area, wherein the traffic processing scheme comprises the following steps: road section information, time period information, and congestion levels;
traffic pre-management of each road section is carried out according to the obtained traffic processing scheme;
the analysis data is obtained by the following steps:
extracting data conforming to the running of a current target vehicle from vehicle history data according to the position of the target vehicle, marking the data as initial data, namely screening vehicle history records comprising the current road according to the position and the running direction of the target vehicle, counting optional roads which are subsequently arranged on the target vehicle in the initial data, wherein the optional roads are displayed in a road distribution map mode more intuitively and conveniently for analysis, classifying the initial data according to each optional road to obtain road data of each optional road, namely, the historical passing times of the road and time periods corresponding to each time, evaluating the effective value of the time according to the current time of the target vehicle and the time periods corresponding to each passing time of each optional road, and setting the effective value of the corresponding passing when weights represented by different times are different;
taking part in calculation by using a valid value, marking as YZi, marking the traffic record in the optional road as i, wherein i=1, 2, … …, n and n are positive integers, and the traffic record is one-time traffic record; calculating the representative value of each selectable road according to the representative value model;
marking each selectable road as j, wherein j=1, 2, … …, m and m are positive integers, and calculating the candidate value of each selectable road according to a candidate value model to obtain the candidate road, wherein:
the representative value model is as follows:wherein LR is a value to be selected;
the formula of the candidate value is thatWherein alpha is a conversion coefficient used for converting the corresponding probability value into a value to be selected;
the method for determining the candidate road comprises the following steps:
setting a threshold value X1, marking an optional road with a candidate value not smaller than the threshold value X1 as a candidate road, and integrating the candidate value corresponding to the candidate road and the estimated time into analysis data;
analyzing the road condition in the working area according to the obtained analysis data to obtain a traffic processing scheme in the working area, wherein the traffic processing scheme comprises the following steps:
establishing a traffic display model of a working area, inputting analysis data of each target vehicle into the traffic display model, obtaining analysis data corresponding to each road section in the working area, classifying according to estimated time in the analysis data, and obtaining a set of to-be-selected values corresponding to each time period of each road section;
and establishing a traffic scheme library, evaluating corresponding traffic congestion levels according to the obtained set of candidate values, and matching corresponding traffic processing schemes from the traffic scheme library according to the obtained traffic congestion levels.
2. The internet of vehicles-based big data analysis method according to claim 1, wherein the vehicle information further includes a vehicle number, vehicle history data, a vehicle model number, and a vehicle use.
3. The internet of vehicles-based big data analysis method of claim 1, further comprising: a component history database;
acquiring vehicles entering and exiting the working area in the acquisition period within the set acquisition period, and marking the vehicles as initial vehicles;
generating unique codes for different vehicles, and establishing a history database, wherein the history database comprises the vehicle history data;
and when the vehicles entering the working area need to be identified, matching historical vehicle information from the historical database according to the unique codes.
4. A big data analysis method based on internet of vehicles according to claim 3, wherein the component history database:
acquiring a historical driving record of the initial vehicle in an acquisition limit period;
extracting a driving route and a corresponding time period in a driving record, integrating the obtained driving route and the corresponding time period into the vehicle history data and storing the vehicle history data;
in the running process, the running route and the time period of each vehicle are recorded in real time and are supplemented to corresponding vehicle history data in a history database.
5. A big data analysis system based on the internet of vehicles, characterized in that a big data analysis method based on the internet of vehicles according to any one of claims 1-4 is performed.
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