CN113160551B - Traffic big data based accompanying model application method - Google Patents
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- CN113160551B CN113160551B CN202110045394.2A CN202110045394A CN113160551B CN 113160551 B CN113160551 B CN 113160551B CN 202110045394 A CN202110045394 A CN 202110045394A CN 113160551 B CN113160551 B CN 113160551B
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- G—PHYSICS
- G08—SIGNALLING
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- 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
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- G—PHYSICS
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
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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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- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
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Abstract
The invention discloses a traffic big data-based accompanying model application method, which comprises the steps of utilizing a data acquisition unit in an accompanying model to acquire traffic passage data and send the traffic passage data to a data storage unit, extracting the traffic passage data by a data processing unit to carry out accompanying processing, then transmitting a vehicle time sequence, a code detection time sequence and voiceprint binding data to a data analysis unit, carrying out matching analysis by the data analysis unit to obtain the highest matching degree traffic record starting time, the highest matching degree traffic record ending time, the highest matching degree detection record starting time and the highest matching degree detection record ending time, further obtaining the corresponding highest matching degree vehicle record data and the highest matching degree code record data, establishing contact among vehicles, passengers and mobile phone detection codes and carrying out matching according to similarity by arranging the data processing unit and the data analysis unit, and facilitating tracing query on the traffic data and accompanying personnel of the vehicles, the information retrieval time is saved, and the traffic safety control strength is improved.
Description
Technical Field
The invention relates to an application method, in particular to an application method based on a traffic big data accompanying model.
Background
In the prior art, vehicles and drivers are usually bound, vehicle information is recorded, and when the vehicle information is required to be applied to public security prevention and control, information tracing is carried out, time is wasted, and positioning is not accurate.
Disclosure of Invention
The invention aims to provide an application method based on a traffic big data accompanying model, which establishes a relation among vehicles, passengers and mobile phone detection codes and matches the relation according to similarity by arranging a data processing unit and a data analysis unit, is convenient for tracing and inquiring traffic data and accompanying personnel of the vehicles, saves information retrieval time and improves traffic safety control.
The technical problem solved by the invention is as follows: how to detect and identify the vehicle passing condition and the unique identification code of the mobile phone by setting the data processing unit and the data analysis unit and record the voiceprint data of passengers, and the problem that the tracing of the accompanying relationship between the vehicle and the personnel at the inspection station in the prior art cannot be realized is solved.
The purpose of the invention can be realized by the following technical scheme: an operation method based on a traffic big data accompanying model comprises the following steps:
the method comprises the following steps: collecting station data, vehicle record data, detection code record data and passenger voiceprint record data by using a data collection unit in the accompanying model, integrating the station data, the vehicle record data, the detection code record data and the passenger voiceprint record data into traffic data, and transmitting the traffic data to a data storage unit for storage;
step two: the data processing unit extracts traffic data from the data storage unit for accompanying processing, generates a corresponding vehicle time sequence according to the vehicle identification code, generates a corresponding detection code time sequence according to the unique identification code of the mobile phone, binds the passenger voiceprint recording data and the vehicle recording data to generate voiceprint binding data, and then transmits the vehicle time sequence, the detection code time sequence and the voiceprint binding data to the data analysis unit;
step three: the vehicle time sequence and the code detection time sequence are marked and substituted into a similarity function, similarity analysis is carried out on the vehicle time sequence and the code detection time sequence by adopting a DTW algorithm, an LCS (Long term evolution) forward tracing algorithm and an LCS backward tracing algorithm are carried out simultaneously to obtain the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree, and then the corresponding vehicle record data and the corresponding code detection record data with the highest matching degree are obtained and integrated to generate matching result data which are transmitted to a data storage unit for storage.
The invention has further technical improvements that: the adjoint model body comprises a data acquisition unit, a data analysis unit, a data processing unit and a data storage unit;
the data acquisition unit acquires traffic data and sends the traffic data to the data storage unit, the traffic data comprises station data, vehicle record data, detection code record data and passenger voiceprint record data, the station data represents station number data and vehicle acquisition time data, the vehicle record data comprises vehicle type data and license plate data, the detection code record data comprises a mobile phone unique identification code, identification start time data and identification end time data, and the passenger voiceprint record data represents sound wave spectrum information of passenger sound;
the data processing unit extracts traffic data from the data storage unit, carries out accompanying processing, and transmits the obtained vehicle time sequence, the obtained detection code time sequence and the obtained voiceprint binding data to the data analysis unit;
the data analysis unit receives the vehicle time sequence, the detection code time sequence and the voiceprint binding data and performs matching analysis on the vehicle time sequence, the detection code time sequence and the voiceprint binding data to obtain the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree, and then the corresponding vehicle record data and the detection code record data with the highest matching degree are obtained and integrated to generate matching result data which are transmitted to the data storage unit to be stored;
because data in the vehicle time sequence and the detection code time sequence cannot be simply defined as points on a plane, the Euclidean distance calculation method in the DWT is improved, the distance between the vehicle acquisition time data and the identification start time data and the identification end time data is defined in a probability mode, the probability that the vehicle and the detection code are identified at the same time is defined as P and is determined to be in accordance with normal distribution, and the distance between the vehicle and the detection code pair is defined as D1-P.
The invention has the further technical improvements that: the data processing unit performs the accompanying processing specifically as follows:
s31: extracting station data and vehicle record data in the traffic data, integrating vehicle type data and license plate data in the vehicle record data, automatically generating a vehicle mark code for a corresponding vehicle, generating a corresponding vehicle time sequence for the vehicle mark code, arranging the vehicle time sequence according to the sequence of vehicle acquisition time data, and binding station number data corresponding to the vehicle acquisition time data in the vehicle time sequence;
s32: extracting detection code recording data in the traffic data as a unique identification code of the mobile phone to generate a corresponding detection code time sequence, arranging the detection code time sequence according to the sequence of identification starting time data, and setting identification ending time data and the corresponding identification starting time data in the same item of the detection code time sequence;
s33: and extracting passenger voiceprint recording data in the traffic data, and binding the passenger voiceprint recording data with the vehicle recording data to generate voiceprint binding data.
The invention has the further technical improvements that: the specific steps of the data analysis unit for performing matching analysis are as follows:
s41: the vehicle time series is marked as s [ is1, ie1], the detection code time series is marked as t [ js2, je2], wherein is1 and ie1 respectively represent the vehicle traffic record start time and the vehicle traffic record end time, and is1 ═ ie1, js2 and je2 respectively represent the detection record start time and the detection record end time of the detection code;
s42: substituting the vehicle time series and the detection code time series into a function max sim (s [ is1, ie1, t [ js2, je2]), wherein the function sim represents the similarity of the vehicle time series and the detection code time series, performing similarity analysis on the two time series by adopting a DTW algorithm, and converting the function into max dwt (s [ is1, ie1], t [ js2, je2]) by making sim (x, y) be DTW (x, y);
s43: calculating by using a DTW algorithm, and simultaneously performing an LCS (Long-term storage) forward-tracing algorithm and an LCS backward-tracing algorithm to obtain parameters of is, ie, js and je, wherein the parameters respectively represent the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree;
s44: the data analysis unit is preset with a matching frequency limit value, voiceprint binding data are identified and counted regularly, when the frequency of the same voiceprint binding data is larger than or equal to the matching frequency limit value, the matching degree of a corresponding passenger and a corresponding vehicle is judged to be high, the binding state of the voiceprint recording data of the passenger and the vehicle recording data is kept, and when the frequency of the same voiceprint binding data is smaller than the matching frequency limit value, the voiceprint recording data of the passenger and the vehicle recording data are unbound.
Compared with the prior art, the invention has the beneficial effects that:
the data acquisition unit in the accompanying model is used for acquiring station data, vehicle record data, detection code record data and passenger voiceprint record data and integrating the station data, the vehicle record data, the detection code record data and the passenger voiceprint record data into traffic data, then the traffic data is transmitted to the data storage unit for storage, the data processing unit extracts the traffic data from the data storage unit for accompanying processing, generates a corresponding vehicle time sequence according to a vehicle identification code, generates a corresponding detection code time sequence according to a mobile phone unique identification code, binds the passenger voiceprint record data and the vehicle record data to generate voiceprint binding data, then transmits the vehicle time sequence, the detection code time sequence and the voiceprint binding data to the data analysis unit, marks the vehicle time sequence and the detection code time sequence, substitutes the vehicle time sequence and the detection code time sequence into a similarity function, and carries out similarity analysis by adopting a DTW algorithm, and meanwhile, performing an LCS (Long range traffic analysis) forward tracing algorithm and an LCS backward tracing algorithm to obtain the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree, further obtaining the corresponding vehicle record data and the detection code record data with the highest matching degree, integrating the vehicle record data and the detection code record data, generating matching result data, transmitting the matching result data to a data storage unit for storage, establishing a relation among the vehicle, the passenger and the mobile phone detection code by arranging a data processing unit and a data analysis unit, and matching according to the similarity, so that the traffic data and the accompanying personnel of the vehicle can be traced and inquired conveniently, the information retrieval time is saved, and the traffic safety control strength is improved.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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, a method for using an association model based on traffic big data includes the following steps:
the method comprises the following steps: collecting station data, vehicle record data, detection code record data and passenger voiceprint record data by using a data collection unit in an accompanying model, integrating the station data, the vehicle record data, the detection code record data and the passenger voiceprint record data into traffic data, and transmitting the traffic data to a data storage unit for storage;
step two: the data processing unit extracts traffic data from the data storage unit for accompanying processing, generates a corresponding vehicle time sequence according to the vehicle identification code, generates a corresponding detection code time sequence according to the unique identification code of the mobile phone, binds the passenger voiceprint recording data and the vehicle recording data to generate voiceprint binding data, and then transmits the vehicle time sequence, the detection code time sequence and the voiceprint binding data to the data analysis unit;
step three: the vehicle time sequence and the code detection time sequence are marked and substituted into a similarity function, similarity analysis is carried out on the vehicle time sequence and the code detection time sequence by adopting a DTW algorithm, an LCS (Long term evolution) forward tracing algorithm and an LCS backward tracing algorithm are carried out simultaneously to obtain the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree, and then the corresponding vehicle record data and the corresponding code detection record data with the highest matching degree are obtained and integrated to generate matching result data which are transmitted to a data storage unit for storage.
The adjoint model body comprises a data acquisition unit, a data analysis unit, a data processing unit and a data storage unit;
the data acquisition unit acquires traffic data and sends the traffic data to the data storage unit, the traffic data comprises station data, vehicle record data, detection code record data and passenger voiceprint record data, the station data represents station number data and vehicle acquisition time data, the vehicle record data comprises vehicle type data and license plate data, the detection code record data comprises a mobile phone unique identification code, identification start time data and identification end time data, and the passenger voiceprint record data represents sound wave spectrum information of passenger sound;
the data processing unit extracts traffic data from the data storage unit and carries out accompanying processing, and transmits the obtained vehicle time sequence, the obtained detection code time sequence and the obtained voiceprint binding data to the data analysis unit;
the data analysis unit receives the vehicle time sequence, the detection code time sequence and the voiceprint binding data, performs matching analysis on the vehicle time sequence, the detection code time sequence and the voiceprint binding data to obtain the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree, further obtains the corresponding vehicle record data and the detection code record data with the highest matching degree, integrates the vehicle record data and the detection code record data, generates matching result data and transmits the matching result data to the data storage unit for storage.
The data processing unit performs the following specific steps:
s31: extracting station data and vehicle record data in the traffic data, integrating vehicle type data and license plate data in the vehicle record data, automatically generating a vehicle mark code for a corresponding vehicle, generating a corresponding vehicle time sequence for the vehicle mark code, arranging the vehicle time sequence according to the sequence of vehicle acquisition time data, and binding station number data corresponding to the vehicle acquisition time data in the vehicle time sequence;
s32: extracting detection code recording data in the traffic data as a unique identification code of the mobile phone to generate a corresponding detection code time sequence, arranging the detection code time sequence according to the sequence of identification starting time data, and setting identification ending time data and the corresponding identification starting time data in the same item of the detection code time sequence;
s33: and extracting passenger voiceprint recording data in the traffic data, and binding the passenger voiceprint recording data with the vehicle recording data to generate voiceprint binding data.
The invention has the further technical improvements that: the specific steps of the data analysis unit for performing matching analysis are as follows:
s41: marking a vehicle time series as s [ is1, ie1], marking a detection code time series as t [ js2, je2], wherein is1 and ie1 respectively represent a traffic record start time of the vehicle and a traffic record end time of the vehicle, and making is1 ═ ie1, js2 and je2 respectively represent a detection record start time of the detection code and a detection record end time of the detection code;
s42: substituting the vehicle time series and the detection code time series into a function max sim (s is1, ie1, t js2, je 2), wherein the function sim represents the similarity of the vehicle time series and the detection code time series, performing similarity analysis on the two time series by adopting a DTW algorithm, and converting the function into max dwt (s 1, ie 1), t js2, je 2) by making sim (x, y) equal to DTW (x, y);
s43: calculating by using a DTW algorithm, and simultaneously performing an LCS (Long-term storage) forward tracing algorithm and an LCS backward tracing algorithm to obtain is, ie, js and je parameters which respectively represent the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree;
s44: the data analysis unit is preset with a matching frequency limit value, voiceprint binding data are identified and counted regularly, when the frequency of the same voiceprint binding data is larger than or equal to the matching frequency limit value, the matching degree of a corresponding passenger and a corresponding vehicle is judged to be high, the binding state of the voiceprint recording data of the passenger and the vehicle recording data is kept, and when the frequency of the same voiceprint binding data is smaller than the matching frequency limit value, the voiceprint recording data of the passenger and the vehicle recording data are unbound.
The working principle is as follows: when the invention is used, firstly, a data acquisition unit in an accompanying model is used for acquiring station data, vehicle record data, detection code record data and passenger voiceprint record data and integrating the station data, the vehicle record data, the detection code record data and the passenger voiceprint record data into traffic data, then the traffic data is transmitted to a data storage unit for storage, a data processing unit extracts the traffic data from the data storage unit for accompanying processing, generates a corresponding vehicle time sequence according to a vehicle identification code, generates a corresponding detection code time sequence according to a mobile phone unique identification code, binds the passenger voiceprint record data and the vehicle record data to generate voiceprint binding data, then transmits the vehicle time sequence, the detection code time sequence and the voiceprint binding data to a data analysis unit, marks the vehicle time sequence and the detection code time sequence and substitutes the vehicle time sequence and the detection code time sequence into a similarity function, and performing similarity analysis on the vehicle data by adopting a DTW algorithm, and simultaneously performing an LCS forward tracing algorithm and an LCS backward tracing algorithm to obtain the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree, further obtaining the corresponding vehicle record data and the detection code record data with the highest matching degree, integrating the vehicle record data and the detection code record data, generating matching result data and transmitting the matching result data to a data storage unit for storage.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "left", "right", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are only for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation and a specific orientation configuration and operation, and thus, should not be construed as limiting the present invention. Furthermore, "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that unless otherwise explicitly stated or limited, the terms "mounted," "connected," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be directly connected or indirectly connected through an intermediate member, or they may be connected through two or more elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
Although one embodiment of the present invention has been described in detail, the description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (4)
1. A traffic big data accompanying model application method is characterized in that: the application method of the accompanying model comprises the following steps:
the method comprises the following steps: collecting station data, vehicle record data, detection code record data and passenger voiceprint record data by using a data collection unit in the accompanying model, integrating the station data, the vehicle record data, the detection code record data and the passenger voiceprint record data into traffic data, and transmitting the traffic data to a data storage unit for storage;
step two: the data processing unit extracts traffic data from the data storage unit for accompanying processing, generates a corresponding vehicle time sequence according to the vehicle identification code, generates a corresponding detection code time sequence according to the unique identification code of the mobile phone, binds the passenger voiceprint recording data and the vehicle recording data to generate voiceprint binding data, and then transmits the vehicle time sequence, the detection code time sequence and the voiceprint binding data to the data analysis unit;
step three: marking the vehicle time sequence and the code detection time sequence, substituting the vehicle time sequence and the code detection time sequence into a similarity function, carrying out similarity analysis on the vehicle time sequence and the code detection time sequence by adopting a DTW algorithm, simultaneously carrying out an LCS (Long term communication) forward tracing algorithm and an LCS backward tracing algorithm to obtain the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree, further obtaining the corresponding vehicle record data and the corresponding code detection record data with the highest matching degree, integrating the vehicle record data and the code detection record data, generating matching result data and transmitting the matching result data to a data storage unit for storage.
2. The method for applying the traffic big data-based accompanying model as claimed in claim 1, wherein the accompanying model body comprises a data acquisition unit, a data analysis unit, a data processing unit and a data storage unit;
the data acquisition unit acquires traffic passing data and sends the traffic passing data to the data storage unit, the traffic passing data comprises station data, vehicle recording data, detection code recording data and passenger voiceprint recording data, the station data represents station number data and vehicle acquisition time data, the vehicle recording data comprises vehicle type data and license plate data, the detection code recording data comprises a mobile phone unique identification code, identification starting time data and identification ending time data, and the passenger voiceprint recording data represents sound wave spectrum information of passenger sound;
the data processing unit extracts traffic data from the data storage unit, carries out accompanying processing, and transmits the obtained vehicle time sequence, the obtained detection code time sequence and the obtained voiceprint binding data to the data analysis unit;
the data analysis unit receives the vehicle time sequence, the code detection time sequence and the voiceprint binding data, performs matching analysis on the vehicle time sequence, the code detection time sequence and the voiceprint binding data to obtain the highest-matching-degree traffic record starting time, the highest-matching-degree traffic record ending time, the highest-matching-degree detection record starting time and the highest-matching-degree detection record ending time, further obtains the corresponding highest-matching-degree vehicle record data and the corresponding highest-matching-degree code detection record data, integrates the highest-matching-degree traffic record starting time, the highest-matching-degree detection record ending time and the corresponding highest-matching-degree vehicle record data and the corresponding highest-matching-degree code detection record ending time, generates matching result data and transmits the matching result data to the data storage unit for storage.
3. The method as claimed in claim 1, wherein the data processing unit performs the accompanying processing by the following steps:
s31: extracting station data and vehicle record data in the traffic data, integrating vehicle type data and license plate data in the vehicle record data, automatically generating a vehicle identification code for a corresponding vehicle, generating a corresponding vehicle time sequence for the vehicle identification code, arranging the vehicle time sequence according to the sequence of vehicle acquisition time data, and binding station number data corresponding to the vehicle acquisition time data in the vehicle time sequence;
s32: extracting detection code record data in the traffic data to generate a corresponding detection code time sequence for a unique identification code of the mobile phone, arranging the detection code time sequence according to the sequence of identification starting time data, and setting identification ending time data and the corresponding identification starting time data in the same entry of the detection code time sequence;
s33: and extracting passenger voiceprint recording data in the traffic data, and binding the passenger voiceprint recording data with the vehicle recording data to generate voiceprint binding data.
4. The method as claimed in claim 1, wherein the data analysis unit performs the matching analysis by the following steps:
s41: the vehicle time series is marked as s [ is1, ie1], the detection code time series is marked as t [ js2, je2], wherein is1 and ie1 respectively represent the traffic record starting time of the vehicle and the traffic record ending time of the vehicle, and is1= ie1, js2 and je2 respectively represent the detection record starting time and the detection record ending time of the detection code;
s42: substituting the vehicle time series and the code detection time series into a function
In the method, a function sim represents the similarity between a vehicle time sequence and a code detection time sequence, the similarity between the two time sequences is analyzed by adopting a DTW algorithm, and if sim (x, y) = DTW (x, y), the function is converted into a function
；
S43: calculating by using a DTW algorithm, and simultaneously performing an LCS (Long-term storage) forward-tracing algorithm and an LCS backward-tracing algorithm to obtain is, ie, js and je parameters which respectively represent the traffic record starting time, the traffic record ending time, the detection record starting time and the detection record ending time with the highest matching degree;
s44: the data analysis unit is preset with a matching frequency limit value, voiceprint binding data are identified and counted regularly, when the frequency of the same voiceprint binding data is larger than or equal to the matching frequency limit value, the matching degree of a corresponding passenger and a corresponding vehicle is judged to be high, the binding state of the voiceprint recording data of the passenger and the vehicle recording data is kept, and when the frequency of the same voiceprint binding data is smaller than the matching frequency limit value, the voiceprint recording data of the passenger and the vehicle recording data are unbound.
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| CN109635059A (en) * | 2018-11-23 | 2019-04-16 | 武汉烽火众智数字技术有限责任公司 | People's vehicle association analysis method and system based on track similarity mode |
| CN109634946B (en) * | 2018-12-06 | 2020-08-18 | 南京森根科技股份有限公司 | Intelligent track matching correlation analysis method based on big data mining |
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